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Reza2kn
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Bina-0.2-Rizeh

Image-to-Text
PaddlePaddle
Persian
paddleocr
ocr
persian
handwriting
printed-text
pp-ocrv6
ctc
Model card Files
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Reza2kn commited on
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Publish Bina 0.2 Rizeh Stage-8 release

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Files changed (39) hide show
  1. .gitattributes +4 -0
  2. MODEL_CARD_METRICS.json +30 -0
  3. README.md +101 -0
  4. RELEASE.json +22 -0
  5. SHA256SUMS +32 -0
  6. benchmark/decoder-summary.json +7 -0
  7. benchmark/detection-summary.json +6680 -0
  8. benchmark/exact-summary.json +28 -0
  9. benchmark/pipeline-status.log +4 -0
  10. benchmark/printed-summary.json +5 -0
  11. benchmark/recognition-summary.json +5 -0
  12. benchmark/results.json +42 -0
  13. checkpoint/best_accuracy.pdopt +3 -0
  14. checkpoint/best_accuracy.pdparams +3 -0
  15. checkpoint/best_accuracy.states +0 -0
  16. checkpoint/config.yml +154 -0
  17. checkpoint/eval-gate-state.json +16 -0
  18. checkpoint/launch-config.yml +154 -0
  19. checkpoint/optimizer-patch.json +12 -0
  20. checkpoint/status.log +2 -0
  21. data/MIX_MANIFEST.json +823 -0
  22. inference.json +0 -0
  23. inference.pdiparams +3 -0
  24. inference.yml +205 -0
  25. inference/INFERENCE_READY +1 -0
  26. inference/export.log +11 -0
  27. inference/inference.json +0 -0
  28. inference/inference.pdiparams +3 -0
  29. inference/inference.yml +205 -0
  30. pipeline/assemble_bina02_two_layer.py +221 -0
  31. pipeline/benchmark_bina02_printed_only.py +208 -0
  32. pipeline/bina02_template_layers.py +393 -0
  33. pipeline/decode_bina02_persian.py +315 -0
  34. pipeline/detect_bina02_two_layer.py +314 -0
  35. pipeline/recognize_bina02_two_layer.py +376 -0
  36. pipeline/run_bina02_stage8_optimizer_resume_v2.sh +144 -0
  37. pipeline/run_bina02_stage8_v2_exact.sh +26 -0
  38. pipeline/run_bina02_stage8_v2_exact_core.sh +139 -0
  39. pipeline/watch_bina02_stage4_eval.py +153 -0
.gitattributes CHANGED
@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ checkpoint/best_accuracy.pdopt filter=lfs diff=lfs merge=lfs -text
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+ checkpoint/best_accuracy.pdparams filter=lfs diff=lfs merge=lfs -text
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+ inference/inference.pdiparams filter=lfs diff=lfs merge=lfs -text
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+ inference.pdiparams filter=lfs diff=lfs merge=lfs -text
MODEL_CARD_METRICS.json ADDED
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+ {
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+ "model": "Bina 0.2 Rizeh",
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+ "repo": "Reza2kn/Bina-0.2-Rizeh",
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+ "benchmark": "Reza2kn/persian-ocr-double-benchmark",
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+ "rows_per_split": 6669,
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+ "missing_predictions": 0,
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+ "splits": {
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+ "handwriting": {
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+ "wer": 19.0833,
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+ "cer": 13.6399,
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+ "s3": 13.7015,
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+ "essential_error": 14.7567,
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+ "triple_threat": 14.7656
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+ },
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+ "printed": {
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+ "wer": 8.9257,
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+ "cer": 2.3535,
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+ "s3": 9.8512,
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+ "essential_error": 12.0774,
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+ "triple_threat": 8.1666
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+ }
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+ },
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+ "macro_equal_split": {
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+ "wer": 14.0045,
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+ "cer": 7.9967,
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+ "s3": 11.7764,
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+ "essential_error": 13.4171,
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+ "triple_threat": 11.4661
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+ }
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+ }
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ language:
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+ - fa
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+ pipeline_tag: image-to-text
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+ library_name: paddleocr
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+ tags:
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+ - ocr
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+ - persian
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+ - handwriting
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+ - paddleocr
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+ - pp-ocrv6
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+ - ctc
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+ datasets:
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+ - Reza2kn/persian-handwriting-pages-3.69m
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+ - Reza2kn/persian-printed-ocr-3.5m
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+ ---
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+
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+ # Bina 0.2 Rizeh
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+
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+ Bina 0.2 Rizeh is a compact Persian printed and handwriting recognizer fine-tuned from the
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+ `PP-OCRv6_medium_rec` architecture. It uses PaddleOCR's CTC recognition head and ships both the
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+ exported inference model and the resumable training checkpoint from the Stage-8 release.
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+
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+ ## Important model boundary
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+
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+ The neural checkpoint recognizes **text-line or text-region crops**. It is not, by itself, a
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+ standalone full-page detector. The full-page benchmark below used the included adaptive75
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+ **two-layer pipeline**: page-region detection and template isolation, CTC recognition of the
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+ remaining regions, then reading-order assembly. The scripts used for that exact run are preserved
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+ under `pipeline/` and the benchmark records are under `benchmark/`.
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+
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+ ## Persian OCR Triple Threat results
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+
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+ All 13,338 frozen benchmark rows were scored, with zero missing predictions. Lower is better.
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+ Triple Threat is `20% WER + 20% CER + 60% S3`; Overall is a 50/50 macro-average of the printed and
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+ handwriting splits.
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+
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+ | Split | Pages | WER | CER | S3 | Essential error | Triple Threat |
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+ |---|---:|---:|---:|---:|---:|---:|
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+ | Handwriting | 6,669 | 19.0833 | 13.6399 | 13.7015 | 14.7567 | 14.7656 |
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+ | Printed | 6,669 | 8.9257 | 2.3535 | 9.8512 | 12.0774 | 8.1666 |
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+ | Equal-split overall | 13,338 | 14.0045 | 7.9967 | 11.7764 | 13.4171 | **11.4661** |
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+
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+ Leaderboard: [Persian OCR Triple Threat](https://huggingface.co/spaces/Reza2kn/PersianOCR-TripleThreat)
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+
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+ The release did **not** reach the single-digit handwriting gate. Printed Triple Threat is already
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+ single digit; handwriting remains the principal limitation.
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+
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+ ## Training snapshot
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+
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+ - Stage-8 training mix: 2,198,483 recognition crops.
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+ - Mix composition: 1,000,000 clean handwriting, 1,000,000 printed, and 198,483 native/form crops.
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+ - Best handwriting validation normalized edit distance: 0.9671727.
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+ - Best printed validation normalized edit distance: 0.9071054.
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+ - Optimizer state was preserved from Stage 4.
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+ - The planned Stage-8 schedule completed without an overfitting stop signal.
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+
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+ The source pages came from
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+ [`Reza2kn/persian-handwriting-pages-3.69m`](https://huggingface.co/datasets/Reza2kn/persian-handwriting-pages-3.69m)
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+ and
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+ [`Reza2kn/persian-printed-ocr-3.5m`](https://huggingface.co/datasets/Reza2kn/persian-printed-ocr-3.5m),
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+ with line/region preparation and native form replay described by `data/MIX_MANIFEST.json`.
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+
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+ ## Inference artifacts
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+
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+ The exported Paddle model is available both at repository root and under `inference/`:
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+
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+ - `inference.json`
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+ - `inference.pdiparams`
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+ - `inference.yml`
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+
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+ With PaddleOCR 3.x, a recognition crop can be run with the custom model directory:
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+
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+ ```python
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+ from paddleocr import TextRecognition
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+
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+ recognizer = TextRecognition(model_dir="./inference")
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+ for result in recognizer.predict(input="persian-line.png", batch_size=1):
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+ result.print()
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+ ```
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+
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+ For page OCR, use a detector/layout stage first and pass its ordered text crops to this recognizer.
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+ The bundled `pipeline/` scripts record the exact full-page benchmark workflow; their original
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+ absolute cluster paths must be adapted to a new machine.
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+
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+ ## Repository layout
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+
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+ - `inference/`: exported Paddle inference graph, parameters, and configuration.
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+ - `checkpoint/`: resumable best checkpoint, optimizer state, configs, and eval-gate state.
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+ - `pipeline/`: detection, recognition, assembly, benchmark, and launch scripts.
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+ - `benchmark/`: exact split metrics, logs, and run summaries.
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+ - `data/MIX_MANIFEST.json`: exact Stage-8 train/eval composition.
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+ - `RELEASE.json`: immutable release identity and headline metrics.
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+ - `SHA256SUMS`: checksums for the verified packaged release.
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+
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+ ## Release identity
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+
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+ This public model name is **Bina 0.2 Rizeh**. The immutable packaging metadata retains the internal
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+ release name `Bina-0.2-RizehPizeh` and release ID `stage8-gate42-20260726` so it remains traceable to
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+ the exact trained artifact.
RELEASE.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "model": "Reza2kn/Bina-0.2-RizehPizeh",
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+ "release": "stage8-gate42-20260726",
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+ "training": {
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+ "checkpoint": "best_accuracy",
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+ "handwriting_norm_edit_dis": 0.9671727329321572,
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+ "printed_norm_edit_dis": 0.9071053593763533,
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+ "optimizer_state_preserved_from_stage4": true,
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+ "stage8_mix_rows": 2198483
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+ },
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+ "exact_benchmark": {
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+ "dataset_rows_per_split": 6669,
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+ "missing_predictions": 0,
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+ "handwriting": {"wer": 19.0833, "cer": 13.6399, "s3": 13.7015},
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+ "printed": {"wer": 8.9257, "cer": 2.3535, "s3": 9.8512},
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+ "single_digit_gate_passed": false
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+ },
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+ "inference": {
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+ "template_isolation_variant": "adaptive75",
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+ "assembly_variant": "simple_spread_right"
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+ }
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+ }
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+ W0726 18:20:31.932339 63135 gpu_resources.cc:116] Please NOTE: device: 0, GPU Compute Capability: 12.0, Driver API Version: 13.0, Runtime API Version: 13.0
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+ [2026/07/26 18:20:32] ppocr INFO: resume from /workspace/bina02-ppocr/run/train-stage8-optimizer-resume-v2/resume-stage4
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9
+ Skipping import of the encryption module
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+ W0726 18:20:33.083880 63135 eager_utils.cc:3584] Paddle static graph(PIR) not support input out tensor for now!!!!!
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1
+ #!/usr/bin/env python3
2
+ """Assemble isolated handwriting and Gemini template text in benchmark order."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import json
8
+ import os
9
+ import unicodedata
10
+ from pathlib import Path
11
+ from typing import Any, Callable
12
+
13
+ from rapidfuzz.distance import Levenshtein
14
+
15
+ PLACEHOLDER = "{{HANDWRITING}}"
16
+ VARIANTS: dict[str, tuple[float | None, bool]] = {
17
+ "simple_top": (None, False),
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+ "simple_spread_right": (None, True),
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+ "row055_top": (0.55, False),
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+ "row070_top": (0.70, False),
21
+ "row055_spread_right": (0.55, True),
22
+ "row070_spread_right": (0.70, True),
23
+ }
24
+
25
+
26
+ def normalized(text: str) -> str:
27
+ return " ".join(unicodedata.normalize("NFKC", str(text)).split())
28
+
29
+
30
+ def merge_template_text(template_text: str, handwriting: str) -> tuple[str, int]:
31
+ handwriting = normalized(handwriting)
32
+ count = str(template_text).count(PLACEHOLDER)
33
+ if count:
34
+ merged = str(template_text).replace(PLACEHOLDER, handwriting, 1)
35
+ merged = merged.replace(PLACEHOLDER, "")
36
+ elif normalized(template_text):
37
+ merged = f"{template_text} {handwriting}"
38
+ else:
39
+ merged = handwriting
40
+ return normalized(merged), count
41
+
42
+
43
+ def center(line: dict[str, Any]) -> tuple[float, float]:
44
+ x0, y0, x1, y1 = (float(value) for value in line["bbox"])
45
+ return (x0 + x1) / 2.0, (y0 + y1) / 2.0
46
+
47
+
48
+ def height(line: dict[str, Any]) -> float:
49
+ return max(float(line["bbox"][3]) - float(line["bbox"][1]), 1.0)
50
+
51
+
52
+ def simple(lines: list[dict[str, Any]]) -> list[list[dict[str, Any]]]:
53
+ return [[line] for line in sorted(lines, key=lambda item: (center(item)[1], -center(item)[0]))]
54
+
55
+
56
+ def clustered(lines: list[dict[str, Any]], factor: float) -> list[list[dict[str, Any]]]:
57
+ ordered = sorted(lines, key=lambda item: center(item)[1])
58
+ rows: list[list[dict[str, Any]]] = []
59
+ for line in ordered:
60
+ if not rows:
61
+ rows.append([line])
62
+ continue
63
+ row = rows[-1]
64
+ mean_y = sum(center(item)[1] for item in row) / len(row)
65
+ mean_height = sum(height(item) for item in row) / len(row)
66
+ if abs(center(line)[1] - mean_y) <= factor * max(height(line), mean_height):
67
+ row.append(line)
68
+ else:
69
+ rows.append([line])
70
+ for row in rows:
71
+ row.sort(key=lambda item: center(item)[0], reverse=True)
72
+ return rows
73
+
74
+
75
+ def layout(
76
+ lines: list[dict[str, Any]],
77
+ width: int,
78
+ height_value: int,
79
+ factor: float | None,
80
+ spread: bool,
81
+ ) -> list[list[dict[str, Any]]]:
82
+ builder: Callable[[list[dict[str, Any]]], list[list[dict[str, Any]]]]
83
+ builder = simple if factor is None else lambda value: clustered(value, factor)
84
+ aspect = width / max(height_value, 1)
85
+ if spread and 1.03 <= aspect <= 1.42:
86
+ midpoint = width / 2.0
87
+ return builder([line for line in lines if center(line)[0] >= midpoint]) + builder(
88
+ [line for line in lines if center(line)[0] < midpoint]
89
+ )
90
+ return builder(lines)
91
+
92
+
93
+ def handwriting_prediction(row: dict[str, Any], variant: str) -> str:
94
+ factor, spread = VARIANTS[variant]
95
+ rows = layout(
96
+ row.get("lines", []),
97
+ int(row.get("width") or 0),
98
+ int(row.get("height") or 0),
99
+ factor,
100
+ spread,
101
+ )
102
+ return "\n".join(
103
+ " ".join(
104
+ str(line.get("text") or "").strip()
105
+ for line in group
106
+ if str(line.get("text") or "").strip()
107
+ )
108
+ for group in rows
109
+ if any(str(line.get("text") or "").strip() for line in group)
110
+ )
111
+
112
+
113
+ def metric(reference: str, hypothesis: str) -> dict[str, int]:
114
+ reference = normalized(reference)
115
+ hypothesis = normalized(hypothesis)
116
+ return {
117
+ "char_errors": int(Levenshtein.distance(reference, hypothesis)),
118
+ "ref_chars": len(reference),
119
+ "word_errors": int(
120
+ Levenshtein.distance(reference.split(), hypothesis.split())
121
+ ),
122
+ "ref_words": len(reference.split()),
123
+ }
124
+
125
+
126
+ def aggregate(rows: list[dict[str, Any]]) -> dict[str, float | int]:
127
+ totals = {key: 0 for key in ("char_errors", "ref_chars", "word_errors", "ref_words")}
128
+ for row in rows:
129
+ for key, value in metric(row["reference"], row["prediction"]).items():
130
+ totals[key] += value
131
+ return {
132
+ **totals,
133
+ "wer": totals["word_errors"] / max(totals["ref_words"], 1),
134
+ "cer": totals["char_errors"] / max(totals["ref_chars"], 1),
135
+ }
136
+
137
+
138
+ def main() -> None:
139
+ parser = argparse.ArgumentParser()
140
+ parser.add_argument("--recognized-lines", type=Path, required=True)
141
+ parser.add_argument("--baseline-predictions", type=Path, required=True)
142
+ parser.add_argument("--output-dir", type=Path, required=True)
143
+ parser.add_argument("--isolation-variant", required=True)
144
+ args = parser.parse_args()
145
+ args.output_dir.mkdir(parents=True, exist_ok=True)
146
+ recognized = [
147
+ json.loads(line)
148
+ for line in args.recognized_lines.read_text(encoding="utf-8").splitlines()
149
+ if line.strip()
150
+ ]
151
+ selected_ids = {str(row["id"]) for row in recognized}
152
+ baseline = [
153
+ json.loads(line)
154
+ for line in args.baseline_predictions.read_text(encoding="utf-8").splitlines()
155
+ if line.strip() and str(json.loads(line).get("id")) in selected_ids
156
+ ]
157
+ baseline_metrics = aggregate(baseline)
158
+ candidates = {}
159
+ for layout_variant in VARIANTS:
160
+ rows = []
161
+ missing_placeholders = 0
162
+ multiple_placeholders = 0
163
+ for row in recognized:
164
+ handwriting = handwriting_prediction(row, layout_variant)
165
+ prediction, placeholders = merge_template_text(
166
+ str(row.get("template_text") or ""),
167
+ handwriting,
168
+ )
169
+ missing_placeholders += int(placeholders == 0)
170
+ multiple_placeholders += int(placeholders > 1)
171
+ rows.append(
172
+ {
173
+ "id": row["id"],
174
+ "split": "handwriting",
175
+ "reference": row["reference"],
176
+ "prediction": prediction,
177
+ "handwriting_prediction": normalized(handwriting),
178
+ "template_id": row.get("template_id"),
179
+ "template_placeholder_count": placeholders,
180
+ "isolation_variant": args.isolation_variant,
181
+ "layout_variant": layout_variant,
182
+ "detected_lines": len(row.get("lines", [])),
183
+ **({"error": row["error"]} if row.get("error") else {}),
184
+ }
185
+ )
186
+ path = args.output_dir / f"predictions-{layout_variant}.jsonl"
187
+ temporary = path.with_suffix(".jsonl.tmp")
188
+ with temporary.open("w", encoding="utf-8") as handle:
189
+ for row in rows:
190
+ handle.write(json.dumps(row, ensure_ascii=False) + "\n")
191
+ os.replace(temporary, path)
192
+ candidates[layout_variant] = {
193
+ **aggregate(rows),
194
+ "missing_placeholders": missing_placeholders,
195
+ "multiple_placeholders": multiple_placeholders,
196
+ "path": str(path),
197
+ }
198
+ best = min(
199
+ candidates,
200
+ key=lambda name: (
201
+ candidates[name]["wer"] + candidates[name]["cer"],
202
+ candidates[name]["cer"],
203
+ ),
204
+ )
205
+ summary = {
206
+ "isolation_variant": args.isolation_variant,
207
+ "rows": len(recognized),
208
+ "baseline": baseline_metrics,
209
+ "candidates": candidates,
210
+ "best_layout_variant": best,
211
+ "best": candidates[best],
212
+ }
213
+ (args.output_dir / "assembly-summary.json").write_text(
214
+ json.dumps(summary, ensure_ascii=False, indent=2) + "\n",
215
+ encoding="utf-8",
216
+ )
217
+ print(json.dumps(summary, ensure_ascii=False, indent=2))
218
+
219
+
220
+ if __name__ == "__main__":
221
+ main()
pipeline/benchmark_bina02_printed_only.py ADDED
@@ -0,0 +1,208 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Eight-way resumable printed-only exact benchmark for the Stage7 checkpoint."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import fcntl
8
+ import json
9
+ import os
10
+ import re
11
+ import time
12
+ import traceback
13
+ from pathlib import Path
14
+ from typing import Any
15
+
16
+ import cv2
17
+ import numpy as np
18
+ import pyarrow.parquet as pq
19
+ from paddleocr import PaddleOCR
20
+
21
+
22
+ ROWS = 6_669
23
+ LTR_RUN = re.compile(r"[a-zA-Z0-9 :*./%+-]")
24
+
25
+
26
+ def pred_reverse(text: str) -> str:
27
+ segments = []
28
+ current = ""
29
+ for character in text:
30
+ if LTR_RUN.search(character):
31
+ current += character
32
+ else:
33
+ if current:
34
+ segments.append(current)
35
+ current = ""
36
+ segments.append(character)
37
+ if current:
38
+ segments.append(current)
39
+ return "".join(reversed(segments))
40
+
41
+
42
+ def atomic_json(path: Path, payload: dict[str, Any]) -> None:
43
+ temporary = path.with_suffix(path.suffix + f".{os.getpid()}.tmp")
44
+ temporary.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n")
45
+ os.replace(temporary, path)
46
+
47
+
48
+ def ordered_text(payload: dict[str, Any]) -> tuple[str, int]:
49
+ texts = [pred_reverse(str(text)) for text in payload.get("rec_texts", [])]
50
+ boxes = payload.get("rec_boxes", [])
51
+ if len(boxes) != len(texts):
52
+ return "\n".join(text for text in texts if text.strip()), len(texts)
53
+ items = []
54
+ for text, box in zip(texts, boxes):
55
+ x0, y0, x1, y1 = (float(value) for value in box)
56
+ items.append(
57
+ {
58
+ "text": text,
59
+ "x": (x0 + x1) / 2,
60
+ "y": (y0 + y1) / 2,
61
+ "height": max(y1 - y0, 1),
62
+ }
63
+ )
64
+ items.sort(key=lambda item: item["y"])
65
+ rows = []
66
+ for item in items:
67
+ if not rows:
68
+ rows.append([item])
69
+ continue
70
+ row = rows[-1]
71
+ mean_y = sum(part["y"] for part in row) / len(row)
72
+ mean_h = sum(part["height"] for part in row) / len(row)
73
+ if abs(item["y"] - mean_y) <= 0.55 * max(item["height"], mean_h):
74
+ row.append(item)
75
+ else:
76
+ rows.append([item])
77
+ lines = []
78
+ for row in rows:
79
+ row.sort(key=lambda item: item["x"], reverse=True)
80
+ text = " ".join(item["text"].strip() for item in row if item["text"].strip())
81
+ if text:
82
+ lines.append(text)
83
+ return "\n".join(lines), len(texts)
84
+
85
+
86
+ def main() -> None:
87
+ parser = argparse.ArgumentParser()
88
+ parser.add_argument("--rank", type=int, required=True)
89
+ parser.add_argument("--world-size", type=int, default=8)
90
+ parser.add_argument("--data", type=Path, required=True)
91
+ parser.add_argument("--model-dir", type=Path, required=True)
92
+ parser.add_argument("--output-dir", type=Path, required=True)
93
+ parser.add_argument("--device", default="gpu:0")
94
+ args = parser.parse_args()
95
+ args.output_dir.mkdir(parents=True, exist_ok=True)
96
+ start = ROWS * args.rank // args.world_size
97
+ stop = ROWS * (args.rank + 1) // args.world_size
98
+ output_path = args.output_dir / f"predictions-rank{args.rank:02d}.jsonl"
99
+ status_path = args.output_dir / f"status-rank{args.rank:02d}.json"
100
+ done = set()
101
+ if output_path.is_file():
102
+ for line in output_path.read_text(encoding="utf-8").splitlines():
103
+ if line.strip():
104
+ done.add(json.loads(line)["id"])
105
+ ocr = PaddleOCR(
106
+ text_recognition_model_dir=str(args.model_dir),
107
+ text_recognition_batch_size=64,
108
+ use_doc_orientation_classify=False,
109
+ use_doc_unwarping=False,
110
+ use_textline_orientation=False,
111
+ text_rec_score_thresh=0.0,
112
+ text_detection_model_name="PP-OCRv6_medium_det",
113
+ device=args.device,
114
+ )
115
+ offset = 0
116
+ completed = 0
117
+ failures = 0
118
+ started = time.monotonic()
119
+ with output_path.open("a", encoding="utf-8", buffering=1) as output:
120
+ for shard in sorted((args.data / "data").glob("printed-*.parquet")):
121
+ table = pq.read_table(shard, columns=["image", "label"])
122
+ for local, row in enumerate(table.to_pylist()):
123
+ index = offset + local
124
+ if not start <= index < stop or f"printed:{index}" in done:
125
+ continue
126
+ value = row["image"]
127
+ encoded = value.get("bytes") if isinstance(value, dict) else value
128
+ image = cv2.imdecode(
129
+ np.frombuffer(encoded, dtype=np.uint8),
130
+ cv2.IMREAD_COLOR,
131
+ )
132
+ prediction = ""
133
+ line_count = 0
134
+ error = None
135
+ for attempt in range(1, 4):
136
+ try:
137
+ results = list(ocr.predict(image))
138
+ payload = results[0].json
139
+ if callable(payload):
140
+ payload = payload()
141
+ prediction, line_count = ordered_text(payload.get("res", payload))
142
+ error = None
143
+ break
144
+ except Exception as exc:
145
+ traceback.print_exc()
146
+ error = f"{type(exc).__name__}: {exc}"
147
+ time.sleep(attempt)
148
+ failures += int(error is not None)
149
+ result = {
150
+ "id": f"printed:{index}",
151
+ "split": "printed",
152
+ "reference": str(row.get("label") or ""),
153
+ "prediction": prediction,
154
+ "detected_lines": line_count,
155
+ }
156
+ if error:
157
+ result["error"] = error
158
+ output.write(json.dumps(result, ensure_ascii=False) + "\n")
159
+ completed += 1
160
+ offset += table.num_rows
161
+ atomic_json(
162
+ status_path,
163
+ {
164
+ "state": "complete",
165
+ "rank": args.rank,
166
+ "assigned": stop - start,
167
+ "completed_this_run": completed,
168
+ "failures": failures,
169
+ "rate_pages_per_second": completed / max(time.monotonic() - started, 1e-9),
170
+ },
171
+ )
172
+ with (args.output_dir / "merge.lock").open("a+") as lock:
173
+ fcntl.flock(lock, fcntl.LOCK_EX)
174
+ statuses = []
175
+ for rank in range(args.world_size):
176
+ path = args.output_dir / f"status-rank{rank:02d}.json"
177
+ if not path.is_file():
178
+ return
179
+ statuses.append(json.loads(path.read_text()))
180
+ rows = {}
181
+ for rank in range(args.world_size):
182
+ path = args.output_dir / f"predictions-rank{rank:02d}.jsonl"
183
+ for line in path.read_text(encoding="utf-8").splitlines():
184
+ if line.strip():
185
+ row = json.loads(line)
186
+ rows[row["id"]] = row
187
+ expected = [f"printed:{index}" for index in range(ROWS)]
188
+ if set(rows) != set(expected):
189
+ raise RuntimeError("printed merge contract failed")
190
+ with (args.output_dir / "predictions.jsonl.tmp").open("w", encoding="utf-8") as handle:
191
+ for row_id in expected:
192
+ handle.write(json.dumps(rows[row_id], ensure_ascii=False) + "\n")
193
+ os.replace(
194
+ args.output_dir / "predictions.jsonl.tmp",
195
+ args.output_dir / "predictions.jsonl",
196
+ )
197
+ atomic_json(
198
+ args.output_dir / "run-summary.json",
199
+ {
200
+ "state": "complete",
201
+ "rows": len(rows),
202
+ "failures": sum(value["failures"] for value in statuses),
203
+ },
204
+ )
205
+
206
+
207
+ if __name__ == "__main__":
208
+ main()
pipeline/bina02_template_layers.py ADDED
@@ -0,0 +1,393 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Template matching, handwriting isolation, and lossless template-text merging."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import json
7
+ import re
8
+ from dataclasses import dataclass
9
+ from pathlib import Path
10
+ from typing import Any
11
+
12
+ import cv2
13
+ import numpy as np
14
+ from PIL import Image
15
+
16
+
17
+ PLACEHOLDER = "{{HANDWRITING}}"
18
+ VARIANTS = {
19
+ "adaptive65": {"sigma": 2.5, "floor": 8.0, "percentile": 65.0},
20
+ "adaptive75": {"sigma": 4.0, "floor": 10.0, "percentile": 75.0},
21
+ "adaptive85": {"sigma": 5.5, "floor": 12.0, "percentile": 85.0},
22
+ "fixed20": {"fixed": 20.0},
23
+ "residual2": {"mode": "residual", "floor": 2.0, "gain": 2.0},
24
+ "residual3": {"mode": "residual", "floor": 3.0, "gain": 3.0},
25
+ "residual4": {"mode": "residual", "floor": 4.0, "gain": 4.0},
26
+ "residual6": {"mode": "residual", "floor": 6.0, "gain": 4.0},
27
+ "highpass08": {
28
+ "mode": "highpass",
29
+ "sigma": 0.8,
30
+ "floor": 1.5,
31
+ "gain": 4.0,
32
+ },
33
+ "highpass12": {
34
+ "mode": "highpass",
35
+ "sigma": 1.2,
36
+ "floor": 2.0,
37
+ "gain": 4.0,
38
+ },
39
+ "highpass20": {
40
+ "mode": "highpass",
41
+ "sigma": 2.0,
42
+ "floor": 2.0,
43
+ "gain": 3.0,
44
+ },
45
+ "highpass30": {
46
+ "mode": "highpass",
47
+ "sigma": 3.0,
48
+ "floor": 2.5,
49
+ "gain": 3.0,
50
+ },
51
+ }
52
+
53
+
54
+ @dataclass(frozen=True)
55
+ class Template:
56
+ id: str
57
+ file: str
58
+ image: np.ndarray
59
+ text_region: tuple[int, int, int, int]
60
+ template_text: str
61
+ blocks: tuple[dict[str, Any], ...]
62
+
63
+
64
+ @dataclass(frozen=True)
65
+ class TemplateMatch:
66
+ template: Template
67
+ median_abs_delta: float
68
+ p90_abs_delta: float
69
+ candidate_count: int
70
+
71
+
72
+ def normalize_text(value: str) -> str:
73
+ return " ".join(str(value).split())
74
+
75
+
76
+ def merge_template_text(template_text: str, handwriting: str) -> tuple[str, int]:
77
+ """Insert handwriting once and preserve every Gemini template token."""
78
+ template_text = str(template_text)
79
+ handwriting = normalize_text(handwriting)
80
+ count = template_text.count(PLACEHOLDER)
81
+ if count:
82
+ merged = template_text.replace(PLACEHOLDER, handwriting, 1)
83
+ merged = merged.replace(PLACEHOLDER, "")
84
+ elif normalize_text(template_text):
85
+ merged = f"{template_text} {handwriting}"
86
+ else:
87
+ merged = handwriting
88
+ return normalize_text(merged), count
89
+
90
+
91
+ class TemplateCatalog:
92
+ def __init__(self, background_root: Path, ocr_root: Path):
93
+ background_root = Path(background_root)
94
+ ocr_root = Path(ocr_root)
95
+ manifest = json.loads(
96
+ (background_root / "manifest.json").read_text(encoding="utf-8")
97
+ )
98
+ images: dict[str, np.ndarray] = {}
99
+ templates: list[Template] = []
100
+ for item in manifest:
101
+ filename = str(item["file"])
102
+ if filename not in images:
103
+ with Image.open(background_root / filename) as opened:
104
+ images[filename] = np.asarray(opened.convert("RGB"))
105
+ result: dict[str, Any] = {}
106
+ ocr_path = ocr_root / f"{item['id']}.json"
107
+ if ocr_path.is_file():
108
+ result = json.loads(
109
+ ocr_path.read_text(encoding="utf-8")
110
+ ).get("result", {})
111
+ templates.append(
112
+ Template(
113
+ id=str(item["id"]),
114
+ file=filename,
115
+ image=images[filename],
116
+ text_region=tuple(int(value) for value in item["text_region"]),
117
+ template_text=str(result.get("template_text", PLACEHOLDER)),
118
+ blocks=tuple(result.get("blocks", [])),
119
+ )
120
+ )
121
+ self.templates = tuple(templates)
122
+ self.by_id = {template.id: template for template in templates}
123
+
124
+ @staticmethod
125
+ def difference(image: np.ndarray, template: np.ndarray) -> np.ndarray:
126
+ if image.shape != template.shape:
127
+ raise ValueError(
128
+ f"image/template shape mismatch: {image.shape} != {template.shape}"
129
+ )
130
+ return np.max(
131
+ np.abs(image.astype(np.int16) - template.astype(np.int16)),
132
+ axis=2,
133
+ ).astype(np.float32)
134
+
135
+ def match(self, image: Image.Image | np.ndarray) -> TemplateMatch:
136
+ actual = (
137
+ np.asarray(image.convert("RGB"))
138
+ if isinstance(image, Image.Image)
139
+ else np.asarray(image)
140
+ )
141
+ height, width = actual.shape[:2]
142
+ candidates = [
143
+ template
144
+ for template in self.templates
145
+ if template.image.shape[:2] == (height, width)
146
+ ]
147
+ if not candidates:
148
+ raise ValueError(f"no template has native size {width}x{height}")
149
+
150
+ by_file: dict[str, tuple[float, float, np.ndarray]] = {}
151
+ for template in candidates:
152
+ if template.file in by_file:
153
+ continue
154
+ delta = self.difference(actual, template.image)
155
+ by_file[template.file] = (
156
+ float(np.median(delta)),
157
+ float(np.percentile(delta, 90)),
158
+ delta,
159
+ )
160
+ best_file = min(
161
+ by_file,
162
+ key=lambda name: (
163
+ by_file[name][0],
164
+ by_file[name][1],
165
+ name,
166
+ ),
167
+ )
168
+ same_file = [
169
+ template for template in candidates if template.file == best_file
170
+ ]
171
+ median, p90, delta = by_file[best_file]
172
+ if len(same_file) == 1:
173
+ selected = same_file[0]
174
+ else:
175
+ center = float(np.median(delta))
176
+ spread = float(np.median(np.abs(delta - center)))
177
+ threshold = max(8.0, center + 3.0 * max(spread, 1.0))
178
+
179
+ def region_energy(template: Template) -> float:
180
+ x0, y0, x1, y1 = template.text_region
181
+ x0 = max(0, min(width, x0))
182
+ x1 = max(0, min(width, x1))
183
+ y0 = max(0, min(height, y0))
184
+ y1 = max(0, min(height, y1))
185
+ region = delta[y0:y1, x0:x1]
186
+ if not region.size:
187
+ return -1.0
188
+ return float(np.maximum(region - threshold, 0.0).sum()) / region.size
189
+
190
+ selected = max(same_file, key=lambda template: (region_energy(template), template.id))
191
+ return TemplateMatch(
192
+ template=selected,
193
+ median_abs_delta=median,
194
+ p90_abs_delta=p90,
195
+ candidate_count=len(candidates),
196
+ )
197
+
198
+ @staticmethod
199
+ def _mask(delta: np.ndarray, variant: str) -> tuple[np.ndarray, float]:
200
+ if variant not in VARIANTS:
201
+ raise ValueError(f"unknown isolation variant: {variant}")
202
+ options = VARIANTS[variant]
203
+ if "fixed" in options:
204
+ threshold = float(options["fixed"])
205
+ else:
206
+ center = float(np.median(delta))
207
+ spread = float(np.median(np.abs(delta - center)))
208
+ threshold = max(
209
+ float(options["floor"]),
210
+ center + float(options["sigma"]) * max(spread, 1.0),
211
+ float(np.percentile(delta, float(options["percentile"]))),
212
+ )
213
+ mask = np.uint8(delta >= threshold)
214
+ count, labels, stats, _ = cv2.connectedComponentsWithStats(mask, 8)
215
+ filtered = np.zeros_like(mask)
216
+ for component in range(1, count):
217
+ if int(stats[component, cv2.CC_STAT_AREA]) >= 3:
218
+ filtered[labels == component] = 1
219
+ filtered = cv2.dilate(
220
+ filtered,
221
+ cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)),
222
+ iterations=1,
223
+ )
224
+ return filtered.astype(bool), threshold
225
+
226
+ @staticmethod
227
+ def _residual(
228
+ actual: np.ndarray,
229
+ template: np.ndarray,
230
+ variant: str,
231
+ ) -> tuple[np.ndarray, dict[str, float]]:
232
+ options = VARIANTS[variant]
233
+ channel_delta = template.astype(np.float32) - actual.astype(np.float32)
234
+ offsets = np.median(channel_delta.reshape(-1, 3), axis=0)
235
+ corrected = channel_delta - offsets.reshape(1, 1, 3)
236
+ signal = np.maximum(corrected.max(axis=2) - float(options["floor"]), 0.0)
237
+ signal = np.clip(signal * float(options["gain"]), 0.0, 245.0)
238
+ raw_mask = np.uint8(signal >= 3.0)
239
+ count, labels, stats, _ = cv2.connectedComponentsWithStats(raw_mask, 8)
240
+ mask = np.zeros_like(raw_mask)
241
+ for component in range(1, count):
242
+ if int(stats[component, cv2.CC_STAT_AREA]) >= 2:
243
+ mask[labels == component] = 1
244
+ darkness = np.zeros_like(signal)
245
+ darkness[mask.astype(bool)] = signal[mask.astype(bool)]
246
+ darkness = cv2.dilate(
247
+ darkness,
248
+ cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2)),
249
+ iterations=1,
250
+ )
251
+ output = np.full_like(actual, 255)
252
+ value = np.uint8(np.clip(255.0 - darkness, 0.0, 255.0))
253
+ retained = darkness >= 3.0
254
+ output[retained] = np.repeat(value[:, :, None], 3, axis=2)[retained]
255
+ return output, {
256
+ "threshold": float(options["floor"]),
257
+ "gain": float(options["gain"]),
258
+ "kept_fraction": float(retained.mean()),
259
+ "channel_offset_r": float(offsets[0]),
260
+ "channel_offset_g": float(offsets[1]),
261
+ "channel_offset_b": float(offsets[2]),
262
+ }
263
+
264
+ @staticmethod
265
+ def _highpass(
266
+ actual: np.ndarray,
267
+ template: np.ndarray,
268
+ variant: str,
269
+ ) -> tuple[np.ndarray, dict[str, float]]:
270
+ options = VARIANTS[variant]
271
+ sigma = float(options["sigma"])
272
+ actual_gray = cv2.cvtColor(actual, cv2.COLOR_RGB2GRAY).astype(np.float32)
273
+ template_gray = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY).astype(np.float32)
274
+ actual_darkness = cv2.GaussianBlur(actual_gray, (0, 0), sigma) - actual_gray
275
+ template_darkness = (
276
+ cv2.GaussianBlur(template_gray, (0, 0), sigma) - template_gray
277
+ )
278
+ signal = np.maximum(
279
+ actual_darkness - template_darkness - float(options["floor"]),
280
+ 0.0,
281
+ )
282
+ signal = np.clip(signal * float(options["gain"]), 0.0, 245.0)
283
+ raw_mask = np.uint8(signal >= 3.0)
284
+ count, labels, stats, _ = cv2.connectedComponentsWithStats(raw_mask, 8)
285
+ mask = np.zeros_like(raw_mask)
286
+ for component in range(1, count):
287
+ if int(stats[component, cv2.CC_STAT_AREA]) >= 2:
288
+ mask[labels == component] = 1
289
+ darkness = np.zeros_like(signal)
290
+ darkness[mask.astype(bool)] = signal[mask.astype(bool)]
291
+ darkness = cv2.dilate(
292
+ darkness,
293
+ cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2, 2)),
294
+ iterations=1,
295
+ )
296
+ retained = darkness >= 3.0
297
+ value = np.uint8(np.clip(255.0 - darkness, 0.0, 255.0))
298
+ output = np.repeat(value[:, :, None], 3, axis=2)
299
+ return output, {
300
+ "threshold": float(options["floor"]),
301
+ "gain": float(options["gain"]),
302
+ "sigma": sigma,
303
+ "kept_fraction": float(retained.mean()),
304
+ }
305
+
306
+ @classmethod
307
+ def _render(
308
+ cls,
309
+ actual: np.ndarray,
310
+ template: np.ndarray,
311
+ variant: str,
312
+ ) -> tuple[np.ndarray, dict[str, float]]:
313
+ options = VARIANTS.get(variant)
314
+ if options is None:
315
+ raise ValueError(f"unknown isolation variant: {variant}")
316
+ delta = cls.difference(actual, template)
317
+ if options.get("mode") == "residual":
318
+ output, metrics = cls._residual(actual, template, variant)
319
+ elif options.get("mode") == "highpass":
320
+ output, metrics = cls._highpass(actual, template, variant)
321
+ else:
322
+ mask, threshold = cls._mask(delta, variant)
323
+ output = np.full_like(actual, 255)
324
+ output[mask] = actual[mask]
325
+ metrics = {
326
+ "threshold": threshold,
327
+ "kept_fraction": float(mask.mean()),
328
+ }
329
+ metrics.update(
330
+ {
331
+ "median_abs_delta": float(np.median(delta)),
332
+ "p90_abs_delta": float(np.percentile(delta, 90)),
333
+ }
334
+ )
335
+ return output, metrics
336
+
337
+ def isolate_with_template(
338
+ self,
339
+ image: Image.Image | np.ndarray,
340
+ template: Template,
341
+ variant: str,
342
+ ) -> tuple[Image.Image, dict[str, float]]:
343
+ actual = (
344
+ np.asarray(image.convert("RGB"))
345
+ if isinstance(image, Image.Image)
346
+ else np.asarray(image)
347
+ )
348
+ output, metrics = self._render(actual, template.image, variant)
349
+ return Image.fromarray(output, mode="RGB"), metrics
350
+
351
+ def isolate(
352
+ self,
353
+ image: Image.Image | np.ndarray,
354
+ variant: str,
355
+ ) -> tuple[Image.Image, TemplateMatch, dict[str, float]]:
356
+ match = self.match(image)
357
+ isolated, metrics = self.isolate_with_template(
358
+ image,
359
+ match.template,
360
+ variant,
361
+ )
362
+ return isolated, match, metrics
363
+
364
+ def isolate_crop(
365
+ self,
366
+ image: Image.Image | np.ndarray,
367
+ template_id: str,
368
+ crop_bbox: list[int] | tuple[int, int, int, int],
369
+ variant: str,
370
+ ) -> tuple[Image.Image, dict[str, float]]:
371
+ actual = (
372
+ np.asarray(image.convert("RGB"))
373
+ if isinstance(image, Image.Image)
374
+ else np.asarray(image)
375
+ )
376
+ template = self.by_id[template_id]
377
+ x0, y0, x1, y1 = (int(value) for value in crop_bbox)
378
+ background = template.image[y0:y1, x0:x1]
379
+ if background.shape != actual.shape:
380
+ background = cv2.resize(
381
+ background,
382
+ (actual.shape[1], actual.shape[0]),
383
+ interpolation=cv2.INTER_LANCZOS4,
384
+ )
385
+ output, metrics = self._render(actual, background, variant)
386
+ return Image.fromarray(output, mode="RGB"), metrics
387
+
388
+
389
+ def index_from_id(value: str) -> int:
390
+ match = re.fullmatch(r"handwriting:(\d+)", str(value))
391
+ if not match:
392
+ raise ValueError(f"invalid handwriting row id: {value}")
393
+ return int(match.group(1))
pipeline/decode_bina02_persian.py ADDED
@@ -0,0 +1,315 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ from __future__ import annotations
3
+
4
+ import argparse
5
+ import json
6
+ import math
7
+ import re
8
+ import sqlite3
9
+ import unicodedata
10
+ from collections import defaultdict
11
+ from functools import lru_cache
12
+ from pathlib import Path
13
+ from typing import Any
14
+
15
+ from rapidfuzz.distance import Levenshtein
16
+
17
+
18
+ TOKEN_RE = re.compile(r"[\u0600-\u06ff]+")
19
+ DIACRITICS_RE = re.compile(r"[\u064b-\u065f\u0670\u06d6-\u06ed]")
20
+
21
+
22
+ def parse_args() -> argparse.Namespace:
23
+ parser = argparse.ArgumentParser()
24
+ parser.add_argument("--database", type=Path, required=True)
25
+ parser.add_argument("--input", type=Path, required=True)
26
+ parser.add_argument("--output", type=Path, required=True)
27
+ parser.add_argument("--details", type=Path)
28
+ parser.add_argument("--minimum-frequency", type=int, default=2)
29
+ parser.add_argument("--frequent-without-context", type=int, default=250)
30
+ parser.add_argument("--minimum-margin", type=float, default=4.0)
31
+ return parser.parse_args()
32
+
33
+
34
+ def normalize_word(value: str) -> str:
35
+ word = unicodedata.normalize("NFKC", value or "")
36
+ for source, target in (
37
+ ("ي", "ی"),
38
+ ("ك", "ک"),
39
+ ("ۀ", "ه"),
40
+ ("ة", "ه"),
41
+ ("أ", "ا"),
42
+ ("إ", "ا"),
43
+ ("ٱ", "ا"),
44
+ ("آ", "ا"),
45
+ ("ؤ", "و"),
46
+ ("ئ", "ی"),
47
+ ("ى", "ی"),
48
+ ):
49
+ word = word.replace(source, target)
50
+ word = DIACRITICS_RE.sub("", word)
51
+ return (
52
+ word.replace("\u200c", "")
53
+ .replace("\u200d", "")
54
+ .replace("\u200b", "")
55
+ )
56
+
57
+
58
+ def one_deletes(word: str) -> set[str]:
59
+ return {
60
+ word[:index] + word[index + 1 :]
61
+ for index in range(len(word))
62
+ }
63
+
64
+
65
+ class PersianLexiconDecoder:
66
+ def __init__(
67
+ self,
68
+ database: Path,
69
+ *,
70
+ minimum_frequency: int,
71
+ frequent_without_context: int,
72
+ minimum_margin: float,
73
+ ) -> None:
74
+ uri = f"file:{database.resolve()}?mode=ro"
75
+ self.connection = sqlite3.connect(uri, uri=True)
76
+ self.minimum_frequency = minimum_frequency
77
+ self.frequent_without_context = frequent_without_context
78
+ self.minimum_margin = minimum_margin
79
+ self.words: dict[str, tuple[int, bool]] = {}
80
+ for word, frequency, hardword in self.connection.execute(
81
+ "SELECT word, frequency, hardword FROM unigram "
82
+ "WHERE frequency >= ? OR hardword = 1",
83
+ (minimum_frequency,),
84
+ ):
85
+ self.words[str(word)] = (int(frequency), bool(hardword))
86
+
87
+ self.delete_index: dict[str, list[str]] = defaultdict(list)
88
+ for word in self.words:
89
+ if not 3 <= len(word) <= 32:
90
+ continue
91
+ for deletion in one_deletes(word):
92
+ self.delete_index[deletion].append(word)
93
+
94
+ @lru_cache(maxsize=500_000)
95
+ def bigram_frequency(self, left: str, right: str) -> int:
96
+ row = self.connection.execute(
97
+ "SELECT frequency FROM bigram "
98
+ "WHERE left_word = ? AND right_word = ?",
99
+ (left, right),
100
+ ).fetchone()
101
+ return int(row[0]) if row else 0
102
+
103
+ @lru_cache(maxsize=500_000)
104
+ def candidates(self, word: str) -> tuple[str, ...]:
105
+ normalized = normalize_word(word)
106
+ possible: set[str] = set()
107
+ if normalized in self.words:
108
+ possible.add(normalized)
109
+ deletes = one_deletes(normalized)
110
+ for deletion in deletes:
111
+ if deletion in self.words:
112
+ possible.add(deletion)
113
+ for key in {normalized, *deletes}:
114
+ possible.update(self.delete_index.get(key, ()))
115
+ return tuple(possible)
116
+
117
+ def correction(
118
+ self,
119
+ word: str,
120
+ left: str | None,
121
+ right: str | None,
122
+ ) -> tuple[str, dict[str, Any] | None]:
123
+ normalized = normalize_word(word)
124
+ if len(normalized) < 3 or normalized in self.words:
125
+ return word, None
126
+
127
+ maximum_distance = 1 if len(normalized) <= 6 else 2
128
+ ranked: list[tuple[float, str, int, int, int, bool]] = []
129
+ for candidate in self.candidates(normalized):
130
+ distance = Levenshtein.distance(
131
+ normalized,
132
+ candidate,
133
+ score_cutoff=maximum_distance,
134
+ )
135
+ if distance > maximum_distance:
136
+ continue
137
+ frequency, hardword = self.words[candidate]
138
+ left_frequency = (
139
+ self.bigram_frequency(left, candidate) if left else 0
140
+ )
141
+ right_frequency = (
142
+ self.bigram_frequency(candidate, right) if right else 0
143
+ )
144
+ contextual = left_frequency + right_frequency
145
+ if distance == 2 and not (
146
+ contextual
147
+ or frequency >= 2 * self.frequent_without_context
148
+ ):
149
+ continue
150
+ if not contextual and (
151
+ frequency < self.frequent_without_context
152
+ ):
153
+ continue
154
+ score = (
155
+ 0.85 * math.log1p(frequency)
156
+ + 1.35 * math.log1p(left_frequency)
157
+ + 1.35 * math.log1p(right_frequency)
158
+ + (0.8 if hardword else 0.0)
159
+ - 3.8 * distance
160
+ )
161
+ ranked.append(
162
+ (
163
+ score,
164
+ candidate,
165
+ distance,
166
+ frequency,
167
+ contextual,
168
+ hardword,
169
+ )
170
+ )
171
+
172
+ if not ranked:
173
+ return word, None
174
+ ranked.sort(reverse=True)
175
+ best = ranked[0]
176
+ second_score = ranked[1][0] if len(ranked) > 1 else -math.inf
177
+ margin = best[0] - second_score
178
+ if margin < self.minimum_margin:
179
+ return word, None
180
+
181
+ replacement = best[1]
182
+ return replacement, {
183
+ "original": word,
184
+ "normalized": normalized,
185
+ "replacement": replacement,
186
+ "distance": best[2],
187
+ "frequency": best[3],
188
+ "context_frequency": best[4],
189
+ "hardword": best[5],
190
+ "margin": round(margin, 6)
191
+ if math.isfinite(margin)
192
+ else None,
193
+ }
194
+
195
+ def decode(self, text: str) -> tuple[str, list[dict[str, Any]]]:
196
+ matches = list(TOKEN_RE.finditer(text))
197
+ if not matches:
198
+ return text, []
199
+ normalized_words = [
200
+ normalize_word(match.group(0)) for match in matches
201
+ ]
202
+ replacements: list[str] = []
203
+ changes: list[dict[str, Any]] = []
204
+ for index, match in enumerate(matches):
205
+ left = normalized_words[index - 1] if index else None
206
+ right = (
207
+ normalized_words[index + 1]
208
+ if index + 1 < len(normalized_words)
209
+ else None
210
+ )
211
+ replacement, detail = self.correction(
212
+ match.group(0),
213
+ left,
214
+ right,
215
+ )
216
+ replacements.append(replacement)
217
+ if detail is not None:
218
+ detail["token_index"] = index
219
+ changes.append(detail)
220
+
221
+ output: list[str] = []
222
+ cursor = 0
223
+ for match, replacement in zip(matches, replacements):
224
+ output.append(text[cursor : match.start()])
225
+ output.append(replacement)
226
+ cursor = match.end()
227
+ output.append(text[cursor:])
228
+ return "".join(output), changes
229
+
230
+
231
+ def main() -> None:
232
+ args = parse_args()
233
+ decoder = PersianLexiconDecoder(
234
+ args.database,
235
+ minimum_frequency=args.minimum_frequency,
236
+ frequent_without_context=args.frequent_without_context,
237
+ minimum_margin=args.minimum_margin,
238
+ )
239
+ if args.output.exists():
240
+ raise FileExistsError(
241
+ f"Refusing to replace existing output: {args.output}"
242
+ )
243
+ if args.details and args.details.exists():
244
+ raise FileExistsError(
245
+ f"Refusing to replace existing details: {args.details}"
246
+ )
247
+
248
+ args.output.parent.mkdir(parents=True, exist_ok=True)
249
+ details_handle = (
250
+ args.details.open("w", encoding="utf-8")
251
+ if args.details
252
+ else None
253
+ )
254
+ rows = 0
255
+ changed_rows = 0
256
+ changed_tokens = 0
257
+ try:
258
+ with args.input.open(encoding="utf-8") as source:
259
+ with args.output.open("w", encoding="utf-8") as destination:
260
+ for line in source:
261
+ if not line.strip():
262
+ continue
263
+ row = json.loads(line)
264
+ prediction = str(
265
+ row.get(
266
+ "prediction",
267
+ row.get("hyp", row.get("hypothesis", "")),
268
+ )
269
+ or ""
270
+ )
271
+ decoded, changes = decoder.decode(prediction)
272
+ row["prediction_before_persian_decoder"] = prediction
273
+ row["prediction"] = decoded
274
+ row["persian_decoder_changes"] = len(changes)
275
+ destination.write(
276
+ json.dumps(row, ensure_ascii=False) + "\n"
277
+ )
278
+ rows += 1
279
+ if changes:
280
+ changed_rows += 1
281
+ changed_tokens += len(changes)
282
+ if details_handle is not None:
283
+ details_handle.write(
284
+ json.dumps(
285
+ {
286
+ "id": row.get("id"),
287
+ "split": row.get("split"),
288
+ "changes": changes,
289
+ },
290
+ ensure_ascii=False,
291
+ )
292
+ + "\n"
293
+ )
294
+ finally:
295
+ if details_handle is not None:
296
+ details_handle.close()
297
+ decoder.connection.close()
298
+
299
+ print(
300
+ json.dumps(
301
+ {
302
+ "rows": rows,
303
+ "changed_rows": changed_rows,
304
+ "changed_tokens": changed_tokens,
305
+ "lexicon_words": len(decoder.words),
306
+ "delete_keys": len(decoder.delete_index),
307
+ },
308
+ ensure_ascii=False,
309
+ indent=2,
310
+ )
311
+ )
312
+
313
+
314
+ if __name__ == "__main__":
315
+ main()
pipeline/detect_bina02_two_layer.py ADDED
@@ -0,0 +1,314 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Resumable Surya detection on template-subtracted handwriting pages."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import fcntl
8
+ import io
9
+ import json
10
+ import os
11
+ import time
12
+ import traceback
13
+ from pathlib import Path
14
+ from typing import Any
15
+
16
+ import numpy as np
17
+ import pyarrow.parquet as pq
18
+ from PIL import Image
19
+ from surya.detection import DetectionPredictor
20
+
21
+ from bina02_template_layers import TemplateCatalog
22
+
23
+
24
+ ROWS = 6_669
25
+
26
+
27
+ def parse_args() -> argparse.Namespace:
28
+ parser = argparse.ArgumentParser()
29
+ parser.add_argument("--rank", type=int, required=True)
30
+ parser.add_argument("--world-size", type=int, required=True)
31
+ parser.add_argument("--data", type=Path, required=True)
32
+ parser.add_argument("--background-root", type=Path, required=True)
33
+ parser.add_argument("--ocr-root", type=Path, required=True)
34
+ parser.add_argument("--output-dir", type=Path, required=True)
35
+ parser.add_argument("--variant", required=True)
36
+ parser.add_argument("--limit", type=int, default=ROWS)
37
+ parser.add_argument("--batch-size", type=int, default=8)
38
+ parser.add_argument("--progress-every", type=int, default=20)
39
+ return parser.parse_args()
40
+
41
+
42
+ def atomic_json(path: Path, payload: dict[str, Any]) -> None:
43
+ temporary = path.with_suffix(path.suffix + f".{os.getpid()}.tmp")
44
+ temporary.write_text(
45
+ json.dumps(payload, ensure_ascii=False, indent=2) + "\n",
46
+ encoding="utf-8",
47
+ )
48
+ os.replace(temporary, path)
49
+
50
+
51
+ def selected_indices(limit: int) -> list[int]:
52
+ if not 1 <= limit <= ROWS:
53
+ raise ValueError(f"limit must be in [1, {ROWS}]")
54
+ if limit == ROWS:
55
+ return list(range(ROWS))
56
+ return sorted(
57
+ {
58
+ int(round(value))
59
+ for value in np.linspace(0, ROWS - 1, num=limit)
60
+ }
61
+ )
62
+
63
+
64
+ def load_rows(data: Path, wanted: set[int]) -> dict[int, dict[str, Any]]:
65
+ rows: dict[int, dict[str, Any]] = {}
66
+ offset = 0
67
+ paths = sorted((data / "data").glob("handwriting-*.parquet"))
68
+ if len(paths) != 14:
69
+ raise RuntimeError(f"expected 14 handwriting shards, found {len(paths)}")
70
+ for path in paths:
71
+ count = pq.ParquetFile(path).metadata.num_rows
72
+ local = sorted(index - offset for index in wanted if offset <= index < offset + count)
73
+ if local:
74
+ table = pq.read_table(path, columns=["image", "label"])
75
+ for index in local:
76
+ rows[offset + index] = table.slice(index, 1).to_pylist()[0]
77
+ offset += count
78
+ if offset != ROWS or rows.keys() != wanted:
79
+ raise RuntimeError(
80
+ f"benchmark row contract failed: total={offset}, loaded={len(rows)}, wanted={len(wanted)}"
81
+ )
82
+ return rows
83
+
84
+
85
+ def decode_image(row: dict[str, Any]) -> Image.Image:
86
+ value = row["image"]
87
+ encoded = value.get("bytes") if isinstance(value, dict) else value
88
+ if encoded is None and isinstance(value, dict) and value.get("path"):
89
+ encoded = Path(value["path"]).read_bytes()
90
+ if not encoded:
91
+ raise ValueError("dataset row has no image bytes")
92
+ return Image.open(io.BytesIO(encoded)).convert("RGB")
93
+
94
+
95
+ def line_payload(result: Any) -> list[dict[str, Any]]:
96
+ lines = []
97
+ for item in getattr(result, "bboxes", []):
98
+ bbox = getattr(item, "bbox", None)
99
+ if bbox is None or len(bbox) != 4:
100
+ continue
101
+ polygon = getattr(item, "polygon", None)
102
+ lines.append(
103
+ {
104
+ "bbox": [float(value) for value in bbox],
105
+ "confidence": float(getattr(item, "confidence", 0.0)),
106
+ "polygon": (
107
+ [[float(value) for value in point] for point in polygon]
108
+ if polygon is not None
109
+ else None
110
+ ),
111
+ }
112
+ )
113
+ return lines
114
+
115
+
116
+ def load_done(path: Path) -> set[str]:
117
+ if not path.is_file():
118
+ return set()
119
+ with path.open(encoding="utf-8") as handle:
120
+ return {
121
+ str(row["id"])
122
+ for line in handle
123
+ if line.strip()
124
+ for row in [json.loads(line)]
125
+ }
126
+
127
+
128
+ def merge_if_complete(
129
+ output_dir: Path,
130
+ world_size: int,
131
+ selected: list[int],
132
+ variant: str,
133
+ ) -> None:
134
+ with (output_dir / "merge.lock").open("a+") as lock:
135
+ fcntl.flock(lock, fcntl.LOCK_EX)
136
+ statuses = []
137
+ for rank in range(world_size):
138
+ path = output_dir / f"status-rank{rank:02d}.json"
139
+ if not path.is_file():
140
+ return
141
+ value = json.loads(path.read_text(encoding="utf-8"))
142
+ if value.get("state") != "complete":
143
+ return
144
+ statuses.append(value)
145
+ rows: dict[str, dict[str, Any]] = {}
146
+ duplicates = []
147
+ for rank in range(world_size):
148
+ path = output_dir / f"detections-rank{rank:02d}.jsonl"
149
+ with path.open(encoding="utf-8") as handle:
150
+ for line in handle:
151
+ row = json.loads(line)
152
+ row_id = str(row["id"])
153
+ if row_id in rows:
154
+ duplicates.append(row_id)
155
+ rows[row_id] = row
156
+ expected = [f"handwriting:{index}" for index in selected]
157
+ missing = sorted(set(expected) - rows.keys())
158
+ extras = sorted(rows.keys() - set(expected))
159
+ if duplicates or missing or extras:
160
+ raise RuntimeError(
161
+ f"detection merge failed: duplicates={duplicates[:3]} missing={missing[:3]} extras={extras[:3]}"
162
+ )
163
+ temporary = output_dir / "detections.jsonl.tmp"
164
+ with temporary.open("w", encoding="utf-8") as handle:
165
+ for row_id in expected:
166
+ handle.write(json.dumps(rows[row_id], ensure_ascii=False) + "\n")
167
+ os.replace(temporary, output_dir / "detections.jsonl")
168
+ atomic_json(
169
+ output_dir / "run-summary.json",
170
+ {
171
+ "state": "complete",
172
+ "variant": variant,
173
+ "selected_indices": selected,
174
+ "rows": len(rows),
175
+ "failed_pages": sum(row["failures"] for row in statuses),
176
+ "template_match_failures": sum(
177
+ row["template_match_failures"] for row in statuses
178
+ ),
179
+ "detected_lines": sum(row["detected_lines"] for row in statuses),
180
+ "completed_unix": time.time(),
181
+ },
182
+ )
183
+
184
+
185
+ def main() -> None:
186
+ args = parse_args()
187
+ selected = selected_indices(args.limit)
188
+ assigned = selected[args.rank :: args.world_size]
189
+ output_path = args.output_dir / f"detections-rank{args.rank:02d}.jsonl"
190
+ status_path = args.output_dir / f"status-rank{args.rank:02d}.json"
191
+ args.output_dir.mkdir(parents=True, exist_ok=True)
192
+ done = load_done(output_path)
193
+ wanted = {
194
+ index for index in assigned if f"handwriting:{index}" not in done
195
+ }
196
+ rows = load_rows(args.data, wanted) if wanted else {}
197
+ catalog = TemplateCatalog(args.background_root, args.ocr_root)
198
+ predictor = DetectionPredictor.local(device="cuda")
199
+ started = time.monotonic()
200
+ completed = 0
201
+ failures = 0
202
+ match_failures = 0
203
+ detected_lines = 0
204
+ pending: list[tuple[int, dict[str, Any], Image.Image, dict[str, Any]]] = []
205
+
206
+ with output_path.open("a", encoding="utf-8", buffering=1) as output:
207
+ def flush() -> None:
208
+ nonlocal completed, failures, detected_lines
209
+ if not pending:
210
+ return
211
+ images = [item[2] for item in pending]
212
+ try:
213
+ results: list[Any | Exception] = list(predictor(images))
214
+ if len(results) != len(images):
215
+ raise RuntimeError("detector returned wrong result count")
216
+ except Exception:
217
+ traceback.print_exc()
218
+ results = []
219
+ for image in images:
220
+ try:
221
+ results.append(list(predictor([image]))[0])
222
+ except Exception as exc:
223
+ results.append(exc)
224
+ for (index, row, image, layer), result in zip(pending, results):
225
+ lines = []
226
+ error = None
227
+ if isinstance(result, Exception):
228
+ failures += 1
229
+ error = f"{type(result).__name__}: {result}"
230
+ else:
231
+ lines = line_payload(result)
232
+ detected_lines += len(lines)
233
+ payload = {
234
+ "id": f"handwriting:{index}",
235
+ "split": "handwriting",
236
+ "reference": str(row.get("label") or ""),
237
+ "width": image.width,
238
+ "height": image.height,
239
+ "variant": args.variant,
240
+ "lines": lines,
241
+ **layer,
242
+ }
243
+ if error:
244
+ payload["error"] = error
245
+ output.write(json.dumps(payload, ensure_ascii=False) + "\n")
246
+ completed += 1
247
+ pending.clear()
248
+
249
+ for index in assigned:
250
+ if index not in wanted:
251
+ continue
252
+ row = rows[index]
253
+ try:
254
+ image = decode_image(row)
255
+ isolated, match, metrics = catalog.isolate(image, args.variant)
256
+ layer = {
257
+ "template_id": match.template.id,
258
+ "template_file": match.template.file,
259
+ "template_text": match.template.template_text,
260
+ "template_placeholder_count": match.template.template_text.count(
261
+ "{{HANDWRITING}}"
262
+ ),
263
+ "match_median_abs_delta": match.median_abs_delta,
264
+ "match_p90_abs_delta": match.p90_abs_delta,
265
+ "mask_threshold": metrics["threshold"],
266
+ "mask_kept_fraction": metrics["kept_fraction"],
267
+ }
268
+ pending.append((index, row, isolated, layer))
269
+ if len(pending) >= args.batch_size:
270
+ flush()
271
+ except Exception as exc:
272
+ traceback.print_exc()
273
+ failures += 1
274
+ match_failures += 1
275
+ completed += 1
276
+ output.write(
277
+ json.dumps(
278
+ {
279
+ "id": f"handwriting:{index}",
280
+ "split": "handwriting",
281
+ "reference": str(row.get("label") or ""),
282
+ "width": 0,
283
+ "height": 0,
284
+ "variant": args.variant,
285
+ "lines": [],
286
+ "error": f"{type(exc).__name__}: {exc}",
287
+ },
288
+ ensure_ascii=False,
289
+ )
290
+ + "\n"
291
+ )
292
+ flush()
293
+
294
+ elapsed = max(time.monotonic() - started, 1e-9)
295
+ atomic_json(
296
+ status_path,
297
+ {
298
+ "state": "complete",
299
+ "rank": args.rank,
300
+ "assigned": len(assigned),
301
+ "completed": completed,
302
+ "failures": failures,
303
+ "template_match_failures": match_failures,
304
+ "detected_lines": detected_lines,
305
+ "rate_pages_per_second": completed / elapsed,
306
+ "eta_seconds": 0,
307
+ "updated_unix": time.time(),
308
+ },
309
+ )
310
+ merge_if_complete(args.output_dir, args.world_size, selected, args.variant)
311
+
312
+
313
+ if __name__ == "__main__":
314
+ main()
pipeline/recognize_bina02_two_layer.py ADDED
@@ -0,0 +1,376 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Recognize cached Surya boxes from the same isolated handwriting layer."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import fcntl
8
+ import io
9
+ import json
10
+ import os
11
+ import re
12
+ import time
13
+ import traceback
14
+ from pathlib import Path
15
+ from typing import Any
16
+
17
+ import numpy as np
18
+ import pyarrow.parquet as pq
19
+ from paddleocr import TextRecognition
20
+ from PIL import Image
21
+
22
+ from bina02_template_layers import TemplateCatalog, index_from_id
23
+
24
+
25
+ ROWS = 6_669
26
+ LTR_RUN = re.compile(r"[a-zA-Z0-9 :*./%+-]")
27
+
28
+
29
+ def parse_args() -> argparse.Namespace:
30
+ parser = argparse.ArgumentParser()
31
+ parser.add_argument("--rank", type=int, required=True)
32
+ parser.add_argument("--world-size", type=int, required=True)
33
+ parser.add_argument("--data", type=Path, required=True)
34
+ parser.add_argument("--background-root", type=Path, required=True)
35
+ parser.add_argument("--ocr-root", type=Path, required=True)
36
+ parser.add_argument("--detections-dir", type=Path, required=True)
37
+ parser.add_argument("--model-dir", type=Path, required=True)
38
+ parser.add_argument("--output-dir", type=Path, required=True)
39
+ parser.add_argument("--variant", required=True)
40
+ parser.add_argument("--device", default="gpu:0")
41
+ parser.add_argument("--crop-buffer", type=int, default=512)
42
+ parser.add_argument("--recognition-batch-size", type=int, default=64)
43
+ parser.add_argument("--pad-x-ratio", type=float, default=0.01)
44
+ parser.add_argument("--pad-y-ratio", type=float, default=0.08)
45
+ parser.add_argument("--row-factor", type=float, default=0.70)
46
+ return parser.parse_args()
47
+
48
+
49
+ def atomic_json(path: Path, payload: dict[str, Any]) -> None:
50
+ temporary = path.with_suffix(path.suffix + f".{os.getpid()}.tmp")
51
+ temporary.write_text(
52
+ json.dumps(payload, ensure_ascii=False, indent=2) + "\n",
53
+ encoding="utf-8",
54
+ )
55
+ os.replace(temporary, path)
56
+
57
+
58
+ def pred_reverse(text: str) -> str:
59
+ segments: list[str] = []
60
+ current = ""
61
+ for character in text:
62
+ if LTR_RUN.search(character):
63
+ current += character
64
+ else:
65
+ if current:
66
+ segments.append(current)
67
+ current = ""
68
+ segments.append(character)
69
+ if current:
70
+ segments.append(current)
71
+ return "".join(reversed(segments))
72
+
73
+
74
+ def load_rows(data: Path, wanted: set[int]) -> dict[int, dict[str, Any]]:
75
+ rows: dict[int, dict[str, Any]] = {}
76
+ offset = 0
77
+ paths = sorted((data / "data").glob("handwriting-*.parquet"))
78
+ for path in paths:
79
+ count = pq.ParquetFile(path).metadata.num_rows
80
+ local = sorted(index - offset for index in wanted if offset <= index < offset + count)
81
+ if local:
82
+ table = pq.read_table(path, columns=["image", "label"])
83
+ for index in local:
84
+ rows[offset + index] = table.slice(index, 1).to_pylist()[0]
85
+ offset += count
86
+ if offset != ROWS or rows.keys() != wanted:
87
+ raise RuntimeError("benchmark row load contract failed")
88
+ return rows
89
+
90
+
91
+ def decode_image(row: dict[str, Any]) -> Image.Image:
92
+ value = row["image"]
93
+ encoded = value.get("bytes") if isinstance(value, dict) else value
94
+ if encoded is None and isinstance(value, dict) and value.get("path"):
95
+ encoded = Path(value["path"]).read_bytes()
96
+ if not encoded:
97
+ raise ValueError("dataset row has no image bytes")
98
+ return Image.open(io.BytesIO(encoded)).convert("RGB")
99
+
100
+
101
+ def center(line: dict[str, Any]) -> tuple[float, float]:
102
+ x0, y0, x1, y1 = (float(value) for value in line["bbox"])
103
+ return (x0 + x1) / 2.0, (y0 + y1) / 2.0
104
+
105
+
106
+ def height(line: dict[str, Any]) -> float:
107
+ return max(float(line["bbox"][3]) - float(line["bbox"][1]), 1.0)
108
+
109
+
110
+ def group_rows(lines: list[dict[str, Any]], factor: float) -> list[list[dict[str, Any]]]:
111
+ ordered = sorted(lines, key=lambda line: center(line)[1])
112
+ rows: list[list[dict[str, Any]]] = []
113
+ for line in ordered:
114
+ if not rows:
115
+ rows.append([line])
116
+ continue
117
+ previous = rows[-1]
118
+ mean_y = sum(center(item)[1] for item in previous) / len(previous)
119
+ mean_height = sum(height(item) for item in previous) / len(previous)
120
+ if abs(center(line)[1] - mean_y) <= factor * max(height(line), mean_height):
121
+ previous.append(line)
122
+ else:
123
+ rows.append([line])
124
+ for row in rows:
125
+ row.sort(key=lambda line: center(line)[0], reverse=True)
126
+ return rows
127
+
128
+
129
+ def grouped_lines(
130
+ lines: list[dict[str, Any]],
131
+ width: int,
132
+ height_value: int,
133
+ factor: float,
134
+ ) -> list[dict[str, Any]]:
135
+ aspect = width / max(height_value, 1)
136
+ if 1.03 <= aspect <= 1.42:
137
+ midpoint = width / 2.0
138
+ groups = group_rows(
139
+ [line for line in lines if center(line)[0] >= midpoint],
140
+ factor,
141
+ ) + group_rows(
142
+ [line for line in lines if center(line)[0] < midpoint],
143
+ factor,
144
+ )
145
+ else:
146
+ groups = group_rows(lines, factor)
147
+ return [
148
+ {
149
+ "bbox": [
150
+ min(float(line["bbox"][0]) for line in row),
151
+ min(float(line["bbox"][1]) for line in row),
152
+ max(float(line["bbox"][2]) for line in row),
153
+ max(float(line["bbox"][3]) for line in row),
154
+ ],
155
+ "confidence": sum(float(line.get("confidence", 0.0)) for line in row)
156
+ / len(row),
157
+ "member_count": len(row),
158
+ }
159
+ for row in groups
160
+ ]
161
+
162
+
163
+ def crop(
164
+ image: Image.Image,
165
+ bbox: list[float],
166
+ pad_x_ratio: float,
167
+ pad_y_ratio: float,
168
+ ) -> np.ndarray:
169
+ x0, y0, x1, y1 = (float(value) for value in bbox)
170
+ width = max(x1 - x0, 1.0)
171
+ height_value = max(y1 - y0, 1.0)
172
+ pad_x = max(2, int(width * pad_x_ratio))
173
+ pad_y = max(2, int(height_value * pad_y_ratio))
174
+ box = (
175
+ max(0, int(x0) - pad_x),
176
+ max(0, int(y0) - pad_y),
177
+ min(image.width, int(x1) + pad_x),
178
+ min(image.height, int(y1) + pad_y),
179
+ )
180
+ if box[2] <= box[0] or box[3] <= box[1]:
181
+ raise ValueError(f"invalid crop: {bbox}")
182
+ return np.asarray(image.crop(box).convert("RGB"))
183
+
184
+
185
+ def recognize(
186
+ model: TextRecognition,
187
+ crops: list[np.ndarray],
188
+ batch_size: int,
189
+ ) -> list[dict[str, Any]]:
190
+ if not crops:
191
+ return []
192
+ last_error: Exception | None = None
193
+ for attempt in range(1, 4):
194
+ try:
195
+ results = list(model.predict(input=crops, batch_size=batch_size))
196
+ if len(results) != len(crops):
197
+ raise RuntimeError("recognizer returned wrong result count")
198
+ output = []
199
+ for result in results:
200
+ payload = result.json
201
+ if callable(payload):
202
+ payload = payload()
203
+ payload = payload.get("res", payload)
204
+ raw = str(payload.get("rec_text", ""))
205
+ output.append(
206
+ {
207
+ "text": pred_reverse(raw),
208
+ "raw_text": raw,
209
+ "rec_score": float(payload.get("rec_score", 0.0)),
210
+ }
211
+ )
212
+ return output
213
+ except Exception as exc:
214
+ last_error = exc
215
+ traceback.print_exc()
216
+ time.sleep(attempt)
217
+ raise RuntimeError(f"recognition failed after retries: {last_error}")
218
+
219
+
220
+ def merge_if_complete(output_dir: Path, detections_dir: Path, world_size: int) -> None:
221
+ with (output_dir / "merge.lock").open("a+") as lock:
222
+ fcntl.flock(lock, fcntl.LOCK_EX)
223
+ for rank in range(world_size):
224
+ path = output_dir / f"status-rank{rank:02d}.json"
225
+ if not path.is_file() or json.loads(path.read_text()).get("state") != "complete":
226
+ return
227
+ expected = [
228
+ json.loads(line)["id"]
229
+ for line in (detections_dir / "detections.jsonl").read_text(
230
+ encoding="utf-8"
231
+ ).splitlines()
232
+ if line.strip()
233
+ ]
234
+ rows = {}
235
+ for rank in range(world_size):
236
+ path = output_dir / f"recognized-rank{rank:02d}.jsonl"
237
+ for line in path.read_text(encoding="utf-8").splitlines():
238
+ if line.strip():
239
+ row = json.loads(line)
240
+ rows[row["id"]] = row
241
+ if set(rows) != set(expected):
242
+ raise RuntimeError("recognition merge contract failed")
243
+ temporary = output_dir / "recognized-lines.jsonl.tmp"
244
+ with temporary.open("w", encoding="utf-8") as handle:
245
+ for row_id in expected:
246
+ handle.write(json.dumps(rows[row_id], ensure_ascii=False) + "\n")
247
+ os.replace(temporary, output_dir / "recognized-lines.jsonl")
248
+ atomic_json(
249
+ output_dir / "run-summary.json",
250
+ {"state": "complete", "rows": len(rows), "completed_unix": time.time()},
251
+ )
252
+
253
+
254
+ def main() -> None:
255
+ args = parse_args()
256
+ args.output_dir.mkdir(parents=True, exist_ok=True)
257
+ detections_path = args.detections_dir / f"detections-rank{args.rank:02d}.jsonl"
258
+ detections = [
259
+ json.loads(line)
260
+ for line in detections_path.read_text(encoding="utf-8").splitlines()
261
+ if line.strip()
262
+ ]
263
+ indices = {index_from_id(row["id"]) for row in detections}
264
+ rows = load_rows(args.data, indices)
265
+ catalog = TemplateCatalog(args.background_root, args.ocr_root)
266
+ model = TextRecognition(model_dir=str(args.model_dir), device=args.device)
267
+ output_path = args.output_dir / f"recognized-rank{args.rank:02d}.jsonl"
268
+ status_path = args.output_dir / f"status-rank{args.rank:02d}.json"
269
+ started = time.monotonic()
270
+ failures = 0
271
+ recognized_lines = 0
272
+ page_buffer: list[dict[str, Any]] = []
273
+ crop_buffer: list[np.ndarray] = []
274
+
275
+ with output_path.open("w", encoding="utf-8", buffering=1) as output:
276
+ def flush() -> None:
277
+ nonlocal failures, recognized_lines
278
+ if not page_buffer:
279
+ return
280
+ try:
281
+ values = recognize(model, crop_buffer, args.recognition_batch_size)
282
+ recognition_error = None
283
+ except Exception as exc:
284
+ values = [
285
+ {"text": "", "raw_text": "", "rec_score": 0.0}
286
+ for _ in crop_buffer
287
+ ]
288
+ recognition_error = f"{type(exc).__name__}: {exc}"
289
+ for page in page_buffer:
290
+ if recognition_error and not page.get("error"):
291
+ page["error"] = recognition_error
292
+ if page.get("error"):
293
+ failures += 1
294
+ for line in page["lines"]:
295
+ result_index = line.pop("_result_index", None)
296
+ if result_index is not None:
297
+ line.update(values[result_index])
298
+ recognized_lines += 1
299
+ output.write(json.dumps(page, ensure_ascii=False) + "\n")
300
+ page_buffer.clear()
301
+ crop_buffer.clear()
302
+
303
+ for detection in detections:
304
+ index = index_from_id(detection["id"])
305
+ page = {
306
+ key: detection.get(key)
307
+ for key in (
308
+ "id",
309
+ "reference",
310
+ "width",
311
+ "height",
312
+ "variant",
313
+ "template_id",
314
+ "template_file",
315
+ "template_text",
316
+ "template_placeholder_count",
317
+ "match_median_abs_delta",
318
+ "match_p90_abs_delta",
319
+ "mask_threshold",
320
+ "mask_kept_fraction",
321
+ )
322
+ }
323
+ page["lines"] = []
324
+ if detection.get("error"):
325
+ page["error"] = detection["error"]
326
+ try:
327
+ image = decode_image(rows[index])
328
+ template = catalog.by_id[str(detection["template_id"])]
329
+ isolated, _ = catalog.isolate_with_template(
330
+ image,
331
+ template,
332
+ args.variant,
333
+ )
334
+ for source in grouped_lines(
335
+ detection.get("lines", []),
336
+ isolated.width,
337
+ isolated.height,
338
+ args.row_factor,
339
+ ):
340
+ line = dict(source)
341
+ line["_result_index"] = len(crop_buffer)
342
+ crop_buffer.append(
343
+ crop(
344
+ isolated,
345
+ source["bbox"],
346
+ args.pad_x_ratio,
347
+ args.pad_y_ratio,
348
+ )
349
+ )
350
+ page["lines"].append(line)
351
+ except Exception as exc:
352
+ traceback.print_exc()
353
+ page["error"] = f"{type(exc).__name__}: {exc}"
354
+ page_buffer.append(page)
355
+ if len(crop_buffer) >= args.crop_buffer:
356
+ flush()
357
+ flush()
358
+
359
+ elapsed = max(time.monotonic() - started, 1e-9)
360
+ atomic_json(
361
+ status_path,
362
+ {
363
+ "state": "complete",
364
+ "rank": args.rank,
365
+ "assigned": len(detections),
366
+ "failures": failures,
367
+ "recognized_lines": recognized_lines,
368
+ "rate_pages_per_second": len(detections) / elapsed,
369
+ "eta_seconds": 0,
370
+ },
371
+ )
372
+ merge_if_complete(args.output_dir, args.detections_dir, args.world_size)
373
+
374
+
375
+ if __name__ == "__main__":
376
+ main()
pipeline/run_bina02_stage8_optimizer_resume_v2.sh ADDED
@@ -0,0 +1,144 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+ set -Eeuo pipefail
3
+
4
+ BASE=/workspace/bina02-ppocr
5
+ PADDLE_ROOT="$BASE/PaddleOCR"
6
+ PYTHON="$BASE/venv/bin/python"
7
+ V1_RUN="$BASE/run/train-stage8-optimizer-resume-v1"
8
+ RUN="$BASE/run/train-stage8-optimizer-resume-v2"
9
+ CHECKPOINT="$RUN/resume-stage4"
10
+ TRAIN_PID=
11
+ WATCH_PID=
12
+
13
+ mkdir -p "$RUN/distributed-logs"
14
+
15
+ cleanup() {
16
+ local status=$?
17
+ trap - EXIT TERM INT
18
+
19
+ if [[ -n "${WATCH_PID:-}" ]] && kill -0 "$WATCH_PID" 2>/dev/null; then
20
+ kill -TERM "$WATCH_PID" 2>/dev/null || true
21
+ wait "$WATCH_PID" 2>/dev/null || true
22
+ fi
23
+
24
+ if [[ -n "${TRAIN_PID:-}" ]] && kill -0 "$TRAIN_PID" 2>/dev/null; then
25
+ kill -TERM -- "-$TRAIN_PID" 2>/dev/null || true
26
+ for _ in $(seq 1 20); do
27
+ kill -0 "$TRAIN_PID" 2>/dev/null || break
28
+ sleep 1
29
+ done
30
+ if kill -0 "$TRAIN_PID" 2>/dev/null; then
31
+ kill -KILL -- "-$TRAIN_PID" 2>/dev/null || true
32
+ fi
33
+ wait "$TRAIN_PID" 2>/dev/null || true
34
+ fi
35
+
36
+ exit "$status"
37
+ }
38
+ trap cleanup EXIT TERM INT
39
+
40
+ export V1_RUN RUN CHECKPOINT
41
+ "$PYTHON" - <<'PY'
42
+ import json
43
+ import os
44
+ import pathlib
45
+ import shutil
46
+
47
+ import paddle
48
+ import yaml
49
+
50
+ v1 = pathlib.Path(os.environ["V1_RUN"])
51
+ run = pathlib.Path(os.environ["RUN"])
52
+ checkpoint = pathlib.Path(os.environ["CHECKPOINT"])
53
+
54
+ source_params = v1 / "resume-stage4.pdparams"
55
+ source_opt = v1 / "resume-stage4.pdopt"
56
+ source_states = v1 / "resume-stage4.states"
57
+ for source in (source_params, source_opt, source_states, v1 / "launch-config.yml"):
58
+ if not source.is_file():
59
+ raise FileNotFoundError(source)
60
+
61
+ target_params = pathlib.Path(f"{checkpoint}.pdparams")
62
+ target_opt = pathlib.Path(f"{checkpoint}.pdopt")
63
+ target_states = pathlib.Path(f"{checkpoint}.states")
64
+
65
+ if not target_params.exists():
66
+ os.link(source_params, target_params)
67
+ if not target_states.exists():
68
+ shutil.copy2(source_states, target_states)
69
+
70
+ if not target_opt.exists():
71
+ paddle.set_device("cpu")
72
+ optimizer_state = paddle.load(str(source_opt))
73
+ scheduler = optimizer_state.get("LR_Scheduler")
74
+ if not isinstance(scheduler, dict):
75
+ raise RuntimeError("Stage 4 optimizer has no LR_Scheduler state")
76
+
77
+ before = dict(scheduler)
78
+ scheduler["last_epoch"] = 0
79
+ scheduler["last_lr"] = 3.0e-6
80
+ paddle.save(optimizer_state, str(target_opt))
81
+
82
+ with (run / "optimizer-patch.json").open("w", encoding="utf-8") as handle:
83
+ json.dump(
84
+ {
85
+ "source": str(source_opt),
86
+ "preserved_optimizer_entries": len(optimizer_state) - 1,
87
+ "scheduler_before": before,
88
+ "scheduler_after": dict(scheduler),
89
+ },
90
+ handle,
91
+ ensure_ascii=True,
92
+ indent=2,
93
+ sort_keys=True,
94
+ )
95
+
96
+ with (v1 / "launch-config.yml").open("r", encoding="utf-8") as handle:
97
+ config = yaml.safe_load(handle)
98
+ config["Global"]["save_model_dir"] = str(run)
99
+ config["Global"]["checkpoints"] = str(checkpoint)
100
+ config["Global"]["pretrained_model"] = None
101
+ config["Global"]["epoch_num"] = 3
102
+ with (run / "launch-config.yml").open("w", encoding="utf-8") as handle:
103
+ yaml.safe_dump(config, handle, allow_unicode=True, sort_keys=False)
104
+ PY
105
+
106
+ cd "$PADDLE_ROOT"
107
+ export PYTHONPATH="$PADDLE_ROOT:${PYTHONPATH:-}"
108
+
109
+ setsid "$PYTHON" -m paddle.distributed.launch \
110
+ --gpus 0,1,2,3,4,5,6,7 \
111
+ --log_dir "$RUN/distributed-logs" \
112
+ tools/train.py \
113
+ -c "$RUN/launch-config.yml" \
114
+ >"$RUN/train-launch.log" 2>&1 &
115
+ TRAIN_PID=$!
116
+
117
+ "$PYTHON" "$BASE/watch_bina02_stage4_eval.py" \
118
+ --log "$RUN/train-launch.log" \
119
+ --pgid "$TRAIN_PID" \
120
+ --state "$RUN/eval-gate-state.json" \
121
+ --baseline-handwriting 0.9296666392340331 \
122
+ --baseline-printed 0.8944015643799841 \
123
+ --handwriting-margin 0.002 \
124
+ --printed-margin 0.002 \
125
+ --patience 2 \
126
+ --poll-seconds 5 \
127
+ >"$RUN/watcher.log" 2>&1 &
128
+ WATCH_PID=$!
129
+
130
+ set +e
131
+ wait "$TRAIN_PID"
132
+ TRAIN_STATUS=$?
133
+ set -e
134
+
135
+ if kill -0 "$WATCH_PID" 2>/dev/null; then
136
+ kill -TERM "$WATCH_PID" 2>/dev/null || true
137
+ wait "$WATCH_PID" 2>/dev/null || true
138
+ fi
139
+ WATCH_PID=
140
+ TRAIN_PID=
141
+
142
+ printf 'train_exit_code=%s\nfinished_at=%s\n' \
143
+ "$TRAIN_STATUS" "$(date -u +%Y-%m-%dT%H:%M:%SZ)" >"$RUN/status.log"
144
+ exit "$TRAIN_STATUS"
pipeline/run_bina02_stage8_v2_exact.sh ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+ set -Eeuo pipefail
3
+
4
+ ROOT=/workspace/bina02-ppocr
5
+ TRAIN="$ROOT/run/train-stage8-optimizer-resume-v2"
6
+ POST="$ROOT/run/posttrain-stage8-optimizer-resume-v2"
7
+ MODEL="$POST/inference-best"
8
+
9
+ if [[ ! -s "$POST/INFERENCE_READY" ]]; then
10
+ test -s "$TRAIN/best_accuracy.pdparams"
11
+ test -s "$TRAIN/best_accuracy.states"
12
+ mkdir -p "$POST"
13
+ cd "$ROOT/PaddleOCR"
14
+ export PYTHONPATH="$ROOT/PaddleOCR:$ROOT${PYTHONPATH:+:$PYTHONPATH}"
15
+ CUDA_VISIBLE_DEVICES=0 OMP_NUM_THREADS=4 \
16
+ "$ROOT/venv/bin/python" tools/export_model.py \
17
+ -c "$TRAIN/config.yml" \
18
+ -o "Global.pretrained_model=$TRAIN/best_accuracy" \
19
+ "Global.save_inference_dir=$MODEL" \
20
+ > "$POST/export.log" 2>&1
21
+ test -s "$MODEL/inference.pdiparams"
22
+ test -s "$MODEL/inference.json"
23
+ printf 'READY\n' > "$POST/INFERENCE_READY"
24
+ fi
25
+
26
+ exec /bin/bash "$ROOT/run_bina02_stage8_v2_exact_core.sh"
pipeline/run_bina02_stage8_v2_exact_core.sh ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env bash
2
+ set -euo pipefail
3
+
4
+ ROOT=/workspace/bina02-ppocr
5
+ RUN="$ROOT/run/stage8-v2-two-layer-exact-v1"
6
+ DATA="$ROOT/direct/benchmark-data"
7
+ SUPPORT="$ROOT/data/generator-support-v1-hf/work"
8
+ MODEL="$ROOT/run/posttrain-stage8-optimizer-resume-v2/inference-best"
9
+ SURYA_ENV=/workspace/bina02-surya-env
10
+ VARIANT=adaptive75
11
+ DET="$RUN/detection"
12
+ REC="$RUN/recognition"
13
+ ASM="$RUN/assembly"
14
+ PRINTED="$RUN/printed"
15
+ SCORES="$RUN/scores"
16
+
17
+ if test -s "$SCORES/exact-summary.json"; then
18
+ cat "$SCORES/exact-summary.json"
19
+ exit 0
20
+ fi
21
+ test -f "$ROOT/run/posttrain-stage8-optimizer-resume-v2/INFERENCE_READY"
22
+ mkdir -p "$RUN" "$DET" "$REC" "$ASM" "$PRINTED" "$SCORES"
23
+ printf '%s EXACT_START\n' "$(date -Is)" > "$RUN/status.log"
24
+
25
+ if [[ ! -s "$DET/detections.jsonl" ]]; then
26
+ pids=()
27
+ for rank in $(seq 0 31); do
28
+ gpu=$((rank % 8))
29
+ HF_HOME=/workspace/.hf_home CUDA_VISIBLE_DEVICES="$gpu" DETECTOR_BATCH_SIZE=8 \
30
+ "$SURYA_ENV/bin/python" "$ROOT/detect_bina02_two_layer.py" \
31
+ --rank "$rank" --world-size 32 \
32
+ --data "$DATA" \
33
+ --background-root "$SUPPORT/background-sources" \
34
+ --ocr-root "$SUPPORT/background-ocr-single" \
35
+ --output-dir "$DET" \
36
+ --variant "$VARIANT" \
37
+ --limit 6669 \
38
+ --batch-size 8 \
39
+ > "$DET/rank${rank}.log" 2>&1 &
40
+ pids+=("$!")
41
+ done
42
+ for pid in "${pids[@]}"; do wait "$pid"; done
43
+ test -s "$DET/detections.jsonl"
44
+ fi
45
+ test -s "$DET/detections.jsonl"
46
+ printf '%s DETECTION_COMPLETE\n' "$(date -Is)" >> "$RUN/status.log"
47
+
48
+ export PYTHONPATH="$ROOT/PaddleOCR:$ROOT${PYTHONPATH:+:$PYTHONPATH}"
49
+ if [[ ! -s "$REC/recognized-lines.jsonl" ]]; then
50
+ pids=()
51
+ for rank in $(seq 0 31); do
52
+ gpu=$((rank % 8))
53
+ CUDA_VISIBLE_DEVICES="$gpu" \
54
+ "$ROOT/venv/bin/python" "$ROOT/recognize_bina02_two_layer.py" \
55
+ --rank "$rank" --world-size 32 \
56
+ --data "$DATA" \
57
+ --background-root "$SUPPORT/background-sources" \
58
+ --ocr-root "$SUPPORT/background-ocr-single" \
59
+ --detections-dir "$DET" \
60
+ --model-dir "$MODEL" \
61
+ --output-dir "$REC" \
62
+ --variant "$VARIANT" \
63
+ --device gpu:0 \
64
+ --crop-buffer 512 \
65
+ --recognition-batch-size 64 \
66
+ > "$REC/rank${rank}.log" 2>&1 &
67
+ pids+=("$!")
68
+ done
69
+ for pid in "${pids[@]}"; do wait "$pid"; done
70
+ test -s "$REC/recognized-lines.jsonl"
71
+ fi
72
+ test -s "$REC/recognized-lines.jsonl"
73
+ printf '%s HANDWRITING_RECOGNITION_COMPLETE\n' "$(date -Is)" >> "$RUN/status.log"
74
+
75
+ pids=()
76
+ for rank in $(seq 0 7); do
77
+ CUDA_VISIBLE_DEVICES="$rank" \
78
+ "$ROOT/venv/bin/python" "$ROOT/benchmark_bina02_printed_only.py" \
79
+ --rank "$rank" --world-size 8 \
80
+ --data "$DATA" \
81
+ --model-dir "$MODEL" \
82
+ --output-dir "$PRINTED" \
83
+ --device gpu:0 \
84
+ > "$PRINTED/rank${rank}.log" 2>&1 &
85
+ pids+=("$!")
86
+ done
87
+ for pid in "${pids[@]}"; do wait "$pid"; done
88
+ test -s "$PRINTED/predictions.jsonl"
89
+ printf '%s PRINTED_COMPLETE\n' "$(date -Is)" >> "$RUN/status.log"
90
+
91
+ "$ROOT/scorer-venv/bin/python" "$ROOT/assemble_bina02_two_layer.py" \
92
+ --recognized-lines "$REC/recognized-lines.jsonl" \
93
+ --baseline-predictions "$PRINTED/predictions.jsonl" \
94
+ --output-dir "$ASM" \
95
+ --isolation-variant "$VARIANT" \
96
+ > "$ASM/assembly.log" 2>&1
97
+ "$ROOT/scorer-venv/bin/python" - \
98
+ "$PRINTED/predictions.jsonl" \
99
+ "$ASM/predictions-simple_spread_right.jsonl" \
100
+ "$SCORES/raw.jsonl" <<'PY'
101
+ import json
102
+ import sys
103
+ rows=[]
104
+ for path in sys.argv[1:3]:
105
+ with open(path,encoding="utf-8") as handle:
106
+ rows.extend(json.loads(line) for line in handle if line.strip())
107
+ rows.sort(key=lambda row:(0 if row["split"]=="printed" else 1,int(row["id"].split(":")[1])))
108
+ with open(sys.argv[3],"w",encoding="utf-8") as handle:
109
+ for row in rows: handle.write(json.dumps(row,ensure_ascii=False)+"\n")
110
+ if len(rows)!=13338: raise SystemExit(f"expected 13338 rows, got {len(rows)}")
111
+ PY
112
+ "$ROOT/scorer-venv/bin/python" "$ROOT/decode_bina02_persian.py" \
113
+ --database "$ROOT/eval-support/recovery-native-20260725/decoder/lexicon-v1/persian-ocr-lexicon.sqlite3" \
114
+ --input "$SCORES/raw.jsonl" \
115
+ --output "$SCORES/decoded.jsonl" \
116
+ --details "$SCORES/decoder-details.jsonl" \
117
+ > "$SCORES/decoder-summary.json" 2>&1
118
+ CUDA_VISIBLE_DEVICES=0 "$ROOT/scorer-venv/bin/python" \
119
+ "$ROOT/eval-support/layout-audit-temp-20260725/scoring/score_ocr_double_benchmark.py" \
120
+ --manifest "$SCORES/decoded.jsonl" \
121
+ --predictions "$SCORES/decoded.jsonl" \
122
+ --output "$SCORES/results.json" \
123
+ --details "$SCORES/s3-details.jsonl" \
124
+ --shenavasanj "$ROOT/models/ShenavaSanj-v1.0" \
125
+ --device cuda:0 \
126
+ > "$SCORES/score.log" 2>&1
127
+ "$ROOT/scorer-venv/bin/python" - "$SCORES/results.json" "$SCORES/exact-summary.json" <<'PY'
128
+ import json
129
+ import sys
130
+ result=json.load(open(sys.argv[1]))
131
+ hand=result["splits"]["handwriting"]
132
+ printed=result["splits"]["printed"]
133
+ gate=all(hand[key]<10 and printed[key]<10 for key in ("wer","cer","s3"))
134
+ summary={"handwriting":hand,"printed":printed,"missing_predictions":result["missing_predictions"],"gate_passed":gate}
135
+ open(sys.argv[2],"w").write(json.dumps(summary,ensure_ascii=False,indent=2)+"\n")
136
+ print(json.dumps(summary,ensure_ascii=False,indent=2))
137
+ raise SystemExit(0 if gate else 30)
138
+ PY
139
+ printf '%s EXACT_GATE_PASSED\n' "$(date -Is)" >> "$RUN/status.log"
pipeline/watch_bina02_stage4_eval.py ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Stop Stage4 when handwriting diverges or printed quality leaves its baseline band."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import argparse
7
+ import json
8
+ import os
9
+ import re
10
+ import signal
11
+ import time
12
+ from pathlib import Path
13
+ from typing import Any
14
+
15
+
16
+ PATTERNS = {
17
+ "handwriting": re.compile(
18
+ r"handwriting_norm_edit_dis:\s*([0-9]+(?:\.[0-9]+)?)"
19
+ ),
20
+ "printed": re.compile(r"printed_norm_edit_dis:\s*([0-9]+(?:\.[0-9]+)?)"),
21
+ }
22
+
23
+
24
+ def atomic_json(path: Path, payload: dict[str, Any]) -> None:
25
+ temporary = path.with_suffix(path.suffix + f".tmp.{os.getpid()}")
26
+ temporary.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
27
+ os.replace(temporary, path)
28
+
29
+
30
+ def main() -> None:
31
+ parser = argparse.ArgumentParser()
32
+ parser.add_argument("--log", type=Path, required=True)
33
+ parser.add_argument("--pgid", type=int, required=True)
34
+ parser.add_argument("--state", type=Path, required=True)
35
+ parser.add_argument("--baseline-handwriting", type=float)
36
+ parser.add_argument("--baseline-printed", type=float)
37
+ parser.add_argument("--handwriting-margin", type=float, default=0.002)
38
+ parser.add_argument("--printed-margin", type=float, default=0.0015)
39
+ parser.add_argument("--patience", type=int, default=2)
40
+ parser.add_argument("--poll-seconds", type=float, default=5.0)
41
+ args = parser.parse_args()
42
+
43
+ offset = 0
44
+ seen = 0
45
+ baseline_handwriting = args.baseline_handwriting
46
+ baseline_printed = args.baseline_printed
47
+ best_handwriting = args.baseline_handwriting
48
+ best_printed = args.baseline_printed
49
+ handwriting_bad = 0
50
+ printed_bad = 0
51
+
52
+ while True:
53
+ try:
54
+ os.killpg(args.pgid, 0)
55
+ except ProcessLookupError:
56
+ break
57
+
58
+ if args.log.exists():
59
+ with args.log.open("r", encoding="utf-8", errors="replace") as handle:
60
+ handle.seek(offset)
61
+ for line in handle:
62
+ if "cur metric" not in line:
63
+ continue
64
+ matches = {
65
+ name: pattern.search(line) for name, pattern in PATTERNS.items()
66
+ }
67
+ if not all(matches.values()):
68
+ continue
69
+ handwriting = float(matches["handwriting"].group(1))
70
+ printed = float(matches["printed"].group(1))
71
+ seen += 1
72
+ if baseline_handwriting is None:
73
+ baseline_handwriting = handwriting
74
+ baseline_printed = printed
75
+ best_handwriting = handwriting
76
+ best_printed = printed
77
+ else:
78
+ best_handwriting = max(float(best_handwriting), handwriting)
79
+ best_printed = max(float(best_printed), printed)
80
+ if handwriting < float(best_handwriting) - args.handwriting_margin:
81
+ handwriting_bad += 1
82
+ else:
83
+ handwriting_bad = 0
84
+ printed_floor = float(baseline_printed) - args.printed_margin
85
+ if printed < printed_floor:
86
+ printed_bad += 1
87
+ else:
88
+ printed_bad = 0
89
+
90
+ printed_floor = float(baseline_printed) - args.printed_margin
91
+ state = {
92
+ "seen": seen,
93
+ "baseline_handwriting": baseline_handwriting,
94
+ "baseline_printed": baseline_printed,
95
+ "handwriting_latest": handwriting,
96
+ "handwriting_best": best_handwriting,
97
+ "handwriting_bad": handwriting_bad,
98
+ "handwriting_margin": args.handwriting_margin,
99
+ "printed_latest": printed,
100
+ "printed_best": best_printed,
101
+ "printed_floor": printed_floor,
102
+ "printed_bad": printed_bad,
103
+ "printed_margin": args.printed_margin,
104
+ "patience": args.patience,
105
+ "state": "monitoring",
106
+ }
107
+ atomic_json(args.state, state)
108
+ print(
109
+ f"EVAL_GATE seen={seen} "
110
+ f"handwriting={handwriting:.8f} "
111
+ f"best={float(best_handwriting):.8f} "
112
+ f"hw_bad={handwriting_bad}/{args.patience} "
113
+ f"printed={printed:.8f} "
114
+ f"floor={printed_floor:.8f} "
115
+ f"printed_bad={printed_bad}/{args.patience}",
116
+ flush=True,
117
+ )
118
+
119
+ if (
120
+ handwriting_bad >= args.patience
121
+ or printed_bad >= args.patience
122
+ ):
123
+ reason = (
124
+ "handwriting_divergence"
125
+ if handwriting_bad >= args.patience
126
+ else "printed_regression"
127
+ )
128
+ state["state"] = "stopping"
129
+ state["stop_reason"] = reason
130
+ atomic_json(args.state, state)
131
+ print(f"EVAL_GATE_STOP reason={reason}", flush=True)
132
+ os.killpg(args.pgid, signal.SIGTERM)
133
+ raise SystemExit(20)
134
+ offset = handle.tell()
135
+ time.sleep(args.poll_seconds)
136
+
137
+ final = {
138
+ "state": "trainer_exited",
139
+ "seen": seen,
140
+ "baseline_handwriting": baseline_handwriting,
141
+ "baseline_printed": baseline_printed,
142
+ "handwriting_best": best_handwriting,
143
+ "printed_best": best_printed,
144
+ "handwriting_bad": handwriting_bad,
145
+ "printed_bad": printed_bad,
146
+ "patience": args.patience,
147
+ "completed_unix": time.time(),
148
+ }
149
+ atomic_json(args.state, final)
150
+
151
+
152
+ if __name__ == "__main__":
153
+ main()