[REQ] start image_id=001.jpg models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/001.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=0
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/001.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x-restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png', '--model', 'x-restormer.dehaze'] with GPU=0
[MODEL] Failed (2): x-restormer.dehaze
python: can't open file '/hdd/Restoration/Inference/x-restormer_api.py': [Errno 2] No such file or directory

ERROR conda.cli.main_run:execute(125): `conda run python x-restormer_api.py --input /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png --output /hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png --model x-restormer.dehaze` failed. (See above for error)

[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x-restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png', '--model', 'x-restormer.dehaze'] with GPU=0
[MODEL] Failed (2): x-restormer.dehaze
python: can't open file '/hdd/Restoration/Inference/x-restormer_api.py': [Errno 2] No such file or directory

ERROR conda.cli.main_run:execute(125): `conda run python x-restormer_api.py --input /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png --output /hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png --model x-restormer.dehaze` failed. (See above for error)

[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png', '--model', 'x-restormer.dehaze'] with GPU=0
[MODEL] Failed (1): x-restormer.dehaze
/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
usage: inference.py [-h] --input INPUT --output OUTPUT --model
                    {xrestormer.denoise_50,xrestormer.derain,xrestormer.dehaze,xrestormer.deblur,xrestormer.super_resolution}
inference.py: error: argument --model: invalid choice: 'x-restormer.dehaze' (choose from 'xrestormer.denoise_50', 'xrestormer.derain', 'xrestormer.dehaze', 'xrestormer.deblur', 'xrestormer.super_resolution')
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 68, in <module>
    run_single_inference(args.input, args.output, args.model)
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 38, in run_single_inference
    subprocess.run(cmd, check=True)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/subprocess.py", line 526, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['python', 'X-Restormer/inference.py', '--input', '/tmp/xrestormer_e00b830d__s3gar72/input', '--output', '/tmp/xrestormer_e00b830d__s3gar72/output', '--model', 'x-restormer.dehaze']' returned non-zero exit status 2.

ERROR conda.cli.main_run:execute(125): `conda run python x_restormer_api.py --input /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png --output /hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png --model x-restormer.dehaze` failed. (See above for error)
[INFO] Running: python X-Restormer/inference.py --input /tmp/xrestormer_e00b830d__s3gar72/input --output /tmp/xrestormer_e00b830d__s3gar72/output --model x-restormer.dehaze


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'xrestormer', 'python', 'xrestormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[MODEL] Failed (1): xrestormer.dehaze

EnvironmentLocationNotFound: Not a conda environment: /home/ubuntu/anaconda3/envs/xrestormer


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'xrestormer', 'python', 'xrestormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[MODEL] Failed (1): xrestormer.dehaze

EnvironmentLocationNotFound: Not a conda environment: /home/ubuntu/anaconda3/envs/xrestormer


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'xrestormer', 'python', 'xrestormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[MODEL] Failed (1): xrestormer.dehaze

EnvironmentLocationNotFound: Not a conda environment: /home/ubuntu/anaconda3/envs/xrestormer


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: name 'hq_path' is not defined
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: 
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: 
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: 
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: 
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: PosixPath('/hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png') and PosixPath('/hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png') are the same file
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[ERROR] exception processing request: PosixPath('/hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png') and PosixPath('/hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png') are the same file
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=001.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.denoise']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_d25fd2dc888c6bbf/out.png', '--model', 'xrestormer.denoise'] with GPU=0
[MODEL] Failed (1): xrestormer.denoise
/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
usage: inference.py [-h] --input INPUT --output OUTPUT --model
                    {xrestormer.denoise_50,xrestormer.derain,xrestormer.dehaze,xrestormer.deblur,xrestormer.super_resolution}
inference.py: error: argument --model: invalid choice: 'xrestormer.denoise' (choose from 'xrestormer.denoise_50', 'xrestormer.derain', 'xrestormer.dehaze', 'xrestormer.deblur', 'xrestormer.super_resolution')
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 68, in <module>
    run_single_inference(args.input, args.output, args.model)
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 38, in run_single_inference
    subprocess.run(cmd, check=True)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/subprocess.py", line 526, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['python', 'X-Restormer/inference.py', '--input', '/tmp/xrestormer_eeea5817_ow_8xp44/input', '--output', '/tmp/xrestormer_eeea5817_ow_8xp44/output', '--model', 'xrestormer.denoise']' returned non-zero exit status 2.

ERROR conda.cli.main_run:execute(125): `conda run python x_restormer_api.py --input /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png --output /hdd/Restoration/Inference/Cache/tmp/001.png_d25fd2dc888c6bbf/out.png --model xrestormer.denoise` failed. (See above for error)
[INFO] Running: python X-Restormer/inference.py --input /tmp/xrestormer_eeea5817_ow_8xp44/input --output /tmp/xrestormer_eeea5817_ow_8xp44/output --model xrestormer.denoise


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.denoise_50']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_487ebe34f5fe4fb9/out.png', '--model', 'xrestormer.denoise_50'] with GPU=0
[MODEL] Success: xrestormer.denoise_50 -> /hdd/Restoration/Inference/Cache/tmp/001.png_487ebe34f5fe4fb9/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/487ebe34f5fe4fb9/output.png
[REQ] start image_id=001.png models=['restormer.real_denoise', 'xrestormer.deblur']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/001.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_d03c2e1c1b842c81/out.png', '--model', 'restormer.real_denoise'] with GPU=0
[REQ] start image_id=002.png models=['restormer.real_denoise', 'xrestormer.deblur']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/002.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/002.png_d03c2e1c1b842c81/out.png', '--model', 'restormer.real_denoise'] with GPU=1
[MODEL] Success: restormer.real_denoise -> /hdd/Restoration/Inference/Cache/tmp/001.png_d03c2e1c1b842c81/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/d03c2e1c1b842c81/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/d03c2e1c1b842c81/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_441a541e674dcdc8/out.png', '--model', 'xrestormer.deblur'] with GPU=0
[MODEL] Success: restormer.real_denoise -> /hdd/Restoration/Inference/Cache/tmp/002.png_d03c2e1c1b842c81/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/002.png/d03c2e1c1b842c81/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/002.png/d03c2e1c1b842c81/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/002.png_441a541e674dcdc8/out.png', '--model', 'xrestormer.deblur'] with GPU=1
[MODEL] Failed (1): xrestormer.deblur
/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
2025-10-06 12:26:54,065 INFO: 
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    
Version Information: 
	BasicSR: 1.4.2
	PyTorch: 2.7.1+cu126
	TorchVision: 0.22.1+cu126
2025-10-06 12:26:54,065 INFO: 
  name: 003_xrestormer_deblur
  model_type: XRestormerModel
  scale: 1
  num_gpu: 8
  manual_seed: 123
  padding_size: 64
  datasets:[
    test_1:[
      name: test
      type: SingleImageDataset
      dataroot_lq: /tmp/xrestormer_8be67e24_by3eix6r/input
      in_ch: 3
      io_backend:[
        type: disk
      ]
      phase: test
      scale: 1
    ]
  ]
  network_g:[
    type: XRestormer
    inp_channels: 3
    out_channels: 3
    dim: 48
    num_blocks: [2, 4, 4, 4]
    num_refinement_blocks: 4
    channel_heads: [1, 2, 4, 8]
    spatial_heads: [1, 2, 4, 8]
    overlap_ratio: [0.5, 0.5, 0.5, 0.5]
    window_size: 8
    spatial_dim_head: 16
    ffn_expansion_factor: 2.66
    bias: False
    LayerNorm_type: WithBias
    dual_pixel_task: False
    scale: 1
  ]
  path:[
    pretrain_network_g: /hdd/Restoration/Inference/X-Restormer/pretrained_models/deblur.pth
    strict_load_g: True
    resume_state: None
    results_root: /tmp/xrestormer_8be67e24_by3eix6r/output
    log: /tmp/xrestormer_8be67e24_by3eix6r/output
    visualization: /tmp/xrestormer_8be67e24_by3eix6r/output/visualization
  ]
  val:[
    save_img: True
    suffix: None
    pbar: True
  ]
  dist_params:[
    backend: nccl
    port: 29500
  ]
  auto_resume: False
  is_train: False

2025-10-06 12:26:54,065 INFO: Dataset [SingleImageDataset] - test is built.
2025-10-06 12:26:54,065 INFO: Number of test images in test: 1
2025-10-06 12:26:54,156 INFO: Network [XRestormer] is created.
2025-10-06 12:26:54,255 INFO: Network: DataParallel - XRestormer, with parameters: 26,041,208
2025-10-06 12:26:54,255 INFO: XRestormer(
  (patch_embed): OverlapPatchEmbed(
    (proj): Conv2d(3, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  )
  (encoder_level1): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (project_out): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (project_out): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down1_2): Downsample(
    (body): Sequential(
      (0): Conv2d(48, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (encoder_level2): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down2_3): Downsample(
    (body): Sequential(
      (0): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (encoder_level3): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down3_4): Downsample(
    (body): Sequential(
      (0): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (latent): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up4_3): Upsample(
    (body): Sequential(
      (0): Conv2d(384, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (reduce_chan_level3): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (decoder_level3): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up3_2): Upsample(
    (body): Sequential(
      (0): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (reduce_chan_level2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (decoder_level2): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up2_1): Upsample(
    (body): Sequential(
      (0): Conv2d(96, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (decoder_level1): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (refinement): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (output): Conv2d(96, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/X-Restormer/inference.py", line 172, in <module>
    custom_test_pipeline(root_path)
  File "/hdd/Restoration/Inference/X-Restormer/inference.py", line 162, in custom_test_pipeline
    model: SRModel = build_model(opt)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/__init__.py", line 26, in build_model
    model = MODEL_REGISTRY.get(opt['model_type'])(opt)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/sr_model.py", line 30, in __init__
    self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/base_model.py", line 290, in load_network
    load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 1479, in load
    with _open_file_like(f, "rb") as opened_file:
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 759, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 740, in __init__
    super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/hdd/Restoration/Inference/X-Restormer/pretrained_models/deblur.pth'
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 68, in <module>
    run_single_inference(args.input, args.output, args.model)
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 38, in run_single_inference
    subprocess.run(cmd, check=True)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/subprocess.py", line 526, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['python', 'X-Restormer/inference.py', '--input', '/tmp/xrestormer_8be67e24_by3eix6r/input', '--output', '/tmp/xrestormer_8be67e24_by3eix6r/output', '--model', 'xrestormer.deblur']' returned non-zero exit status 1.

ERROR conda.cli.main_run:execute(125): `conda run python x_restormer_api.py --input /hdd/Restoration/Inference/Cache/001.png/d03c2e1c1b842c81/output.png --output /hdd/Restoration/Inference/Cache/tmp/001.png_441a541e674dcdc8/out.png --model xrestormer.deblur` failed. (See above for error)
Path already exists. Rename it to /tmp/xrestormer_8be67e24_by3eix6r/output_archived_20251006_122654
Initializing XRestormer
[INFO] Running: python X-Restormer/inference.py --input /tmp/xrestormer_8be67e24_by3eix6r/input --output /tmp/xrestormer_8be67e24_by3eix6r/output --model xrestormer.deblur


[MODEL] Failed (1): xrestormer.deblur
/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/timm/models/layers/__init__.py:48: FutureWarning: Importing from timm.models.layers is deprecated, please import via timm.layers
  warnings.warn(f"Importing from {__name__} is deprecated, please import via timm.layers", FutureWarning)
2025-10-06 12:26:55,286 INFO: 
                ____                _       _____  ____
               / __ ) ____ _ _____ (_)_____/ ___/ / __ \
              / __  |/ __ `// ___// // ___/\__ \ / /_/ /
             / /_/ // /_/ /(__  )/ // /__ ___/ // _, _/
            /_____/ \__,_//____//_/ \___//____//_/ |_|
     ______                   __   __                 __      __
    / ____/____   ____   ____/ /  / /   __  __ _____ / /__   / /
   / / __ / __ \ / __ \ / __  /  / /   / / / // ___// //_/  / /
  / /_/ // /_/ // /_/ // /_/ /  / /___/ /_/ // /__ / /<    /_/
  \____/ \____/ \____/ \____/  /_____/\____/ \___//_/|_|  (_)
    
Version Information: 
	BasicSR: 1.4.2
	PyTorch: 2.7.1+cu126
	TorchVision: 0.22.1+cu126
2025-10-06 12:26:55,286 INFO: 
  name: 003_xrestormer_deblur
  model_type: XRestormerModel
  scale: 1
  num_gpu: 8
  manual_seed: 123
  padding_size: 64
  datasets:[
    test_1:[
      name: test
      type: SingleImageDataset
      dataroot_lq: /tmp/xrestormer_50d84fe2_wl36gv50/input
      in_ch: 3
      io_backend:[
        type: disk
      ]
      phase: test
      scale: 1
    ]
  ]
  network_g:[
    type: XRestormer
    inp_channels: 3
    out_channels: 3
    dim: 48
    num_blocks: [2, 4, 4, 4]
    num_refinement_blocks: 4
    channel_heads: [1, 2, 4, 8]
    spatial_heads: [1, 2, 4, 8]
    overlap_ratio: [0.5, 0.5, 0.5, 0.5]
    window_size: 8
    spatial_dim_head: 16
    ffn_expansion_factor: 2.66
    bias: False
    LayerNorm_type: WithBias
    dual_pixel_task: False
    scale: 1
  ]
  path:[
    pretrain_network_g: /hdd/Restoration/Inference/X-Restormer/pretrained_models/deblur.pth
    strict_load_g: True
    resume_state: None
    results_root: /tmp/xrestormer_50d84fe2_wl36gv50/output
    log: /tmp/xrestormer_50d84fe2_wl36gv50/output
    visualization: /tmp/xrestormer_50d84fe2_wl36gv50/output/visualization
  ]
  val:[
    save_img: True
    suffix: None
    pbar: True
  ]
  dist_params:[
    backend: nccl
    port: 29500
  ]
  auto_resume: False
  is_train: False

2025-10-06 12:26:55,286 INFO: Dataset [SingleImageDataset] - test is built.
2025-10-06 12:26:55,286 INFO: Number of test images in test: 1
2025-10-06 12:26:55,374 INFO: Network [XRestormer] is created.
2025-10-06 12:26:55,474 INFO: Network: DataParallel - XRestormer, with parameters: 26,041,208
2025-10-06 12:26:55,474 INFO: XRestormer(
  (patch_embed): OverlapPatchEmbed(
    (proj): Conv2d(3, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
  )
  (encoder_level1): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (project_out): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(48, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
        (project_out): Conv2d(48, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(48, 254, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(254, 254, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=254, bias=False)
        (project_out): Conv2d(127, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down1_2): Downsample(
    (body): Sequential(
      (0): Conv2d(48, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (encoder_level2): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down2_3): Downsample(
    (body): Sequential(
      (0): Conv2d(96, 48, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (encoder_level3): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (down3_4): Downsample(
    (body): Sequential(
      (0): Conv2d(192, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelUnshuffle(downscale_factor=2)
    )
  )
  (latent): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(128, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(384, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)
        (project_out): Conv2d(384, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(384, 2042, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(2042, 2042, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2042, bias=False)
        (project_out): Conv2d(1021, 384, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up4_3): Upsample(
    (body): Sequential(
      (0): Conv2d(384, 768, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (reduce_chan_level3): Conv2d(384, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (decoder_level3): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(64, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(192, 576, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(576, 576, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=576, bias=False)
        (project_out): Conv2d(192, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(192, 1020, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(1020, 1020, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1020, bias=False)
        (project_out): Conv2d(510, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up3_2): Upsample(
    (body): Sequential(
      (0): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (reduce_chan_level2): Conv2d(192, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
  (decoder_level2): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(32, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (up2_1): Upsample(
    (body): Sequential(
      (0): Conv2d(96, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): PixelShuffle(upscale_factor=2)
    )
  )
  (decoder_level1): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (refinement): Sequential(
    (0): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (1): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (2): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
    (3): TransformerBlock(
      (spatial_attn): OCAB(
        (unfold): Unfold(kernel_size=(12, 12), dilation=1, padding=2, stride=8)
        (qkv): Conv2d(96, 48, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (project_out): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (rel_pos_emb): RelPosEmb()
      )
      (channel_attn): ChannelAttention(
        (qkv): Conv2d(96, 288, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (qkv_dwconv): Conv2d(288, 288, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=288, bias=False)
        (project_out): Conv2d(96, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (norm1): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm2): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm3): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (norm4): LayerNorm(
        (body): WithBias_LayerNorm()
      )
      (channel_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
      (spatial_ffn): FeedForward(
        (project_in): Conv2d(96, 510, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (dwconv): Conv2d(510, 510, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=510, bias=False)
        (project_out): Conv2d(255, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)
      )
    )
  )
  (output): Conv2d(96, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
)
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/X-Restormer/inference.py", line 172, in <module>
    custom_test_pipeline(root_path)
  File "/hdd/Restoration/Inference/X-Restormer/inference.py", line 162, in custom_test_pipeline
    model: SRModel = build_model(opt)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/__init__.py", line 26, in build_model
    model = MODEL_REGISTRY.get(opt['model_type'])(opt)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/sr_model.py", line 30, in __init__
    self.load_network(self.net_g, load_path, self.opt['path'].get('strict_load_g', True), param_key)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/basicsr/models/base_model.py", line 290, in load_network
    load_net = torch.load(load_path, map_location=lambda storage, loc: storage)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 1479, in load
    with _open_file_like(f, "rb") as opened_file:
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 759, in _open_file_like
    return _open_file(name_or_buffer, mode)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/site-packages/torch/serialization.py", line 740, in __init__
    super().__init__(open(name, mode))
FileNotFoundError: [Errno 2] No such file or directory: '/hdd/Restoration/Inference/X-Restormer/pretrained_models/deblur.pth'
Traceback (most recent call last):
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 68, in <module>
    run_single_inference(args.input, args.output, args.model)
  File "/hdd/Restoration/Inference/x_restormer_api.py", line 38, in run_single_inference
    subprocess.run(cmd, check=True)
  File "/home/ubuntu/anaconda3/envs/basicsr/lib/python3.10/subprocess.py", line 526, in run
    raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['python', 'X-Restormer/inference.py', '--input', '/tmp/xrestormer_50d84fe2_wl36gv50/input', '--output', '/tmp/xrestormer_50d84fe2_wl36gv50/output', '--model', 'xrestormer.deblur']' returned non-zero exit status 1.

ERROR conda.cli.main_run:execute(125): `conda run python x_restormer_api.py --input /hdd/Restoration/Inference/Cache/002.png/d03c2e1c1b842c81/output.png --output /hdd/Restoration/Inference/Cache/tmp/002.png_441a541e674dcdc8/out.png --model xrestormer.deblur` failed. (See above for error)
Path already exists. Rename it to /tmp/xrestormer_50d84fe2_wl36gv50/output_archived_20251006_122655
Initializing XRestormer
[INFO] Running: python X-Restormer/inference.py --input /tmp/xrestormer_50d84fe2_wl36gv50/input --output /tmp/xrestormer_50d84fe2_wl36gv50/output --model xrestormer.deblur


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/001.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=0
[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[REQ] start image_id=001.png models=['restormer.derain', 'x-restormer.dehaze']
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/001.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[CACHE HIT after lock] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[CACHE HIT after lock] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'x-restormer', 'python', 'x-restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/001.png_9546679a84edcdab/out.png', '--model', 'x-restormer.dehaze'] with GPU=1
[MODEL] Failed (1): x-restormer.dehaze

EnvironmentLocationNotFound: Not a conda environment: /home/ubuntu/anaconda3/envs/x-restormer


[REQ] start image_id=001.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=001.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/001.png/fa661d22fd83f746/output.png
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/001.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[CACHE HIT after lock] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[CACHE HIT after lock] image=001.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/001.png/3ed52a7265db8c5d/output.png
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/002.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/002.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=0
[REQ] start image_id=003.png models=['restormer.derain', 'xrestormer.dehaze']
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/003.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/003.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=1
[REQ] start image_id=004.png models=['restormer.derain', 'xrestormer.dehaze']
[REQ] start image_id=005.png models=['restormer.derain', 'xrestormer.dehaze']
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/002.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/005.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/005.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=0
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/003.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/003.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/002.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=1
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/005.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/005.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'restormer', 'python', 'restormer_api.py', '--input', '/hdd/Restoration/Inference/LQ/004.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/004.png_fa661d22fd83f746/out.png', '--model', 'restormer.derain'] with GPU=0
[MODEL] Success: restormer.derain -> /hdd/Restoration/Inference/Cache/tmp/004.png_fa661d22fd83f746/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/004.png/fa661d22fd83f746/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/004.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/004.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=0
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/002.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/003.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/003.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=1
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/003.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/003.png/3ed52a7265db8c5d/output.png
[MODEL] Running ['conda', 'run', '-n', 'basicsr', 'python', 'x_restormer_api.py', '--input', '/hdd/Restoration/Inference/Cache/005.png/fa661d22fd83f746/output.png', '--output', '/hdd/Restoration/Inference/Cache/tmp/005.png_3ed52a7265db8c5d/out.png', '--model', 'xrestormer.dehaze'] with GPU=1
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/004.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/004.png/3ed52a7265db8c5d/output.png
[MODEL] Success: xrestormer.dehaze -> /hdd/Restoration/Inference/Cache/tmp/005.png_3ed52a7265db8c5d/out.png
[CACHE WRITE] wrote /hdd/Restoration/Inference/Cache/005.png/3ed52a7265db8c5d/output.png
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14
[REQ] start image_id=002.png models=['restormer.derain', 'xrestormer.dehaze']
[CACHE HIT] image=002.png prefix=['restormer.derain'] -> /hdd/Restoration/Inference/Cache/002.png/fa661d22fd83f746/output.png
[CACHE HIT] image=002.png prefix=['restormer.derain', 'xrestormer.dehaze'] -> /hdd/Restoration/Inference/Cache/002.png/3ed52a7265db8c5d/output.png
[SCORE CACHE HIT] image=002.png models=['restormer.derain', 'xrestormer.dehaze'] score=1145.14