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XPixelGroup / RankSRGAN

ICCV 2019 (oral) RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution. PyTorch implementation
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[Prepare perceptual data] RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED #13

Open zhhezhhe opened 4 years ago

zhhezhhe commented 4 years ago

Thans for your great work! When dataroot_LQ has only one image, the code runs OK! But when I put more images (DIV2K dataset) to the dir of dataroot_LQ: /DIV2K/DIV2K_train_HR, the error occurs.

export CUDA_VISIBLE_DEVICES=4
20-01-15 19:31:02.816 - INFO:   name: DIV2K
  suffix: None
  model: sr
  distortion: sr
  scale: 4
  crop_border: None
  gpu_ids: [4]
  datasets:[
    test_1:[
      name: DIV2K_train_srres
      mode: LQ
      dataroot_GT: None
      dataroot_LQ: DIV2K/DIV2K_train_HR
      phase: test
      scale: 4
      data_type: img
    ]
  ]
  network_G:[
    which_model_G: SRResNet
    in_nc: 3
    out_nc: 3
    nf: 64
    nb: 16
    upscale: 4
    scale: 4
  ]
  path:[
    pretrain_model_G: ../experiments/pretrained_models/mmsr_RankSRGAN_NIQE.pth
    root: /RankSRGAN
    results_root: /RankSRGAN/results/DIV2K
    log: /RankSRGAN/results/DIV2K
  ]
  is_train: False

20-01-15 19:31:02.822 - INFO: Dataset [LQDataset - DIV2K_train_srres] is created.
20-01-15 19:31:02.822 - INFO: Number of test images in [DIV2K_train_srres]: 800
20-01-15 19:31:08.614 - INFO: Network G structure: DataParallel - SRResNet, with parameters: 1,554,499
20-01-15 19:31:08.615 - INFO: SRResNet(
  (conv_first): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (recon_trunk): Sequential(
    (0): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (1): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (2): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (3): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (4): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (5): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (6): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (7): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (8): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (9): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (10): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (11): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (12): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (13): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (14): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (15): ResidualBlock_noBN(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
  (LRconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (upconv1): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (upconv2): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (pixel_shuffle): PixelShuffle(upscale_factor=2)
  (HRconv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (conv_last): Conv2d(64, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (relu): ReLU(inplace)
)
20-01-15 19:31:08.615 - INFO: Loading model for G [../experiments/pretrained_models/mmsr_RankSRGAN_NIQE.pth] ...
20-01-15 19:31:08.625 - INFO: Model [SRModel] is created.
20-01-15 19:31:08.625 - INFO:
Testing [DIV2K_train_srres]...
Traceback (most recent call last):
  File "test.py", line 55, in <module>
    model.test()
  File "/RankSRGAN/codes/models/SR_model.py", line 102, in test
    self.fake_H = self.netG(self.var_L)
  File "/anaconda3/envs/pytorch_sr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/anaconda3/envs/pytorch_sr/lib/python3.6/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/anaconda3/envs/pytorch_sr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/RankSRGAN/codes/models/archs/RankSRGAN_arch.py", line 50, in forward
    out = self.relu(self.pixel_shuffle(self.upconv2(out)))
  File "/anaconda3/envs/pytorch_sr/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/anaconda3/envs/pytorch_sr/lib/python3.6/site-packages/torch/nn/modules/conv.py", line 338, in forward
    self.padding, self.dilation, self.groups)
RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED. This error may appear if you passed in a non-contiguous input.
zhhezhhe commented 4 years ago

The error occurs when I test Flickr2K/Flickr2K/000001.png. Its size is 20401356. When I test another image with a smaller size (some size like 680 680), the code runs OK.