[Prepare perceptual data] RuntimeError: cuDNN error: CUDNN_STATUS_NOT_SUPPORTED #13
Description
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.