what caused?

[jackylee1]

python3 prepare.py Prepare test_real.... 100%|██████████████████████████████████████████████████████████████████████▉| 617/618 [01:11<00:00, 7.49it/s]libpng warning: Image width is zero in IHDR libpng warning: Image height is zero in IHDR libpng error: Invalid IHDR data libpng warning: Image width is zero in IHDR libpng warning: Image height is zero in IHDR libpng error: Invalid IHDR data libpng warning: Image width is zero in IHDR libpng warning: Image height is zero in IHDR libpng error: Invalid IHDR data 100%|███████████████████████████████████████████████████████████████████████| 618/618 [01:11<00:00, 8.65it/s] Prepare test_sync.... CustomDatasetDataLoader dataset [AlignedDataset] was created GlobalGenerator( (model): Sequential( (0): ReflectionPad2d((3, 3, 3, 3)) (1): Conv2d(18, 64, kernel_size=(7, 7), stride=(1, 1)) (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (6): ReLU(inplace) (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (9): ReLU(inplace) (10): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (11): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (12): ReLU(inplace) (13): Conv2d(512, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)) (14): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (15): ReLU(inplace) (16): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (17): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (18): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (19): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (20): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (21): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (22): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (23): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (24): ResnetBlock( (conv_block): Sequential( (0): ReflectionPad2d((1, 1, 1, 1)) (1): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (2): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (3): ReLU(inplace) (4): ReflectionPad2d((1, 1, 1, 1)) (5): Conv2d(1024, 1024, kernel_size=(3, 3), stride=(1, 1)) (6): InstanceNorm2d(1024, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) ) ) (25): ConvTranspose2d(1024, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (26): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (27): ReLU(inplace) (28): ConvTranspose2d(512, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (29): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (30): ReLU(inplace) (31): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (32): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (33): ReLU(inplace) (34): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1)) (35): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (36): ReLU(inplace) (37): ReflectionPad2d((3, 3, 3, 3)) (38): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1)) (39): Tanh() ) )

0%| | 0/1235 [00:00<?, ?it/s] ../src/pix2pixHD/models/pix2pixHD_model.py:134: UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead. input_label = Variable(input_label, volatile=infer) 100%|████████████████████████████████████████████████████████████████████▉| 1234/1235 [02:54<00:00, 7.15it/s]Traceback (most recent call last): File "prepare.py", line 64, in for data in tqdm(dataset): File "/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py", line 937, in iter for obj in iterable: File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 615, in next batch = self.collate_fn([self.dataset[i] for i in indices]) File "/usr/local/lib/python3.5/dist-packages/torch/utils/data/dataloader.py", line 615, in batch = self.collate_fn([self.dataset[i] for i in indices]) File "../src/pix2pixHD/data/aligned_dataset.py", line 40, in getitem A = Image.open(A_path) File "/usr/local/lib/python3.5/dist-packages/PIL/Image.py", line 2622, in open % (filename if filename else fp)) OSError: cannot identify image file '../data/target/test_label/00617.png'