the train size is little can't find face .

[yutao007]

because my gpu memory is only 4G so I train_pose2vid.py fineSize= 96 loadSize=96
but i can't find face when i python prepare.py Prepare test_real.... 100%|███████████████████████████████████████████████████████████████████████████████████| 166/166 [00:05<00:00, 29.95it/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() ) ) 0it [00:00, ?it/s] Copy the synthesized images... 0it [00:00, ?it/s]

[yutao007]

the file data/face/test_real and test_sync is nothing

[yutao007]

i find the reason : cv2.imwrite(str(test_real_dir)+'/{:05}.png'.format(img_idx),img) cv2.imwrite(str(test_img)+'/{:05}.png'.format(img_idx),img) cv2.imwrite(str(test_label)+'/{:05}.png'.format(img_idx),label)

[Dian-Yi]

i find the reason : cv2.imwrite(str(test_real_dir)+'/{:05}.png'.format(img_idx),img) cv2.imwrite(str(test_img)+'/{:05}.png'.format(img_idx),img) cv2.imwrite(str(test_label)+'/{:05}.png'.format(img_idx),label)

good job. 不同系统下，不同版本的包，这个项目里一些路径设置的确实有问题，还得慢慢改。

[zhikangSu]

How was this problem solved in the end？