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VisiumCH / AMLD2020-Dirty-GANcing

AMLD 2020
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error while running python src/GANcing/train_pose2vid.py -t data/targets/target -r danesh #5

Closed silexxx closed 4 years ago

silexxx commented 4 years ago

nceNow-master/AMLD2020-Dirty-GANcing$ python src/GANcing/train_pose2vid.py -t data/targets/target -r danesh CustomDatasetDataLoader dataset [AlignedDataset] was created

training images = 203

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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (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=True) (37): ReflectionPad2d((3, 3, 3, 3)) (38): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1)) (39): Tanh() ) ) MultiscaleDiscriminator( (scale0_layer0): Sequential( (0): Conv2d(21, 64, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale0_layer1): Sequential( (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale0_layer2): Sequential( (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale0_layer3): Sequential( (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale0_layer4): Sequential( (0): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) ) (scale1_layer0): Sequential( (0): Conv2d(21, 64, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale1_layer1): Sequential( (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale1_layer2): Sequential( (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(2, 2)) (1): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale1_layer3): Sequential( (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) (1): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False) (2): LeakyReLU(negative_slope=0.2, inplace=True) ) (scale1_layer4): Sequential( (0): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), padding=(2, 2)) ) (downsample): AvgPool2d(kernel_size=3, stride=2, padding=[1, 1]) ) WARNING:tensorflow:From src/GANcing/../pix2pixHD/util/visualizer.py:26: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

create web directory ./checkpoints/danesh/web... torch_shm_managertorch_shm_manager: error while loading shared libraries: libnvToolsExt.so.1: cannot open shared object file: No such file or directory : error while loading shared libraries: libnvToolsExt.so.1: cannot open shared object file: No such file or directory Traceback (most recent call last): File "src/GANcing/train_pose2vid.py", line 183, in train_pose2vid(args.target_dir, args.run_name, args.temporal_smoothing) File "src/GANcing/train_pose2vid.py", line 61, in train_pose2vid for i, data in enumerate(dataset, start=epoch_iter): File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 819, in next return self._process_data(data) File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data data.reraise() File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/_utils.py", line 385, in reraise raise self.exc_type(msg) RuntimeError: Caught RuntimeError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 47, in fetch return self.collate_fn(data) File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 74, in default_collate return {key: default_collate([d[key] for d in batch]) for key in elem} File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 74, in return {key: default_collate([d[key] for d in batch]) for key in elem} File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/utils/data/_utils/collate.py", line 53, in default_collate storage = elem.storage()._new_shared(numel) File "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/storage.py", line 128, in _new_shared return cls._new_using_filename(size) RuntimeError: error executing torch_shm_manager at "/home/daneshwar/Desktop/pytorch-EverybodyDanceNow-master/AMLD2020-Dirty-GANcing/env/lib/python3.7/site-packages/torch/bin/torch_shm_manager" at /pytorch/torch/lib/libshm/core.cpp:99

silexxx commented 4 years ago

From src/GANcing/../pix2pixHD/util/visualizer.py:26: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

actually this issue I am encountering with all the GitHub everybody dance now repositories I tried to fix it but still getting stuck can u help me with this, please Thank you

Gramet commented 4 years ago

This looks more like a pytorch/cuda problem than a bug in the code to me...

There is nothing I can change in the code that would solve this unfortunately

silexxx commented 4 years ago

yes, I would look more into it whats causing the error to thank you so much.

silexxx commented 4 years ago

yes I got the problem solved by changing the tensorflow-gpu from 1.14.0 to 1.15.2 in colab. Thank you again. for the quick response.