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Please send me this .json file or test me how to fix this

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Bulding VGG19
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:207: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(m.weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:209: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_.
init.constant(m.bias, 0.0)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:212: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model1_1[8].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:213: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model1_2[8].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:215: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model2_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:216: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model3_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:217: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model4_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:218: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model5_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:219: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model6_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:221: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model2_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:222: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model3_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:223: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model4_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:224: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model5_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:225: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model6_2[12].weight, std=0.01)
Traceback (most recent call last):
File "src/data_preparation/prepare_source.py", line 131, in
prepare_source(args.save_dir)
File "src/data_preparation/prepare_source.py", line 40, in prepare_source
model = load_openpose_model()
File "src/data_preparation/prepare_source.py", line 62, in load_openpose_model
model.load_state_dict(torch.load(weights))
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 426, in load
return _load(f, map_location, pickle_module, **pickle_load_args)
File "/usr/local/lib/python3.6/dist-packages/torch/serialization.py", line 603, in _load
magic_number = pickle_module.load(f, **pickle_load_args)
_pickle.UnpicklingError: invalid load key, 'v'.

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Bulding VGG19
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:204: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(m.weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:206: UserWarning: nn.init.constant is now deprecated in favor of nn.init.constant_.
init.constant(m.bias, 0.0)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:209: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model1_1[8].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:210: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model1_2[8].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:212: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model2_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:213: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model3_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:214: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model4_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:215: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model5_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:216: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model6_1[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:218: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model2_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:219: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model3_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:220: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model4_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:221: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model5_2[12].weight, std=0.01)
src/data_preparation/../PoseEstimation/network/rtpose_vgg.py:222: UserWarning: nn.init.normal is now deprecated in favor of nn.init.normal_.
init.normal(self.model6_2[12].weight, std=0.01)
97% 448/464 [22:24<00:48, 3.00s/it]Traceback (most recent call last):
File "src/data_preparation/prepare_source.py", line 131, in
prepare_source(args.save_dir)
File "src/data_preparation/prepare_source.py", line 42, in prepare_source
extract_poses(model, save_dir)
File "src/data_preparation/prepare_source.py", line 86, in extract_poses
shape_dst = np.min(img.shape[:2])
AttributeError: 'NoneType' object has no attribute 'shape'
97% 448/464 [22:24<00:48, 3.00s/it]

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

when I run the last module command !python src/data_postprocessing/make_gif.py -s data/sources/source -r results/danesh
MovieWriter imagemagick unavailable; trying to use pillow instead.
Traceback (most recent call last):
File "src/data_postprocessing/make_gif.py", line 72, in
make_gif(args.source_dir, args.results_dir)
File "src/data_postprocessing/make_gif.py", line 60, in make_gif
writer="imagemagick")
File "/usr/local/lib/python3.6/dist-packages/matplotlib/animation.py", line 1111, in save
extra_args=extra_args, metadata=metadata)
TypeError: 'str' object is not callable

please help me I have almost at the end of it .