Comments (2)
Hi @Gigibulid did you solve your problem? I am working with 100 images and I get:
warnings.warn(msg, SourceChangeWarning)
INFO - 04/22/20 18:39:46 - 0:00:25 - 100 / 0 / 0 images with attributes for train / valid / test sets
/home/ericd/tests/FaderNetworks/src/loader.py:140: UserWarning: volatile was removed and now has no effect. Use `with torch.no_grad():` instead.
return Variable(batch_x, volatile=True), Variable(batch_y, volatile=True)
Traceback (most recent call last):
File "interpolate.py", line 96, in <module>
interpolations.append(get_interpolations(ae, images, attributes, params))
File "interpolate.py", line 72, in get_interpolations
enc_outputs = ae.encode(images)
File "/home/ericd/tests/FaderNetworks/src/model.py", line 106, in encode
enc_outputs.append(layer(enc_outputs[-1]))
File "/home/ericd/anaconda/envs/myPytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/ericd/anaconda/envs/myPytorch/lib/python3.6/site-packages/torch/nn/modules/container.py", line 100, in forward
input = module(input)
File "/home/ericd/anaconda/envs/myPytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/home/ericd/anaconda/envs/myPytorch/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py", line 106, in forward
self.training or not self.track_running_stats,
File "/home/ericd/anaconda/envs/myPytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 576, in __getattr__
type(self).__name__, name))
AttributeError: 'BatchNorm2d' object has no attribute 'track_running_stats'
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Apparently my problem has to do with the Pytorch version, perhaps this should be on dependencies. https://discuss.pytorch.org/t/batchnorm2d-object-has-no-attribute-track-running-stats/17525
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Related Issues (14)
- interpolate on own images HOT 2
- does someone reproducing the results HOT 7
- About the losses used for the pre-trained model. HOT 3
- Memory usage during pre-processing HOT 2
- The model must use a single boolean attribute only HOT 1
- should use detach to make the gradient do not propagate from discriminator to autoencoder HOT 1
- multiple attributes
- Adversarial loss of encoder not identical to the paper HOT 1
- fader network training failure
- Can you explain the difference between your work and ALI?
- pytorch version? HOT 2
- jpg vs png CelebA images
- training with continous attributes
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