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View Code? Open in Web Editor NEWVariational Autoencoder (VAE) with perception loss implementation in pytorch
License: MIT License
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bobiblazeski xuchensjtu fdujay escu0001 szxuhongye joekhoa allenpu tmbj-jerry ekonyagin meanna rliumines keikinn junaidmq saiddddd miergeduo ashlinrichardsoncnn-vae's Issues
Thank you
Hey I just wanted to say I really like your project. I have tried out other vae repos in the past and this one is much easier to use and performs much better. I was able to very quickly get setup training on my dataset. The VGG19 model vastly improves training performance for me compared to other network structures I've used before. Code is very clean as well. Cheers!
size of VAE image
Can the size of VAE image input be changed？Why must it be 64x64？
Question about class ResUp
In class Decoder, the number of convolutional patterns decreaes (from z to ch8 -> ch8 -> ch4 -> ch2 -> ch).
In class ResUp, the convolution operation goes in this way: self.conv1 = nn.Conv2d(channel_in, channel_out//2, 3, 1, 1).
In this way, the number of convolutional patterns is smaller than channel_out.
Take ResUp(ch*8, ch*4) as an example, the number of convolutional patterns varies by ch*8 -> ch*2 ->ch*4.
I suggest that are there any mistakes in self.conv1 in class ResUp?
I think it should be self.conv1 = nn.Conv2d(channel_in, channel_in//2,3,1,1) or nn.Conv2d(channel_in, channel_out*2, 3, 1, 1) or something that doesn't "shrink" the convolutional patterns during the operation.
Cool! how well does it work?
I've been thinking about implementing a resnet style vae for images. In preparation I came across your repo -- very interesting! I'm wondering if you'd be willing to share your thoughts about how well this works? In particular, I'd be interested to understand the impact of the skip connections. Also, qualitatively, what fidelity do you see in the reconstructed images?
Kind regards,
Hudson
How to modify the image size?
Thanks for your code! It did work when I trained with image_size=64, but when I tried to modify the image_size=128, some error
occurred:
ValueError Traceback (most recent call last)
<ipython-input-23-cd633620fbd2> in <module>
6
7 #VAE loss
----> 8 loss = vae_loss(recon_data, data[0].to(device), mu, logvar)
9
10 #Perception loss
<ipython-input-4-ba4d722462c7> in vae_loss(recon, x, mu, logvar)
14
15 def vae_loss(recon, x, mu, logvar):
---> 16 recon_loss = F.binary_cross_entropy_with_logits(recon, x)
17 KL_loss = -0.5 * (1 + logvar - mu.pow(2) - logvar.exp()).mean()
18 loss = recon_loss + 0.01 * KL_loss
~/anaconda3/envs/pt1.0/lib/python3.6/site-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
2096
2097 if not (target.size() == input.size()):
-> 2098 raise ValueError("Target size ({}) must be the same as input size ({})".format(target.size(), input.size()))
2099
2100 return torch.binary_cross_entropy_with_logits(input, target, weight, pos_weight, reduction_enum)
ValueError: Target size (torch.Size([8, 3, 128, 128])) must be the same as input size (torch.Size([8, 3, 320, 320]))
So how can I modify the input size of image correctly? Thank you very much, if you can answer my question！
A virtualenv describing what version of packages works
I'm having trouble figuring out which python packages and versions are needed to get this project working.
A requirements.txt file with the version numbers would be nice :)
About training for1024*1024 imaging
Dear LukeDitria,
I am a long-time user of this repo and I have raised an issue about scaling to 256 resolutions earlier (if you remember). But now I want to train on much higher resolution images (1024), do you think it is possible or what necessary changes do I need to make?
Best regards,
Jay
How about 1-channel image reconstrustion?
Thanks for your code! It did work when I trained with image_channel=3, but when I tried to test the image with channel=1, some error occurred:
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-38-0565c2bab7d7> in <module>
9
10 #Perception loss
---> 11 loss_feature = feature_loss(data[0].to(device), recon_data, feature_extractor)
12
13 loss += loss_feature
<ipython-input-30-2deca954bbe0> in feature_loss(img, recon_data, feature_extractor)
26 def feature_loss(img, recon_data, feature_extractor):
27 img_cat = torch.cat((img, torch.sigmoid(recon_data)), 0)
---> 28 out = feature_extractor(img_cat)
29 loss = 0
30 for i in range(len(feature_extractor)):
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
115 def forward(self, input):
116 for module in self:
--> 117 input = module(input)
118 return input
119
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py in forward(self, input)
421
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
424
425 class Conv3d(_ConvNd):
~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py in _conv_forward(self, input, weight)
417 weight, self.bias, self.stride,
418 _pair(0), self.dilation, self.groups)
--> 419 return F.conv2d(input, weight, self.bias, self.stride,
420 self.padding, self.dilation, self.groups)
421
RuntimeError: Given groups=1, weight of size [64, 3, 3, 3], expected input[64, 1, 256, 256] to have 3 channels, but got 1 channels instead
So how can I modify the code correctly? Thank you very much, if you could answer my question！
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