Comments (6)
First you need to gather zs from blocks. Then you can reverse using these zs, but you will need to skip sampling (reparameterization) using prior. You can may refer to this: 4beed5d.
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Hi I just tried it out and it works perfectly now! Thank you very much your help!
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for reconstruction do we need to normalize zs (obtained during the forward pass)?
Currently, I'm training glow on mnist with affine coupling, during the forward pass zs blows up to 100k sometimes, due to which reconstructions are Nan always. Let me if normalization is required or if I'm missing any other step during reconstruction.
Thank you!
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@koriavinash1 No, you don't need a normalization. It should be enough to give zs from forward to reverse with reconstruct=True.
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@rosinality thanks for the quick response, do you have any idea on how to resolve the NaN issue?
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@koriavinash1 You can try to reduce --n_flow and --n_block to make the model more stable.
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Related Issues (20)
- Something wrong with affine coupling? HOT 4
- Question: Does this work with larger resolutions? HOT 2
- coupling.py is error:The size of tensor a (2) must match the size of tensor b (62) at non-singleton dimension 1
- Conditional Gaussian prior parameters produce unnormalized likelihoods HOT 2
- Change image_size to 256 HOT 7
- File Checkpoint HOT 1
- a question about dataset
- Loss value HOT 2
- a question about the sigmoid function in the affine coupling layer HOT 2
- loss NAN HOT 1
- Maybe something wrong with affine paramter in argparse? HOT 1
- Act Norm Output issue HOT 1
- z_list HOT 9
- Why my sample pictures are black? HOT 2
- 如果对图像生成,glow感兴趣,或者需要帮助,可以联系我
- any pretrained models?
- too smalll value of logP
- Flow not perfectly invertible HOT 4
- why with torch.no_grad() when i == 0: HOT 1
- what's the difference between the " reconstruct=True" and " reconstruct=False"
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