StyleGAN_PyTorch
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Some questions about `networks_stylegan.py`
Thanks for your reimplementation!
- In networks_stylegan.py, line 107
f = f[:, :, ::-1, ::-1]
, I think you should consider usef = torch.flip(f, [2, 3])
- In networks_stylegan.py, line 247,
x = self.noise(x, noise)
, I think you should consider add a condition judgement before this line, likeif self.noise is not None:
- In defination of forward function of the
class GBlock
, do you miss a 3x3 convolution between upsmaple and the first adaIn1?
Thanks a lot!
It has been a few months that I haven't kept pace with progress in PyTorch, could you please explain is there a difference between f = f[:, :, ::-1, ::-1]
and torch.flip(f, [2, 3])
?
The advice 2, 3 looks good to me, you could pull request a version to improve it and i will have it look and merge if it works well.
^.^ Samuel
Thanks for your reply.
For the first problem, if you use [::-1]
index for a torch.tensor, you will get an error ValueError: negative step not yet supported
, i.e., cannot flip a torch.tensor like numpy array.
Besides, I'm working on this project and it will be my pleasure to pull a request later.
Thanks for your reply. For the first problem, if you use
[::-1]
index for a torch.tensor, you will get an errorValueError: negative step not yet supported
, i.e., cannot flip a torch.tensor like numpy array. Besides, I'm working on this project and it will be my pleasure to pull a request later.
Thanks!
Thanks for your reimplementation!
- In networks_stylegan.py, line 107
f = f[:, :, ::-1, ::-1]
, I think you should consider usef = torch.flip(f, [2, 3])
- In networks_stylegan.py, line 247,
x = self.noise(x, noise)
, I think you should consider add a condition judgement before this line, likeif self.noise is not None:
- In defination of forward function of the
class GBlock
, do you miss a 3x3 convolution between upsmaple and the first adaIn1?
Did you find the answer for 3? I was thinking the same about missing a convolution between upsample and first adaln1 so I checked the issues and saw your question. I think the layer is missing as well but I haven't tested the code myself yet so I don't know if it works or not.