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You will have to implement a GPU version for micro-patch-to-macro-patch conversion. Otherwise, there will be no gradient propagation from the discriminator for the generator adversarial training.
The text was updated successfully, but these errors were encountered:
Thank you for pointing out, appreciated. Will update the code.
The code was debugged and run on the cpu for implementation, I should have added this in the description, my bad. Anyways I will also add the gpu implementation of the functions soon.
Thanks for your interest in COCO-GAN and your effort in implementing it in Pytorch!
I took a quick look and found two issues (not sure if there is still any other) that may cause your model failed to converge:
The implementation of CBN is wrong, the
mean_rec
andvar_rec
needs to be tracked for each iteration. It should work very similarly to the standard batch norm. You may refer to this:https://discuss.pytorch.org/t/conditional-batch-normalization/14412/2
You will have to implement a GPU version for micro-patch-to-macro-patch conversion. Otherwise, there will be no gradient propagation from the discriminator for the generator adversarial training.
The text was updated successfully, but these errors were encountered: