Comments (1)
For e.g. DCGAN or pix2pix people usually start with discriminator training with detached generated fakes and then optimize Generator. Not sure why it is the other way round here.
Dont know if that helps 😅 ...
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Related Issues (20)
- about results HOT 2
- PatchGAN loss calculated incorrectly (perhaps) HOT 3
- SOLUTION: IndexError: invalid index of a 0-dim tensor. Use tensor.item() to convert a 0-dim tensor to a Python number HOT 3
- Getting some random noise in generated fake images while training after few epochs and not generating quite clear images even after 60 epochs
- input and target shapes do not match: input [1 x 1], target [1] at /pytorch/aten/src/THCUNN/generic/MSECriterion.cu:15
- question for the dataset structur HOT 2
- why not fix D while updating G? HOT 1
- Training did not converge,how to change the parameters?plz! HOT 1
- UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. HOT 8
- Training problems:Setting up a new session HOT 1
- RuntimeError: The expanded size of the tensor (1) must match the existing size (3) at non-singleton dimension 0 HOT 2
- RuntimeError: The expanded size of the tensor (1224) must match the existing size (370) at non-singleton dimension 2
- question about train HOT 1
- Pls tell me why derive the discriminator loss function w.r.t. the label rather than the input data.
- about identity loss HOT 1
- Can the results same as the official implement? HOT 1
- UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimiz er.step()` before `lr_scheduler.step()`. Failure to do this will result in PyTorch skipping the first value of the learning rate schedule.
- picture size HOT 1
- Beginners' question: Is there a way to use a pre-trained model, or that I have to train it myself?
- Traceback (most recent call last): File "E:\Desktop\PyTorch-CycleGAN-master\test", line 73, in <module> real_A = Variable(input_A.copy_(batch['A'])) RuntimeError: The size of tensor a (256) must match the size of tensor b (1920) at non-singleton dimension 3
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