Comments (2)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
netG_A2B = torch.nn.DataParallel(netG_A2B)
netG_B2A = torch.nn.DataParallel(netG_B2A)
netD_A = torch.nn.DataParallel(netD_A)
netD_B = torch.nn.DataParallel(netD_B)
if opt.cuda:
netG_A2B.cuda()
netG_B2A.cuda()
netD_A.cuda()
netD_B.cuda()
from pytorch-cyclegan.
I have the same problem. Have you solved it?
from pytorch-cyclegan.
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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from pytorch-cyclegan.