There seem to be some issues in your code.
myMSELoss uses the .data attribute to calculate the loss, which is not recommended. It might break the computation graph, which is most likely the case in your code. Have a look at this small code snippet:
x = torch.randn(1, requires_grad=True)
y = torch.randn(1)
loss = torch.sum((x-y)**2)  # .data will yield an error
loss.backward()
print(x.grad)

If you add .data to the loss calculation, you’ll get an error:

RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

I guess this error might be masked, since you are using different losses, where some of them might still calculate the correct gradients. You could just remove the .data op in this case.
Also, your transformations like transform_RGB2YCBCR detach the tensor from the computation graph, as you are leaving PyTorch and use methods of PIL, which calls numpy under the hood.
I haven’t run your code, but I think your generator won’t be updated at all, since imgSR_Y is detached from it before being passed to the discriminator.
Based on your code it looks like you would like to feed RGB images wo the generator and CBCR to the discriminator. If that’s the case, you would have to write the transformation using PyTorch operations.