Hi there!

I’m having some problems with my GAN code, any help would be much appreciated.

I am implementing an SR model which involves 3 loss functions, a linear combination of two for the generator and one for the discriminator.

G1) Loss function between the generator output and target image.

G2) Loss function A between outputs of the discriminator for real (target image) and fake (generator output) inputs.

D) Loss function B between outputs of the discriminator for real (target image) and fake (generator output) inputs.

G2 and D are distinct loss functions.

Problem I am having is the error below, setting anomaly_detection = True does not give much more information. I am fairly sure the issue stems from optimizer_D.step() acting inplace.

RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [512]] is at version 4; expected version 3 instead.

Here is my code:

```
batch_hr = batch["hr"].to(opt.local_rank)
batch_lr = batch["lr"].to(opt.local_rank)
g_hr = generator(batch_lr)
d_real = discriminator(batch_hr)
d_fake = discriminator(g_hr.detach()) # detached from generator graph
loss_D = D(d_real, d_fake)
optimizer_D.zero_grad(set_to_none=True)
loss_D.backward(retain_graph=True)
optimizer_D.step()
loss_G1 = G1(batch_hr, g_hr)
d_real = discrim(batch_hr).detach() # detached from discrim graph
d_fake = discrim(g_hr)
loss_G2 = G2(d_real, d_fake)
loss_G = loss_G1 + loss_G2
optimizer_G.zero_grad(set_to_none=True)
loss_G.backward()
optimizer_G.step()
```

I have tried various rearrangements and combinations of clone(), detach() and retain_graph=True but cannot work around this. Further to this, I would ideally only make one call to the generator and two to the discriminator (real/fake once each) but I am not sure this is possible with this combination of losses.

Thank you in advance for any help or advice!