Code broken in backward

[FrancescoMandru]

Code broken in backward

[ry85]

Hi,

The code should work fine. There might some difference with respect to the python version or similar to that. The error you are getting is not an uncommon error, did you tried to google it. I think modifying the version should work.

[ILYSHI]

It's rather common error, i found it in several VAE-GAN models.

[GloryyrolG]

Hi,

The code should work fine. There might some difference with respect to the python version or similar to that. The error you are getting is not an uncommon error, did you tried to google it. I think modifying the version should work.

Could you please provide your environment, i.e., the requirements of this repo code?

[wqzdhrdhr]

Hi,

The code should work fine. There might some difference with respect to the python version or similar to that. The error you are getting is not an uncommon error, did you tried to google it. I think modifying the version should work.

I encountered the same problem, my environment is pytorch1.7+python3.9. I guess this is because in the newer version of pytorch, .step() directly changes the parameters in the graph, instead of creating a clone like in the old version. So I tried to put all optimizer.step() after all loss.backward() was completed.

        `net.zero_grad()
        # encoder
        loss_encoder.backward(retain_graph=True)  #someone likes to clamp the grad here: [p.grad.data.clamp_(-1,1) for p in net.encoder.parameters()]
        net.decoder.zero_grad()
        net.discriminator.zero_grad()

        if (train_dec and train_dis == True):
            loss_decoder.backward(retain_graph=True)
            net.discriminator.zero_grad()
            loss_discriminator.backward()
            optimizer_encoder.step()
            optimizer_decoder.step()
            optimizer_discriminator.step()
        #decoder
        if (train_dec == True and train_dis == False):
            loss_decoder.backward()  #[p.grad.data.clamp_(-1,1) for p in net.decoder.parameters()]
            optimizer_decoder.step()
            optimizer_encoder.step()
            optimizer_discriminator.step()


        #discriminator
        if (train_dec == False and train_dis == True):
            loss_discriminator.backward() 
            optimizer_encoder.step()
            optimizer_decoder.step()
            optimizer_discriminator.step()

        if (train_dec == False and train_dis == False):
            optimizer_encoder.step()`