3f62a3f23930c17170dc2e03f0de56da83f8ec80,implementations/unit/unit.py,,,#,152

Before Change

        // Fake loss
        loss_fake = criterion_GAN(D1(fake_X1.detach()), fake)
        // Total loss
        loss_D1 = (loss_real + loss_fake) / 2

        loss_D1.backward()
        optimizer_D1.step()

        // -----------------------
        //  Train Discriminator B
        // -----------------------

        optimizer_D2.zero_grad()

        // Real loss
        loss_real = criterion_GAN(D2(X2), valid)
        // Fake loss
        loss_fake = criterion_GAN(D2(fake_X2.detach()), fake)
        // Total loss
        loss_D2 = (loss_real + loss_fake) / 2

        loss_D2.backward()
        optimizer_D2.step()

        loss_D = (loss_D1 + loss_D2) / 2

        // --------------
        //  Log Progress
        // --------------

After Change

        optimizer_D2.zero_grad()

        loss_D2 =   criterion_GAN(D2(X2), valid) + \
                    criterion_GAN(D2(fake_X2.detach()), fake)

        loss_D2.backward()
        optimizer_D2.step()

Instances