e7a02b6293100e21ef15870fd0f9069beaae5290,train.py,,,#,21

Before Change

        real_img = Variable(target)
        if torch.cuda.is_available():
            real_img = real_img.cuda()
            real_label = real_label.cuda()

        // compute loss of real_img
        real_out = netD(real_img)
        d_loss_real = discriminator_criterion(real_out, real_label)

After Change

        fake_out = netD(fake_img)
        fake_scores = fake_out.data.sum()

        d_loss = - torch.mean(torch.log(real_out) + torch.log(1 - fake_out))

        // bp and optimize
        optimizerD.zero_grad()
        d_loss.backward(retain_graph=True)

Instances