24387ddc838a9eb4273c03bf19e3f35587e3f201,implementations/wgan/wgan.py,,,#,88

Before Change

    for i in range(len(data_iter) // opt.n_critic):
        // Train discriminator for n_critic times
        for _ in range(opt.n_critic):
            (imgs, _) = data_iter.next()

            // Configure input
            real_imgs = Variable(imgs.type(Tensor))

After Change

            p.data.clamp_(-opt.clip_value, opt.clip_value)

        // Train the generator every n_critic iterations
        if i % opt.n_critic == 0:

            // -----------------
            //  Train Generator
            // -----------------

            optimizer_G.zero_grad()

            // Generate a batch of images
            gen_imgs = generator(z)
            // Adversarial loss
            loss_G = -torch.mean(discriminator(gen_imgs))

            loss_G.backward()
            optimizer_G.step()

            print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, opt.n_epochs,
                                                            batches_done % len(dataloader), len(dataloader),
                                                            loss_D.item(), loss_G.item()))

        if batches_done % opt.sample_interval == 0:
            save_image(gen_imgs.data[:25], "images/%d.png" % batches_done, nrow=5, normalize=True)
        batches_done += 1

Instances