fc8951bbd36b14099e41fe171ab57e9f3937fe4f,gan_train.py,,calc_gradient_penalty,#Any#Any#Any#,98

Before Change

    if cuda_available:
        interpolates = interpolates.cuda()
    interpolates = autograd.Variable(interpolates, requires_grad=True)

    disc_interpolates = netD(interpolates)

After Change

    alpha = alpha.cuda() if cuda_available else alpha
    
    fake_data = fake_data.view(BATCH_SIZE, 3, DIM, DIM)
    interpolates = alpha * real_data.detach() + ((1 - alpha) * fake_data.detach())

    if cuda_available:
        interpolates = interpolates.cuda()
    interpolates.requires_grad_(True)

    disc_interpolates = netD(interpolates)

Instances