cp_train.py 文件源码

def calc_gradient_penalty(netD, real_data, fake_data, sketch):
    alpha = torch.rand(opt.batchSize, 1, 1, 1)
    alpha = alpha.cuda() if opt.cuda else alpha

    interpolates = alpha * real_data + ((1 - alpha) * fake_data)

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

    disc_interpolates = netD(interpolates, Variable(sketch))[0]

    gradients = grad(outputs=disc_interpolates, inputs=interpolates,
                     grad_outputs=torch.ones(disc_interpolates.size()).cuda() if opt.cuda else torch.ones(
                         disc_interpolates.size()),
                     create_graph=True, retain_graph=True, only_inputs=True)[0]

    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean() * opt.gpW
    return gradient_penalty