032e88438b1b2690e538c2cf399ebceb76adbea9,models/networks.py,GANLoss,__call__,#GANLoss#Any#Any#,132

Before Change

    def __call__(self, input, target_is_real):
        target_tensor = self.get_target_tensor(input, target_is_real)
        return self.loss(input, target_tensor)


// Defines the generator that consists of Resnet blocks between a few
// downsampling/upsampling operations.

After Change

        
        if self.gan_mode in ["lsgan", "vanilla"]:
            target_tensor = self.get_target_tensor(prediction, target_is_real)
            loss = self.loss(prediction, target_tensor)
        elif self.gan_mode == "wgangp":
            if target_is_real:
                loss = -prediction.mean()
            else:
                loss = prediction.mean()
        return loss


def cal_gradient_penalty(netD, real_data, fake_data, device, type="mixed", constant=1.0, lambda_gp=10.0):
    calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028

Instances