请问一下，下面两个损失都输针对判别器的损失，输入都是pred_fake,但是target确不一样，这样每一次优化的时候会不会造成混淆？

[cjt222]

No description provided.

[cjt222]

def compute_generator_loss(self, input_semantics, real_image):
    G_losses = {}

    fake_image = self.generate_fake(input_semantics)

    pred_fake, pred_real = self.discriminate(input_semantics, fake_image, real_image)

    G_losses['GAN'] = self.criterionGAN(pred_fake, True,
                                        for_discriminator=False)

    if not self.opt.no_ganFeat_loss:
        num_D = len(pred_fake)
        GAN_Feat_loss = self.FloatTensor(1).fill_(0)
        for i in range(num_D):  # for each discriminator
            # last output is the final prediction, so we exclude it
            num_intermediate_outputs = len(pred_fake[i]) - 1
            for j in range(num_intermediate_outputs):  # for each layer output
                unweighted_loss = self.criterionFeat(pred_fake[i][j], pred_real[i][j].detach())
                GAN_Feat_loss += unweighted_loss * self.opt.lambda_feat / num_D
        G_losses['GAN_Feat'] = GAN_Feat_loss
    
    h ,w = fake_image.shape[-2:]
    if not self.opt.no_vgg_loss and min(w,h)>=64:
        G_losses['VGG'] = self.criterionVGG(fake_image, real_image) \
                          * self.opt.lambda_vgg

    return G_losses, fake_image

def compute_discriminator_loss(self, input_semantics, real_image):
    D_losses = {}
    with torch.no_grad():
        fake_image = self.generate_fake(input_semantics)
        fake_image = fake_image.detach()
        fake_image.requires_grad_()

    pred_fake, pred_real = self.discriminate(
        input_semantics, fake_image, real_image)

    D_losses['D_Fake'] = self.criterionGAN(pred_fake, False,
                                           for_discriminator=True)
    D_losses['D_real'] = self.criterionGAN(pred_real, True,
                                           for_discriminator=True)

    return D_losses

gan_mode是ls，所以采用的是mse loss，这里面的 G_losses['GAN']和D_losses['D_Fake']貌似是相反的操作