| import torch | |
| import torch.nn.functional as F | |
| def hinge_d_loss(logits_real, logits_fake): | |
| loss_real = torch.mean(F.relu(1.0 - logits_real)) | |
| loss_fake = torch.mean(F.relu(1.0 + logits_fake)) | |
| d_loss = 0.5 * (loss_real + loss_fake) | |
| return d_loss | |
| def vanilla_d_loss(logits_real, logits_fake): | |
| d_loss = 0.5 * ( | |
| torch.mean(torch.nn.functional.softplus(-logits_real)) | |
| + torch.mean(torch.nn.functional.softplus(logits_fake)) | |
| ) | |
| return d_loss | |