| import torch | |
| from torch import nn | |
| class WNormLoss(nn.Module): | |
| def __init__(self, opts): | |
| super(WNormLoss, self).__init__() | |
| self.opts = opts | |
| def forward(self, latent, latent_avg=None): | |
| if self.opts.start_from_latent_avg or self.opts.start_from_encoded_w_plus: | |
| latent = latent - latent_avg | |
| return torch.sum(latent.norm(2, dim=(1, 2))) / latent.shape[0] | |