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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]
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