def forward(self, x):
mean = torch.mean(x, dim=0).pow(2)
cov = Ops.cov(x)
cov_loss = torch.mean(
(Variable(torch.eye(cov.size()[0]).cuda()) - cov).pow(2))
return torch.mean(mean) + cov_loss
def forward(self, x):
real, fake = x
_, real_features = real
_, fake_features = fake
real_features = torch.cat(real_features, dim=1)
fake_features = torch.cat(fake_features, dim=1)
real_mean = torch.mean(real_features, dim=0)
fake_mean = torch.mean(fake_features, dim=0)
real_cov = Ops.cov(real_features)
fake_cov = Ops.cov(fake_features)
mean_loss = torch.sum((real_mean - fake_mean).pow(2))
cov_loss = torch.sum((real_cov - fake_cov).pow(2))
return mean_loss + cov_loss