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harisreedhar
2025-04-15 14:05:27 +05:30
parent 39ce14b590
commit 4a319ec9bd
1 changed files with 4 additions and 4 deletions
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face_swapper/src/models/loss.py
+4 -4
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@@ -54,7 +54,7 @@ class CycleLoss(nn.Module):
super().__init__()
self.config_batch_size = config_parser.getint('training.loader', 'batch_size')
self.config_cycle_weight = config_parser.getfloat('training.losses', 'cycle_weight')
self.l1_loss = nn.L1Loss()
self.mae_loss = nn.L1Loss()
def forward(self, target_tensor : Tensor, cycle_tensor : Tensor, target_features : Tuple[Feature, ...], cycle_features : Tuple[Feature, ...]) -> Tuple[Loss, Loss]:
temp_tensors = []
@@ -63,9 +63,9 @@ class CycleLoss(nn.Module):
temp_tensor = torch.mean(torch.pow(output_feature - target_feature, 2).reshape(self.config_batch_size, -1), dim = 1).mean()
temp_tensors.append(temp_tensor)
cycle_feature_loss = torch.stack(temp_tensors).mean()
cycle_l1_loss = self.l1_loss(target_tensor, cycle_tensor)
cycle_loss = (cycle_feature_loss + cycle_l1_loss) * 0.5
feature_loss = torch.stack(temp_tensors).mean()
mae_loss = self.mae_loss(target_tensor, cycle_tensor)
cycle_loss = (feature_loss + mae_loss) * 0.5
weighted_feature_loss = cycle_loss * self.config_cycle_weight
return cycle_loss, weighted_feature_loss