Fixes more typing errors

Author: marksgraham

monai/losses/adversarial_loss.py

@@ -64,7 +64,7 @@ def __init__(
         # Depending on the criterion, a different activation layer is used.
         self.real_label = 1.0
         self.fake_label = 0.0
-        self.loss_fct : _Loss
+        self.loss_fct: _Loss
         if criterion == AdversarialCriterions.BCE.value:
             self.activation = get_act_layer("SIGMOID")
             self.loss_fct = torch.nn.BCELoss(reduction=reduction)
@@ -153,16 +153,18 @@ def forward(
                 loss_ = self.forward_single(disc_out, target_[disc_ind])
             loss_list.append(loss_)
 
+        loss: torch.Tensor | list[torch.Tensor]
         if loss_list is not None:
             if self.reduction == LossReduction.MEAN.value:
                 loss = torch.mean(torch.stack(loss_list))
             elif self.reduction == LossReduction.SUM.value:
                 loss = torch.sum(torch.stack(loss_list))
-
+            else:
+                loss = loss_list
         return loss
 
     def forward_single(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
-        forward : torch.Tensor
+        forward: torch.Tensor
         if (
             self.criterion == AdversarialCriterions.BCE.value
             or self.criterion == AdversarialCriterions.LEAST_SQUARE.value

monai/losses/perceptual.py

@@ -20,7 +20,6 @@
 torchvision, _ = optional_import("torchvision")
 
 
-
 class PerceptualLoss(nn.Module):
     """
     Perceptual loss using features from pretrained deep neural networks trained. The function supports networks
@@ -78,7 +77,7 @@ def __init__(
             torch.hub.set_dir(cache_dir)
 
         self.spatial_dims = spatial_dims
-        self.perceptual_function : nn.Module
+        self.perceptual_function: nn.Module
         if spatial_dims == 3 and is_fake_3d is False:
             self.perceptual_function = MedicalNetPerceptualSimilarity(net=network_type, verbose=False)
         elif "radimagenet_" in network_type:
@@ -196,7 +195,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         feats_input = normalize_tensor(outs_input)
         feats_target = normalize_tensor(outs_target)
 
-        results : torch.Tensor = (feats_input - feats_target) ** 2
+        results: torch.Tensor = (feats_input - feats_target) ** 2
         results = spatial_average_3d(results.sum(dim=1, keepdim=True), keepdim=True)
 
         return results
@@ -345,7 +344,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         feats_input = normalize_tensor(outs_input)
         feats_target = normalize_tensor(outs_target)
 
-        results : torch.Tensor = (feats_input - feats_target) ** 2
+        results: torch.Tensor = (feats_input - feats_target) ** 2
         results = spatial_average(results.sum(dim=1, keepdim=True), keepdim=True)
 
         return results