Fixes more typing errors

Project-MONAI · Jul 31, 2023 · cd01b59 · cd01b59
1 parent 4b1d801
commit cd01b59
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Showing 2 changed files with 8 additions and 7 deletions.
diff --git a/monai/losses/adversarial_loss.py b/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

diff --git a/monai/losses/perceptual.py b/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