[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

monai/losses/unified_focal_loss.py

@@ -71,26 +71,26 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
             y_pred: prediction logits or probabilities. Shape should be (B, C, spatial_dims).
             y_true: ground truth labels. Shape should match y_pred.
         """
-        
+
         # Track whether we've already converted to probabilities
         is_converted_to_prob = self.is_prob_input
-        
+
         # Handle single-channel binary case
         if y_pred.shape[1] == 1:
             # For binary segmentation, enforce num_classes == 2
             if self.num_classes != 2:
                 raise ValueError(
                     f"Single-channel input requires num_classes=2, but got num_classes={self.num_classes}"
                 )
-            
+
             # Convert to probability if needed
             if not is_converted_to_prob:
                 y_pred = torch.sigmoid(y_pred)
                 is_converted_to_prob = True
-            
+
             # Expand to 2 channels
             y_pred = torch.cat([1 - y_pred, y_pred], dim=1)
-            
+
             # Convert y_true to one-hot with 2 classes (matching expanded y_pred)
             if y_true.shape[1] == 1:
                 y_true = one_hot(y_true, num_classes=2)
@@ -213,23 +213,23 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
 
         # Track whether we've already converted to probabilities
         is_converted_to_prob = self.is_prob_input
-        
+
         # Handle single-channel binary case
         if y_pred.shape[1] == 1:
             # For binary segmentation, enforce num_classes == 2
             if self.num_classes != 2:
                 raise ValueError(
                     f"Single-channel input requires num_classes=2, but got num_classes={self.num_classes}"
                 )
-            
+
             # Convert to probability if needed
             if not is_converted_to_prob:
                 y_pred = torch.sigmoid(y_pred)
                 is_converted_to_prob = True
-            
+
             # Expand to 2 channels
             y_pred = torch.cat([1 - y_pred, y_pred], dim=1)
-            
+
             # Convert y_true to one-hot with 2 classes (matching expanded y_pred)
             if y_true.shape[1] == 1:
                 y_true = one_hot(y_true, num_classes=2)
@@ -334,13 +334,13 @@ def __init__(
             >>> fl(pred, grnd)
         """
         super().__init__(reduction=LossReduction(reduction).value)
-        
+
         # Validate configuration
         if use_softmax and num_classes < 2:
             raise ValueError(
                 f"use_softmax=True requires num_classes >= 2, but got num_classes={num_classes}"
             )
-        
+
         self.to_onehot_y = to_onehot_y
         self.num_classes = num_classes
         self.gamma = gamma
@@ -382,18 +382,18 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
             ValueError: When the number of classes in ground truth exceeds the configured `num_classes`.
             ValueError: When use_softmax=True with single-channel input.
         """
-        
+
         # Validate input dimensions
         if len(y_pred.shape) not in [4, 5]:
             raise ValueError(f"Input shape must be 4 (2D) or 5 (3D), but got {y_pred.shape}")
-        
+
         # Validate use_softmax with single channel
         if self.use_softmax and y_pred.shape[1] == 1:
             raise ValueError(
                 "use_softmax=True is invalid with single-channel input (C=1). "
                 "Use use_softmax=False for binary segmentation with sigmoid activation."
             )
-        
+
         # Shape validation
         if y_pred.shape != y_true.shape:
             # Allow mismatch only for valid cases
@@ -413,7 +413,7 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
                 raise ValueError(
                     f"Ground truth contains class indices >= {self.num_classes}, which exceeds num_classes."
                 )
-        
+
         # Get losses from sub-losses
         # AsymmetricFocalLoss with NONE reduction returns (B, C, H, W, [D])
         # AsymmetricFocalTverskyLoss with NONE reduction returns (B, C)
@@ -442,4 +442,4 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
         else:
             loss = torch.mean(combined_loss)
 
-        return loss
+        return loss