fix: add missing stacklevel=2 to warnings.warn() in losses/ (Fixes #8929)

monai/losses/adversarial_loss.py

@@ -129,7 +129,8 @@ def forward(
             target_is_real = True  # With generator, we always want this to be true!
             warnings.warn(
                 "Variable target_is_real has been set to False, but for_discriminator is set"
-                "to False. To optimise a generator, target_is_real must be set to True."
+                "to False. To optimise a generator, target_is_real must be set to True.",
+                stacklevel=2,
             )
 
         if not isinstance(input, list):

monai/losses/dice.py

@@ -156,7 +156,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         n_pred_ch = input.shape[1]
         if self.softmax:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `softmax=True` ignored.")
+                warnings.warn("single channel prediction, `softmax=True` ignored.", stacklevel=2)
             else:
                 input = torch.softmax(input, 1)
 
@@ -165,13 +165,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 # if skipping background, removing first channel
                 target = target[:, 1:]
@@ -405,7 +405,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         n_pred_ch = input.shape[1]
         if self.softmax:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `softmax=True` ignored.")
+                warnings.warn("single channel prediction, `softmax=True` ignored.", stacklevel=2)
             else:
                 input = torch.softmax(input, 1)
 
@@ -414,13 +414,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 # if skipping background, removing first channel
                 target = target[:, 1:]
@@ -987,7 +987,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         if self.to_onehot_y:
             n_pred_ch = input.shape[1]
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
         dice_loss = self.dice(input, target)

monai/losses/focal_loss.py

@@ -146,13 +146,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 # if skipping background, removing first channel
                 target = target[:, 1:]

monai/losses/hausdorff_loss.py

@@ -154,7 +154,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         n_pred_ch = input.shape[1]
         if self.softmax:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `softmax=True` ignored.")
+                warnings.warn("single channel prediction, `softmax=True` ignored.", stacklevel=2)
             else:
                 input = torch.softmax(input, 1)
 
@@ -163,13 +163,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 # If skipping background, removing first channel
                 target = target[:, 1:]

monai/losses/mcc_loss.py

@@ -133,7 +133,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         n_pred_ch = input.shape[1]
         if self.softmax:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `softmax=True` ignored.")
+                warnings.warn("single channel prediction, `softmax=True` ignored.", stacklevel=2)
             else:
                 input = torch.softmax(input, 1)
 
@@ -142,13 +142,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 target = target[:, 1:]
                 input = input[:, 1:]

monai/losses/perceptual.py

@@ -112,7 +112,7 @@ def __init__(
                 )
             if not channel_wise:
                 warnings.warn(
-                    "MedicalNet networks supp, ort channel-wise loss. Consider setting channel_wise=True.", stacklevel=2
+                    "MedicalNet networks support channel-wise loss. Consider setting channel_wise=True.", stacklevel=2
                 )
 
         # Channel-wise only for MedicalNet
@@ -127,7 +127,8 @@ def __init__(
             torch.hub.set_dir(cache_dir)
             # raise a warning that this may change the default cache dir for all torch.hub calls
             warnings.warn(
-                f"Setting cache_dir to {cache_dir}, this may change the default cache dir for all torch.hub calls."
+                f"Setting cache_dir to {cache_dir}, this may change the default cache dir for all torch.hub calls.",
+                stacklevel=2,
             )
 
         self.spatial_dims = spatial_dims

monai/losses/spatial_mask.py

@@ -55,16 +55,18 @@ def forward(self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
             mask: the shape should be B1H[WD] or 11H[WD].
         """
         if mask is None:
-            warnings.warn("No mask value specified for the MaskedLoss.")
+            warnings.warn("No mask value specified for the MaskedLoss.", stacklevel=2)
             return self.loss(input, target)
 
         if input.dim() != mask.dim():
-            warnings.warn(f"Dim of input ({input.shape}) is different from mask ({mask.shape}).")
+            warnings.warn(f"Dim of input ({input.shape}) is different from mask ({mask.shape}).", stacklevel=2)
         if input.shape[0] != mask.shape[0] and mask.shape[0] != 1:
             raise ValueError(f"Batch size of mask ({mask.shape}) must be one or equal to input ({input.shape}).")
         if target.dim() > 1:
             if mask.shape[1] != 1:
                 raise ValueError(f"Mask ({mask.shape}) must have only one channel.")
             if input.shape[2:] != mask.shape[2:]:
-                warnings.warn(f"Spatial size of input ({input.shape}) is different from mask ({mask.shape}).")
+                warnings.warn(
+                    f"Spatial size of input ({input.shape}) is different from mask ({mask.shape}).", stacklevel=2
+                )
         return self.loss(input * mask, target * mask)

monai/losses/tversky.py

@@ -118,7 +118,7 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
         n_pred_ch = input.shape[1]
         if self.softmax:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `softmax=True` ignored.")
+                warnings.warn("single channel prediction, `softmax=True` ignored.", stacklevel=2)
             else:
                 input = torch.softmax(input, 1)
 
@@ -127,13 +127,13 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 target = one_hot(target, num_classes=n_pred_ch)
 
         if not self.include_background:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `include_background=False` ignored.")
+                warnings.warn("single channel prediction, `include_background=False` ignored.", stacklevel=2)
             else:
                 # if skipping background, removing first channel
                 target = target[:, 1:]

monai/losses/unified_focal_loss.py

@@ -58,7 +58,7 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 y_true = one_hot(y_true, num_classes=n_pred_ch)
 
@@ -122,7 +122,7 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
 
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 y_true = one_hot(y_true, num_classes=n_pred_ch)
 
@@ -223,7 +223,7 @@ def forward(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> torch.Tensor:
         n_pred_ch = y_pred.shape[1]
         if self.to_onehot_y:
             if n_pred_ch == 1:
-                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+                warnings.warn("single channel prediction, `to_onehot_y=True` ignored.", stacklevel=2)
             else:
                 y_true = one_hot(y_true, num_classes=n_pred_ch)