Merge branch 'dev' into optimize/vectorize-dice-metric

Author: aymuos15

monai/metrics/active_learning_metrics.py

@@ -137,7 +137,7 @@ def compute_variance(
     n_len = len(y_pred.shape)
 
     if n_len < 4 and spatial_map:
-        warnings.warn("Spatial map requires a 2D/3D image with N-repeats and C-channels")
+        warnings.warn("Spatial map requires a 2D/3D image with N-repeats and C-channels", stacklevel=2)
         return None
 
     # Create new shape list
@@ -190,7 +190,10 @@ def label_quality_score(
 
     n_len = len(y_pred.shape)
     if n_len < 4 and scalar_reduction == "none":
-        warnings.warn("Reduction set to None, Spatial map return requires a 2D/3D image of B-Batchsize and C-channels")
+        warnings.warn(
+            "Reduction set to None, Spatial map return requires a 2D/3D image of B-Batchsize and C-channels",
+            stacklevel=2,
+        )
         return None
 
     abs_diff_map = torch.abs(y_pred - y)

monai/metrics/average_precision.py

@@ -88,10 +88,12 @@ def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
         raise AssertionError("y and y_pred must be 1 dimension data with same length.")
     y_unique = y.unique()
     if len(y_unique) == 1:
-        warnings.warn(f"y values can not be all {y_unique.item()}, skip AP computation and return `Nan`.")
+        warnings.warn(f"y values can not be all {y_unique.item()}, skip AP computation and return `Nan`.", stacklevel=2)
         return float("nan")
     if not y_unique.equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
-        warnings.warn(f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AP computation and return `Nan`.")
+        warnings.warn(
+            f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AP computation and return `Nan`.", stacklevel=2
+        )
         return float("nan")
 
     n = len(y)

monai/metrics/confusion_matrix.py

@@ -93,7 +93,7 @@ def _compute_tensor(self, y_pred: torch.Tensor, y: torch.Tensor) -> torch.Tensor
             raise ValueError("y_pred should have at least two dimensions.")
         if dims == 2 or (dims == 3 and y_pred.shape[-1] == 1):
             if self.compute_sample:
-                warnings.warn("As for classification task, compute_sample should be False.")
+                warnings.warn("As for classification task, compute_sample should be False.", stacklevel=2)
                 self.compute_sample = False
 
         return get_confusion_matrix(y_pred=y_pred, y=y, include_background=self.include_background)

monai/metrics/cumulative_average.py

@@ -154,7 +154,7 @@ def append(self, val: Any, count: Any | None = 1) -> None:
         # account for possible non-finite numbers in val and replace them with 0s
         nfin = torch.isfinite(val)
         if not torch.all(nfin):
-            warnings.warn(f"non-finite inputs received: val: {val}, count: {count}")
+            warnings.warn(f"non-finite inputs received: val: {val}, count: {count}", stacklevel=2)
             count = torch.where(nfin, count, torch.zeros_like(count))
             val = torch.where(nfin, val, torch.zeros_like(val))
 

monai/metrics/rocauc.py

@@ -77,10 +77,14 @@ def _calculate(y_pred: torch.Tensor, y: torch.Tensor) -> float:
         raise AssertionError("y and y_pred must be 1 dimension data with same length.")
     y_unique = y.unique()
     if len(y_unique) == 1:
-        warnings.warn(f"y values can not be all {y_unique.item()}, skip AUC computation and return `Nan`.")
+        warnings.warn(
+            f"y values can not be all {y_unique.item()}, skip AUC computation and return `Nan`.", stacklevel=2
+        )
         return float("nan")
     if not y_unique.equal(torch.tensor([0, 1], dtype=y.dtype, device=y.device)):
-        warnings.warn(f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AUC computation and return `Nan`.")
+        warnings.warn(
+            f"y values must be 0 or 1, but in {y_unique.tolist()}, skip AUC computation and return `Nan`.", stacklevel=2
+        )
         return float("nan")
 
     n = len(y)

monai/metrics/utils.py

@@ -341,12 +341,14 @@ def get_edge_surface_distance(
     if not edges_gt.any():
         warnings.warn(
             f"the ground truth of class {class_index if class_index != -1 else 'Unknown'} is all 0,"
-            " this may result in nan/inf distance."
+            " this may result in nan/inf distance.",
+            stacklevel=2,
         )
     if not edges_pred.any():
         warnings.warn(
             f"the prediction of class {class_index if class_index != -1 else 'Unknown'} is all 0,"
-            " this may result in nan/inf distance."
+            " this may result in nan/inf distance.",
+            stacklevel=2,
         )
     distances: tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor]
     if symmetric:
@@ -375,7 +377,7 @@ def is_binary_tensor(input: torch.Tensor, name: str) -> None:
     if not isinstance(input, torch.Tensor):
         raise ValueError(f"{name} must be of type PyTorch Tensor.")
     if not torch.all(input.byte() == input) or input.max() > 1 or input.min() < 0:
-        warnings.warn(f"{name} should be a binarized tensor.")
+        warnings.warn(f"{name} should be a binarized tensor.", stacklevel=2)
 
 
 def remap_instance_id(pred: torch.Tensor, by_size: bool = False) -> torch.Tensor:
@@ -510,7 +512,8 @@ def compute_voronoi_regions_fast(labels: np.ndarray | torch.Tensor) -> torch.Ten
         if isinstance(labels, torch.Tensor):
             warnings.warn(
                 "Voronoi computation is running on CPU. "
-                "To accelerate, move the input tensor to GPU and ensure 'cupy' with 'cupyx.scipy.ndimage' is installed."
+                "To accelerate, move the input tensor to GPU and ensure 'cupy' with 'cupyx.scipy.ndimage' is installed.",
+                stacklevel=2,
             )
             x = labels.cpu().numpy()
         else: