Change default labels_getter of SanitizeKeypoints to None.

Author: Callidior

test/test_transforms_v2.py

@@ -7740,22 +7740,21 @@ def test_errors_transform(self):
         # Test missing labels key
         with pytest.raises(ValueError, match="Could not infer where the labels are"):
             bad_sample = {"keypoints": good_keypoints, "BAD_KEY": torch.tensor([0])}
-            transforms.SanitizeKeyPoints()(bad_sample)
+            transforms.SanitizeKeyPoints(labels_getter="default")(bad_sample)
 
         # Test labels not a tensor
         with pytest.raises(ValueError, match="must be a tensor"):
             bad_sample = {"keypoints": good_keypoints, "labels": [0]}
-            transforms.SanitizeKeyPoints()(bad_sample)
+            transforms.SanitizeKeyPoints(labels_getter="default")(bad_sample)
 
         # Test mismatched sizes
         with pytest.raises(ValueError, match="Number of"):
             bad_sample = {"keypoints": good_keypoints, "labels": torch.tensor([0, 1, 2])}
-            transforms.SanitizeKeyPoints()(bad_sample)
+            transforms.SanitizeKeyPoints(labels_getter="default")(bad_sample)
 
         # Test min_invalid_points > 1 for 2D keypoints
         with pytest.raises(ValueError, match="so min_invalid_points must be 1"):
-            sample = {"keypoints": good_keypoints, "labels": torch.tensor([0])}
-            transforms.SanitizeKeyPoints(min_invalid_points=2)(sample)
+            transforms.SanitizeKeyPoints(min_invalid_points=2)(good_keypoints)
 
     def test_no_label(self):
         """Test transform without labels."""
@@ -7764,7 +7763,7 @@ def test_no_label(self):
 
         # Should raise error without labels_getter=None
         with pytest.raises(ValueError, match="or a two-tuple whose second item is a dict"):
-            transforms.SanitizeKeyPoints()(img, keypoints)
+            transforms.SanitizeKeyPoints(labels_getter="default")(img, keypoints)
 
         # Should work with labels_getter=None
         out_img, out_keypoints = transforms.SanitizeKeyPoints(labels_getter=None)(img, keypoints)

torchvision/transforms/v2/_misc.py

@@ -503,22 +503,22 @@ class SanitizeKeyPoints(Transform):
             Default is 1.
         labels_getter (callable or str or None, optional): indicates how to identify the labels in the input
             (or anything else that needs to be sanitized along with the keypoints).
-            By default, this will try to find a "labels" key in the input (case-insensitive), if
+            If set to the string ``"default"``, this will try to find a "labels" key in the input (case-insensitive), if
             the input is a dict or it is a tuple whose second element is a dict.
 
             It can also be a callable that takes the same input as the transform, and returns either:
 
             - A single tensor (the labels)
             - A tuple/list of tensors, each of which will be subject to the same sanitization as the keypoints.
 
-            If ``labels_getter`` is None then only keypoints are sanitized.
+            If ``labels_getter`` is None (the default), then only keypoints are sanitized.
     """
 
     def __init__(
         self,
         min_valid_edge_distance: int = 0,
         min_invalid_points: int | float = 1,
-        labels_getter: Union[Callable[[Any], Any], str, None] = "default",
+        labels_getter: Union[Callable[[Any], Any], str, None] = None,
     ) -> None:
         super().__init__()
         self.min_valid_edge_distance = min_valid_edge_distance