Stricter SanitizeBoundingBoxes labels_getter heuristic (#7880)

gallery/v2_transforms/plot_transforms_v2.py

@@ -99,10 +99,9 @@ bboxes = datapoints.BoundingBoxes(
     format="XYXY", canvas_size=img.shape[-2:])
 
 transforms = v2.Compose([
-    v2.RandomPhotometricDistort(),
-    v2.RandomIoUCrop(),
-    v2.RandomHorizontalFlip(p=0.5),
-    v2.SanitizeBoundingBoxes(),
+    v2.RandomResizedCrop(size=(224, 224), antialias=True),
+    v2.RandomPhotometricDistort(p=1),
+    v2.RandomHorizontalFlip(p=1),
 ])
 out_img, out_bboxes = transforms(img, bboxes)
 

test/test_transforms_v2.py

@@ -1256,6 +1256,20 @@ def test_sanitize_bounding_boxes(min_size, labels_getter, sample_type):
         assert out_labels.tolist() == valid_indices
 
 
+def test_sanitize_bounding_boxes_no_label():
+    # Non-regression test for https://github.com/pytorch/vision/issues/7878
+
+    img = make_image()
+    boxes = make_bounding_boxes()
+
+    with pytest.raises(ValueError, match="or a two-tuple whose second item is a dict"):
+        transforms.SanitizeBoundingBoxes()(img, boxes)
+
+    out_img, out_boxes = transforms.SanitizeBoundingBoxes(labels_getter=None)(img, boxes)
+    assert isinstance(out_img, datapoints.Image)
+    assert isinstance(out_boxes, datapoints.BoundingBoxes)
+
+
 def test_sanitize_bounding_boxes_errors():
 
     good_bbox = datapoints.BoundingBoxes(

torchvision/transforms/v2/_utils.py

@@ -112,7 +112,7 @@ def _find_labels_default_heuristic(inputs: Any) -> torch.Tensor:
         inputs = inputs[1]
 
     # MixUp, CutMix
-    if isinstance(inputs, torch.Tensor):
+    if is_pure_tensor(inputs):
         return inputs
 
     if not isinstance(inputs, collections.abc.Mapping):