Extend default heuristic of SanitizeBoundingBoxes to support tuples

test/test_transforms_v2.py

@@ -1935,7 +1935,14 @@ def test_detection_preset(image_type, data_augmentation, to_tensor, sanitize):
 @pytest.mark.parametrize(
     "labels_getter", ("default", "labels", lambda inputs: inputs["labels"], None, lambda inputs: None)
 )
-def test_sanitize_bounding_boxes(min_size, labels_getter):
+@pytest.mark.parametrize("sample_type", (tuple, dict))
+def test_sanitize_bounding_boxes(min_size, labels_getter, sample_type):
+
+    if sample_type is tuple and not isinstance(labels_getter, str):
+        # The "lambda inputs: inputs["labels"]" labels_getter used in this test
+        # doesn't work if the input is a tuple.
+        return
+
     H, W = 256, 128
 
     boxes_and_validity = [
@@ -1970,35 +1977,56 @@ def test_sanitize_bounding_boxes(min_size, labels_getter):
     )
 
     masks = datapoints.Mask(torch.randint(0, 2, size=(boxes.shape[0], H, W)))
-
+    whatever = torch.rand(10)
+    input_img = torch.randint(0, 256, size=(1, 3, H, W), dtype=torch.uint8)
     sample = {
-        "image": torch.randint(0, 256, size=(1, 3, H, W), dtype=torch.uint8),
+        "image": input_img,
         "labels": labels,
         "boxes": boxes,
-        "whatever": torch.rand(10),
+        "whatever": whatever,
         "None": None,
         "masks": masks,
     }
 
+    if sample_type is tuple:
+        img = sample.pop("image")
+        sample = (img, sample)
+
     out = transforms.SanitizeBoundingBoxes(min_size=min_size, labels_getter=labels_getter)(sample)
 
-    assert out["image"] is sample["image"]
-    assert out["whatever"] is sample["whatever"]
+    if sample_type is tuple:
+        out_image = out[0]
+        out_labels = out[1]["labels"]
+        out_boxes = out[1]["boxes"]
+        out_masks = out[1]["masks"]
+        out_whatever = out[1]["whatever"]
+    else:
+        out_image = out["image"]
+        out_labels = out["labels"]
+        out_boxes = out["boxes"]
+        out_masks = out["masks"]
+        out_whatever = out["whatever"]
+
+    assert out_image is input_img
+    assert out_whatever is whatever
 
     if labels_getter is None or (callable(labels_getter) and labels_getter({"labels": "blah"}) is None):
-        assert out["labels"] is sample["labels"]
+        assert out_labels is labels
     else:
-        assert isinstance(out["labels"], torch.Tensor)
-        assert out["boxes"].shape[0] == out["labels"].shape[0] == out["masks"].shape[0]
+        assert isinstance(out_labels, torch.Tensor)
+        assert out_boxes.shape[0] == out_labels.shape[0] == out_masks.shape[0]
         # This works because we conveniently set labels to arange(num_boxes)
-        assert out["labels"].tolist() == valid_indices
+        assert out_labels.tolist() == valid_indices
 
 
 @pytest.mark.parametrize("key", ("labels", "LABELS", "LaBeL", "SOME_WEIRD_KEY_THAT_HAS_LABeL_IN_IT"))
-def test_sanitize_bounding_boxes_default_heuristic(key):
+@pytest.mark.parametrize("sample_type", (tuple, dict))
+def test_sanitize_bounding_boxes_default_heuristic(key, sample_type):
     labels = torch.arange(10)
-    d = {key: labels}
-    assert transforms.SanitizeBoundingBoxes._find_labels_default_heuristic(d) is labels
+    sample = {key: labels, "another_key": "whatever"}
+    if sample_type is tuple:
+        sample = (None, sample, "whatever_again")
+    assert transforms.SanitizeBoundingBoxes._find_labels_default_heuristic(sample) is labels
 
     if key.lower() != "labels":
         # If "labels" is in the dict (case-insensitive),

torchvision/transforms/v2/_misc.py

@@ -1,7 +1,7 @@
 import collections
 import warnings
 from contextlib import suppress
-from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Type, Union
+from typing import Any, Callable, cast, Dict, List, Mapping, Optional, Sequence, Type, Union
 
 import PIL.Image
 
@@ -269,7 +269,9 @@ def __init__(
         elif callable(labels_getter):
             self._labels_getter = labels_getter
         elif isinstance(labels_getter, str):
-            self._labels_getter = lambda inputs: inputs[labels_getter]
+            self._labels_getter = lambda inputs: SanitizeBoundingBoxes._get_dict_or_second_tuple_entry(inputs)[
+                labels_getter  # type: ignore[index]
+            ]
         elif labels_getter is None:
             self._labels_getter = None
         else:
@@ -278,10 +280,27 @@ def __init__(
                 f"Got {labels_getter} of type {type(labels_getter)}."
             )
 
+    @staticmethod
+    def _get_dict_or_second_tuple_entry(inputs: Any) -> Mapping[str, Any]:
+        # datasets outputs may be plain dicts like {"img": ..., "labels": ..., "bbox": ...}
+        # or tuples like (img, {"labels":..., "bbox": ...})
+        # This hacky helper accounts for both structures.
+        if isinstance(inputs, tuple):
+            inputs = inputs[1]
+
+        if not isinstance(inputs, collections.abc.Mapping):
+            raise ValueError(
+                f"If labels_getter is a str or 'default', "
+                f"then the input to forward() must be a dict or a tuple whose second element is a dict."
+                f" Got {type(inputs)} instead."
+            )
+        return inputs
+
     @staticmethod
     def _find_labels_default_heuristic(inputs: Dict[str, Any]) -> Optional[torch.Tensor]:
-        # Tries to find a "label" key, otherwise tries for the first key that contains "label" - case insensitive
+        # Tries to find a "labels" key, otherwise tries for the first key that contains "label" - case insensitive
         # Returns None if nothing is found
+        inputs = SanitizeBoundingBoxes._get_dict_or_second_tuple_entry(inputs)
         candidate_key = None
         with suppress(StopIteration):
             candidate_key = next(key for key in inputs.keys() if key.lower() == "labels")
@@ -298,12 +317,6 @@ def _find_labels_default_heuristic(inputs: Dict[str, Any]) -> Optional[torch.Ten
     def forward(self, *inputs: Any) -> Any:
         inputs = inputs if len(inputs) > 1 else inputs[0]
 
-        if isinstance(self.labels_getter, str) and not isinstance(inputs, collections.abc.Mapping):
-            raise ValueError(
-                f"If labels_getter is a str or 'default' (got {self.labels_getter}), "
-                f"then the input to forward() must be a dict. Got {type(inputs)} instead."
-            )
-
         if self._labels_getter is None:
             labels = None
         else: