Add more tests for bounding boxes (#7276)

test/test_prototype_transforms.py

@@ -269,6 +269,21 @@ class TestSmoke:
             else:
                 assert output_item is input_item
 
+            if isinstance(input_item, datapoints.BoundingBox) and not isinstance(
+                transform, transforms.ConvertBoundingBoxFormat
+            ):
+                assert output_item.format == input_item.format
+
+        # Enforce that the transform does not turn a degenerate box marked by RandomIoUCrop (or any other future
+        # transform that does this), back into a valid one.
+        # TODO: we should test that against all degenerate boxes above
+        for format in list(datapoints.BoundingBoxFormat):
+            sample = dict(
+                boxes=datapoints.BoundingBox([[0, 0, 0, 0]], format=format, spatial_size=(224, 244)),
+                labels=torch.tensor([3]),
+            )
+            assert transforms.SanitizeBoundingBoxes()(sample)["boxes"].shape == (0, 4)
+
     @parametrize(
         [
             (

test/test_prototype_transforms_functional.py

@@ -508,6 +508,22 @@ class TestDispatchers:
         with pytest.raises(TypeError, match=re.escape(str(type(unkown_input)))):
             info.dispatcher(unkown_input, *other_args, **kwargs)
 
+    @make_info_args_kwargs_parametrization(
+        [
+            info
+            for info in DISPATCHER_INFOS
+            if datapoints.BoundingBox in info.kernels and info.dispatcher is not F.convert_format_bounding_box
+        ],
+        args_kwargs_fn=lambda info: info.sample_inputs(datapoints.BoundingBox),
+    )
+    def test_bounding_box_format_consistency(self, info, args_kwargs):
+        (bounding_box, *other_args), kwargs = args_kwargs.load()
+        format = bounding_box.format
+
+        output = info.dispatcher(bounding_box, *other_args, **kwargs)
+
+        assert output.format == format
+
 
 @pytest.mark.parametrize(
     ("alias", "target"),