Expose transforms.v2 utils for writing custom transforms.

test/test_transforms_v2.py

@@ -6169,3 +6169,50 @@ def test_transform_sequence_len_error(self, quality):
     def test_transform_invalid_quality_error(self, quality):
         with pytest.raises(ValueError, match="quality must be an integer from 1 to 100"):
             transforms.JPEG(quality=quality)
+
+
+class TestUtils:
+    # TODO: Still need to test has_all, has_any, check_type and get_bouding_boxes
+    @pytest.mark.parametrize(
+        "make_input1", [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask]
+    )
+    @pytest.mark.parametrize(
+        "make_input2", [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask]
+    )
+    @pytest.mark.parametrize("query", [transforms.query_size, transforms.query_chw])
+    def test_query_size_and_query_chw(self, make_input1, make_input2, query):
+        size = (32, 64)
+        input1 = make_input1(size)
+        input2 = make_input2(size)
+
+        if query is transforms.query_chw and not any(
+            transforms.check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video))
+            for inpt in (input1, input2)
+        ):
+            return
+
+        expected = size if query is transforms.query_size else ((3,) + size)
+        assert query([input1, input2]) == expected
+
+    @pytest.mark.parametrize(
+        "make_input1", [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask]
+    )
+    @pytest.mark.parametrize(
+        "make_input2", [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask]
+    )
+    @pytest.mark.parametrize("query", [transforms.query_size, transforms.query_chw])
+    def test_different_sizes(self, make_input1, make_input2, query):
+        input1 = make_input1((10, 10))
+        input2 = make_input2((20, 20))
+        if query is transforms.query_chw and not all(
+            transforms.check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video))
+            for inpt in (input1, input2)
+        ):
+            return
+        with pytest.raises(ValueError, match="Found multiple"):
+            query([input1, input2])
+
+    @pytest.mark.parametrize("query", [transforms.query_size, transforms.query_chw])
+    def test_no_valid_input(self, query):
+        with pytest.raises(TypeError, match="No image"):
+            query(["blah"])

torchvision/transforms/v2/__init__.py

@@ -55,5 +55,6 @@
 )
 from ._temporal import UniformTemporalSubsample
 from ._type_conversion import PILToTensor, ToImage, ToPILImage, ToPureTensor
+from ._utils import check_type, get_bounding_boxes, has_all, has_any, query_chw, query_size
 
 from ._deprecated import ToTensor  # usort: skip