allow dispatch to PIL image subclasses

test/test_transforms_v2_refactored.py

@@ -3,6 +3,7 @@
 import inspect
 import math
 import re
+from pathlib import Path
 from unittest import mock
 
 import numpy as np
@@ -2126,16 +2127,10 @@ class TestGetKernel:
         datapoints.Video: F.resize_video,
     }
 
-    def test_unsupported_types(self):
-        class MyTensor(torch.Tensor):
-            pass
-
-        class MyPILImage(PIL.Image.Image):
-            pass
-
-        for input_type in [str, int, object, MyTensor, MyPILImage]:
-            with pytest.raises(TypeError, match="supports inputs of type"):
-                _get_kernel(F.resize, input_type)
+    @pytest.mark.parametrize("input_type", [str, int, object])
+    def test_unsupported_types(self, input_type):
+        with pytest.raises(TypeError, match="supports inputs of type"):
+            _get_kernel(F.resize, input_type)
 
     def test_exact_match(self):
         # We cannot use F.resize together with self.KERNELS mapping here directly here, since this is only the
@@ -2197,6 +2192,24 @@ def resize_my_datapoint():
 
         assert _get_kernel(F.resize, MyDatapoint) is resize_my_datapoint
 
+    def test_pil_image_subclass(self):
+        opened_image = PIL.Image.open(Path(__file__).parent / "assets" / "encode_jpeg" / "grace_hopper_517x606.jpg")
+        loaded_image = opened_image.convert("RGB")
+
+        # check the assumptions
+        assert isinstance(opened_image, PIL.Image.Image)
+        assert type(opened_image) is not PIL.Image.Image
+
+        assert type(loaded_image) is PIL.Image.Image
+
+        size = [17, 11]
+        for image in [opened_image, loaded_image]:
+            kernel = _get_kernel(F.resize, type(image))
+
+            output = kernel(image, size=size)
+
+            assert F.get_size(output) == size
+
 
 class TestPermuteChannels:
     _DEFAULT_PERMUTATION = [2, 0, 1]

torchvision/transforms/v2/functional/_utils.py

@@ -100,21 +100,14 @@ def _get_kernel(functional, input_type, *, allow_passthrough=False):
     if not registry:
         raise ValueError(f"No kernel registered for functional {functional.__name__}.")
 
-    # In case we have an exact type match, we take a shortcut.
-    if input_type in registry:
-        return registry[input_type]
-
-    # In case of datapoints, we check if we have a kernel for a superclass registered
-    if issubclass(input_type, datapoints.Datapoint):
-        # Since we have already checked for an exact match above, we can start the traversal at the superclass.
-        for cls in input_type.__mro__[1:]:
-            if cls is datapoints.Datapoint:
-                # We don't want user-defined datapoints to dispatch to the pure Tensor kernels, so we explicit stop the
-                # MRO traversal before hitting torch.Tensor. We can even stop at datapoints.Datapoint, since we don't
-                # allow kernels to be registered for datapoints.Datapoint anyway.
-                break
-            elif cls in registry:
-                return registry[cls]
+    for cls in input_type.__mro__:
+        if cls in registry:
+            return registry[cls]
+        elif cls is datapoints.Datapoint:
+            # We don't want user-defined datapoints to dispatch to the pure Tensor kernels, so we explicit stop the
+            # MRO traversal before hitting torch.Tensor. We can even stop at datapoints.Datapoint, since we don't
+            # allow kernels to be registered for datapoints.Datapoint anyway.
+            break
 
     if allow_passthrough:
         return lambda inpt, *args, **kwargs: inpt