add tests for the output types of prototype functional dispatchers (#7118)

test/prototype_transforms_dispatcher_infos.py

@@ -112,6 +112,15 @@ skip_dispatch_datapoint = TestMark(
     pytest.mark.skip(reason="Dispatcher doesn't support arbitrary datapoint dispatch."),
 )
 
+multi_crop_skips = [
+    TestMark(
+        ("TestDispatchers", test_name),
+        pytest.mark.skip(reason="Multi-crop dispatchers return a sequence of items rather than a single one."),
+    )
+    for test_name in ["test_simple_tensor_output_type", "test_pil_output_type", "test_datapoint_output_type"]
+]
+multi_crop_skips.append(skip_dispatch_datapoint)
+
 
 def fill_sequence_needs_broadcast(args_kwargs):
     (image_loader, *_), kwargs = args_kwargs
@@ -404,7 +413,7 @@ DISPATCHER_INFOS = [
         pil_kernel_info=PILKernelInfo(F.five_crop_image_pil),
         test_marks=[
             xfail_jit_python_scalar_arg("size"),
-            skip_dispatch_datapoint,
+            *multi_crop_skips,
         ],
     ),
     DispatcherInfo(
@@ -415,7 +424,7 @@ DISPATCHER_INFOS = [
         },
         test_marks=[
             xfail_jit_python_scalar_arg("size"),
-            skip_dispatch_datapoint,
+            *multi_crop_skips,
         ],
         pil_kernel_info=PILKernelInfo(F.ten_crop_image_pil),
     ),

test/test_prototype_transforms_functional.py

@@ -362,6 +362,16 @@ class TestDispatchers:
 
         spy.assert_called_once()
 
+    @image_sample_inputs
+    def test_simple_tensor_output_type(self, info, args_kwargs):
+        (image_datapoint, *other_args), kwargs = args_kwargs.load()
+        image_simple_tensor = image_datapoint.as_subclass(torch.Tensor)
+
+        output = info.dispatcher(image_simple_tensor, *other_args, **kwargs)
+
+        # We cannot use `isinstance` here since all datapoints are instances of `torch.Tensor` as well
+        assert type(output) is torch.Tensor
+
     @make_info_args_kwargs_parametrization(
         [info for info in DISPATCHER_INFOS if info.pil_kernel_info is not None],
         args_kwargs_fn=lambda info: info.sample_inputs(datapoints.Image),
@@ -381,6 +391,22 @@ class TestDispatchers:
 
         spy.assert_called_once()
 
+    @make_info_args_kwargs_parametrization(
+        [info for info in DISPATCHER_INFOS if info.pil_kernel_info is not None],
+        args_kwargs_fn=lambda info: info.sample_inputs(datapoints.Image),
+    )
+    def test_pil_output_type(self, info, args_kwargs):
+        (image_datapoint, *other_args), kwargs = args_kwargs.load()
+
+        if image_datapoint.ndim > 3:
+            pytest.skip("Input is batched")
+
+        image_pil = F.to_image_pil(image_datapoint)
+
+        output = info.dispatcher(image_pil, *other_args, **kwargs)
+
+        assert isinstance(output, PIL.Image.Image)
+
     @make_info_args_kwargs_parametrization(
         DISPATCHER_INFOS,
         args_kwargs_fn=lambda info: info.sample_inputs(),
@@ -397,6 +423,17 @@ class TestDispatchers:
 
         spy.assert_called_once()
 
+    @make_info_args_kwargs_parametrization(
+        DISPATCHER_INFOS,
+        args_kwargs_fn=lambda info: info.sample_inputs(),
+    )
+    def test_datapoint_output_type(self, info, args_kwargs):
+        (datapoint, *other_args), kwargs = args_kwargs.load()
+
+        output = info.dispatcher(datapoint, *other_args, **kwargs)
+
+        assert isinstance(output, type(datapoint))
+
     @pytest.mark.parametrize(
         ("dispatcher_info", "datapoint_type", "kernel_info"),
         [