Addressed comments and added another test.

test/test_transforms_v2.py

@@ -5004,7 +5004,6 @@ def test_image_correctness(self, fn):
         actual = fn(image)
         expected = F.to_image(F.grayscale_to_rgb(F.to_pil_image(image)))
 
-        print(f"ahmad: {expected.shape=} {actual.shape=}")
         assert_equal(actual, expected, rtol=0, atol=1)
 
     def test_expanded_channels_are_not_views_into_the_same_underlying_tensor(self):
@@ -5015,6 +5014,19 @@ def test_expanded_channels_are_not_views_into_the_same_underlying_tensor(self):
         output_image[0][0][0] = output_image[0][0][0] + 1
         assert output_image[0][0][0] != output_image[1][0][0]
 
+    def test_rgb_image_is_unchanged(self):
+        image = make_image(dtype=torch.uint8, device="cpu", color_space="RGB")
+        assert_equal(image.shape[-3], 3)
+        image[0][0][0] = 0
+        image[1][0][0] = 100
+        image[2][0][0] = 200
+        output_image = F.grayscale_to_rgb(image)
+        assert output_image[0][0][0] == 0
+        assert output_image[1][0][0] == 100
+        assert output_image[2][0][0] == 200
+        print(image)
+        print(output_image)
+
 
 class TestRandomZoomOut:
     # Tests are light because this largely relies on the already tested `pad` kernels.

torchvision/transforms/v2/functional/_color.py

@@ -79,14 +79,16 @@ def grayscale_to_rgb(inpt: torch.Tensor) -> torch.Tensor:
 @_register_kernel_internal(grayscale_to_rgb, torch.Tensor)
 @_register_kernel_internal(grayscale_to_rgb, tv_tensors.Image)
 def grayscale_to_rgb_image(image: torch.Tensor) -> torch.Tensor:
+    if image.shape[-3] >= 3:
+        # Image already has RGB channels. We don't need to do anything.
+        return image
     # rgb_to_grayscale can be used to add channels so we reuse that function.
     return _rgb_to_grayscale_image(image, num_output_channels=3, preserve_dtype=True)
 
 
 @_register_kernel_internal(grayscale_to_rgb, PIL.Image.Image)
 def grayscale_to_rgb_image_pil(image: PIL.Image.Image) -> PIL.Image.Image:
-    # to_grayscale can expand channels from 1 to 3 so we reuse that function.
-    return _FP.to_grayscale(image, num_output_channels=3)
+    return image.convert(mode="RGB")
 
 
 def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor: