Add a GrayscaleToRgb transform that can expand channels to 3 (#8247)

docs/source/transforms.rst

@@ -347,6 +347,7 @@ Color
     v2.RandomChannelPermutation
     v2.RandomPhotometricDistort
     v2.Grayscale
+    v2.RGB
     v2.RandomGrayscale
     v2.GaussianBlur
     v2.RandomInvert
@@ -364,6 +365,7 @@ Functionals
 
     v2.functional.permute_channels
     v2.functional.rgb_to_grayscale
+    v2.functional.grayscale_to_rgb
     v2.functional.to_grayscale
     v2.functional.gaussian_blur
     v2.functional.invert
@@ -584,7 +586,7 @@ Conversion
     while performing the conversion, while some may not do any scaling. By
     scaling, we mean e.g. that a ``uint8`` -> ``float32`` would map the [0,
     255] range into [0, 1] (and vice-versa). See :ref:`range_and_dtype`.
-    
+
 .. autosummary::
     :toctree: generated/
     :template: class.rst

test/test_transforms_v2.py

@@ -5005,6 +5005,54 @@ class TestRgbToGrayscale:
         assert_equal(actual, expected, rtol=0, atol=1)
 
 
+class TestGrayscaleToRgb:
+    @pytest.mark.parametrize("dtype", [torch.uint8, torch.float32])
+    @pytest.mark.parametrize("device", cpu_and_cuda())
+    def test_kernel_image(self, dtype, device):
+        check_kernel(F.grayscale_to_rgb_image, make_image(dtype=dtype, device=device))
+
+    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image])
+    def test_functional(self, make_input):
+        check_functional(F.grayscale_to_rgb, make_input())
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.rgb_to_grayscale_image, torch.Tensor),
+            (F._rgb_to_grayscale_image_pil, PIL.Image.Image),
+            (F.rgb_to_grayscale_image, tv_tensors.Image),
+        ],
+    )
+    def test_functional_signature(self, kernel, input_type):
+        check_functional_kernel_signature_match(F.grayscale_to_rgb, kernel=kernel, input_type=input_type)
+
+    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image_pil, make_image])
+    def test_transform(self, make_input):
+        check_transform(transforms.RGB(), make_input(color_space="GRAY"))
+
+    @pytest.mark.parametrize("fn", [F.grayscale_to_rgb, transform_cls_to_functional(transforms.RGB)])
+    def test_image_correctness(self, fn):
+        image = make_image(dtype=torch.uint8, device="cpu", color_space="GRAY")
+
+        actual = fn(image)
+        expected = F.to_image(F.grayscale_to_rgb(F.to_pil_image(image)))
+
+        assert_equal(actual, expected, rtol=0, atol=1)
+
+    def test_expanded_channels_are_not_views_into_the_same_underlying_tensor(self):
+        image = make_image(dtype=torch.uint8, device="cpu", color_space="GRAY")
+
+        output_image = F.grayscale_to_rgb(image)
+        assert_equal(output_image[0][0][0], output_image[1][0][0])
+        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)
+        assert_equal(F.grayscale_to_rgb(image), image)
+
+
 class TestRandomZoomOut:
     # Tests are light because this largely relies on the already tested `pad` kernels.
 

torchvision/transforms/v2/__init__.py

@@ -18,6 +18,7 @@ from ._color import (
     RandomPhotometricDistort,
     RandomPosterize,
     RandomSolarize,
+    RGB,
 )
 from ._container import Compose, RandomApply, RandomChoice, RandomOrder
 from ._geometry import (

torchvision/transforms/v2/_color.py

@@ -54,6 +54,20 @@ class RandomGrayscale(_RandomApplyTransform):
         return self._call_kernel(F.rgb_to_grayscale, inpt, num_output_channels=params["num_input_channels"])
 
 
+class RGB(Transform):
+    """Convert images or videos to RGB (if they are already not RGB).
+
+    If the input is a :class:`torch.Tensor`, it is expected
+    to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions
+    """
+
+    def __init__(self):
+        super().__init__()
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return self._call_kernel(F.grayscale_to_rgb, inpt)
+
+
 class ColorJitter(Transform):
     """Randomly change the brightness, contrast, saturation and hue of an image or video.
 

torchvision/transforms/v2/functional/__init__.py

@@ -63,6 +63,8 @@ from ._color import (
     equalize,
     equalize_image,
     equalize_video,
+    grayscale_to_rgb,
+    grayscale_to_rgb_image,
     invert,
     invert_image,
     invert_video,

torchvision/transforms/v2/functional/_color.py

@@ -65,6 +65,32 @@ def _rgb_to_grayscale_image_pil(image: PIL.Image.Image, num_output_channels: int
     return _FP.to_grayscale(image, num_output_channels=num_output_channels)
 
 
+def grayscale_to_rgb(inpt: torch.Tensor) -> torch.Tensor:
+    """See :class:`~torchvision.transforms.v2.GrayscaleToRgb` for details."""
+    if torch.jit.is_scripting():
+        return grayscale_to_rgb_image(inpt)
+
+    _log_api_usage_once(grayscale_to_rgb)
+
+    kernel = _get_kernel(grayscale_to_rgb, type(inpt))
+    return kernel(inpt)
+
+
+@_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:
+    return image.convert(mode="RGB")
+
+
 def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor:
     ratio = float(ratio)
     fp = image1.is_floating_point()