Undeprecate ToGrayScale transforms and functionals

test/test_prototype_transforms_consistency.py

@@ -149,6 +149,8 @@ def __init__(
             ArgsKwargs(num_output_channels=3),
         ],
         make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, color_spaces=["RGB", "GRAY"]),
+        # Use default tolerances of `torch.testing.assert_close`
+        closeness_kwargs=dict(rtol=None, atol=None),
     ),
     ConsistencyConfig(
         prototype_transforms.ConvertDtype,
@@ -271,6 +273,9 @@ def __init__(
             ArgsKwargs(p=0),
             ArgsKwargs(p=1),
         ],
+        make_images_kwargs=dict(DEFAULT_MAKE_IMAGES_KWARGS, color_spaces=["RGB", "GRAY"]),
+        # Use default tolerances of `torch.testing.assert_close`
+        closeness_kwargs=dict(rtol=None, atol=None),
     ),
     ConsistencyConfig(
         prototype_transforms.RandomResizedCrop,

torchvision/prototype/datapoints/_datapoint.py

@@ -230,6 +230,9 @@ def elastic(
     ) -> Datapoint:
         return self
 
+    def to_grayscale(self, num_output_channels: int = 1) -> Datapoint:
+        return self
+
     def adjust_brightness(self, brightness_factor: float) -> Datapoint:
         return self
 

torchvision/prototype/datapoints/_image.py

@@ -169,6 +169,12 @@ def elastic(
         )
         return Image.wrap_like(self, output)
 
+    def to_grayscale(self, num_output_channels: int = 1) -> Image:
+        output = self._F.rgb_to_grayscale_image_tensor(
+            self.as_subclass(torch.Tensor), num_output_channels=num_output_channels
+        )
+        return Image.wrap_like(self, output)
+
     def adjust_brightness(self, brightness_factor: float) -> Image:
         output = self._F.adjust_brightness_image_tensor(
             self.as_subclass(torch.Tensor), brightness_factor=brightness_factor

torchvision/prototype/datapoints/_video.py

@@ -173,6 +173,12 @@ def elastic(
         )
         return Video.wrap_like(self, output)
 
+    def to_grayscale(self, num_output_channels: int = 1) -> Video:
+        output = self._F.rgb_to_grayscale_image_tensor(
+            self.as_subclass(torch.Tensor), num_output_channels=num_output_channels
+        )
+        return Video.wrap_like(self, output)
+
     def adjust_brightness(self, brightness_factor: float) -> Video:
         output = self._F.adjust_brightness_video(self.as_subclass(torch.Tensor), brightness_factor=brightness_factor)
         return Video.wrap_like(self, output)

torchvision/prototype/transforms/__init__.py

@@ -9,9 +9,11 @@
 from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
 from ._color import (
     ColorJitter,
+    Grayscale,
     RandomAdjustSharpness,
     RandomAutocontrast,
     RandomEqualize,
+    RandomGrayscale,
     RandomInvert,
     RandomPhotometricDistort,
     RandomPosterize,
@@ -54,4 +56,4 @@
 from ._temporal import UniformTemporalSubsample
 from ._type_conversion import LabelToOneHot, PILToTensor, ToImagePIL, ToImageTensor, ToPILImage
 
-from ._deprecated import Grayscale, RandomGrayscale, ToTensor  # usort: skip
+from ._deprecated import ToTensor  # usort: skip

torchvision/prototype/transforms/_color.py

@@ -11,6 +11,41 @@
 from .utils import is_simple_tensor, query_chw
 
 
+class Grayscale(Transform):
+    _transformed_types = (
+        datapoints.Image,
+        PIL.Image.Image,
+        is_simple_tensor,
+        datapoints.Video,
+    )
+
+    def __init__(self, num_output_channels: int = 1):
+        super().__init__()
+        self.num_output_channels = num_output_channels
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.rgb_to_grayscale(inpt, num_output_channels=self.num_output_channels)
+
+
+class RandomGrayscale(_RandomApplyTransform):
+    _transformed_types = (
+        datapoints.Image,
+        PIL.Image.Image,
+        is_simple_tensor,
+        datapoints.Video,
+    )
+
+    def __init__(self, p: float = 0.1) -> None:
+        super().__init__(p=p)
+
+    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
+        num_input_channels, *_ = query_chw(flat_inputs)
+        return dict(num_input_channels=num_input_channels)
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.rgb_to_grayscale(inpt, num_output_channels=params["num_input_channels"])
+
+
 class ColorJitter(Transform):
     def __init__(
         self,

torchvision/prototype/transforms/_deprecated.py

@@ -1,17 +1,12 @@
 import warnings
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, Union
 
 import numpy as np
 import PIL.Image
 import torch
 
-from torchvision.prototype import datapoints
 from torchvision.prototype.transforms import Transform
 from torchvision.transforms import functional as _F
-from typing_extensions import Literal
-
-from ._transform import _RandomApplyTransform
-from .utils import is_simple_tensor, query_chw
 
 
 class ToTensor(Transform):
@@ -26,78 +21,3 @@ def __init__(self) -> None:
 
     def _transform(self, inpt: Union[PIL.Image.Image, np.ndarray], params: Dict[str, Any]) -> torch.Tensor:
         return _F.to_tensor(inpt)
-
-
-# TODO: in other PR (?) undeprecate those and make them use _rgb_to_gray?
-class Grayscale(Transform):
-    _transformed_types = (
-        datapoints.Image,
-        PIL.Image.Image,
-        is_simple_tensor,
-        datapoints.Video,
-    )
-
-    def __init__(self, num_output_channels: Literal[1, 3] = 1) -> None:
-        deprecation_msg = (
-            f"The transform `Grayscale(num_output_channels={num_output_channels})` "
-            f"is deprecated and will be removed in a future release."
-        )
-        if num_output_channels == 1:
-            replacement_msg = (
-                "transforms.ConvertImageColorSpace(old_color_space=ColorSpace.RGB, color_space=ColorSpace.GRAY)"
-            )
-        else:
-            replacement_msg = (
-                "transforms.Compose(\n"
-                "    transforms.ConvertImageColorSpace(old_color_space=ColorSpace.RGB, color_space=ColorSpace.GRAY),\n"
-                "    transforms.ConvertImageColorSpace(old_color_space=ColorSpace.GRAY, color_space=ColorSpace.RGB),\n"
-                ")"
-            )
-        warnings.warn(f"{deprecation_msg} Instead, please use\n\n{replacement_msg}")
-
-        super().__init__()
-        self.num_output_channels = num_output_channels
-
-    def _transform(
-        self, inpt: Union[datapoints.ImageType, datapoints.VideoType], params: Dict[str, Any]
-    ) -> Union[datapoints.ImageType, datapoints.VideoType]:
-        output = _F.rgb_to_grayscale(inpt, num_output_channels=self.num_output_channels)
-        if isinstance(inpt, (datapoints.Image, datapoints.Video)):
-            output = inpt.wrap_like(inpt, output)  # type: ignore[arg-type]
-        return output
-
-
-class RandomGrayscale(_RandomApplyTransform):
-    _transformed_types = (
-        datapoints.Image,
-        PIL.Image.Image,
-        is_simple_tensor,
-        datapoints.Video,
-    )
-
-    def __init__(self, p: float = 0.1) -> None:
-        warnings.warn(
-            "The transform `RandomGrayscale(p=...)` is deprecated and will be removed in a future release. "
-            "Instead, please use\n\n"
-            "transforms.RandomApply(\n"
-            "    transforms.Compose(\n"
-            "        transforms.ConvertImageColorSpace(old_color_space=ColorSpace.RGB, color_space=ColorSpace.GRAY),\n"
-            "        transforms.ConvertImageColorSpace(old_color_space=ColorSpace.GRAY, color_space=ColorSpace.RGB),\n"
-            "    )\n"
-            "    p=...,\n"
-            ")"
-        )
-
-        super().__init__(p=p)
-
-    def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
-        num_input_channels, *_ = query_chw(flat_inputs)
-        return dict(num_input_channels=num_input_channels)
-
-    def _transform(
-        self, inpt: Union[datapoints.ImageType, datapoints.VideoType], params: Dict[str, Any]
-    ) -> Union[datapoints.ImageType, datapoints.VideoType]:
-        output = _F.rgb_to_grayscale(inpt, num_output_channels=params["num_input_channels"])
-        if isinstance(inpt, (datapoints.Image, datapoints.Video)):
-            output = inpt.wrap_like(inpt, output)  # type: ignore[arg-type]
-        return output

torchvision/prototype/transforms/functional/__init__.py

@@ -71,6 +71,9 @@
     posterize_image_pil,
     posterize_image_tensor,
     posterize_video,
+    rgb_to_grayscale,
+    rgb_to_grayscale_image_pil,
+    rgb_to_grayscale_image_tensor,
     solarize,
     solarize_image_pil,
     solarize_image_tensor,
@@ -167,4 +170,4 @@
 from ._temporal import uniform_temporal_subsample, uniform_temporal_subsample_video
 from ._type_conversion import pil_to_tensor, to_image_pil, to_image_tensor, to_pil_image
 
-from ._deprecated import get_image_size, rgb_to_grayscale, to_grayscale, to_tensor  # usort: skip
+from ._deprecated import get_image_size, to_grayscale, to_tensor  # usort: skip

torchvision/prototype/transforms/functional/_color.py

@@ -1,3 +1,5 @@
+from typing import Union
+
 import PIL.Image
 import torch
 from torch.nn.functional import conv2d
@@ -7,10 +9,53 @@
 
 from torchvision.utils import _log_api_usage_once
 
-from ._meta import _num_value_bits, _rgb_to_gray, convert_dtype_image_tensor
+from ._meta import _num_value_bits, convert_dtype_image_tensor
 from ._utils import is_simple_tensor
 
 
+def _rgb_to_grayscale_image_tensor(
+    image: torch.Tensor, num_output_channels: int = 1, preserve_dtype: bool = True
+) -> torch.Tensor:
+    if image.shape[-3] == 1:
+        return image.clone()
+
+    r, g, b = image.unbind(dim=-3)
+    l_img = r.mul(0.2989).add_(g, alpha=0.587).add_(b, alpha=0.114)
+    l_img = l_img.unsqueeze(dim=-3)
+    if preserve_dtype:
+        l_img = l_img.to(image.dtype)
+    if num_output_channels == 3:
+        l_img = l_img.expand(image.shape)
+    return l_img
+
+
+def rgb_to_grayscale_image_tensor(image: torch.Tensor, num_output_channels: int = 1) -> torch.Tensor:
+    return _rgb_to_grayscale_image_tensor(image, num_output_channels=num_output_channels, preserve_dtype=True)
+
+
+rgb_to_grayscale_image_pil = _FP.to_grayscale
+
+
+def rgb_to_grayscale(
+    inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT], num_output_channels: int = 1
+) -> Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT]:
+    if not torch.jit.is_scripting():
+        _log_api_usage_once(rgb_to_grayscale)
+    if num_output_channels not in (1, 3):
+        raise ValueError(f"num_output_channels must be 1 or 3, got {num_output_channels}.")
+    if torch.jit.is_scripting() or is_simple_tensor(inpt):
+        return rgb_to_grayscale_image_tensor(inpt, num_output_channels=num_output_channels)
+    elif isinstance(inpt, datapoints._datapoint.Datapoint):
+        return inpt.to_grayscale(num_output_channels=num_output_channels)
+    elif isinstance(inpt, PIL.Image.Image):
+        return rgb_to_grayscale_image_pil(inpt, num_output_channels=num_output_channels)
+    else:
+        raise TypeError(
+            f"Input can either be a plain tensor, any TorchVision datapoint, or a PIL image, "
+            f"but got {type(inpt)} instead."
+        )
+
+
 def _blend(image1: torch.Tensor, image2: torch.Tensor, ratio: float) -> torch.Tensor:
     ratio = float(ratio)
     fp = image1.is_floating_point()
@@ -68,7 +113,7 @@ def adjust_saturation_image_tensor(image: torch.Tensor, saturation_factor: float
     if c == 1:  # Match PIL behaviour
         return image
 
-    grayscale_image = _rgb_to_gray(image, cast=False)
+    grayscale_image = _rgb_to_grayscale_image_tensor(image, num_output_channels=1, preserve_dtype=False)
     if not image.is_floating_point():
         grayscale_image = grayscale_image.floor_()
 
@@ -110,7 +155,7 @@ def adjust_contrast_image_tensor(image: torch.Tensor, contrast_factor: float) ->
         raise TypeError(f"Input image tensor permitted channel values are 1 or 3, but found {c}")
     fp = image.is_floating_point()
     if c == 3:
-        grayscale_image = _rgb_to_gray(image, cast=False)
+        grayscale_image = _rgb_to_grayscale_image_tensor(image, num_output_channels=1, preserve_dtype=False)
         if not fp:
             grayscale_image = grayscale_image.floor_()
     else:

torchvision/prototype/transforms/functional/_deprecated.py

@@ -7,8 +7,6 @@
 from torchvision.prototype import datapoints
 from torchvision.transforms import functional as _F
 
-from ._utils import is_simple_tensor
-
 
 @torch.jit.unused
 def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Image.Image:
@@ -24,33 +22,6 @@ def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Ima
     return _F.to_grayscale(inpt, num_output_channels=num_output_channels)
 
 
-def rgb_to_grayscale(
-    inpt: Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT], num_output_channels: int = 1
-) -> Union[datapoints.ImageTypeJIT, datapoints.VideoTypeJIT]:
-    old_color_space = None  # TODO: remove when un-deprecating
-    if not (torch.jit.is_scripting() or is_simple_tensor(inpt)) and isinstance(
-        inpt, (datapoints.Image, datapoints.Video)
-    ):
-        inpt = inpt.as_subclass(torch.Tensor)
-
-    call = ", num_output_channels=3" if num_output_channels == 3 else ""
-    replacement = (
-        f"convert_color_space(..., color_space=datapoints.ColorSpace.GRAY"
-        f"{f', old_color_space=datapoints.ColorSpace.{old_color_space}' if old_color_space is not None else ''})"
-    )
-    if num_output_channels == 3:
-        replacement = (
-            f"convert_color_space({replacement}, color_space=datapoints.ColorSpace.RGB"
-            f"{f', old_color_space=datapoints.ColorSpace.GRAY' if old_color_space is not None else ''})"
-        )
-    warnings.warn(
-        f"The function `rgb_to_grayscale(...{call})` is deprecated in will be removed in a future release. "
-        f"Instead, please use `{replacement}`.",
-    )
-
-    return _F.rgb_to_grayscale(inpt, num_output_channels=num_output_channels)
-
-
 @torch.jit.unused
 def to_tensor(inpt: Any) -> torch.Tensor:
     warnings.warn(

torchvision/prototype/transforms/functional/_meta.py

@@ -225,15 +225,6 @@ def clamp_bounding_box(
     return convert_format_bounding_box(xyxy_boxes, old_format=BoundingBoxFormat.XYXY, new_format=format, inplace=True)
 
 
-def _rgb_to_gray(image: torch.Tensor, cast: bool = True) -> torch.Tensor:
-    r, g, b = image.unbind(dim=-3)
-    l_img = r.mul(0.2989).add_(g, alpha=0.587).add_(b, alpha=0.114)
-    if cast:
-        l_img = l_img.to(image.dtype)
-    l_img = l_img.unsqueeze(dim=-3)
-    return l_img
-
-
 def _num_value_bits(dtype: torch.dtype) -> int:
     if dtype == torch.uint8:
         return 8