Add GaussianNoise transforms (#8381)

Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>

docs/source/transforms.rst

@@ -350,6 +350,7 @@ Color
     v2.RGB
     v2.RandomGrayscale
     v2.GaussianBlur
+    v2.GaussianNoise
     v2.RandomInvert
     v2.RandomPosterize
     v2.RandomSolarize
@@ -368,6 +369,7 @@ Functionals
     v2.functional.grayscale_to_rgb
     v2.functional.to_grayscale
     v2.functional.gaussian_blur
+    v2.functional.gaussian_noise
     v2.functional.invert
     v2.functional.posterize
     v2.functional.solarize

test/test_transforms_v2.py

@@ -111,7 +111,9 @@ def _check_kernel_scripted_vs_eager(kernel, input, *args, rtol, atol, **kwargs):
 
     input = input.as_subclass(torch.Tensor)
     with ignore_jit_no_profile_information_warning():
+        with freeze_rng_state():
             actual = kernel_scripted(input, *args, **kwargs)
+    with freeze_rng_state():
         expected = kernel(input, *args, **kwargs)
 
     assert_close(actual, expected, rtol=rtol, atol=atol)
@@ -3238,6 +3240,78 @@ class TestGaussianBlur:
         torch.testing.assert_close(actual, expected, rtol=0, atol=1)
 
 
+class TestGaussianNoise:
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image, make_video],
+    )
+    def test_kernel(self, make_input):
+        check_kernel(
+            F.gaussian_noise,
+            make_input(dtype=torch.float32),
+            # This cannot pass because the noise on a batch in not per-image
+            check_batched_vs_unbatched=False,
+        )
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image, make_video],
+    )
+    def test_functional(self, make_input):
+        check_functional(F.gaussian_noise, make_input(dtype=torch.float32))
+
+    @pytest.mark.parametrize(
+        ("kernel", "input_type"),
+        [
+            (F.gaussian_noise, torch.Tensor),
+            (F.gaussian_noise_image, tv_tensors.Image),
+            (F.gaussian_noise_video, tv_tensors.Video),
+        ],
+    )
+    def test_functional_signature(self, kernel, input_type):
+        check_functional_kernel_signature_match(F.gaussian_noise, kernel=kernel, input_type=input_type)
+
+    @pytest.mark.parametrize(
+        "make_input",
+        [make_image_tensor, make_image, make_video],
+    )
+    def test_transform(self, make_input):
+        def adapter(_, input, __):
+            # This transform doesn't support uint8 so we have to convert the auto-generated uint8 tensors to float32
+            # Same for PIL images
+            for key, value in input.items():
+                if isinstance(value, torch.Tensor) and not value.is_floating_point():
+                    input[key] = value.to(torch.float32)
+                if isinstance(value, PIL.Image.Image):
+                    input[key] = F.pil_to_tensor(value).to(torch.float32)
+            return input
+
+        check_transform(transforms.GaussianNoise(), make_input(dtype=torch.float32), check_sample_input=adapter)
+
+    def test_bad_input(self):
+        with pytest.raises(ValueError, match="Gaussian Noise is not implemented for PIL images."):
+            F.gaussian_noise(make_image_pil())
+        with pytest.raises(ValueError, match="Input tensor is expected to be in float dtype"):
+            F.gaussian_noise(make_image(dtype=torch.uint8))
+        with pytest.raises(ValueError, match="sigma shouldn't be negative"):
+            F.gaussian_noise(make_image(dtype=torch.float32), sigma=-1)
+
+    def test_clip(self):
+        img = make_image(dtype=torch.float32)
+
+        out = F.gaussian_noise(img, mean=100, clip=False)
+        assert out.min() > 50
+
+        out = F.gaussian_noise(img, mean=100, clip=True)
+        assert (out == 1).all()
+
+        out = F.gaussian_noise(img, mean=-100, clip=False)
+        assert out.min() < -50
+
+        out = F.gaussian_noise(img, mean=-100, clip=True)
+        assert (out == 0).all()
+
+
 class TestAutoAugmentTransforms:
     # These transforms have a lot of branches in their `forward()` passes which are conditioned on random sampling.
     # It's typically very hard to test the effect on some parameters without heavy mocking logic.

torchvision/transforms/v2/__init__.py

@@ -45,6 +45,7 @@ from ._meta import ClampBoundingBoxes, ConvertBoundingBoxFormat
 from ._misc import (
     ConvertImageDtype,
     GaussianBlur,
+    GaussianNoise,
     Identity,
     Lambda,
     LinearTransformation,

torchvision/transforms/v2/_misc.py

@@ -205,6 +205,33 @@ class GaussianBlur(Transform):
         return self._call_kernel(F.gaussian_blur, inpt, self.kernel_size, **params)
 
 
+class GaussianNoise(Transform):
+    """Add gaussian noise to images or videos.
+
+    The input tensor is expected to be in [..., 1 or 3, H, W] format,
+    where ... means it can have an arbitrary number of leading dimensions.
+    Each image or frame in a batch will be transformed independently i.e. the
+    noise added to each image will be different.
+
+    The input tensor is also expected to be of float dtype in ``[0, 1]``.
+    This transform does not support PIL images.
+
+    Args:
+        mean (float): Mean of the sampled normal distribution. Default is 0.
+        sigma (float): Standard deviation of the sampled normal distribution. Default is 0.1.
+        clip (bool, optional): Whether to clip the values in ``[0, 1]`` after adding noise. Default is True.
+    """
+
+    def __init__(self, mean: float = 0.0, sigma: float = 0.1, clip=True) -> None:
+        super().__init__()
+        self.mean = mean
+        self.sigma = sigma
+        self.clip = clip
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return self._call_kernel(F.gaussian_noise, inpt, mean=self.mean, sigma=self.sigma, clip=self.clip)
+
+
 class ToDtype(Transform):
     """Converts the input to a specific dtype, optionally scaling the values for images or videos.
 

torchvision/transforms/v2/functional/__init__.py

@@ -136,6 +136,9 @@ from ._misc import (
     gaussian_blur,
     gaussian_blur_image,
     gaussian_blur_video,
+    gaussian_noise,
+    gaussian_noise_image,
+    gaussian_noise_video,
     normalize,
     normalize_image,
     normalize_video,

torchvision/transforms/v2/functional/_misc.py

@@ -181,6 +181,44 @@ def gaussian_blur_video(
     return gaussian_blur_image(video, kernel_size, sigma)
 
 
+def gaussian_noise(inpt: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor:
+    """See :class:`~torchvision.transforms.v2.GaussianNoise`"""
+    if torch.jit.is_scripting():
+        return gaussian_noise_image(inpt, mean=mean, sigma=sigma)
+
+    _log_api_usage_once(gaussian_noise)
+
+    kernel = _get_kernel(gaussian_noise, type(inpt))
+    return kernel(inpt, mean=mean, sigma=sigma, clip=clip)
+
+
+@_register_kernel_internal(gaussian_noise, torch.Tensor)
+@_register_kernel_internal(gaussian_noise, tv_tensors.Image)
+def gaussian_noise_image(image: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor:
+    if not image.is_floating_point():
+        raise ValueError(f"Input tensor is expected to be in float dtype, got dtype={image.dtype}")
+    if sigma < 0:
+        raise ValueError(f"sigma shouldn't be negative. Got {sigma}")
+
+    noise = mean + torch.randn_like(image) * sigma
+    out = image + noise
+    if clip:
+        out = torch.clamp(out, 0, 1)
+    return out
+
+
+@_register_kernel_internal(gaussian_noise, tv_tensors.Video)
+def gaussian_noise_video(video: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor:
+    return gaussian_noise_image(video, mean=mean, sigma=sigma, clip=clip)
+
+
+@_register_kernel_internal(gaussian_noise, PIL.Image.Image)
+def _gaussian_noise_pil(
+    video: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True
+) -> PIL.Image.Image:
+    raise ValueError("Gaussian Noise is not implemented for PIL images.")
+
+
 def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
     """See :func:`~torchvision.transforms.v2.ToDtype` for details."""
     if torch.jit.is_scripting():