[proto] Added GaussianBlur transform and tests

test/test_prototype_transforms.py

@@ -644,3 +644,58 @@ def test_forward(self, padding, pad_if_needed, fill, padding_mode, mocker):
         else:
             # vfdev-5: I do not know how to mock and test this case
             pass
+
+
+class TestGaussianBlur:
+    def test_assertions(self):
+        with pytest.raises(ValueError, match="Kernel size should be a tuple/list of two integers"):
+            transforms.GaussianBlur([10, 12, 14])
+
+        with pytest.raises(ValueError, match="Kernel size value should be an odd and positive number"):
+            transforms.GaussianBlur(4)
+
+        with pytest.raises(TypeError, match="sigma should be a single float or a list/tuple with length 2"):
+            transforms.GaussianBlur(3, sigma=[1, 2, 3])
+
+        with pytest.raises(ValueError, match="If sigma is a single number, it must be positive"):
+            transforms.GaussianBlur(3, sigma=-1.0)
+
+        with pytest.raises(ValueError, match="sigma values should be positive and of the form"):
+            transforms.GaussianBlur(3, sigma=[2.0, 1.0])
+
+    @pytest.mark.parametrize("sigma", [10.0, [10.0, 12.0]])
+    def test__get_params(self, sigma):
+        transform = transforms.GaussianBlur(3, sigma=sigma)
+        params = transform._get_params(None)
+
+        if isinstance(sigma, float):
+            assert params["sigma"][0] == params["sigma"][1] == 10
+        else:
+            assert sigma[0] <= params["sigma"][0] <= sigma[1]
+            assert sigma[0] <= params["sigma"][1] <= sigma[1]
+
+    @pytest.mark.parametrize("kernel_size", [3, [3, 5], (5, 3)])
+    @pytest.mark.parametrize("sigma", [2.0, [2.0, 3.0]])
+    def test__transform(self, kernel_size, sigma, mocker):
+        transform = transforms.GaussianBlur(kernel_size=kernel_size, sigma=sigma)
+
+        if isinstance(kernel_size, (tuple, list)):
+            assert transform.kernel_size == kernel_size
+        else:
+            assert transform.kernel_size == (kernel_size, kernel_size)
+
+        if isinstance(sigma, (tuple, list)):
+            assert transform.sigma == sigma
+        else:
+            assert transform.sigma == (sigma, sigma)
+
+        fn = mocker.patch("torchvision.prototype.transforms.functional.gaussian_blur")
+        inpt = features.Image(torch.rand(1, 3, 32, 32))
+        # vfdev-5, Feature Request: let's store params as Transform attribute
+        # This could be also helpful for users
+        torch.manual_seed(12)
+        _ = transform(inpt)
+        torch.manual_seed(12)
+        params = transform._get_params(inpt)
+
+        fn.assert_called_once_with(inpt, **params)

test/test_prototype_transforms_functional.py

@@ -1,6 +1,7 @@
 import functools
 import itertools
 import math
+import os
 
 import numpy as np
 import pytest
@@ -495,6 +496,7 @@ def center_crop_bounding_box():
         )
 
 
+@register_kernel_info_from_sample_inputs_fn
 def center_crop_segmentation_mask():
     for mask, output_size in itertools.product(
         make_segmentation_masks(image_sizes=((16, 16), (7, 33), (31, 9))),
@@ -503,6 +505,16 @@ def center_crop_segmentation_mask():
         yield SampleInput(mask, output_size)
 
 
+@register_kernel_info_from_sample_inputs_fn
+def gaussian_blur_image_tensor():
+    for image, kernel_size, sigma in itertools.product(
+        make_images(extra_dims=((4,),)),
+        [[3, 3]],
+        [None, [3.0, 3.0]],
+    ):
+        yield SampleInput(image, kernel_size=kernel_size, sigma=sigma)
+
+
 @pytest.mark.parametrize(
     "kernel",
     [
@@ -1555,3 +1567,58 @@ def _compute_expected_segmentation_mask(mask, output_size):
 
     expected = _compute_expected_segmentation_mask(mask, output_size)
     torch.testing.assert_close(expected, actual)
+
+
+# Copied from test/test_functional_tensor.py
+@pytest.mark.parametrize("device", cpu_and_gpu())
+@pytest.mark.parametrize("image_size", ("small", "large"))
+@pytest.mark.parametrize("dt", [None, torch.float32, torch.float64, torch.float16])
+@pytest.mark.parametrize("ksize", [(3, 3), [3, 5], (23, 23)])
+@pytest.mark.parametrize("sigma", [[0.5, 0.5], (0.5, 0.5), (0.8, 0.8), (1.7, 1.7)])
+def test_correctness_gaussian_blur_image_tensor(device, image_size, dt, ksize, sigma):
+    fn = F.gaussian_blur_image_tensor
+
+    # true_cv2_results = {
+    #     # np_img = np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))
+    #     # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.8)
+    #     "3_3_0.8": ...
+    #     # cv2.GaussianBlur(np_img, ksize=(3, 3), sigmaX=0.5)
+    #     "3_3_0.5": ...
+    #     # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.8)
+    #     "3_5_0.8": ...
+    #     # cv2.GaussianBlur(np_img, ksize=(3, 5), sigmaX=0.5)
+    #     "3_5_0.5": ...
+    #     # np_img2 = np.arange(26 * 28, dtype="uint8").reshape((26, 28))
+    #     # cv2.GaussianBlur(np_img2, ksize=(23, 23), sigmaX=1.7)
+    #     "23_23_1.7": ...
+    # }
+    p = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "gaussian_blur_opencv_results.pt")
+    true_cv2_results = torch.load(p)
+
+    if image_size == "small":
+        tensor = (
+            torch.from_numpy(np.arange(3 * 10 * 12, dtype="uint8").reshape((10, 12, 3))).permute(2, 0, 1).to(device)
+        )
+    else:
+        tensor = torch.from_numpy(np.arange(26 * 28, dtype="uint8").reshape((1, 26, 28))).to(device)
+
+    if dt == torch.float16 and device == "cpu":
+        # skip float16 on CPU case
+        return
+
+    if dt is not None:
+        tensor = tensor.to(dtype=dt)
+
+    _ksize = (ksize, ksize) if isinstance(ksize, int) else ksize
+    _sigma = sigma[0] if sigma is not None else None
+    shape = tensor.shape
+    gt_key = f"{shape[-2]}_{shape[-1]}_{shape[-3]}__{_ksize[0]}_{_ksize[1]}_{_sigma}"
+    if gt_key not in true_cv2_results:
+        return
+
+    true_out = (
+        torch.tensor(true_cv2_results[gt_key]).reshape(shape[-2], shape[-1], shape[-3]).permute(2, 0, 1).to(tensor)
+    )
+
+    out = fn(tensor, kernel_size=ksize, sigma=sigma)
+    torch.testing.assert_close(out, true_out, rtol=0.0, atol=1.0, msg=f"{ksize}, {sigma}")

torchvision/prototype/features/_feature.py

@@ -189,3 +189,6 @@ def equalize(self) -> Any:
 
     def invert(self) -> Any:
         return self
+
+    def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Any:
+        return self

torchvision/prototype/features/_image.py

@@ -309,3 +309,9 @@ def invert(self) -> Image:
 
         output = _F.invert_image_tensor(self)
         return Image.new_like(self, output)
+
+    def gaussian_blur(self, kernel_size: List[int], sigma: Optional[List[float]] = None) -> Image:
+        from torchvision.prototype.transforms import functional as _F
+
+        output = _F.gaussian_blur_image_tensor(self, kernel_size=kernel_size, sigma=sigma)
+        return Image.new_like(self, output)

torchvision/prototype/transforms/__init__.py

@@ -22,7 +22,10 @@
     RandomAffine,
 )
 from ._meta import ConvertBoundingBoxFormat, ConvertImageDtype, ConvertImageColorSpace
-from ._misc import Identity, Normalize, ToDtype, Lambda
+from ._misc import Identity, GaussianBlur, Normalize, ToDtype, Lambda
 from ._type_conversion import DecodeImage, LabelToOneHot
 
 from ._deprecated import Grayscale, RandomGrayscale, ToTensor, ToPILImage, PILToTensor  # usort: skip
+
+# TODO: add RandomPerspective, RandomInvert, RandomPosterize, RandomSolarize,
+# RandomAdjustSharpness, RandomAutocontrast, ElasticTransform

torchvision/prototype/transforms/_misc.py

@@ -1,8 +1,9 @@
 import functools
-from typing import Any, List, Type, Callable, Dict
+from typing import Any, List, Type, Callable, Dict, Sequence, Union
 
 import torch
 from torchvision.prototype.transforms import Transform, functional as F
+from torchvision.transforms.transforms import _setup_size
 
 
 class Identity(Transform):
@@ -46,6 +47,36 @@ def _transform(self, input: Any, params: Dict[str, Any]) -> Any:
             return input
 
 
+class GaussianBlur(Transform):
+    def __init__(
+        self, kernel_size: Union[int, Sequence[int]], sigma: Union[float, Sequence[float]] = (0.1, 2.0)
+    ) -> None:
+        super().__init__()
+        self.kernel_size = _setup_size(kernel_size, "Kernel size should be a tuple/list of two integers")
+        for ks in self.kernel_size:
+            if ks <= 0 or ks % 2 == 0:
+                raise ValueError("Kernel size value should be an odd and positive number.")
+
+        if isinstance(sigma, float):
+            if sigma <= 0:
+                raise ValueError("If sigma is a single number, it must be positive.")
+            sigma = (sigma, sigma)
+        elif isinstance(sigma, Sequence) and len(sigma) == 2:
+            if not 0.0 < sigma[0] <= sigma[1]:
+                raise ValueError("sigma values should be positive and of the form (min, max).")
+        else:
+            raise TypeError("sigma should be a single float or a list/tuple with length 2 floats.")
+
+        self.sigma = sigma
+
+    def _get_params(self, sample: Any) -> Dict[str, Any]:
+        sigma = torch.empty(1).uniform_(self.sigma[0], self.sigma[1]).item()
+        return dict(sigma=[sigma, sigma])
+
+    def _transform(self, inpt: Any, params: Dict[str, Any]) -> Any:
+        return F.gaussian_blur(inpt, **params)
+
+
 class ToDtype(Lambda):
     def __init__(self, dtype: torch.dtype, *types: Type) -> None:
         self.dtype = dtype

torchvision/prototype/transforms/functional/__init__.py

@@ -99,7 +99,12 @@
     ten_crop_image_tensor,
     ten_crop_image_pil,
 )
-from ._misc import normalize_image_tensor, gaussian_blur_image_tensor
+from ._misc import (
+    normalize_image_tensor,
+    gaussian_blur,
+    gaussian_blur_image_tensor,
+    gaussian_blur_image_pil,
+)
 from ._type_conversion import (
     decode_image_with_pil,
     decode_video_with_av,

torchvision/prototype/transforms/functional/_misc.py

@@ -1,14 +1,28 @@
-from typing import Optional, List
+from typing import Optional, List, Union
 
 import PIL.Image
 import torch
+from torchvision.prototype import features
 from torchvision.transforms import functional_tensor as _FT
 from torchvision.transforms.functional import pil_to_tensor, to_pil_image
 
 
+# shortcut type
+DType = Union[torch.Tensor, PIL.Image.Image, features._Feature]
+
+
 normalize_image_tensor = _FT.normalize
 
 
+def normalize(inpt: DType, mean: List[float], std: List[float], inplace: bool = False) -> DType:
+    if isinstance(inpt, features.Image):
+        return normalize_image_tensor(inpt, mean=mean, std=std, inplace=inplace)
+    elif type(inpt) == torch.Tensor:
+        return normalize_image_tensor(inpt, mean=mean, std=std, inplace=inplace)
+    else:
+        raise TypeError("Unsupported input type")
+
+
 def gaussian_blur_image_tensor(
     img: torch.Tensor, kernel_size: List[int], sigma: Optional[List[float]] = None
 ) -> torch.Tensor:
@@ -42,3 +56,12 @@ def gaussian_blur_image_pil(img: PIL.Image, kernel_size: List[int], sigma: Optio
     t_img = pil_to_tensor(img)
     output = gaussian_blur_image_tensor(t_img, kernel_size=kernel_size, sigma=sigma)
     return to_pil_image(output, mode=img.mode)
+
+
+def gaussian_blur(inpt: DType, kernel_size: List[int], sigma: Optional[List[float]] = None) -> DType:
+    if isinstance(inpt, features._Feature):
+        return inpt.gaussian_blur(kernel_size=kernel_size, sigma=sigma)
+    elif isinstance(inpt, PIL.Image.Image):
+        return gaussian_blur_image_pil(inpt, kernel_size=kernel_size, sigma=sigma)
+    else:
+        return gaussian_blur_image_tensor(inpt, kernel_size=kernel_size, sigma=sigma)