diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py index 3ce603c3ed2..a5e394ec2ba 100644 --- a/test/test_transforms_v2.py +++ b/test/test_transforms_v2.py @@ -21,6 +21,7 @@ import torchvision.transforms.v2 as transforms from common_utils import ( + assert_close, assert_equal, cache, cpu_and_cuda, @@ -41,7 +42,6 @@ ) from torch import nn -from torch.testing import assert_close from torch.utils._pytree import tree_flatten, tree_map from torch.utils.data import DataLoader, default_collate from torchvision import tv_tensors @@ -3936,7 +3936,15 @@ def test_kernel_video(self): @pytest.mark.parametrize( "make_input", - [make_image_tensor, make_image_pil, make_image, make_video], + [ + make_image_tensor, + make_image_pil, + make_image, + make_video, + pytest.param( + make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA is not available") + ), + ], ) def test_functional(self, make_input): check_functional(F.gaussian_blur, make_input(), kernel_size=(3, 3)) @@ -3948,14 +3956,31 @@ def test_functional(self, make_input): (F._misc._gaussian_blur_image_pil, PIL.Image.Image), (F.gaussian_blur_image, tv_tensors.Image), (F.gaussian_blur_video, tv_tensors.Video), + pytest.param( + F._misc._gaussian_blur_image_cvcuda, + None, + marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA is not available"), + ), ], ) def test_functional_signature(self, kernel, input_type): + if kernel is F._misc._gaussian_blur_image_cvcuda: + input_type = _import_cvcuda().Tensor check_functional_kernel_signature_match(F.gaussian_blur, kernel=kernel, input_type=input_type) @pytest.mark.parametrize( "make_input", - [make_image_tensor, make_image_pil, make_image, make_bounding_boxes, make_segmentation_mask, make_video], + [ + make_image_tensor, + make_image_pil, + make_image, + make_bounding_boxes, + make_segmentation_mask, + make_video, + pytest.param( + make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA is not available") + ), + ], ) @pytest.mark.parametrize("device", cpu_and_cuda()) @pytest.mark.parametrize("sigma", [5, 2.0, (0.5, 2), [1.3, 2.7]]) @@ -4018,11 +4043,22 @@ def test_make_params(self, sigma): ((1, 26, 28), (23, 23), 1.7), ], ) - @pytest.mark.parametrize("dtype", [torch.float32, torch.float64, torch.float16]) + @pytest.mark.parametrize("dtype", [torch.uint8, torch.float32, torch.float64, torch.float16]) @pytest.mark.parametrize("device", cpu_and_cuda()) - def test_functional_image_correctness(self, dimensions, kernel_size, sigma, dtype, device): + @pytest.mark.parametrize( + "input_type", + [ + tv_tensors.Image, + pytest.param( + "cvcuda.Tensor", marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA not available") + ), + ], + ) + def test_functional_image_correctness(self, dimensions, kernel_size, sigma, dtype, device, input_type): if dtype is torch.float16 and device == "cpu": pytest.skip("The CPU implementation of float16 on CPU differs from opencv") + if (dtype != torch.float32 and dtype != torch.uint8) and input_type == "cvcuda.Tensor": + pytest.skip("CVCUDA does not support non-float32 or uint8 dtypes for gaussian blur") num_channels, height, width = dimensions @@ -4042,9 +4078,17 @@ def test_functional_image_correctness(self, dimensions, kernel_size, sigma, dtyp device=device, ) - actual = F.gaussian_blur_image(image, kernel_size=kernel_size, sigma=sigma) + if input_type == "cvcuda.Tensor": + image = image.unsqueeze(0) + image = F.to_cvcuda_tensor(image) - torch.testing.assert_close(actual, expected, rtol=0, atol=1) + actual = F.gaussian_blur(image, kernel_size=kernel_size, sigma=sigma) + + if input_type == "cvcuda.Tensor": + actual = F.cvcuda_to_tensor(actual) + actual = actual.squeeze(0).to(device=device) + + assert_close(actual, expected, rtol=0, atol=1) class TestGaussianNoise: diff --git a/torchvision/transforms/v2/_misc.py b/torchvision/transforms/v2/_misc.py index 305149c87b1..e9f6a2f3137 100644 --- a/torchvision/transforms/v2/_misc.py +++ b/torchvision/transforms/v2/_misc.py @@ -9,6 +9,7 @@ from torchvision import transforms as _transforms, tv_tensors from torchvision.transforms.v2 import functional as F, Transform +from torchvision.transforms.v2.functional._utils import _is_cvcuda_tensor from ._utils import ( _parse_labels_getter, @@ -192,6 +193,8 @@ class GaussianBlur(Transform): _v1_transform_cls = _transforms.GaussianBlur + _transformed_types = Transform._transformed_types + (_is_cvcuda_tensor,) + def __init__( self, kernel_size: Union[int, Sequence[int]], sigma: Union[int, float, Sequence[float]] = (0.1, 2.0) ) -> None: diff --git a/torchvision/transforms/v2/functional/_misc.py b/torchvision/transforms/v2/functional/_misc.py index daf263df046..4f7fa8e5722 100644 --- a/torchvision/transforms/v2/functional/_misc.py +++ b/torchvision/transforms/v2/functional/_misc.py @@ -1,5 +1,5 @@ import math -from typing import Optional +from typing import List, Optional, Tuple, TYPE_CHECKING import PIL.Image import torch @@ -13,7 +13,12 @@ from ._meta import _convert_bounding_box_format -from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor +from ._utils import _get_kernel, _import_cvcuda, _is_cvcuda_available, _register_kernel_internal, is_pure_tensor + +CVCUDA_AVAILABLE = _is_cvcuda_available() + +if TYPE_CHECKING: + import cvcuda # type: ignore[import-not-found] def normalize( @@ -99,11 +104,10 @@ def _get_gaussian_kernel2d( return kernel2d -@_register_kernel_internal(gaussian_blur, torch.Tensor) -@_register_kernel_internal(gaussian_blur, tv_tensors.Image) -def gaussian_blur_image( - image: torch.Tensor, kernel_size: list[int], sigma: Optional[list[float]] = None -) -> torch.Tensor: +def _validate_kernel_size_and_sigma( + kernel_size: List[int], + sigma: Optional[List[float]] = None, +) -> Tuple[List[int], List[float]]: # TODO: consider deprecating integers from sigma on the future if isinstance(kernel_size, int): kernel_size = [kernel_size, kernel_size] @@ -132,6 +136,16 @@ def gaussian_blur_image( if s <= 0.0: raise ValueError(f"sigma should have positive values. Got {sigma}") + return kernel_size, sigma + + +@_register_kernel_internal(gaussian_blur, torch.Tensor) +@_register_kernel_internal(gaussian_blur, tv_tensors.Image) +def gaussian_blur_image( + image: torch.Tensor, kernel_size: list[int], sigma: Optional[list[float]] = None +) -> torch.Tensor: + kernel_size, sigma = _validate_kernel_size_and_sigma(kernel_size, sigma) + if image.numel() == 0: return image @@ -181,6 +195,25 @@ def gaussian_blur_video( return gaussian_blur_image(video, kernel_size, sigma) +def _gaussian_blur_image_cvcuda( + image: "cvcuda.Tensor", kernel_size: list[int], sigma: Optional[list[float]] = None +) -> "cvcuda.Tensor": + cvcuda = _import_cvcuda() + + kernel_size, sigma = _validate_kernel_size_and_sigma(kernel_size, sigma) + + return cvcuda.gaussian( + image, + tuple(kernel_size), + tuple(sigma), + border=cvcuda.Border.REFLECT101, + ) + + +if CVCUDA_AVAILABLE: + _register_kernel_internal(gaussian_blur, _import_cvcuda().Tensor)(_gaussian_blur_image_cvcuda) + + 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():