Normalize CV-CUDA Backend

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
@@ -5569,7 +5569,17 @@ def test_kernel_image_inplace(self, device):
     def test_kernel_video(self):
         check_kernel(F.normalize_video, make_video(dtype=torch.float32), mean=self.MEAN, std=self.STD)
 
-    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_video])
+    @pytest.mark.parametrize(
+        "make_input",
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
+        ],
+    )
     def test_functional(self, make_input):
         check_functional(F.normalize, make_input(dtype=torch.float32), mean=self.MEAN, std=self.STD)
 
@@ -5579,9 +5589,16 @@ def test_functional(self, make_input):
             (F.normalize_image, torch.Tensor),
             (F.normalize_image, tv_tensors.Image),
             (F.normalize_video, tv_tensors.Video),
+            pytest.param(
+                F._misc._normalize_image_cvcuda,
+                None,
+                marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA"),
+            ),
         ],
     )
     def test_functional_signature(self, kernel, input_type):
+        if kernel is F._misc._normalize_image_cvcuda:
+            input_type = _import_cvcuda().Tensor
         check_functional_kernel_signature_match(F.normalize, kernel=kernel, input_type=input_type)
 
     def test_functional_error(self):
@@ -5607,7 +5624,17 @@ def _sample_input_adapter(self, transform, input, device):
             adapted_input[key] = value
         return adapted_input
 
-    @pytest.mark.parametrize("make_input", [make_image_tensor, make_image, make_video])
+    @pytest.mark.parametrize(
+        "make_input",
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
+        ],
+    )
     def test_transform(self, make_input):
         check_transform(
             transforms.Normalize(mean=self.MEAN, std=self.STD),
@@ -5622,14 +5649,33 @@ def _reference_normalize_image(self, image, *, mean, std):
 
     @pytest.mark.parametrize(("mean", "std"), MEANS_STDS)
     @pytest.mark.parametrize("dtype", [torch.float16, torch.float32, torch.float64])
+    @pytest.mark.parametrize(
+        "make_input",
+        [
+            make_image,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="test requires CVCUDA")
+            ),
+        ],
+    )
     @pytest.mark.parametrize("fn", [F.normalize, transform_cls_to_functional(transforms.Normalize)])
-    def test_correctness_image(self, mean, std, dtype, fn):
-        image = make_image(dtype=dtype)
+    def test_correctness_image(self, mean, std, dtype, make_input, fn):
+        if make_input is make_image_cvcuda and dtype != torch.float32:
+            pytest.skip("CVCUDA only supports float32 for normalize")
+
+        image = make_input(dtype=dtype)
 
         actual = fn(image, mean=mean, std=std)
+
+        if make_input is make_image_cvcuda:
+            image = F.cvcuda_to_tensor(image)[0].cpu()
+
         expected = self._reference_normalize_image(image, mean=mean, std=std)
 
-        assert_equal(actual, expected)
+        if make_input is make_image_cvcuda:
+            assert_close(actual, expected, rtol=0, atol=1e-6)
+        else:
+            assert_equal(actual, expected)
 
 
 class TestClampBoundingBoxes:

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,
@@ -160,6 +161,8 @@ class Normalize(Transform):
 
     _v1_transform_cls = _transforms.Normalize
 
+    _transformed_types = Transform._transformed_types + (_is_cvcuda_tensor,)
+
     def __init__(self, mean: Sequence[float], std: Sequence[float], inplace: bool = False):
         super().__init__()
         self.mean = list(mean)

torchvision/transforms/v2/functional/_misc.py

@@ -1,5 +1,5 @@
 import math
-from typing import Optional
+from typing import Optional, 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(
@@ -72,6 +77,44 @@ def normalize_video(video: torch.Tensor, mean: list[float], std: list[float], in
     return normalize_image(video, mean, std, inplace=inplace)
 
 
+def _normalize_image_cvcuda(
+    image: "cvcuda.Tensor",
+    mean: list[float],
+    std: list[float],
+    inplace: bool = False,
+) -> "cvcuda.Tensor":
+    cvcuda = _import_cvcuda()
+    if inplace:
+        raise ValueError("Inplace normalization is not supported for CVCUDA.")
+
+    # CV-CUDA supports signed int and float tensors
+    # torchvision only supports uint and float, right now CV-CUDA doesnt expose float16, so only check 32
+    # in the future add float16 once exposed in CV-CUDA
+    if not (image.dtype == cvcuda.Type.F32):
+        raise ValueError(f"Input tensor should be a float tensor. Got {image.dtype}.")
+
+    channels = image.shape[3]
+    if isinstance(mean, float | int):
+        mean = [mean] * channels
+    elif len(mean) != channels:
+        raise ValueError(f"Mean should have {channels} elements. Got {len(mean)}.")
+    if isinstance(std, float | int):
+        std = [std] * channels
+    elif len(std) != channels:
+        raise ValueError(f"Std should have {channels} elements. Got {len(std)}.")
+
+    mt = torch.as_tensor(mean, dtype=torch.float32).reshape(1, 1, 1, channels).cuda()
+    st = torch.as_tensor(std, dtype=torch.float32).reshape(1, 1, 1, channels).cuda()
+    mean_cv = cvcuda.as_tensor(mt, cvcuda.TensorLayout.NHWC)
+    std_cv = cvcuda.as_tensor(st, cvcuda.TensorLayout.NHWC)
+
+    return cvcuda.normalize(image, base=mean_cv, scale=std_cv, flags=cvcuda.NormalizeFlags.SCALE_IS_STDDEV)
+
+
+if CVCUDA_AVAILABLE:
+    _register_kernel_internal(normalize, _import_cvcuda().Tensor)(_normalize_image_cvcuda)
+
+
 def gaussian_blur(inpt: torch.Tensor, kernel_size: list[int], sigma: Optional[list[float]] = None) -> torch.Tensor:
     """See :class:`~torchvision.transforms.v2.GaussianBlur` for details."""
     if torch.jit.is_scripting():