update gaussian blur with main standards

Author: justincdavis

test/test_transforms_v2.py

@@ -25,7 +25,6 @@
     assert_equal,
     cache,
     cpu_and_cuda,
-    cvcuda_to_pil_compatible_tensor,
     freeze_rng_state,
     ignore_jit_no_profile_information_warning,
     make_bounding_boxes,
@@ -3958,7 +3957,7 @@ def test_functional(self, make_input):
             (F.gaussian_blur_image, tv_tensors.Image),
             (F.gaussian_blur_video, tv_tensors.Video),
             pytest.param(
-                F._misc._gaussian_blur_cvcuda,
+                F._misc._gaussian_blur_image_cvcuda,
                 "cvcuda.Tensor",
                 marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CVCUDA is not available"),
             ),

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_available, _is_cvcuda_tensor
 
 from ._utils import (
     _parse_labels_getter,
@@ -20,6 +21,8 @@
     is_pure_tensor,
 )
 
+CVCUDA_AVAILABLE = _is_cvcuda_available()
+
 
 # TODO: do we want/need to expose this?
 class Identity(Transform):
@@ -192,6 +195,9 @@ class GaussianBlur(Transform):
 
     _v1_transform_cls = _transforms.GaussianBlur
 
+    if CVCUDA_AVAILABLE:
+        _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:

torchvision/transforms/v2/_utils.py

@@ -15,8 +15,8 @@
 from torchvision._utils import sequence_to_str
 
 from torchvision.transforms.transforms import _check_sequence_input, _setup_angle, _setup_size  # noqa: F401
-from torchvision.transforms.v2.functional import get_dimensions, get_size, is_cvcuda_tensor, is_pure_tensor
-from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
+from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
+from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT, _is_cvcuda_tensor
 
 
 def _setup_number_or_seq(arg: int | float | Sequence[int | float], name: str) -> Sequence[float]:
@@ -182,7 +182,7 @@ def query_chw(flat_inputs: list[Any]) -> tuple[int, int, int]:
     chws = {
         tuple(get_dimensions(inpt))
         for inpt in flat_inputs
-        if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video, is_cvcuda_tensor))
+        if check_type(inpt, (is_pure_tensor, tv_tensors.Image, PIL.Image.Image, tv_tensors.Video, _is_cvcuda_tensor))
     }
     if not chws:
         raise TypeError("No image or video was found in the sample")
@@ -207,7 +207,7 @@ def query_size(flat_inputs: list[Any]) -> tuple[int, int]:
                 tv_tensors.Mask,
                 tv_tensors.BoundingBoxes,
                 tv_tensors.KeyPoints,
-                is_cvcuda_tensor,
+                _is_cvcuda_tensor,
             ),
         )
     }

torchvision/transforms/v2/functional/_misc.py

@@ -197,7 +197,7 @@ def gaussian_blur_video(
     return gaussian_blur_image(video, kernel_size, sigma)
 
 
-def _gaussian_blur_cvcuda(
+def _gaussian_blur_image_cvcuda(
     image: "cvcuda.Tensor", kernel_size: list[int], sigma: Optional[list[float]] = None
 ) -> "cvcuda.Tensor":
     cvcuda = _import_cvcuda()
@@ -213,7 +213,7 @@ def _gaussian_blur_cvcuda(
 
 
 if CVCUDA_AVAILABLE:
-    _register_kernel_internal(gaussian_blur, _import_cvcuda().Tensor)(_gaussian_blur_cvcuda)
+    _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: