resolve more review comments

Author: justincdavis

test/test_transforms_v2.py

@@ -4085,7 +4085,7 @@ def test_functional_image_correctness(self, dimensions, kernel_size, sigma, dtyp
     @pytest.mark.parametrize("color_space", ["RGB", "GRAY"])
     @pytest.mark.parametrize("batch_dims", [(1,), (2,), (4,)])
     @pytest.mark.parametrize("dtype", [torch.uint8, torch.float32])
-    def test_functional_cvcuda_parity(self, dimensions, kernel_size, sigma, color_space, batch_dims, dtype):
+    def test_functional_cvcuda_correctness(self, dimensions, kernel_size, sigma, color_space, batch_dims, dtype):
         height, width = dimensions
 
         image_tensor = make_image(
@@ -4098,6 +4098,9 @@ def test_functional_cvcuda_parity(self, dimensions, kernel_size, sigma, color_sp
         actual_torch = F.cvcuda_to_tensor(actual)
 
         if dtype.is_floating_point:
+            # floating point precision differences between torch and cvcuda are present
+            # observed greatest absolute error is 0.3
+            # most likely stemming from different implementation
             torch.testing.assert_close(actual_torch, expected, rtol=0, atol=0.3)
         else:
             # uint8/16 gaussians can differ by up to max-value, most likely an overflow issue

torchvision/transforms/v2/functional/_misc.py

@@ -1,5 +1,5 @@
 import math
-from typing import List, Optional, TYPE_CHECKING, Union
+from typing import List, Optional, Tuple, TYPE_CHECKING, Union
 
 import PIL.Image
 import torch
@@ -109,7 +109,7 @@ def _get_gaussian_kernel2d(
 def _validate_kernel_size_and_sigma(
     kernel_size: List[int] | int,
     sigma: Optional[Union[List[float], float, int]] = None,
-) -> tuple[list[int], list[float]]:
+) -> 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]
@@ -213,9 +213,7 @@ def _gaussian_blur_cvcuda(
 
 
 if CVCUDA_AVAILABLE:
-    _gaussian_blur_cvcuda_registered = _register_kernel_internal(gaussian_blur, _import_cvcuda().Tensor)(
-        _gaussian_blur_cvcuda
-    )
+    _register_kernel_internal(gaussian_blur, _import_cvcuda().Tensor)(_gaussian_blur_cvcuda)
 
 
 def gaussian_noise(inpt: torch.Tensor, mean: float = 0.0, sigma: float = 0.1, clip: bool = True) -> torch.Tensor: