consolidate gaussian_blur_image to use new validate_kernel_size_and_sigma

Author: justincdavis

torchvision/transforms/v2/functional/_misc.py

@@ -1,5 +1,5 @@
 import math
-from typing import Optional, Sequence, TYPE_CHECKING
+from typing import List, Optional, TYPE_CHECKING, Union
 
 import PIL.Image
 import torch
@@ -107,10 +107,10 @@ def _get_gaussian_kernel2d(
 
 
 def _validate_kernel_size_and_sigma(
-    kernel_size: Sequence[int] | int,
-    sigma: Sequence[float | int] | float | int | None = None,
+    kernel_size: List[int] | int,
+    sigma: Optional[Union[List[float], float, int]] = None,
 ) -> tuple[list[int], list[float]]:
-    # duplicated logic from gaussian_blur_image for use in gaussian_blur_cvcuda
+    # TODO: consider deprecating integers from sigma on the future
     if isinstance(kernel_size, int):
         kernel_size = [kernel_size, kernel_size]
     elif len(kernel_size) != 2:
@@ -146,33 +146,7 @@ def _validate_kernel_size_and_sigma(
 def gaussian_blur_image(
     image: torch.Tensor, kernel_size: list[int], sigma: Optional[list[float]] = None
 ) -> torch.Tensor:
-    # TODO: consider deprecating integers from sigma on the future
-    if isinstance(kernel_size, int):
-        kernel_size = [kernel_size, kernel_size]
-    elif len(kernel_size) != 2:
-        raise ValueError(f"If kernel_size is a sequence its length should be 2. Got {len(kernel_size)}")
-    for ksize in kernel_size:
-        if ksize % 2 == 0 or ksize < 0:
-            raise ValueError(f"kernel_size should have odd and positive integers. Got {kernel_size}")
-
-    if sigma is None:
-        sigma = [ksize * 0.15 + 0.35 for ksize in kernel_size]
-    else:
-        if isinstance(sigma, (list, tuple)):
-            length = len(sigma)
-            if length == 1:
-                s = sigma[0]
-                sigma = [s, s]
-            elif length != 2:
-                raise ValueError(f"If sigma is a sequence, its length should be 2. Got {length}")
-        elif isinstance(sigma, (int, float)):
-            s = float(sigma)
-            sigma = [s, s]
-        else:
-            raise TypeError(f"sigma should be either float or sequence of floats. Got {type(sigma)}")
-    for s in sigma:
-        if s <= 0.0:
-            raise ValueError(f"sigma should have positive values. Got {sigma}")
+    kernel_size, sigma = _validate_kernel_size_and_sigma(kernel_size, sigma)
 
     if image.numel() == 0:
         return image