Minor improvements on functional.

torchvision/prototype/transforms/functional/_color.py

@@ -188,7 +188,7 @@ def _hsv_to_rgb(img: torch.Tensor) -> torch.Tensor:
     h, s, v = img.unbind(dim=-3)
     h6 = h * 6
     i = torch.floor(h6)
-    f = (h6) - i
+    f = h6 - i
     i = i.to(dtype=torch.int32)
 
     p = (v * (1.0 - s)).clamp_(0.0, 1.0)
@@ -210,9 +210,6 @@ def adjust_hue_image_tensor(image: torch.Tensor, hue_factor: float) -> torch.Ten
     if not (-0.5 <= hue_factor <= 0.5):
         raise ValueError(f"hue_factor ({hue_factor}) is not in [-0.5, 0.5].")
 
-    if not (isinstance(image, torch.Tensor)):
-        raise TypeError("Input img should be Tensor image")
-
     c = get_num_channels_image_tensor(image)
 
     if c not in [1, 3]:
@@ -258,9 +255,6 @@ def adjust_hue(inpt: features.InputTypeJIT, hue_factor: float) -> features.Input
 
 
 def adjust_gamma_image_tensor(image: torch.Tensor, gamma: float, gain: float = 1.0) -> torch.Tensor:
-    if not (isinstance(image, torch.Tensor)):
-        raise TypeError("Input img should be Tensor image")
-
     if gamma < 0:
         raise ValueError("Gamma should be a non-negative real number")
 
@@ -337,10 +331,6 @@ def solarize(inpt: features.InputTypeJIT, threshold: float) -> features.InputTyp
 
 
 def autocontrast_image_tensor(image: torch.Tensor) -> torch.Tensor:
-
-    if not (isinstance(image, torch.Tensor)):
-        raise TypeError("Input img should be Tensor image")
-
     c = get_num_channels_image_tensor(image)
 
     if c not in [1, 3]:

torchvision/prototype/transforms/functional/_meta.py

@@ -183,12 +183,8 @@ def clamp_bounding_box(
     return convert_format_bounding_box(xyxy_boxes, BoundingBoxFormat.XYXY, format)
 
 
-def _split_alpha(image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    return image[..., :-1, :, :], image[..., -1:, :, :]
-
-
 def _strip_alpha(image: torch.Tensor) -> torch.Tensor:
-    image, alpha = _split_alpha(image)
+    image, alpha = torch.tensor_split(image, indices=(-1,), dim=-3)
     if not torch.all(alpha == _FT._max_value(alpha.dtype)):
         raise RuntimeError(
             "Stripping the alpha channel if it contains values other than the max value is not supported."
@@ -237,7 +233,7 @@ def convert_color_space_image_tensor(
     elif old_color_space == ColorSpace.GRAY_ALPHA and new_color_space == ColorSpace.RGB:
         return _gray_to_rgb(_strip_alpha(image))
     elif old_color_space == ColorSpace.GRAY_ALPHA and new_color_space == ColorSpace.RGB_ALPHA:
-        image, alpha = _split_alpha(image)
+        image, alpha = torch.tensor_split(image, indices=(-1,), dim=-3)
         return _add_alpha(_gray_to_rgb(image), alpha)
     elif old_color_space == ColorSpace.RGB and new_color_space == ColorSpace.GRAY:
         return _rgb_to_gray(image)
@@ -248,7 +244,7 @@ def convert_color_space_image_tensor(
     elif old_color_space == ColorSpace.RGB_ALPHA and new_color_space == ColorSpace.GRAY:
         return _rgb_to_gray(_strip_alpha(image))
     elif old_color_space == ColorSpace.RGB_ALPHA and new_color_space == ColorSpace.GRAY_ALPHA:
-        image, alpha = _split_alpha(image)
+        image, alpha = torch.tensor_split(image, indices=(-1,), dim=-3)
         return _add_alpha(_rgb_to_gray(image), alpha)
     elif old_color_space == ColorSpace.RGB_ALPHA and new_color_space == ColorSpace.RGB:
         return _strip_alpha(image)

torchvision/prototype/transforms/functional/_misc.py

@@ -69,18 +69,18 @@ def normalize(
     return normalize_image_tensor(inpt, mean=mean, std=std, inplace=inplace)
 
 
-def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> torch.Tensor:
+def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
     lim = (kernel_size - 1) / (2 * math.sqrt(2) * sigma)
-    x = torch.linspace(-lim, lim, steps=kernel_size)
+    x = torch.linspace(-lim, lim, steps=kernel_size, dtype=dtype, device=device)
     kernel1d = torch.softmax(-x.pow_(2), dim=0)
     return kernel1d
 
 
 def _get_gaussian_kernel2d(
     kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device
 ) -> torch.Tensor:
-    kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0]).to(device, dtype=dtype)
-    kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1]).to(device, dtype=dtype)
+    kernel1d_x = _get_gaussian_kernel1d(kernel_size[0], sigma[0], dtype, device)
+    kernel1d_y = _get_gaussian_kernel1d(kernel_size[1], sigma[1], dtype, device)
     kernel2d = kernel1d_y.unsqueeze(-1) * kernel1d_x
     return kernel2d