Create kernel on-device for `transforms.functional.gaussian_blur` (#8426)

Co-authored-by: Nicolas Hug <nh.nicolas.hug@gmail.com>
Co-authored-by: Nicolas Hug <nicolashug@fb.com>

torchvision/transforms/_functional_tensor.py

@@ -722,10 +722,10 @@ def perspective(
     return _apply_grid_transform(img, grid, interpolation, fill=fill)
 
 
-def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:
+def _get_gaussian_kernel1d(kernel_size: int, sigma: float, dtype: torch.dtype, device: torch.device) -> Tensor:
     ksize_half = (kernel_size - 1) * 0.5
 
-    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size, dtype=dtype, device=device)
     pdf = torch.exp(-0.5 * (x / sigma).pow(2))
     kernel1d = pdf / pdf.sum()
 
@@ -735,8 +735,8 @@ def _get_gaussian_kernel1d(kernel_size: int, sigma: float) -> Tensor:
 def _get_gaussian_kernel2d(
     kernel_size: List[int], sigma: List[float], dtype: torch.dtype, device: torch.device
 ) -> 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 = torch.mm(kernel1d_y[:, None], kernel1d_x[None, :])
     return kernel2d