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import torch
Tensor = torch.Tensor
Device = torch.DeviceObjType
Dtype = torch.Type
pad = torch.nn.functional.pad
def _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]:
ky, kx = _unpack_2d_ks(kernel_size)
return (ky - 1) // 2, (kx - 1) // 2
def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]:
if isinstance(kernel_size, int):
ky = kx = kernel_size
else:
assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.'
ky, kx = kernel_size
ky = int(ky)
kx = int(kx)
return ky, kx
def gaussian(
window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None
) -> Tensor:
batch_size = sigma.shape[0]
x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1)
if window_size % 2 == 0:
x = x + 0.5
gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0)))
return gauss / gauss.sum(-1, keepdim=True)
def get_gaussian_kernel1d(
kernel_size: int,
sigma: float | Tensor,
force_even: bool = False,
*,
device: Device | None = None,
dtype: Dtype | None = None,
) -> Tensor:
return gaussian(kernel_size, sigma, device=device, dtype=dtype)
def get_gaussian_kernel2d(
kernel_size: tuple[int, int] | int,
sigma: tuple[float, float] | Tensor,
force_even: bool = False,
*,
device: Device | None = None,
dtype: Dtype | None = None,
) -> Tensor:
sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype)
ksize_y, ksize_x = _unpack_2d_ks(kernel_size)
sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None]
kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None]
kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None]
return kernel_y * kernel_x.view(-1, 1, ksize_x)
def _bilateral_blur(
input: Tensor,
guidance: Tensor | None,
kernel_size: tuple[int, int] | int,
sigma_color: float | Tensor,
sigma_space: tuple[float, float] | Tensor,
border_type: str = 'reflect',
color_distance_type: str = 'l1',
) -> Tensor:
if isinstance(sigma_color, Tensor):
sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1)
ky, kx = _unpack_2d_ks(kernel_size)
pad_y, pad_x = _compute_zero_padding(kernel_size)
padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type)
unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx)
if guidance is None:
guidance = input
unfolded_guidance = unfolded_input
else:
padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type)
unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx)
diff = unfolded_guidance - guidance.unsqueeze(-1)
if color_distance_type == "l1":
color_distance_sq = diff.abs().sum(1, keepdim=True).square()
elif color_distance_type == "l2":
color_distance_sq = diff.square().sum(1, keepdim=True)
else:
raise ValueError("color_distance_type only acceps l1 or l2")
color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx)
space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype)
space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky)
kernel = space_kernel * color_kernel
out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1)
return out
def bilateral_blur(
input: Tensor,
kernel_size: tuple[int, int] | int = (13, 13),
sigma_color: float | Tensor = 3.0,
sigma_space: tuple[float, float] | Tensor = 3.0,
border_type: str = 'reflect',
color_distance_type: str = 'l1',
) -> Tensor:
return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)
def adaptive_anisotropic_filter(x, g=None):
if g is None:
g = x
s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True)
s = s + 1e-5
guidance = (g - m) / s
y = _bilateral_blur(x, guidance,
kernel_size=(13, 13),
sigma_color=3.0,
sigma_space=3.0,
border_type='reflect',
color_distance_type='l1')
return y
def joint_bilateral_blur(
input: Tensor,
guidance: Tensor,
kernel_size: tuple[int, int] | int,
sigma_color: float | Tensor,
sigma_space: tuple[float, float] | Tensor,
border_type: str = 'reflect',
color_distance_type: str = 'l1',
) -> Tensor:
return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type)
class _BilateralBlur(torch.nn.Module):
def __init__(
self,
kernel_size: tuple[int, int] | int,
sigma_color: float | Tensor,
sigma_space: tuple[float, float] | Tensor,
border_type: str = 'reflect',
color_distance_type: str = "l1",
) -> None:
super().__init__()
self.kernel_size = kernel_size
self.sigma_color = sigma_color
self.sigma_space = sigma_space
self.border_type = border_type
self.color_distance_type = color_distance_type
def __repr__(self) -> str:
return (
f"{self.__class__.__name__}"
f"(kernel_size={self.kernel_size}, "
f"sigma_color={self.sigma_color}, "
f"sigma_space={self.sigma_space}, "
f"border_type={self.border_type}, "
f"color_distance_type={self.color_distance_type})"
)
class BilateralBlur(_BilateralBlur):
def forward(self, input: Tensor) -> Tensor:
return bilateral_blur(
input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type
)
class JointBilateralBlur(_BilateralBlur):
def forward(self, input: Tensor, guidance: Tensor) -> Tensor:
return joint_bilateral_blur(
input,
guidance,
self.kernel_size,
self.sigma_color,
self.sigma_space,
self.border_type,
self.color_distance_type,
)
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