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Change gaussian kernel to anisotropic kernel. (#199)
Change gaussian kernel to anisotropic kernel. (#199)
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version = '1.0.35' | ||
version = '1.0.36' |
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import torch | ||
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Tensor = torch.Tensor | ||
Device = torch.DeviceObjType | ||
Dtype = torch.Type | ||
pad = torch.nn.functional.pad | ||
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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 | ||
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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 | ||
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ky = int(ky) | ||
kx = int(kx) | ||
return ky, kx | ||
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def gaussian( | ||
window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None | ||
) -> Tensor: | ||
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batch_size = sigma.shape[0] | ||
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x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) | ||
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if window_size % 2 == 0: | ||
x = x + 0.5 | ||
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gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) | ||
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return gauss / gauss.sum(-1, keepdim=True) | ||
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def get_gaussian_kernel1d( | ||
kernel_size: int, | ||
sigma: float | Tensor, | ||
force_even: bool = False, | ||
*, | ||
device: Device | None = None, | ||
dtype: Dtype | None = None, | ||
) -> Tensor: | ||
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return gaussian(kernel_size, sigma, device=device, dtype=dtype) | ||
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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: | ||
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sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype) | ||
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ksize_y, ksize_x = _unpack_2d_ks(kernel_size) | ||
sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None] | ||
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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] | ||
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return kernel_y * kernel_x.view(-1, 1, ksize_x) | ||
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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: | ||
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if isinstance(sigma_color, Tensor): | ||
sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1) | ||
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ky, kx = _unpack_2d_ks(kernel_size) | ||
pad_y, pad_x = _compute_zero_padding(kernel_size) | ||
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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) | ||
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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) | ||
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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) | ||
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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) | ||
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kernel = space_kernel * color_kernel | ||
out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1) | ||
return out | ||
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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) | ||
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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) | ||
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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 | ||
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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})" | ||
) | ||
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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 | ||
) | ||
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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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Original file line number | Diff line number | Diff line change |
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@@ -1,3 +1,7 @@ | ||
### 1.0.36 | ||
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* Change gaussian kernel to anisotropic kernel. | ||
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### 1.0.34 | ||
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* Random seed restoring. | ||
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