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Running
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Zero
| # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 | |
| # LICENSE is in incl_licenses directory. | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import math | |
| if "sinc" in dir(torch): | |
| sinc = torch.sinc | |
| else: | |
| # This code is adopted from adefossez's julius.core.sinc under the MIT License | |
| # https://adefossez.github.io/julius/julius/core.html | |
| # LICENSE is in incl_licenses directory. | |
| def sinc(x: torch.Tensor): | |
| """ | |
| Implementation of sinc, i.e. sin(pi * x) / (pi * x) | |
| __Warning__: Different to julius.sinc, the input is multiplied by `pi`! | |
| """ | |
| return torch.where( | |
| x == 0, | |
| torch.tensor(1.0, device=x.device, dtype=x.dtype), | |
| torch.sin(math.pi * x) / math.pi / x, | |
| ) | |
| # This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License | |
| # https://adefossez.github.io/julius/julius/lowpass.html | |
| # LICENSE is in incl_licenses directory. | |
| def kaiser_sinc_filter1d( | |
| cutoff, half_width, kernel_size | |
| ): # return filter [1,1,kernel_size] | |
| even = kernel_size % 2 == 0 | |
| half_size = kernel_size // 2 | |
| # For kaiser window | |
| delta_f = 4 * half_width | |
| A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 | |
| if A > 50.0: | |
| beta = 0.1102 * (A - 8.7) | |
| elif A >= 21.0: | |
| beta = 0.5842 * (A - 21) ** 0.4 + 0.07886 * (A - 21.0) | |
| else: | |
| beta = 0.0 | |
| window = torch.kaiser_window(kernel_size, beta=beta, periodic=False) | |
| # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio | |
| if even: | |
| time = torch.arange(-half_size, half_size) + 0.5 | |
| else: | |
| time = torch.arange(kernel_size) - half_size | |
| if cutoff == 0: | |
| filter_ = torch.zeros_like(time) | |
| else: | |
| filter_ = 2 * cutoff * window * sinc(2 * cutoff * time) | |
| # Normalize filter to have sum = 1, otherwise we will have a small leakage | |
| # of the constant component in the input signal. | |
| filter_ /= filter_.sum() | |
| filter = filter_.view(1, 1, kernel_size) | |
| return filter | |
| class LowPassFilter1d(nn.Module): | |
| def __init__( | |
| self, | |
| cutoff=0.5, | |
| half_width=0.6, | |
| stride: int = 1, | |
| padding: bool = True, | |
| padding_mode: str = "replicate", | |
| kernel_size: int = 12, | |
| ): | |
| # kernel_size should be even number for stylegan3 setup, | |
| # in this implementation, odd number is also possible. | |
| super().__init__() | |
| if cutoff < -0.0: | |
| raise ValueError("Minimum cutoff must be larger than zero.") | |
| if cutoff > 0.5: | |
| raise ValueError("A cutoff above 0.5 does not make sense.") | |
| self.kernel_size = kernel_size | |
| self.even = kernel_size % 2 == 0 | |
| self.pad_left = kernel_size // 2 - int(self.even) | |
| self.pad_right = kernel_size // 2 | |
| self.stride = stride | |
| self.padding = padding | |
| self.padding_mode = padding_mode | |
| filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) | |
| self.register_buffer("filter", filter) | |
| # input [B, C, T] | |
| def forward(self, x): | |
| _, C, _ = x.shape | |
| if self.padding: | |
| x = F.pad(x, (self.pad_left, self.pad_right), mode=self.padding_mode) | |
| out = F.conv1d(x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C) | |
| return out | |