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# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0
# LICENSE is in incl_licenses directory.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
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., 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.:
beta = 0.1102 * (A - 8.7)
elif A >= 21.:
beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
else:
beta = 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,
C=None):
# kernel_size should be even number for stylegan3 setup,
# in this implementation, odd number is also possible.
super().__init__()
if cutoff < -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)
self.conv1d_block = None
if C is not None:
self.conv1d_block = [nn.Conv1d(C,C,kernel_size,stride=self.stride, groups=C, bias=False),]
self.conv1d_block[0].weight = nn.Parameter(self.filter.expand(C, -1, -1))
self.conv1d_block[0].requires_grad_(False)
#input [B, C, T]
def forward(self, x):
if self.conv1d_block[0].weight.device != x.device:
self.conv1d_block[0] = self.conv1d_block[0].to(x.device)
if self.conv1d_block is None:
_, 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)
else:
if self.padding:
x = F.pad(x, (self.pad_left, self.pad_right),
mode=self.padding_mode)
out = self.conv1d_block[0](x)
return out |