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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 torch.nn as nn
from torch.nn import functional as F
from .filter import LowPassFilter1d, kaiser_sinc_filter1d
class UpSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.stride = ratio
self.pad = self.kernel_size // ratio - 1
self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
half_width=0.6 / ratio,
kernel_size=self.kernel_size)
self.register_buffer("filter", filter)
self.conv_transpose1d_block = None
if C is not None:
self.conv_transpose1d_block = [nn.ConvTranspose1d(C,
C,
kernel_size=self.kernel_size,
stride=self.stride,
groups=C,
bias=False
),]
self.conv_transpose1d_block[0].weight = nn.Parameter(self.filter.expand(C, -1, -1).clone())
self.conv_transpose1d_block[0].requires_grad_(False)
# x: [B, C, T]
def forward(self, x, C=None):
if self.conv_transpose1d_block[0].weight.device != x.device:
self.conv_transpose1d_block[0] = self.conv_transpose1d_block[0].to(x.device)
if self.conv_transpose1d_block is None:
if C is None:
_, C, _ = x.shape
# print("snake.conv_t.in:",x.shape)
x = F.pad(x, (self.pad, self.pad), mode='replicate')
x = self.ratio * F.conv_transpose1d(
x, self.filter.expand(C, -1, -1), stride=self.stride, groups=C)
# print("snake.conv_t.out:",x.shape)
x = x[..., self.pad_left:-self.pad_right]
else:
x = F.pad(x, (self.pad, self.pad), mode='replicate')
x = self.ratio * self.conv_transpose1d_block[0](x)
x = x[..., self.pad_left:-self.pad_right]
return x
class DownSample1d(nn.Module):
def __init__(self, ratio=2, kernel_size=None, C=None):
super().__init__()
self.ratio = ratio
self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
half_width=0.6 / ratio,
stride=ratio,
kernel_size=self.kernel_size,
C=C)
def forward(self, x):
xx = self.lowpass(x)
return xx |