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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 |