| | |
| | |
| |
|
| | import torch.nn as nn |
| | from torch.nn import functional as F |
| | from .filter import LowPassFilter1d |
| | from .filter import kaiser_sinc_filter1d |
| |
|
| |
|
| | class UpSample1d(nn.Module): |
| | def __init__(self, ratio=2, kernel_size=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) |
| |
|
| | |
| | def forward(self, x): |
| | _, C, _ = 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) |
| | x = x[..., self.pad_left:-self.pad_right] |
| |
|
| | return x |
| |
|
| |
|
| | class DownSample1d(nn.Module): |
| | def __init__(self, ratio=2, kernel_size=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) |
| |
|
| | def forward(self, x): |
| | xx = self.lowpass(x) |
| |
|
| | return xx |
