| |
| |
|
|
| 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 |
