|
|
|
|
|
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 |
|
|
