| |
|
|
| import torch.nn as nn |
| from .resample import UpSample1d, DownSample1d |
|
|
|
|
| class Activation1d(nn.Module): |
| def __init__( |
| self, |
| activation, |
| up_ratio: int = 2, |
| down_ratio: int = 2, |
| up_kernel_size: int = 12, |
| down_kernel_size: int = 12, |
| ): |
| super().__init__() |
| self.up_ratio = up_ratio |
| self.down_ratio = down_ratio |
| self.act = activation |
| self.upsample = UpSample1d(up_ratio, up_kernel_size) |
| self.downsample = DownSample1d(down_ratio, down_kernel_size) |
|
|
| |
| def forward(self, x): |
| x = self.upsample(x) |
| x = self.act(x) |
| x = self.downsample(x) |
|
|
| return x |
|
|