| | |
| | |
| |
|
| | 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 |