|
|
|
|
| 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
|
|
|
