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on
Zero
Running
on
Zero
# Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 | |
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) | |
# x: [B,C,T] | |
def forward(self, x): | |
x = self.upsample(x) | |
x = self.act(x) | |
x = self.downsample(x) | |
return x | |