import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class ResStack(nn.Module):
def __init__(self, channel):
super(ResStack, self).__init__()
self.blocks = nn.ModuleList([
nn.Sequential(
nn.LeakyReLU(0.2),
nn.ReflectionPad1d(3**i),
nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=3, dilation=3**i)),
nn.LeakyReLU(0.2),
nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=1)),
)
for i in range(3)
])
self.shortcuts = nn.ModuleList([
nn.utils.weight_norm(nn.Conv1d(channel, channel, kernel_size=1))
for i in range(3)
])
def forward(self, x):
for block, shortcut in zip(self.blocks, self.shortcuts):
x = shortcut(x) + block(x)
return x
def remove_weight_norm(self):
for block, shortcut in zip(self.blocks, self.shortcuts):
nn.utils.remove_weight_norm(block[2])
nn.utils.remove_weight_norm(block[4])
nn.utils.remove_weight_norm(shortcut)
