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from torch import nn
from torch.nn.utils.parametrizations import weight_norm
from torch.nn.utils.parametrize import remove_parametrizations
class ResidualStack(nn.Module):
def __init__(self, channels, num_res_blocks, kernel_size):
super().__init__()
assert (kernel_size - 1) % 2 == 0, " [!] kernel_size has to be odd."
base_padding = (kernel_size - 1) // 2
self.blocks = nn.ModuleList()
for idx in range(num_res_blocks):
layer_kernel_size = kernel_size
layer_dilation = layer_kernel_size**idx
layer_padding = base_padding * layer_dilation
self.blocks += [
nn.Sequential(
nn.LeakyReLU(0.2),
nn.ReflectionPad1d(layer_padding),
weight_norm(
nn.Conv1d(channels, channels, kernel_size=kernel_size, dilation=layer_dilation, bias=True)
),
nn.LeakyReLU(0.2),
weight_norm(nn.Conv1d(channels, channels, kernel_size=1, bias=True)),
)
]
self.shortcuts = nn.ModuleList(
[weight_norm(nn.Conv1d(channels, channels, kernel_size=1, bias=True)) for _ in range(num_res_blocks)]
)
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):
remove_parametrizations(block[2], "weight")
remove_parametrizations(block[4], "weight")
remove_parametrizations(shortcut, "weight")
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