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# -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Residual stack module in MelGAN."""
import torch
from . import CausalConv1d
class ResidualStack(torch.nn.Module):
"""Residual stack module introduced in MelGAN."""
def __init__(self,
kernel_size=3,
channels=32,
dilation=1,
bias=True,
nonlinear_activation="LeakyReLU",
nonlinear_activation_params={"negative_slope": 0.2},
pad="ReflectionPad1d",
pad_params={},
use_causal_conv=False,
):
"""Initialize ResidualStack module.
Args:
kernel_size (int): Kernel size of dilation convolution layer.
channels (int): Number of channels of convolution layers.
dilation (int): Dilation factor.
bias (bool): Whether to add bias parameter in convolution layers.
nonlinear_activation (str): Activation function module name.
nonlinear_activation_params (dict): Hyperparameters for activation function.
pad (str): Padding function module name before dilated convolution layer.
pad_params (dict): Hyperparameters for padding function.
use_causal_conv (bool): Whether to use causal convolution.
"""
super(ResidualStack, self).__init__()
# defile residual stack part
if not use_causal_conv:
assert (kernel_size - 1) % 2 == 0, "Not support even number kernel size."
self.stack = torch.nn.Sequential(
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
getattr(torch.nn, pad)((kernel_size - 1) // 2 * dilation, **pad_params),
torch.nn.Conv1d(channels, channels, kernel_size, dilation=dilation, bias=bias),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
torch.nn.Conv1d(channels, channels, 1, bias=bias),
)
else:
self.stack = torch.nn.Sequential(
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
CausalConv1d(channels, channels, kernel_size, dilation=dilation,
bias=bias, pad=pad, pad_params=pad_params),
getattr(torch.nn, nonlinear_activation)(**nonlinear_activation_params),
torch.nn.Conv1d(channels, channels, 1, bias=bias),
)
# defile extra layer for skip connection
self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias)
def forward(self, c):
"""Calculate forward propagation.
Args:
c (Tensor): Input tensor (B, channels, T).
Returns:
Tensor: Output tensor (B, chennels, T).
"""
return self.stack(c) + self.skip_layer(c)
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