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