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| # Copyright 2019 Tomoki Hayashi | |
| # MIT License (https://opensource.org/licenses/MIT) | |
| # Adapted by Florian Lux 2021 | |
| import torch | |
| class ResidualStack(torch.nn.Module): | |
| 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={}, ): | |
| """ | |
| 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. | |
| """ | |
| super(ResidualStack, self).__init__() | |
| # defile residual stack part | |
| 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), ) | |
| # 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) | |