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| import torch |
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| class ResidualStack(torch.nn.Module): |
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| 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. |
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| 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. |
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| """ |
| super(ResidualStack, self).__init__() |
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| 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), ) |
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| self.skip_layer = torch.nn.Conv1d(channels, channels, 1, bias=bias) |
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| def forward(self, c): |
| """ |
| Calculate forward propagation. |
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| Args: |
| c (Tensor): Input tensor (B, channels, T). |
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| Returns: |
| Tensor: Output tensor (B, chennels, T). |
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| """ |
| return self.stack(c) + self.skip_layer(c) |
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