Modules/GeneralLayers/ResidualStack.py

# 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)