modules/parallel_wavegan/layers/causal_conv.py

# -*- coding: utf-8 -*-

# Copyright 2020 Tomoki Hayashi
#  MIT License (https://opensource.org/licenses/MIT)

"""Causal convolusion layer modules."""


import torch


class CausalConv1d(torch.nn.Module):
    """CausalConv1d module with customized initialization."""

    def __init__(self, in_channels, out_channels, kernel_size,
                 dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}):
        """Initialize CausalConv1d module."""
        super(CausalConv1d, self).__init__()
        self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params)
        self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size,
                                    dilation=dilation, bias=bias)

    def forward(self, x):
        """Calculate forward propagation.

        Args:
            x (Tensor): Input tensor (B, in_channels, T).

        Returns:
            Tensor: Output tensor (B, out_channels, T).

        """
        return self.conv(self.pad(x))[:, :, :x.size(2)]


class CausalConvTranspose1d(torch.nn.Module):
    """CausalConvTranspose1d module with customized initialization."""

    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True):
        """Initialize CausalConvTranspose1d module."""
        super(CausalConvTranspose1d, self).__init__()
        self.deconv = torch.nn.ConvTranspose1d(
            in_channels, out_channels, kernel_size, stride, bias=bias)
        self.stride = stride

    def forward(self, x):
        """Calculate forward propagation.

        Args:
            x (Tensor): Input tensor (B, in_channels, T_in).

        Returns:
            Tensor: Output tensor (B, out_channels, T_out).

        """
        return self.deconv(x)[:, :, :-self.stride]