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# -*- 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]
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