wehub-resource-sync
57 lines
1.7 KiB
Python
57 lines
1.7 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright 2020 Tomoki Hayashi
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# MIT License (https://opensource.org/licenses/MIT)
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"""Causal convolusion layer modules."""
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import torch
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class CausalConv1d(torch.nn.Module):
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"""CausalConv1d module with customized initialization."""
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def __init__(self, in_channels, out_channels, kernel_size,
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dilation=1, bias=True, pad="ConstantPad1d", pad_params={"value": 0.0}):
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"""Initialize CausalConv1d module."""
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super(CausalConv1d, self).__init__()
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self.pad = getattr(torch.nn, pad)((kernel_size - 1) * dilation, **pad_params)
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self.conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size,
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dilation=dilation, bias=bias)
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input tensor (B, in_channels, T).
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Returns:
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Tensor: Output tensor (B, out_channels, T).
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"""
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return self.conv(self.pad(x))[:, :, :x.size(2)]
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class CausalConvTranspose1d(torch.nn.Module):
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"""CausalConvTranspose1d module with customized initialization."""
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def __init__(self, in_channels, out_channels, kernel_size, stride, bias=True):
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"""Initialize CausalConvTranspose1d module."""
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super(CausalConvTranspose1d, self).__init__()
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self.deconv = torch.nn.ConvTranspose1d(
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in_channels, out_channels, kernel_size, stride, bias=bias)
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self.stride = stride
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def forward(self, x):
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"""Calculate forward propagation.
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Args:
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x (Tensor): Input tensor (B, in_channels, T_in).
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Returns:
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Tensor: Output tensor (B, out_channels, T_out).
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"""
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return self.deconv(x)[:, :, :-self.stride]
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