106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# Copyright 2019 Tomoki Hayashi
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# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
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"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
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import torch
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class MultiLayeredConv1d(torch.nn.Module):
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"""Multi-layered conv1d for Transformer block.
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This is a module of multi-leyered conv1d designed
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to replace positionwise feed-forward network
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in Transforner block, which is introduced in
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`FastSpeech: Fast, Robust and Controllable Text to Speech`_.
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.. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
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https://arxiv.org/pdf/1905.09263.pdf
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"""
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def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
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"""Initialize MultiLayeredConv1d module.
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Args:
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in_chans (int): Number of input channels.
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hidden_chans (int): Number of hidden channels.
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kernel_size (int): Kernel size of conv1d.
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dropout_rate (float): Dropout rate.
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"""
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super(MultiLayeredConv1d, self).__init__()
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self.w_1 = torch.nn.Conv1d(
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in_chans,
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hidden_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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)
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self.w_2 = torch.nn.Conv1d(
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hidden_chans,
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in_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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)
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self.dropout = torch.nn.Dropout(dropout_rate)
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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): Batch of input tensors (B, ..., in_chans).
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Returns:
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Tensor: Batch of output tensors (B, ..., hidden_chans).
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"""
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x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
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return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
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class Conv1dLinear(torch.nn.Module):
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"""Conv1D + Linear for Transformer block.
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A variant of MultiLayeredConv1d, which replaces second conv-layer to linear.
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"""
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def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
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"""Initialize Conv1dLinear module.
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Args:
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in_chans (int): Number of input channels.
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hidden_chans (int): Number of hidden channels.
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kernel_size (int): Kernel size of conv1d.
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dropout_rate (float): Dropout rate.
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"""
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super(Conv1dLinear, self).__init__()
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self.w_1 = torch.nn.Conv1d(
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in_chans,
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hidden_chans,
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kernel_size,
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stride=1,
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padding=(kernel_size - 1) // 2,
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)
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self.w_2 = torch.nn.Linear(hidden_chans, in_chans)
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self.dropout = torch.nn.Dropout(dropout_rate)
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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): Batch of input tensors (B, ..., in_chans).
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Returns:
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Tensor: Batch of output tensors (B, ..., hidden_chans).
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"""
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x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
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return self.w_2(self.dropout(x))
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