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121 lines
3.7 KiB
121 lines
3.7 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Layer modules for FFT block in FastSpeech (Feed-forward Transformer)."""
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from paddle import nn
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class MultiLayeredConv1d(nn.Layer):
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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):
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Number of input channels.
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hidden_chans (int):
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Number of hidden channels.
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kernel_size (int):
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Kernel size of conv1d.
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dropout_rate (float):
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Dropout rate.
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"""
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super().__init__()
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self.w_1 = 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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self.w_2 = 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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self.dropout = nn.Dropout(dropout_rate)
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self.relu = nn.ReLU()
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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):
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Batch of input tensors (B, T, in_chans).
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Returns:
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Tensor: Batch of output tensors (B, T, in_chans).
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"""
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x = self.relu(self.w_1(x.transpose([0, 2, 1]))).transpose([0, 2, 1])
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out = self.w_2(self.dropout(x).transpose([0, 2, 1])).transpose([0, 2, 1])
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return out
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class Conv1dLinear(nn.Layer):
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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):
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Number of input channels.
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hidden_chans (int):
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Number of hidden channels.
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kernel_size (int):
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Kernel size of conv1d.
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dropout_rate (float):
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Dropout rate.
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"""
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super().__init__()
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self.w_1 = 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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self.w_2 = nn.Linear(hidden_chans, in_chans, bias_attr=True)
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self.dropout = nn.Dropout(dropout_rate)
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self.relu = nn.ReLU()
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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):
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Batch of input tensors (B, T, in_chans).
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Returns:
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Tensor: Batch of output tensors (B, T, in_chans).
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"""
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x = self.relu(self.w_1(x.transpose([0, 2, 1]))).transpose([0, 2, 1])
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return self.w_2(self.dropout(x))
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