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58 lines
1.9 KiB
58 lines
1.9 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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"""Positionwise feed forward layer definition."""
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import paddle
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from paddle import nn
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from deepspeech.utils.log import Log
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logger = Log(__name__).getlog()
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__all__ = ["PositionwiseFeedForward"]
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class PositionwiseFeedForward(nn.Layer):
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"""Positionwise feed forward layer."""
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def __init__(self,
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idim: int,
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hidden_units: int,
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dropout_rate: float,
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activation: nn.Layer=nn.ReLU()):
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"""Construct a PositionwiseFeedForward object.
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FeedForward are appied on each position of the sequence.
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The output dim is same with the input dim.
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Args:
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idim (int): Input dimenstion.
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hidden_units (int): The number of hidden units.
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dropout_rate (float): Dropout rate.
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activation (paddle.nn.Layer): Activation function
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"""
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super().__init__()
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self.w_1 = nn.Linear(idim, hidden_units)
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self.activation = activation
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self.dropout = nn.Dropout(dropout_rate)
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self.w_2 = nn.Linear(hidden_units, idim)
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def forward(self, xs: paddle.Tensor) -> paddle.Tensor:
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"""Forward function.
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Args:
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xs: input tensor (B, Lmax, D)
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Returns:
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output tensor, (B, Lmax, D)
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"""
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return self.w_2(self.dropout(self.activation(self.w_1(xs))))
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