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69 lines
2.2 KiB
69 lines
2.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief Expand feature map to minibatch matrix.
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* - matrix width is: blockH_ * blockW_ * channels_
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* - matirx height is: outputH_ * outputW_
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*
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* \f[
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* outputH\_ = 1 + (2 * paddingH\_ + imgSizeH\_ - blockH\_ + strideH\_ - 1) /
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* strideH\_ \\
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* outputW\_ = 1 + (2 * paddingW\_ + imgSizeW\_ - blockW\_ + strideW\_ - 1) /
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* strideW\_
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* \f]
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*
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* The expand method is the same with ExpandConvLayer, but saved the transposed
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* value. After expanding, output_.sequenceStartPositions will store timeline.
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* The number of time steps are outputH_ * outputW_ and the dimension of each
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* time step is blockH_ * blockW_ * channels_. This layer can be used after
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* convolution neural network, and before recurrent neural network.
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*
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* The config file api is block_expand_layer.
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*/
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class BlockExpandLayer : public Layer {
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protected:
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/**
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* @brief Calculate outputH_ and outputW_ and return block number which
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* actually is time steps.
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* @return time steps, outoutH_ * outputW_.
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*/
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size_t getBlockNum();
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size_t blockH_, blockW_, strideH_, strideW_, paddingH_, paddingW_;
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size_t imgSizeH_, imgSizeW_, outputH_, outputW_, channels_;
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TensorShape inputShape_;
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TensorShape outputShape_;
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public:
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explicit BlockExpandLayer(const LayerConfig& config) : Layer(config) {}
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~BlockExpandLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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} // namespace paddle
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