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100 lines
3.1 KiB
100 lines
3.1 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 <vector>
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#include "Layer.h"
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#include "NormLayer.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 Basic parent layer of normalization
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*
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* @note Normalize the input in local region
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*/
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class NormLayer : public Layer {
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public:
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explicit NormLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override {
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Layer::init(layerMap, parameterMap);
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return true;
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}
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/**
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* @brief create norm layer by norm_type
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*/
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static Layer* create(const LayerConfig& config);
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};
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/**
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* @brief response normalization within feature maps
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* namely normalize in independent channel
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* When code refactoring, we delete the original implementation.
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* Need to implement in the futrue.
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*/
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class ResponseNormLayer : public NormLayer {
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protected:
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size_t channels_, size_, outputX_, imgSize_, outputY_, imgSizeY_;
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real scale_, pow_;
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MatrixPtr denoms_;
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public:
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explicit ResponseNormLayer(const LayerConfig& config) : NormLayer(config) {}
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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 { LOG(FATAL) << "Not implemented"; }
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void backward(const UpdateCallback& callback = nullptr) override {
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LOG(FATAL) << "Not implemented";
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}
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};
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/**
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* This layer applys normalization across the channels of each sample to a
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* conv layer's output, and scales the output by a group of trainable factors
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* whose dimensions equal to the number of channels.
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* - Input: One and only one input layer are accepted.
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* - Output: The normalized data of the input data.
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* Reference:
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* Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed,
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* Cheng-Yang Fu, Alexander C. Berg. SSD: Single Shot MultiBox Detector
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*/
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class CrossChannelNormLayer : public NormLayer {
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public:
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explicit CrossChannelNormLayer(const LayerConfig& config)
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: NormLayer(config) {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback);
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MatrixPtr createSampleMatrix(MatrixPtr data, size_t iter, size_t spatialDim);
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MatrixPtr createSpatialMatrix(MatrixPtr data, size_t iter, size_t spatialDim);
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protected:
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size_t channels_;
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std::unique_ptr<Weight> scale_;
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MatrixPtr scaleDiff_;
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MatrixPtr normBuffer_;
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MatrixPtr dataBuffer_;
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MatrixPtr channelBuffer_;
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MatrixPtr spatialBuffer_;
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MatrixPtr sampleBuffer_;
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};
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} // namespace paddle
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