595 lines
20 KiB
C++
595 lines
20 KiB
C++
/***********************************************************************
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* Software License Agreement (BSD License)
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*
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* Copyright 2008-2009 Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
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* Copyright 2008-2009 David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
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*
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* THE BSD LICENSE
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* 1. Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* 2. Redistributions in binary form must reproduce the above copyright
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* notice, this list of conditions and the following disclaimer in the
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* documentation and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS OR
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* IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
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* OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
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* IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT
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* NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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* DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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* THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF
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* THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*************************************************************************/
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#ifndef OPENCV_FLANN_AUTOTUNED_INDEX_H_
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#define OPENCV_FLANN_AUTOTUNED_INDEX_H_
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//! @cond IGNORED
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#include <sstream>
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#include "nn_index.h"
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#include "ground_truth.h"
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#include "index_testing.h"
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#include "sampling.h"
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#include "kdtree_index.h"
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#include "kdtree_single_index.h"
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#include "kmeans_index.h"
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#include "composite_index.h"
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#include "linear_index.h"
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#include "logger.h"
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namespace cvflann
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{
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template<typename Distance>
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NNIndex<Distance>* create_index_by_type(const Matrix<typename Distance::ElementType>& dataset, const IndexParams& params, const Distance& distance);
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struct AutotunedIndexParams : public IndexParams
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{
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AutotunedIndexParams(float target_precision = 0.8, float build_weight = 0.01, float memory_weight = 0, float sample_fraction = 0.1)
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{
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(*this)["algorithm"] = FLANN_INDEX_AUTOTUNED;
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// precision desired (used for autotuning, -1 otherwise)
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(*this)["target_precision"] = target_precision;
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// build tree time weighting factor
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(*this)["build_weight"] = build_weight;
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// index memory weighting factor
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(*this)["memory_weight"] = memory_weight;
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// what fraction of the dataset to use for autotuning
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(*this)["sample_fraction"] = sample_fraction;
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}
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};
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template <typename Distance>
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class AutotunedIndex : public NNIndex<Distance>
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{
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public:
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typedef typename Distance::ElementType ElementType;
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typedef typename Distance::ResultType DistanceType;
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AutotunedIndex(const Matrix<ElementType>& inputData, const IndexParams& params = AutotunedIndexParams(), Distance d = Distance()) :
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dataset_(inputData), distance_(d)
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{
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target_precision_ = get_param(params, "target_precision",0.8f);
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build_weight_ = get_param(params,"build_weight", 0.01f);
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memory_weight_ = get_param(params, "memory_weight", 0.0f);
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sample_fraction_ = get_param(params,"sample_fraction", 0.1f);
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bestIndex_ = NULL;
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speedup_ = 0;
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}
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AutotunedIndex(const AutotunedIndex&);
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AutotunedIndex& operator=(const AutotunedIndex&);
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virtual ~AutotunedIndex()
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{
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if (bestIndex_ != NULL) {
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delete bestIndex_;
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bestIndex_ = NULL;
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}
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}
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/**
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* Method responsible with building the index.
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*/
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virtual void buildIndex() CV_OVERRIDE
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{
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std::ostringstream stream;
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bestParams_ = estimateBuildParams();
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print_params(bestParams_, stream);
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Logger::info("----------------------------------------------------\n");
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Logger::info("Autotuned parameters:\n");
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Logger::info("%s", stream.str().c_str());
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Logger::info("----------------------------------------------------\n");
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bestIndex_ = create_index_by_type(dataset_, bestParams_, distance_);
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bestIndex_->buildIndex();
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speedup_ = estimateSearchParams(bestSearchParams_);
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stream.str(std::string());
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print_params(bestSearchParams_, stream);
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Logger::info("----------------------------------------------------\n");
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Logger::info("Search parameters:\n");
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Logger::info("%s", stream.str().c_str());
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Logger::info("----------------------------------------------------\n");
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}
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/**
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* Saves the index to a stream
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*/
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virtual void saveIndex(FILE* stream) CV_OVERRIDE
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{
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save_value(stream, (int)bestIndex_->getType());
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bestIndex_->saveIndex(stream);
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save_value(stream, get_param<int>(bestSearchParams_, "checks"));
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}
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/**
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* Loads the index from a stream
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*/
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virtual void loadIndex(FILE* stream) CV_OVERRIDE
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{
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int index_type;
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load_value(stream, index_type);
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IndexParams params;
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params["algorithm"] = (flann_algorithm_t)index_type;
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bestIndex_ = create_index_by_type<Distance>(dataset_, params, distance_);
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bestIndex_->loadIndex(stream);
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int checks;
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load_value(stream, checks);
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bestSearchParams_["checks"] = checks;
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}
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/**
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* Method that searches for nearest-neighbors
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*/
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virtual void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& searchParams) CV_OVERRIDE
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{
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int checks = get_param<int>(searchParams,"checks",FLANN_CHECKS_AUTOTUNED);
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if (checks == FLANN_CHECKS_AUTOTUNED) {
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bestIndex_->findNeighbors(result, vec, bestSearchParams_);
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}
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else {
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bestIndex_->findNeighbors(result, vec, searchParams);
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}
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}
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IndexParams getParameters() const CV_OVERRIDE
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{
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return bestIndex_->getParameters();
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}
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SearchParams getSearchParameters() const
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{
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return bestSearchParams_;
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}
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float getSpeedup() const
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{
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return speedup_;
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}
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/**
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* Number of features in this index.
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*/
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virtual size_t size() const CV_OVERRIDE
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{
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return bestIndex_->size();
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}
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/**
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* The length of each vector in this index.
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*/
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virtual size_t veclen() const CV_OVERRIDE
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{
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return bestIndex_->veclen();
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}
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/**
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* The amount of memory (in bytes) this index uses.
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*/
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virtual int usedMemory() const CV_OVERRIDE
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{
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return bestIndex_->usedMemory();
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}
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/**
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* Algorithm name
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*/
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virtual flann_algorithm_t getType() const CV_OVERRIDE
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{
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return FLANN_INDEX_AUTOTUNED;
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}
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private:
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struct CostData
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{
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float searchTimeCost;
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float buildTimeCost;
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float memoryCost;
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float totalCost;
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IndexParams params;
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};
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void evaluate_kmeans(CostData& cost)
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{
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StartStopTimer t;
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int checks;
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const int nn = 1;
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Logger::info("KMeansTree using params: max_iterations=%d, branching=%d\n",
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get_param<int>(cost.params,"iterations"),
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get_param<int>(cost.params,"branching"));
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KMeansIndex<Distance> kmeans(sampledDataset_, cost.params, distance_);
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// measure index build time
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t.start();
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kmeans.buildIndex();
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t.stop();
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float buildTime = (float)t.value;
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// measure search time
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float searchTime = test_index_precision(kmeans, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
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float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
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cost.memoryCost = (kmeans.usedMemory() + datasetMemory) / datasetMemory;
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cost.searchTimeCost = searchTime;
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cost.buildTimeCost = buildTime;
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Logger::info("KMeansTree buildTime=%g, searchTime=%g, build_weight=%g\n", buildTime, searchTime, build_weight_);
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}
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void evaluate_kdtree(CostData& cost)
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{
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StartStopTimer t;
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int checks;
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const int nn = 1;
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Logger::info("KDTree using params: trees=%d\n", get_param<int>(cost.params,"trees"));
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KDTreeIndex<Distance> kdtree(sampledDataset_, cost.params, distance_);
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t.start();
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kdtree.buildIndex();
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t.stop();
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float buildTime = (float)t.value;
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//measure search time
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float searchTime = test_index_precision(kdtree, sampledDataset_, testDataset_, gt_matches_, target_precision_, checks, distance_, nn);
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float datasetMemory = float(sampledDataset_.rows * sampledDataset_.cols * sizeof(float));
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cost.memoryCost = (kdtree.usedMemory() + datasetMemory) / datasetMemory;
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cost.searchTimeCost = searchTime;
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cost.buildTimeCost = buildTime;
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Logger::info("KDTree buildTime=%g, searchTime=%g\n", buildTime, searchTime);
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}
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// struct KMeansSimpleDownhillFunctor {
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//
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// Autotune& autotuner;
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// KMeansSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
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//
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// float operator()(int* params) {
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//
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// float maxFloat = numeric_limits<float>::max();
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//
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// if (params[0]<2) return maxFloat;
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// if (params[1]<0) return maxFloat;
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//
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// CostData c;
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// c.params["algorithm"] = KMEANS;
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// c.params["centers-init"] = CENTERS_RANDOM;
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// c.params["branching"] = params[0];
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// c.params["max-iterations"] = params[1];
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//
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// autotuner.evaluate_kmeans(c);
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//
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// return c.timeCost;
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//
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// }
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// };
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//
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// struct KDTreeSimpleDownhillFunctor {
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//
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// Autotune& autotuner;
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// KDTreeSimpleDownhillFunctor(Autotune& autotuner_) : autotuner(autotuner_) {}
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//
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// float operator()(int* params) {
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// float maxFloat = numeric_limits<float>::max();
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//
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// if (params[0]<1) return maxFloat;
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//
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// CostData c;
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// c.params["algorithm"] = KDTREE;
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// c.params["trees"] = params[0];
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//
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// autotuner.evaluate_kdtree(c);
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//
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// return c.timeCost;
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//
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// }
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// };
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void optimizeKMeans(std::vector<CostData>& costs)
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{
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Logger::info("KMEANS, Step 1: Exploring parameter space\n");
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// explore kmeans parameters space using combinations of the parameters below
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int maxIterations[] = { 1, 5, 10, 15 };
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int branchingFactors[] = { 16, 32, 64, 128, 256 };
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int kmeansParamSpaceSize = FLANN_ARRAY_LEN(maxIterations) * FLANN_ARRAY_LEN(branchingFactors);
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costs.reserve(costs.size() + kmeansParamSpaceSize);
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// evaluate kmeans for all parameter combinations
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for (size_t i = 0; i < FLANN_ARRAY_LEN(maxIterations); ++i) {
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for (size_t j = 0; j < FLANN_ARRAY_LEN(branchingFactors); ++j) {
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CostData cost;
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cost.params["algorithm"] = FLANN_INDEX_KMEANS;
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cost.params["centers_init"] = FLANN_CENTERS_RANDOM;
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cost.params["iterations"] = maxIterations[i];
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cost.params["branching"] = branchingFactors[j];
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evaluate_kmeans(cost);
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costs.push_back(cost);
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}
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}
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// Logger::info("KMEANS, Step 2: simplex-downhill optimization\n");
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//
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// const int n = 2;
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// // choose initial simplex points as the best parameters so far
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// int kmeansNMPoints[n*(n+1)];
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// float kmeansVals[n+1];
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// for (int i=0;i<n+1;++i) {
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// kmeansNMPoints[i*n] = (int)kmeansCosts[i].params["branching"];
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// kmeansNMPoints[i*n+1] = (int)kmeansCosts[i].params["max-iterations"];
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// kmeansVals[i] = kmeansCosts[i].timeCost;
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// }
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// KMeansSimpleDownhillFunctor kmeans_cost_func(*this);
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// // run optimization
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// optimizeSimplexDownhill(kmeansNMPoints,n,kmeans_cost_func,kmeansVals);
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// // store results
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// for (int i=0;i<n+1;++i) {
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// kmeansCosts[i].params["branching"] = kmeansNMPoints[i*2];
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// kmeansCosts[i].params["max-iterations"] = kmeansNMPoints[i*2+1];
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// kmeansCosts[i].timeCost = kmeansVals[i];
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// }
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}
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void optimizeKDTree(std::vector<CostData>& costs)
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{
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Logger::info("KD-TREE, Step 1: Exploring parameter space\n");
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// explore kd-tree parameters space using the parameters below
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int testTrees[] = { 1, 4, 8, 16, 32 };
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// evaluate kdtree for all parameter combinations
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for (size_t i = 0; i < FLANN_ARRAY_LEN(testTrees); ++i) {
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CostData cost;
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cost.params["algorithm"] = FLANN_INDEX_KDTREE;
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cost.params["trees"] = testTrees[i];
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evaluate_kdtree(cost);
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costs.push_back(cost);
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}
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// Logger::info("KD-TREE, Step 2: simplex-downhill optimization\n");
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//
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// const int n = 1;
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// // choose initial simplex points as the best parameters so far
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// int kdtreeNMPoints[n*(n+1)];
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// float kdtreeVals[n+1];
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// for (int i=0;i<n+1;++i) {
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// kdtreeNMPoints[i] = (int)kdtreeCosts[i].params["trees"];
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// kdtreeVals[i] = kdtreeCosts[i].timeCost;
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// }
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// KDTreeSimpleDownhillFunctor kdtree_cost_func(*this);
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// // run optimization
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// optimizeSimplexDownhill(kdtreeNMPoints,n,kdtree_cost_func,kdtreeVals);
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// // store results
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// for (int i=0;i<n+1;++i) {
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// kdtreeCosts[i].params["trees"] = kdtreeNMPoints[i];
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// kdtreeCosts[i].timeCost = kdtreeVals[i];
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// }
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}
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/**
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* Chooses the best nearest-neighbor algorithm and estimates the optimal
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* parameters to use when building the index (for a given precision).
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* Returns a dictionary with the optimal parameters.
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*/
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IndexParams estimateBuildParams()
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{
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std::vector<CostData> costs;
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int sampleSize = int(sample_fraction_ * dataset_.rows);
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int testSampleSize = std::min(sampleSize / 10, 1000);
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Logger::info("Entering autotuning, dataset size: %d, sampleSize: %d, testSampleSize: %d, target precision: %g\n", dataset_.rows, sampleSize, testSampleSize, target_precision_);
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// For a very small dataset, it makes no sense to build any fancy index, just
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// use linear search
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if (testSampleSize < 10) {
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Logger::info("Choosing linear, dataset too small\n");
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return LinearIndexParams();
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}
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// We use a fraction of the original dataset to speedup the autotune algorithm
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sampledDataset_ = random_sample(dataset_, sampleSize);
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// We use a cross-validation approach, first we sample a testset from the dataset
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testDataset_ = random_sample(sampledDataset_, testSampleSize, true);
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// We compute the ground truth using linear search
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Logger::info("Computing ground truth... \n");
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gt_matches_ = Matrix<int>(new int[testDataset_.rows], testDataset_.rows, 1);
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StartStopTimer t;
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t.start();
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compute_ground_truth<Distance>(sampledDataset_, testDataset_, gt_matches_, 0, distance_);
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t.stop();
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CostData linear_cost;
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linear_cost.searchTimeCost = (float)t.value;
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linear_cost.buildTimeCost = 0;
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linear_cost.memoryCost = 0;
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linear_cost.params["algorithm"] = FLANN_INDEX_LINEAR;
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costs.push_back(linear_cost);
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// Start parameter autotune process
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Logger::info("Autotuning parameters...\n");
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optimizeKMeans(costs);
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optimizeKDTree(costs);
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float bestTimeCost = costs[0].searchTimeCost;
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for (size_t i = 0; i < costs.size(); ++i) {
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float timeCost = costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost;
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if (timeCost < bestTimeCost) {
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bestTimeCost = timeCost;
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}
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}
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float bestCost = costs[0].searchTimeCost / bestTimeCost;
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IndexParams bestParams = costs[0].params;
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if (bestTimeCost > 0) {
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for (size_t i = 0; i < costs.size(); ++i) {
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float crtCost = (costs[i].buildTimeCost * build_weight_ + costs[i].searchTimeCost) / bestTimeCost +
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memory_weight_ * costs[i].memoryCost;
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if (crtCost < bestCost) {
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bestCost = crtCost;
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bestParams = costs[i].params;
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}
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}
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}
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delete[] gt_matches_.data;
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delete[] testDataset_.data;
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delete[] sampledDataset_.data;
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return bestParams;
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}
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/**
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* Estimates the search time parameters needed to get the desired precision.
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* Precondition: the index is built
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* Postcondition: the searchParams will have the optimum params set, also the speedup obtained over linear search.
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*/
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float estimateSearchParams(SearchParams& searchParams)
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{
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const int nn = 1;
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const size_t SAMPLE_COUNT = 1000;
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CV_Assert(bestIndex_ != NULL && "Requires a valid index"); // must have a valid index
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float speedup = 0;
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int samples = (int)std::min(dataset_.rows / 10, SAMPLE_COUNT);
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if (samples > 0) {
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Matrix<ElementType> testDataset = random_sample(dataset_, samples);
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Logger::info("Computing ground truth\n");
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// we need to compute the ground truth first
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Matrix<int> gt_matches(new int[testDataset.rows], testDataset.rows, 1);
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StartStopTimer t;
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t.start();
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compute_ground_truth<Distance>(dataset_, testDataset, gt_matches, 1, distance_);
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t.stop();
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float linear = (float)t.value;
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int checks;
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Logger::info("Estimating number of checks\n");
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float searchTime;
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float cb_index;
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if (bestIndex_->getType() == FLANN_INDEX_KMEANS) {
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Logger::info("KMeans algorithm, estimating cluster border factor\n");
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KMeansIndex<Distance>* kmeans = (KMeansIndex<Distance>*)bestIndex_;
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float bestSearchTime = -1;
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float best_cb_index = -1;
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int best_checks = -1;
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for (cb_index = 0; cb_index < 1.1f; cb_index += 0.2f) {
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kmeans->set_cb_index(cb_index);
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searchTime = test_index_precision(*kmeans, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
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if ((searchTime < bestSearchTime) || (bestSearchTime == -1)) {
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bestSearchTime = searchTime;
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best_cb_index = cb_index;
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best_checks = checks;
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}
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|
}
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|
searchTime = bestSearchTime;
|
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cb_index = best_cb_index;
|
|
checks = best_checks;
|
|
|
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kmeans->set_cb_index(best_cb_index);
|
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Logger::info("Optimum cb_index: %g\n", cb_index);
|
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bestParams_["cb_index"] = cb_index;
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|
}
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else {
|
|
searchTime = test_index_precision(*bestIndex_, dataset_, testDataset, gt_matches, target_precision_, checks, distance_, nn, 1);
|
|
}
|
|
|
|
Logger::info("Required number of checks: %d \n", checks);
|
|
searchParams["checks"] = checks;
|
|
|
|
speedup = linear / searchTime;
|
|
|
|
delete[] gt_matches.data;
|
|
delete[] testDataset.data;
|
|
}
|
|
|
|
return speedup;
|
|
}
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|
|
|
private:
|
|
NNIndex<Distance>* bestIndex_;
|
|
|
|
IndexParams bestParams_;
|
|
SearchParams bestSearchParams_;
|
|
|
|
Matrix<ElementType> sampledDataset_;
|
|
Matrix<ElementType> testDataset_;
|
|
Matrix<int> gt_matches_;
|
|
|
|
float speedup_;
|
|
|
|
/**
|
|
* The dataset used by this index
|
|
*/
|
|
const Matrix<ElementType> dataset_;
|
|
|
|
/**
|
|
* Index parameters
|
|
*/
|
|
float target_precision_;
|
|
float build_weight_;
|
|
float memory_weight_;
|
|
float sample_fraction_;
|
|
|
|
Distance distance_;
|
|
|
|
|
|
};
|
|
}
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|
|
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//! @endcond
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|
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#endif /* OPENCV_FLANN_AUTOTUNED_INDEX_H_ */
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