lsh_index.h

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/***********************************************************************
 * Software License Agreement (BSD License)
 *
 * Copyright 2008-2009  Marius Muja (mariusm@cs.ubc.ca). All rights reserved.
 * Copyright 2008-2009  David G. Lowe (lowe@cs.ubc.ca). All rights reserved.
 *
 * THE BSD LICENSE
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/***********************************************************************
 * Author: Vincent Rabaud
 *************************************************************************/
 
#ifndef RTABMAP_FLANN_LSH_INDEX_H_
#define RTABMAP_FLANN_LSH_INDEX_H_
 
#include <algorithm>
#include <cassert>
#include <cstring>
#include <map>
#include <vector>
 
#include "rtflann/general.h"
#include "rtflann/algorithms/nn_index.h"
#include "rtflann/util/matrix.h"
#include "rtflann/util/result_set.h"
#include "rtflann/util/heap.h"
#include "rtflann/util/lsh_table.h"
#include "rtflann/util/allocator.h"
#include "rtflann/util/random.h"
#include "rtflann/util/saving.h"
 
namespace rtflann
{
 
struct LshIndexParams : public IndexParams
{
    LshIndexParams(unsigned int table_number = 12, unsigned int key_size = 20, unsigned int multi_probe_level = 2)
    {
        (* this)["algorithm"] = FLANN_INDEX_LSH;
        // The number of hash tables to use
        (*this)["table_number"] = table_number;
        // The length of the key in the hash tables
        (*this)["key_size"] = key_size;
        // Number of levels to use in multi-probe (0 for standard LSH)
        (*this)["multi_probe_level"] = multi_probe_level;
    }
};
 
template<typename Distance>
class LshIndex : public NNIndex<Distance>
{
public:
    typedef typename Distance::ElementType ElementType;
    typedef typename Distance::ResultType DistanceType;
 
    typedef NNIndex<Distance> BaseClass;
 
    LshIndex(const IndexParams& params = LshIndexParams(), Distance d = Distance()) :
        BaseClass(params, d)
    {
        table_number_ = get_param<unsigned int>(index_params_,"table_number",12);
        key_size_ = get_param<unsigned int>(index_params_,"key_size",20);
        multi_probe_level_ = get_param<unsigned int>(index_params_,"multi_probe_level",2);
 
        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
    }
 
 
    LshIndex(const Matrix<ElementType>& input_data, const IndexParams& params = LshIndexParams(), Distance d = Distance()) :
        BaseClass(params, d)
    {
        table_number_ = get_param<unsigned int>(index_params_,"table_number",12);
        key_size_ = get_param<unsigned int>(index_params_,"key_size",20);
        multi_probe_level_ = get_param<unsigned int>(index_params_,"multi_probe_level",2);
 
        fill_xor_mask(0, key_size_, multi_probe_level_, xor_masks_);
 
        setDataset(input_data);
    }
 
    LshIndex(const LshIndex& other) : BaseClass(other),
        tables_(other.tables_),
        table_number_(other.table_number_),
        key_size_(other.key_size_),
        multi_probe_level_(other.multi_probe_level_),
        xor_masks_(other.xor_masks_)
    {
    }
    
    LshIndex& operator=(LshIndex other)
    {
        this->swap(other);
        return *this;
    }
 
    virtual ~LshIndex()
    {
        freeIndex();
    }
 
 
    BaseClass* clone() const
    {
        return new LshIndex(*this);
    }
    
    using BaseClass::buildIndex;
 
    void addPoints(const Matrix<ElementType>& points, float rebuild_threshold = 2)
    {
        assert(points.cols==veclen_);
        size_t old_size = size_;
 
        extendDataset(points);
        
        if (rebuild_threshold>1 && size_at_build_*rebuild_threshold<size_) {
            buildIndex();
        }
        else {
            for (unsigned int i = 0; i < table_number_; ++i) {
                lsh::LshTable<ElementType>& table = tables_[i];                
                for (size_t i=old_size;i<size_;++i) {
                    table.add(i, points_[i]);
                }            
            }
        }
    }
 
 
    flann_algorithm_t getType() const
    {
        return FLANN_INDEX_LSH;
    }
 
 
    template<typename Archive>
    void serialize(Archive& ar)
    {
        ar.setObject(this);
 
        ar & *static_cast<NNIndex<Distance>*>(this);
 
        ar & table_number_;
        ar & key_size_;
        ar & multi_probe_level_;
 
        ar & xor_masks_;
        ar & tables_;
 
        if (Archive::is_loading::value) {
            index_params_["algorithm"] = getType();
            index_params_["table_number"] = table_number_;
            index_params_["key_size"] = key_size_;
            index_params_["multi_probe_level"] = multi_probe_level_;
        }
    }
 
    void saveIndex(FILE* stream)
    {
        serialization::SaveArchive sa(stream);
        sa & *this;
    }
 
    void loadIndex(FILE* stream)
    {
        serialization::LoadArchive la(stream);
        la & *this;
    }
 
    int usedMemory() const
    {
        return size_ * sizeof(int);
    }
 
    int knnSearch(const Matrix<ElementType>& queries,
                                        Matrix<size_t>& indices,
                                        Matrix<DistanceType>& dists,
                                        size_t knn,
                                        const SearchParams& params) const
    {
        assert(queries.cols == veclen_);
        assert(indices.rows >= queries.rows);
        assert(dists.rows >= queries.rows);
        assert(indices.cols >= knn);
        assert(dists.cols >= knn);
 
        int count = 0;
        if (params.use_heap==FLANN_True) {
#pragma omp parallel num_threads(params.cores)
                {
                        KNNUniqueResultSet<DistanceType> resultSet(knn);
#pragma omp for schedule(static) reduction(+:count)
                        for (int i = 0; i < (int)queries.rows; i++) {
                                resultSet.clear();
                                findNeighbors(resultSet, queries[i], params);
                                size_t n = std::min(resultSet.size(), knn);
                                resultSet.copy(indices[i], dists[i], n, params.sorted);
                                indices_to_ids(indices[i], indices[i], n);
                                count += n;
                        }
                }
        }
        else {
#pragma omp parallel num_threads(params.cores)
                {
                        KNNResultSet<DistanceType> resultSet(knn);
#pragma omp for schedule(static) reduction(+:count)
                        for (int i = 0; i < (int)queries.rows; i++) {
                                resultSet.clear();
                                findNeighbors(resultSet, queries[i], params);
                                size_t n = std::min(resultSet.size(), knn);
                                resultSet.copy(indices[i], dists[i], n, params.sorted);
                                indices_to_ids(indices[i], indices[i], n);
                                count += n;
                        }
                }
        }
 
        return count;
    }
 
    int knnSearch(const Matrix<ElementType>& queries,
                                        std::vector< std::vector<size_t> >& indices,
                                        std::vector<std::vector<DistanceType> >& dists,
                                size_t knn,
                                const SearchParams& params) const
    {
        assert(queries.cols == veclen_);
                if (indices.size() < queries.rows ) indices.resize(queries.rows);
                if (dists.size() < queries.rows ) dists.resize(queries.rows);
 
                int count = 0;
                if (params.use_heap==FLANN_True) {
#pragma omp parallel num_threads(params.cores)
                        {
                                KNNUniqueResultSet<DistanceType> resultSet(knn);
#pragma omp for schedule(static) reduction(+:count)
                                for (int i = 0; i < (int)queries.rows; i++) {
                                        resultSet.clear();
                                        findNeighbors(resultSet, queries[i], params);
                                        size_t n = std::min(resultSet.size(), knn);
                                        indices[i].resize(n);
                                        dists[i].resize(n);
                                        if (n > 0) {
                                                resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
                                                indices_to_ids(&indices[i][0], &indices[i][0], n);
                                        }
                                        count += n;
                                }
                        }
                }
                else {
#pragma omp parallel num_threads(params.cores)
                        {
                                KNNResultSet<DistanceType> resultSet(knn);
#pragma omp for schedule(static) reduction(+:count)
                                for (int i = 0; i < (int)queries.rows; i++) {
                                        resultSet.clear();
                                        findNeighbors(resultSet, queries[i], params);
                                        size_t n = std::min(resultSet.size(), knn);
                                        indices[i].resize(n);
                                        dists[i].resize(n);
                                        if (n > 0) {
                                                resultSet.copy(&indices[i][0], &dists[i][0], n, params.sorted);
                                                indices_to_ids(&indices[i][0], &indices[i][0], n);
                                        }
                                        count += n;
                                }
                        }
                }
 
                return count;
    }
 
    void findNeighbors(ResultSet<DistanceType>& result, const ElementType* vec, const SearchParams& /*searchParams*/) const
    {
        getNeighbors(vec, result);
    }
 
protected:
 
    void buildIndexImpl()
    {
        tables_.resize(table_number_);
        std::vector<std::pair<size_t,ElementType*> > features;
        features.reserve(points_.size());
        for (size_t i=0;i<points_.size();++i) {
                features.push_back(std::make_pair(i, points_[i]));
        }
        for (unsigned int i = 0; i < table_number_; ++i) {
            lsh::LshTable<ElementType>& table = tables_[i];
            table = lsh::LshTable<ElementType>(veclen_, key_size_);
 
            // Add the features to the table
            table.add(features);
        }
    }
 
    void freeIndex()
    {
        /* nothing to do here */
    }
 
 
private:
    typedef std::pair<float, unsigned int> ScoreIndexPair;
    struct SortScoreIndexPairOnSecond
    {
        bool operator()(const ScoreIndexPair& left, const ScoreIndexPair& right) const
        {
            return left.second < right.second;
        }
    };
 
    void fill_xor_mask(lsh::BucketKey key, int lowest_index, unsigned int level,
                       std::vector<lsh::BucketKey>& xor_masks)
    {
        xor_masks.push_back(key);
        if (level == 0) return;
        for (int index = lowest_index - 1; index >= 0; --index) {
            // Create a new key
            lsh::BucketKey new_key = key | (lsh::BucketKey(1) << index);
            fill_xor_mask(new_key, index, level - 1, xor_masks);
        }
    }
 
    void getNeighbors(const ElementType* vec, bool do_radius, float radius, bool do_k, unsigned int k_nn,
                      float& checked_average)
    {
        static std::vector<ScoreIndexPair> score_index_heap;
 
        if (do_k) {
            unsigned int worst_score = std::numeric_limits<unsigned int>::max();
            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
            for (; table != table_end; ++table) {
                size_t key = table->getKey(vec);
                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
                for (; xor_mask != xor_mask_end; ++xor_mask) {
                    size_t sub_key = key ^ (*xor_mask);
                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
                    if (bucket == 0) continue;
 
                    // Go over each descriptor index
                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
                    DistanceType hamming_distance;
 
                    // Process the rest of the candidates
                    for (; training_index < last_training_index; ++training_index) {
                        if (removed_ && removed_points_.test(*training_index)) continue;
                        hamming_distance = distance_(vec, points_[*training_index].point, veclen_);
 
                        if (hamming_distance < worst_score) {
                            // Insert the new element
                            score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
                            std::push_heap(score_index_heap.begin(), score_index_heap.end());
 
                            if (score_index_heap.size() > (unsigned int)k_nn) {
                                // Remove the highest distance value as we have too many elements
                                std::pop_heap(score_index_heap.begin(), score_index_heap.end());
                                score_index_heap.pop_back();
                                // Keep track of the worst score
                                worst_score = score_index_heap.front().first;
                            }
                        }
                    }
                }
            }
        }
        else {
            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
            typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
            for (; table != table_end; ++table) {
                size_t key = table->getKey(vec);
                std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
                std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
                for (; xor_mask != xor_mask_end; ++xor_mask) {
                    size_t sub_key = key ^ (*xor_mask);
                    const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
                    if (bucket == 0) continue;
 
                    // Go over each descriptor index
                    std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
                    std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
                    DistanceType hamming_distance;
 
                    // Process the rest of the candidates
                    for (; training_index < last_training_index; ++training_index) {
                        if (removed_ && removed_points_.test(*training_index)) continue;
                        // Compute the Hamming distance
                        hamming_distance = distance_(vec, points_[*training_index].point, veclen_);
                        if (hamming_distance < radius) score_index_heap.push_back(ScoreIndexPair(hamming_distance, training_index));
                    }
                }
            }
        }
    }
 
    void getNeighbors(const ElementType* vec, ResultSet<DistanceType>& result) const
    {
        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table = tables_.begin();
        typename std::vector<lsh::LshTable<ElementType> >::const_iterator table_end = tables_.end();
        for (; table != table_end; ++table) {
            size_t key = table->getKey(vec);
            std::vector<lsh::BucketKey>::const_iterator xor_mask = xor_masks_.begin();
            std::vector<lsh::BucketKey>::const_iterator xor_mask_end = xor_masks_.end();
            for (; xor_mask != xor_mask_end; ++xor_mask) {
                size_t sub_key = key ^ (*xor_mask);
                const lsh::Bucket* bucket = table->getBucketFromKey(sub_key);
                if (bucket == 0) continue;
 
                // Go over each descriptor index
                std::vector<lsh::FeatureIndex>::const_iterator training_index = bucket->begin();
                std::vector<lsh::FeatureIndex>::const_iterator last_training_index = bucket->end();
                DistanceType hamming_distance;
 
                // Process the rest of the candidates
                for (; training_index < last_training_index; ++training_index) {
                        if (removed_ && removed_points_.test(*training_index)) continue;
                    // Compute the Hamming distance
                    hamming_distance = distance_(vec, points_[*training_index], veclen_);
                    result.addPoint(hamming_distance, *training_index);
                }
            }
        }
    }
 
 
    void swap(LshIndex& other)
    {
        BaseClass::swap(other);
        std::swap(tables_, other.tables_);
        std::swap(size_at_build_, other.size_at_build_);
        std::swap(table_number_, other.table_number_);
        std::swap(key_size_, other.key_size_);
        std::swap(multi_probe_level_, other.multi_probe_level_);
        std::swap(xor_masks_, other.xor_masks_);
    }
 
    std::vector<lsh::LshTable<ElementType> > tables_;
    
    unsigned int table_number_;
    unsigned int key_size_;
    unsigned int multi_probe_level_;
 
    std::vector<lsh::BucketKey> xor_masks_;
 
    USING_BASECLASS_SYMBOLS
};
}
 
#endif //FLANN_LSH_INDEX_H_