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IndexBinaryIVF.h
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IndexBinaryIVF.h
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/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
#ifndef FAISS_INDEX_BINARY_IVF_H
#define FAISS_INDEX_BINARY_IVF_H
#include <vector>
#include "IndexBinary.h"
#include "IndexIVF.h"
#include "Clustering.h"
#include "Heap.h"
namespace faiss {
/** Index based on a inverted file (IVF)
*
* In the inverted file, the quantizer (an IndexBinary instance) provides a
* quantization index for each vector to be added. The quantization
* index maps to a list (aka inverted list or posting list), where the
* id of the vector is stored.
*
* The inverted list object is required only after trainng. If none is
* set externally, an ArrayInvertedLists is used automatically.
*
* At search time, the vector to be searched is also quantized, and
* only the list corresponding to the quantization index is
* searched. This speeds up the search by making it
* non-exhaustive. This can be relaxed using multi-probe search: a few
* (nprobe) quantization indices are selected and several inverted
* lists are visited.
*/
struct IndexBinaryIVF : IndexBinary {
/// Acess to the actual data
InvertedLists *invlists;
bool own_invlists;
size_t nprobe; ///< number of probes at query time
size_t max_codes; ///< max nb of codes to visit to do a query
/** Select between using a heap or counting to select the k smallest values
* when scanning inverted lists.
*/
bool use_heap = true;
/// map for direct access to the elements. Enables reconstruct().
bool maintain_direct_map;
std::vector<long> direct_map;
IndexBinary *quantizer; ///< quantizer that maps vectors to inverted lists
size_t nlist; ///< number of possible key values
/**
* = 0: use the quantizer as index in a kmeans training
* = 1: just pass on the training set to the train() of the quantizer
* = 2: kmeans training on a flat index + add the centroids to the quantizer
*/
bool own_fields; ///< whether object owns the quantizer
ClusteringParameters cp; ///< to override default clustering params
/// Trains the quantizer and calls train_residual to train sub-quantizers
void train_q1(size_t n, const uint8_t *x, bool verbose);
/** The Inverted file takes a quantizer (an IndexBinary) on input,
* which implements the function mapping a vector to a list
* identifier. The pointer is borrowed: the quantizer should not
* be deleted while the IndexBinaryIVF is in use.
*/
IndexBinaryIVF(IndexBinary *quantizer, size_t d, size_t nlist);
IndexBinaryIVF();
~IndexBinaryIVF() override;
void reset() override;
/// Trains the quantizer and calls train_residual to train sub-quantizers
void train(idx_t n, const uint8_t *x) override;
/// Quantizes x and calls add_with_key
void add(idx_t n, const uint8_t *x) override;
void add_with_ids(idx_t n, const uint8_t *x, const long *xids) override;
/// same as add_with_ids, with precomputed coarse quantizer
void add_core (idx_t n, const uint8_t * x, const long *xids,
const long *precomputed_idx);
/** Search a set of vectors, that are pre-quantized by the IVF
* quantizer. Fill in the corresponding heaps with the query
* results. search() calls this.
*
* @param n nb of vectors to query
* @param x query vectors, size nx * d
* @param assign coarse quantization indices, size nx * nprobe
* @param centroid_dis
* distances to coarse centroids, size nx * nprobe
* @param distance
* output distances, size n * k
* @param labels output labels, size n * k
* @param store_pairs store inv list index + inv list offset
* instead in upper/lower 32 bit of result,
* instead of ids (used for reranking).
* @param params used to override the object's search parameters
*/
void search_preassigned(idx_t n, const uint8_t *x, idx_t k,
const idx_t *assign,
const int32_t *centroid_dis,
int32_t *distances, idx_t *labels,
bool store_pairs,
const IVFSearchParameters *params=nullptr
) const;
/** assign the vectors, then call search_preassign */
virtual void search(idx_t n, const uint8_t *x, idx_t k,
int32_t *distances, idx_t *labels) const override;
void reconstruct(idx_t key, uint8_t *recons) const override;
/** Reconstruct a subset of the indexed vectors.
*
* Overrides default implementation to bypass reconstruct() which requires
* direct_map to be maintained.
*
* @param i0 first vector to reconstruct
* @param ni nb of vectors to reconstruct
* @param recons output array of reconstructed vectors, size ni * d / 8
*/
void reconstruct_n(idx_t i0, idx_t ni, uint8_t *recons) const override;
/** Similar to search, but also reconstructs the stored vectors (or an
* approximation in the case of lossy coding) for the search results.
*
* Overrides default implementation to avoid having to maintain direct_map
* and instead fetch the code offsets through the `store_pairs` flag in
* search_preassigned().
*
* @param recons reconstructed vectors size (n, k, d / 8)
*/
void search_and_reconstruct(idx_t n, const uint8_t *x, idx_t k,
int32_t *distances, idx_t *labels,
uint8_t *recons) const override;
/** Reconstruct a vector given the location in terms of (inv list index +
* inv list offset) instead of the id.
*
* Useful for reconstructing when the direct_map is not maintained and
* the inv list offset is computed by search_preassigned() with
* `store_pairs` set.
*/
virtual void reconstruct_from_offset(long list_no, long offset,
uint8_t* recons) const;
/// Dataset manipulation functions
long remove_ids(const IDSelector& sel) override;
/** moves the entries from another dataset to self. On output,
* other is empty. add_id is added to all moved ids (for
* sequential ids, this would be this->ntotal */
virtual void merge_from(IndexBinaryIVF& other, idx_t add_id);
size_t get_list_size(size_t list_no) const
{ return invlists->list_size(list_no); }
/** intialize a direct map
*
* @param new_maintain_direct_map if true, create a direct map,
* else clear it
*/
void make_direct_map(bool new_maintain_direct_map=true);
/// 1= perfectly balanced, >1: imbalanced
double imbalance_factor() const;
/// display some stats about the inverted lists
void print_stats() const;
void replace_invlists(InvertedLists *il, bool own=false);
};
} // namespace faiss
#endif // FAISS_INDEX_BINARY_IVF_H