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/data/users/matthijs/github_faiss/faiss/ProductQuantizer.h
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the CC-by-NC license found in the
* LICENSE file in the root directory of this source tree.
*/
// Copyright 2004-present Facebook. All Rights Reserved.
// -*- c++ -*-
#ifndef FAISS_PRODUCT_QUANTIZER_H
#define FAISS_PRODUCT_QUANTIZER_H
#include <stdint.h>
#include <vector>
#include "Clustering.h"
#include "Heap.h"
namespace faiss {
/** Product Quantizer. Implemented only for METRIC_L2 */
struct ProductQuantizer {
size_t d; ///< size of the input vectors
size_t M; ///< number of subquantizers
size_t nbits; ///< number of bits per quantization index
// values derived from the above
size_t dsub; ///< dimensionality of each subvector
size_t byte_per_idx; ///< nb bytes per code component (1 or 2)
size_t code_size; ///< byte per indexed vector
size_t ksub; ///< number of centroids for each subquantizer
bool verbose; ///< verbose during training?
/// initialization
enum train_type_t {
Train_default,
Train_hot_start, ///< the centroids are already initialized
Train_shared, ///< share dictionary accross PQ segments
Train_hypercube, ///< intialize centroids with nbits-D hypercube
Train_hypercube_pca, ///< intialize centroids with nbits-D hypercube
};
train_type_t train_type;
ClusteringParameters cp; ///< parameters used during clustering
/// Centroid table, size M * ksub * dsub
std::vector<float> centroids;
/// return the centroids associated with subvector m
float * get_centroids (size_t m, size_t i) {
return &centroids [(m * ksub + i) * dsub];
}
const float * get_centroids (size_t m, size_t i) const {
return &centroids [(m * ksub + i) * dsub];
}
// Train the product quantizer on a set of points. A clustering
// can be set on input to define non-default clustering parameters
void train (int n, const float *x);
ProductQuantizer(size_t d, /* dimensionality of the input vectors */
size_t M, /* number of subquantizers */
size_t nbits); /* number of bit per subvector index */
ProductQuantizer ();
/// compute derived values when d, M and nbits have been set
void set_derived_values ();
/// Define the centroids for subquantizer m
void set_params (const float * centroids, int m);
/// Quantize one vector with the product quantizer
void compute_code (const float * x, uint8_t * code) const ;
/// same as compute_code for several vectors
void compute_codes (const float * x,
uint8_t * codes,
size_t n) const ;
/// decode a vector from a given code (or n vectors if third argument)
void decode (const uint8_t *code, float *x) const;
void decode (const uint8_t *code, float *x, size_t n) const;
/// If we happen to have the distance tables precomputed, this is
/// more efficient to compute the codes.
void compute_code_from_distance_table (const float *tab,
uint8_t *code) const;
/** Compute distance table for one vector.
*
* The distance table for x = [x_0 x_1 .. x_(M-1)] is a M * ksub
* matrix that contains
*
* dis_table (m, j) = || x_m - c_(m, j)||^2
* for m = 0..M-1 and j = 0 .. ksub - 1
*
* where c_(m, j) is the centroid no j of sub-quantizer m.
*
* @param x input vector size d
* @param dis_table output table, size M * ksub
*/
void compute_distance_table (const float * x,
float * dis_table) const;
void compute_inner_prod_table (const float * x,
float * dis_table) const;
/** compute distance table for several vectors
* @param nx nb of input vectors
* @param x input vector size nx * d
* @param dis_table output table, size nx * M * ksub
*/
void compute_distance_tables (size_t nx,
const float * x,
float * dis_tables) const;
void compute_inner_prod_tables (size_t nx,
const float * x,
float * dis_tables) const;
/** perform a search (L2 distance)
* @param x query vectors, size nx * d
* @param nx nb of queries
* @param codes database codes, size ncodes * byte_per_idx
* @param ncodes nb of nb vectors
* @param res heap array to store results (nh == nx)
* @param init_finalize_heap initialize heap (input) and sort (output)?
*/
void search (const float * x,
size_t nx,
const uint8_t * codes,
const size_t ncodes,
float_maxheap_array_t *res,
bool init_finalize_heap = true) const;
/** same as search, but with inner product similarity */
void search_ip (const float * x,
size_t nx,
const uint8_t * codes,
const size_t ncodes,
float_minheap_array_t *res,
bool init_finalize_heap = true) const;
/// Symmetric Distance Table
std::vector<float> sdc_table;
// intitialize the SDC table from the centroids
void compute_sdc_table ();
void search_sdc (const uint8_t * qcodes,
size_t nq,
const uint8_t * bcodes,
const size_t ncodes,
float_maxheap_array_t * res,
bool init_finalize_heap = true) const;
};
} // namespace faiss
#endif
faiss::ProductQuantizer::set_params
void set_params(const float *centroids, int m)
Define the centroids for subquantizer m.
Definition: ProductQuantizer.cpp:179
faiss::ProductQuantizer::Train_hypercube_pca
intialize centroids with nbits-D hypercube
Definition: ProductQuantizer.h:45
faiss::ProductQuantizer::nbits
size_t nbits
number of bits per quantization index
Definition: ProductQuantizer.h:29
faiss::ProductQuantizer::decode
void decode(const uint8_t *code, float *x) const
decode a vector from a given code (or n vectors if third argument)
Definition: ProductQuantizer.cpp:338
faiss::ProductQuantizer::byte_per_idx
size_t byte_per_idx
nb bytes per code component (1 or 2)
Definition: ProductQuantizer.h:33
faiss::ProductQuantizer::Train_hypercube
intialize centroids with nbits-D hypercube
Definition: ProductQuantizer.h:44
faiss::ProductQuantizer::set_derived_values
void set_derived_values()
compute derived values when d, M and nbits have been set
Definition: ProductQuantizer.cpp:166
faiss::ProductQuantizer::sdc_table
std::vector< float > sdc_table
Symmetric Distance Table.
Definition: ProductQuantizer.h:155
faiss::ProductQuantizer::Train_shared
share dictionary accross PQ segments
Definition: ProductQuantizer.h:43
faiss::ProductQuantizer::dsub
size_t dsub
dimensionality of each subvector
Definition: ProductQuantizer.h:32
faiss::ProductQuantizer::compute_distance_tables
void compute_distance_tables(size_t nx, const float *x, float *dis_tables) const
Definition: ProductQuantizer.cpp:439
faiss::ProductQuantizer::compute_code_from_distance_table
void compute_code_from_distance_table(const float *tab, uint8_t *code) const
Definition: ProductQuantizer.cpp:363
faiss::ProductQuantizer::compute_codes
void compute_codes(const float *x, uint8_t *codes, size_t n) const
same as compute_code for several vectors
Definition: ProductQuantizer.cpp:385
faiss::ProductQuantizer::compute_distance_table
void compute_distance_table(const float *x, float *dis_table) const
Definition: ProductQuantizer.cpp:410
faiss::ProductQuantizer::search
void search(const float *x, size_t nx, const uint8_t *codes, const size_t ncodes, float_maxheap_array_t *res, bool init_finalize_heap=true) const
Definition: ProductQuantizer.cpp:540
faiss::ProductQuantizer::code_size
size_t code_size
byte per indexed vector
Definition: ProductQuantizer.h:34
faiss::ProductQuantizer::ksub
size_t ksub
number of centroids for each subquantizer
Definition: ProductQuantizer.h:35
faiss::HeapArray
Definition: Heap.h:350
faiss::ProductQuantizer::search_ip
void search_ip(const float *x, size_t nx, const uint8_t *codes, const size_t ncodes, float_minheap_array_t *res, bool init_finalize_heap=true) const
Definition: ProductQuantizer.cpp:565
faiss::ProductQuantizer::compute_code
void compute_code(const float *x, uint8_t *code) const
Quantize one vector with the product quantizer.
Definition: ProductQuantizer.cpp:311
faiss::ProductQuantizer::Train_hot_start
the centroids are already initialized
Definition: ProductQuantizer.h:42
faiss::ProductQuantizer::cp
ClusteringParameters cp
parameters used during clustering
Definition: ProductQuantizer.h:49
faiss::ClusteringParameters
Definition: Clustering.h:24
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:28
faiss::ProductQuantizer
Definition: ProductQuantizer.h:25
faiss::ProductQuantizer::get_centroids
float * get_centroids(size_t m, size_t i)
return the centroids associated with subvector m
Definition: ProductQuantizer.h:55
faiss::ProductQuantizer::d
size_t d
size of the input vectors
Definition: ProductQuantizer.h:27
faiss::ProductQuantizer::verbose
bool verbose
verbose during training?
Definition: ProductQuantizer.h:36
faiss::ProductQuantizer::centroids
std::vector< float > centroids
Centroid table, size M * ksub * dsub.
Definition: ProductQuantizer.h:52
faiss::ProductQuantizer::train_type_t
train_type_t
initialization
Definition: ProductQuantizer.h:40