faiss/IndexScalarQuantizer.h

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/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
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* This source code is licensed under the BSD+Patents license found in the
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* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
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#ifndef FAISS_INDEX_SCALAR_QUANTIZER_H
#define FAISS_INDEX_SCALAR_QUANTIZER_H
#include <stdint.h>
#include <vector>
#include "IndexIVF.h"
namespace faiss {
/**
* The uniform quantizer has a range [vmin, vmax]. The range can be
* the same for all dimensions (uniform) or specific per dimension
* (default).
*/
struct SQDistanceComputer;
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struct ScalarQuantizer {
enum QuantizerType {
QT_8bit, ///< 8 bits per component
QT_4bit, ///< 4 bits per component
QT_8bit_uniform, ///< same, shared range for all dimensions
QT_4bit_uniform,
QT_fp16,
QT_8bit_direct, /// fast indexing of uint8s
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};
QuantizerType qtype;
/** The uniform encoder can estimate the range of representable
* values of the unform encoder using different statistics. Here
* rs = rangestat_arg */
// rangestat_arg.
enum RangeStat {
RS_minmax, ///< [min - rs*(max-min), max + rs*(max-min)]
RS_meanstd, ///< [mean - std * rs, mean + std * rs]
RS_quantiles, ///< [Q(rs), Q(1-rs)]
RS_optim, ///< alternate optimization of reconstruction error
};
RangeStat rangestat;
float rangestat_arg;
/// dimension of input vectors
size_t d;
/// bytes per vector
size_t code_size;
/// trained values (including the range)
std::vector<float> trained;
ScalarQuantizer (size_t d, QuantizerType qtype);
ScalarQuantizer ();
void train (size_t n, const float *x);
/// 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, size_t n) const;
SQDistanceComputer *get_distance_computer (MetricType metric = METRIC_L2)
const;
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};
struct DistanceComputer;
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struct IndexScalarQuantizer: Index {
/// Used to encode the vectors
ScalarQuantizer sq;
/// Codes. Size ntotal * pq.code_size
std::vector<uint8_t> codes;
size_t code_size;
/** Constructor.
*
* @param d dimensionality of the input vectors
* @param M number of subquantizers
* @param nbits number of bit per subvector index
*/
IndexScalarQuantizer (int d,
ScalarQuantizer::QuantizerType qtype,
MetricType metric = METRIC_L2);
IndexScalarQuantizer ();
void train(idx_t n, const float* x) override;
void add(idx_t n, const float* x) override;
void search(
idx_t n,
const float* x,
idx_t k,
float* distances,
idx_t* labels) const override;
void reset() override;
void reconstruct_n(idx_t i0, idx_t ni, float* recons) const override;
void reconstruct(idx_t key, float* recons) const override;
DistanceComputer *get_distance_computer () const;
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};
/** An IVF implementation where the components of the residuals are
* encoded with a scalar uniform quantizer. All distance computations
* are asymmetric, so the encoded vectors are decoded and approximate
* distances are computed.
*/
struct IndexIVFScalarQuantizer: IndexIVF {
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ScalarQuantizer sq;
bool by_residual;
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IndexIVFScalarQuantizer(Index *quantizer, size_t d, size_t nlist,
ScalarQuantizer::QuantizerType qtype,
MetricType metric = METRIC_L2);
IndexIVFScalarQuantizer();
void train_residual(idx_t n, const float* x) override;
void encode_vectors(idx_t n, const float* x,
const idx_t *list_nos,
uint8_t * codes) const override;
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void add_with_ids(idx_t n, const float* x, const long* xids) override;
InvertedListScanner *get_InvertedListScanner (bool store_pairs)
const override;
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void reconstruct_from_offset (long list_no, long offset,
float* recons) const override;
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};
}
#endif