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demo_ivfpq_indexing.cpp
/**
* 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
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <sys/time.h>
#include "../IndexIVFPQ.h"
#include "../IndexFlat.h"
#include "../index_io.h"
double elapsed ()
{
struct timeval tv;
gettimeofday (&tv, NULL);
return tv.tv_sec + tv.tv_usec * 1e-6;
}
int main ()
{
double t0 = elapsed();
// dimension of the vectors to index
int d = 128;
// size of the database we plan to index
size_t nb = 200 * 1000;
// make a set of nt training vectors in the unit cube
// (could be the database)
size_t nt = 100 * 1000;
// make the index object and train it
faiss::IndexFlatL2 coarse_quantizer (d);
// a reasonable number of centroids to index nb vectors
int ncentroids = int (4 * sqrt (nb));
// the coarse quantizer should not be dealloced before the index
// 4 = nb of bytes per code (d must be a multiple of this)
// 8 = nb of bits per sub-code (almost always 8)
faiss::IndexIVFPQ index (&coarse_quantizer, d,
ncentroids, 4, 8);
{ // training
printf ("[%.3f s] Generating %ld vectors in %dD for training\n",
elapsed() - t0, nt, d);
std::vector <float> trainvecs (nt * d);
for (size_t i = 0; i < nt * d; i++) {
trainvecs[i] = drand48();
}
printf ("[%.3f s] Training the index\n",
elapsed() - t0);
index.verbose = true;
index.train (nt, trainvecs.data());
}
{ // I/O demo
const char *outfilename = "/tmp/index_trained.faissindex";
printf ("[%.3f s] storing the pre-trained index to %s\n",
elapsed() - t0, outfilename);
write_index (&index, outfilename);
}
size_t nq;
std::vector<float> queries;
{ // populating the database
printf ("[%.3f s] Building a dataset of %ld vectors to index\n",
elapsed() - t0, nb);
std::vector <float> database (nb * d);
for (size_t i = 0; i < nb * d; i++) {
database[i] = drand48();
}
printf ("[%.3f s] Adding the vectors to the index\n",
elapsed() - t0);
index.add (nb, database.data());
printf ("[%.3f s] imbalance factor: %g\n",
elapsed() - t0, index.imbalance_factor ());
// remember a few elements from the database as queries
int i0 = 1234;
int i1 = 1243;
nq = i1 - i0;
queries.resize (nq * d);
for (int i = i0; i < i1; i++) {
for (int j = 0; j < d; j++) {
queries [(i - i0) * d + j] = database [i * d + j];
}
}
}
{ // searching the database
int k = 5;
printf ("[%.3f s] Searching the %d nearest neighbors "
"of %ld vectors in the index\n",
elapsed() - t0, k, nq);
std::vector<faiss::Index::idx_t> nns (k * nq);
std::vector<float> dis (k * nq);
index.search (nq, queries.data(), k, dis.data(), nns.data());
printf ("[%.3f s] Query results (vector ids, then distances):\n",
elapsed() - t0);
for (int i = 0; i < nq; i++) {
printf ("query %2d: ", i);
for (int j = 0; j < k; j++) {
printf ("%7ld ", nns[j + i * k]);
}
printf ("\n dis: ");
for (int j = 0; j < k; j++) {
printf ("%7g ", dis[j + i * k]);
}
printf ("\n");
}
printf ("note that the nearest neighbor is not at "
"distance 0 due to quantization errors\n");
}
return 0;
}
faiss::IndexFlatL2
Definition: IndexFlat.h:81
faiss::IndexIVFPQ
Definition: IndexIVFPQ.h:30