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demo_imi_flat.cpp
1 /**
2 * Copyright (c) 2015-present, Facebook, Inc.
3 * All rights reserved.
4 *
5 * This source code is licensed under the BSD+Patents license found in the
6 * LICENSE file in the root directory of this source tree.
7 */
8
9 // Copyright 2004-present Facebook. All Rights Reserved
10
11
12 #include <cmath>
13 #include <cstdio>
14 #include <cstdlib>
15
16 #include <sys/time.h>
17
18
19 #include <faiss/IndexPQ.h>
20 #include <faiss/IndexIVF.h>
21 #include <faiss/IndexFlat.h>
22 #include <faiss/index_io.h>
23
24 double elapsed ()
25 {
26 struct timeval tv;
27 gettimeofday (&tv, nullptr);
28 return tv.tv_sec + tv.tv_usec * 1e-6;
29 }
30
31
32 int main ()
33 {
34 double t0 = elapsed();
35
36 // dimension of the vectors to index
37 int d = 128;
38
39 // size of the database we plan to index
40 size_t nb = 1000 * 1000;
41
42 // make a set of nt training vectors in the unit cube
43 // (could be the database)
44 size_t nt = 100 * 1000;
45
46 //---------------------------------------------------------------
47 // Define the core quantizer
48 // We choose a multiple inverted index for faster training with less data
49 // and because it usually offers best accuracy/speed trade-offs
50 //
51 // We here assume that its lifespan of this coarse quantizer will cover the
52 // lifespan of the inverted-file quantizer IndexIVFFlat below
53 // With dynamic allocation, one may give the responsability to free the
54 // quantizer to the inverted-file index (with attribute do_delete_quantizer)
55 //
56 // Note: a regular clustering algorithm would be defined as:
57 // faiss::IndexFlatL2 coarse_quantizer (d);
58 //
59 // Use nhash=2 subquantizers used to define the product coarse quantizer
60 // Number of bits: we will have 2^nbits_coarse centroids per subquantizer
61 // meaning (2^12)^nhash distinct inverted lists
62 size_t nhash = 2;
63 size_t nbits_subq = int (log2 (nb+1) / 2); // good choice in general
64 size_t ncentroids = 1 << (nhash * nbits_subq); // total # of centroids
65
66 faiss::MultiIndexQuantizer coarse_quantizer (d, nhash, nbits_subq);
67
68 printf ("IMI (%ld,%ld): %ld virtual centroids (target: %ld base vectors)",
69 nhash, nbits_subq, ncentroids, nb);
70
71 // the coarse quantizer should not be dealloced before the index
72 // 4 = nb of bytes per code (d must be a multiple of this)
73 // 8 = nb of bits per sub-code (almost always 8)
74 faiss::MetricType metric = faiss::METRIC_L2; // can be METRIC_INNER_PRODUCT
75 faiss::IndexIVFFlat index (&coarse_quantizer, d, ncentroids, metric);
76 index.quantizer_trains_alone = true;
77
78 // define the number of probes. 2048 is for high-dim, overkilled in practice
79 // Use 4-1024 depending on the trade-off speed accuracy that you want
80 index.nprobe = 2048;
81
82
83 { // training
84 printf ("[%.3f s] Generating %ld vectors in %dD for training\n",
85 elapsed() - t0, nt, d);
86
87 std::vector <float> trainvecs (nt * d);
88 for (size_t i = 0; i < nt * d; i++) {
89 trainvecs[i] = drand48();
90 }
91
92 printf ("[%.3f s] Training the index\n", elapsed() - t0);
93 index.verbose = true;
94 index.train (nt, trainvecs.data());
95 }
96
97 size_t nq;
98 std::vector<float> queries;
99
100 { // populating the database
101 printf ("[%.3f s] Building a dataset of %ld vectors to index\n",
102 elapsed() - t0, nb);
103
104 std::vector <float> database (nb * d);
105 for (size_t i = 0; i < nb * d; i++) {
106 database[i] = drand48();
107 }
108
109 printf ("[%.3f s] Adding the vectors to the index\n", elapsed() - t0);
110
111 index.add (nb, database.data());
112
113 // remember a few elements from the database as queries
114 int i0 = 1234;
115 int i1 = 1244;
116
117 nq = i1 - i0;
118 queries.resize (nq * d);
119 for (int i = i0; i < i1; i++) {
120 for (int j = 0; j < d; j++) {
121 queries [(i - i0) * d + j] = database [i * d + j];
122 }
123 }
124 }
125
126 { // searching the database
127 int k = 5;
128 printf ("[%.3f s] Searching the %d nearest neighbors "
129 "of %ld vectors in the index\n",
130 elapsed() - t0, k, nq);
131
132 std::vector<faiss::Index::idx_t> nns (k * nq);
133 std::vector<float> dis (k * nq);
134
135 index.search (nq, queries.data(), k, dis.data(), nns.data());
136
137 printf ("[%.3f s] Query results (vector ids, then distances):\n",
138 elapsed() - t0);
139
140 for (int i = 0; i < nq; i++) {
141 printf ("query %2d: ", i);
142 for (int j = 0; j < k; j++) {
143 printf ("%7ld ", nns[j + i * k]);
144 }
145 printf ("\n dis: ");
146 for (int j = 0; j < k; j++) {
147 printf ("%7g ", dis[j + i * k]);
148 }
149 printf ("\n");
150 }
151 }
152 return 0;
153 }
faiss::IndexIVFFlat
Definition: IndexIVF.h:132
faiss::MultiIndexQuantizer
Definition: IndexPQ.h:135
faiss::MetricType
MetricType
Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:43