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demo_imi_flat.cpp
1
2 /**
3 * Copyright (c) 2015-present, Facebook, Inc.
4 * All rights reserved.
5 *
6 * This source code is licensed under the CC-by-NC license found in the
7 * LICENSE file in the root directory of this source tree.
8 */
9
10 // Copyright 2004-present Facebook. All Rights Reserved
11
12
13 #include <cmath>
14 #include <cstdio>
15 #include <cstdlib>
16
17 #include <sys/time.h>
18
19
20 #include <faiss/IndexPQ.h>
21 #include <faiss/IndexIVF.h>
22 #include <faiss/IndexFlat.h>
23 #include <faiss/index_io.h>
24
25 double elapsed ()
26 {
27 struct timeval tv;
28 gettimeofday (&tv, nullptr);
29 return tv.tv_sec + tv.tv_usec * 1e-6;
30 }
31
32
33 int main ()
34 {
35 double t0 = elapsed();
36
37 // dimension of the vectors to index
38 int d = 128;
39
40 // size of the database we plan to index
41 size_t nb = 1000 * 1000;
42
43 // make a set of nt training vectors in the unit cube
44 // (could be the database)
45 size_t nt = 100 * 1000;
46
47 //---------------------------------------------------------------
48 // Define the core quantizer
49 // We choose a multiple inverted index for faster training with less data
50 // and because it usually offers best accuracy/speed trade-offs
51 //
52 // We here assume that its lifespan of this coarse quantizer will cover the
53 // lifespan of the inverted-file quantizer IndexIVFFlat below
54 // With dynamic allocation, one may give the responsability to free the
55 // quantizer to the inverted-file index (with attribute do_delete_quantizer)
56 //
57 // Note: a regular clustering algorithm would be defined as:
58 // faiss::IndexFlatL2 coarse_quantizer (d);
59 //
60 // Use nhash=2 subquantizers used to define the product coarse quantizer
61 // Number of bits: we will have 2^nbits_coarse centroids per subquantizer
62 // meaning (2^12)^nhash distinct inverted lists
63 size_t nhash = 2;
64 size_t nbits_subq = int (log2 (nb+1) / 2); // good choice in general
65 size_t ncentroids = 1 << (nhash * nbits_subq); // total # of centroids
66
67 faiss::MultiIndexQuantizer coarse_quantizer (d, nhash, nbits_subq);
68
69 printf ("IMI (%ld,%ld): %ld virtual centroids (target: %ld base vectors)",
70 nhash, nbits_subq, ncentroids, nb);
71
72 // the coarse quantizer should not be dealloced before the index
73 // 4 = nb of bytes per code (d must be a multiple of this)
74 // 8 = nb of bits per sub-code (almost always 8)
75 faiss::MetricType metric = faiss::METRIC_L2; // can be METRIC_INNER_PRODUCT
76 faiss::IndexIVFFlat index (&coarse_quantizer, d, ncentroids, metric);
77 index.quantizer_trains_alone = true;
78
79 // define the number of probes. 2048 is for high-dim, overkilled in practice
80 // Use 4-1024 depending on the trade-off speed accuracy that you want
81 index.nprobe = 2048;
82
83
84 { // training
85 printf ("[%.3f s] Generating %ld vectors in %dD for training\n",
86 elapsed() - t0, nt, d);
87
88 std::vector <float> trainvecs (nt * d);
89 for (size_t i = 0; i < nt * d; i++) {
90 trainvecs[i] = drand48();
91 }
92
93 printf ("[%.3f s] Training the index\n", elapsed() - t0);
94 index.verbose = true;
95 index.train (nt, trainvecs.data());
96 }
97
98 size_t nq;
99 std::vector<float> queries;
100
101 { // populating the database
102 printf ("[%.3f s] Building a dataset of %ld vectors to index\n",
103 elapsed() - t0, nb);
104
105 std::vector <float> database (nb * d);
106 for (size_t i = 0; i < nb * d; i++) {
107 database[i] = drand48();
108 }
109
110 printf ("[%.3f s] Adding the vectors to the index\n", elapsed() - t0);
111
112 index.add (nb, database.data());
113
114 // remember a few elements from the database as queries
115 int i0 = 1234;
116 int i1 = 1244;
117
118 nq = i1 - i0;
119 queries.resize (nq * d);
120 for (int i = i0; i < i1; i++) {
121 for (int j = 0; j < d; j++) {
122 queries [(i - i0) * d + j] = database [i * d + j];
123 }
124 }
125 }
126
127 { // searching the database
128 int k = 5;
129 printf ("[%.3f s] Searching the %d nearest neighbors "
130 "of %ld vectors in the index\n",
131 elapsed() - t0, k, nq);
132
133 std::vector<faiss::Index::idx_t> nns (k * nq);
134 std::vector<float> dis (k * nq);
135
136 index.search (nq, queries.data(), k, dis.data(), nns.data());
137
138 printf ("[%.3f s] Query results (vector ids, then distances):\n",
139 elapsed() - t0);
140
141 for (int i = 0; i < nq; i++) {
142 printf ("query %2d: ", i);
143 for (int j = 0; j < k; j++) {
144 printf ("%7ld ", nns[j + i * k]);
145 }
146 printf ("\n dis: ");
147 for (int j = 0; j < k; j++) {
148 printf ("%7g ", dis[j + i * k]);
149 }
150 printf ("\n");
151 }
152 }
153 return 0;
154 }
faiss::IndexIVFFlat
Definition: IndexIVF.h:113
faiss::MultiIndexQuantizer
Definition: IndexPQ.h:137
faiss::MetricType
MetricType
Some algorithms support both an inner product vetsion and a L2 search version.
Definition: Index.h:44