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