forked from Enet4/faiss
-
Notifications
You must be signed in to change notification settings - Fork 0
/
IndexIVFSpectralHash.cpp
327 lines (266 loc) · 8.43 KB
/
IndexIVFSpectralHash.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
/**
* Copyright (c) Facebook, Inc. and its affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
// -*- c++ -*-
#include "IndexIVFSpectralHash.h"
#include <memory>
#include <algorithm>
#include "hamming.h"
#include "utils.h"
#include "FaissAssert.h"
#include "AuxIndexStructures.h"
#include "VectorTransform.h"
namespace faiss {
IndexIVFSpectralHash::IndexIVFSpectralHash (
Index * quantizer, size_t d, size_t nlist,
int nbit, float period):
IndexIVF (quantizer, d, nlist, (nbit + 7) / 8, METRIC_L2),
nbit (nbit), period (period), threshold_type (Thresh_global)
{
FAISS_THROW_IF_NOT (code_size % 4 == 0);
RandomRotationMatrix *rr = new RandomRotationMatrix (d, nbit);
rr->init (1234);
vt = rr;
own_fields = true;
is_trained = false;
}
IndexIVFSpectralHash::IndexIVFSpectralHash():
IndexIVF(), vt(nullptr), own_fields(false),
nbit(0), period(0), threshold_type(Thresh_global)
{}
IndexIVFSpectralHash::~IndexIVFSpectralHash ()
{
if (own_fields) {
delete vt;
}
}
namespace {
float median (size_t n, float *x) {
std::sort(x, x + n);
if (n % 2 == 1) {
return x [n / 2];
} else {
return (x [n / 2 - 1] + x [n / 2]) / 2;
}
}
}
void IndexIVFSpectralHash::train_residual (idx_t n, const float *x)
{
if (!vt->is_trained) {
vt->train (n, x);
}
if (threshold_type == Thresh_global) {
// nothing to do
return;
} else if (threshold_type == Thresh_centroid ||
threshold_type == Thresh_centroid_half) {
// convert all centroids with vt
std::vector<float> centroids (nlist * d);
quantizer->reconstruct_n (0, nlist, centroids.data());
trained.resize(nlist * nbit);
vt->apply_noalloc (nlist, centroids.data(), trained.data());
if (threshold_type == Thresh_centroid_half) {
for (size_t i = 0; i < nlist * nbit; i++) {
trained[i] -= 0.25 * period;
}
}
return;
}
// otherwise train medians
// assign
std::unique_ptr<idx_t []> idx (new idx_t [n]);
quantizer->assign (n, x, idx.get());
std::vector<size_t> sizes(nlist + 1);
for (size_t i = 0; i < n; i++) {
FAISS_THROW_IF_NOT (idx[i] >= 0);
sizes[idx[i]]++;
}
size_t ofs = 0;
for (int j = 0; j < nlist; j++) {
size_t o0 = ofs;
ofs += sizes[j];
sizes[j] = o0;
}
// transform
std::unique_ptr<float []> xt (vt->apply (n, x));
// transpose + reorder
std::unique_ptr<float []> xo (new float[n * nbit]);
for (size_t i = 0; i < n; i++) {
size_t idest = sizes[idx[i]]++;
for (size_t j = 0; j < nbit; j++) {
xo[idest + n * j] = xt[i * nbit + j];
}
}
trained.resize (n * nbit);
// compute medians
#pragma omp for
for (int i = 0; i < nlist; i++) {
size_t i0 = i == 0 ? 0 : sizes[i - 1];
size_t i1 = sizes[i];
for (int j = 0; j < nbit; j++) {
float *xoi = xo.get() + i0 + n * j;
if (i0 == i1) { // nothing to train
trained[i * nbit + j] = 0.0;
} else if (i1 == i0 + 1) {
trained[i * nbit + j] = xoi[0];
} else {
trained[i * nbit + j] = median(i1 - i0, xoi);
}
}
}
}
namespace {
void binarize_with_freq(size_t nbit, float freq,
const float *x, const float *c,
uint8_t *codes)
{
memset (codes, 0, (nbit + 7) / 8);
for (size_t i = 0; i < nbit; i++) {
float xf = (x[i] - c[i]);
int xi = int(floor(xf * freq));
int bit = xi & 1;
codes[i >> 3] |= bit << (i & 7);
}
}
};
void IndexIVFSpectralHash::encode_vectors(idx_t n, const float* x_in,
const idx_t *list_nos,
uint8_t * codes) const
{
FAISS_THROW_IF_NOT (is_trained);
float freq = 2.0 / period;
// transform with vt
std::unique_ptr<float []> x (vt->apply (n, x_in));
#pragma omp parallel
{
std::vector<float> zero (nbit);
// each thread takes care of a subset of lists
#pragma omp for
for (size_t i = 0; i < n; i++) {
long list_no = list_nos [i];
if (list_no >= 0) {
const float *c;
if (threshold_type == Thresh_global) {
c = zero.data();
} else {
c = trained.data() + list_no * nbit;
}
binarize_with_freq (nbit, freq,
x.get() + i * nbit, c,
codes + i * code_size) ;
}
}
}
}
namespace {
template<class HammingComputer>
struct IVFScanner: InvertedListScanner {
// copied from index structure
const IndexIVFSpectralHash *index;
size_t code_size;
size_t nbit;
bool store_pairs;
float period, freq;
std::vector<float> q;
std::vector<float> zero;
std::vector<uint8_t> qcode;
HammingComputer hc;
using idx_t = Index::idx_t;
IVFScanner (const IndexIVFSpectralHash * index,
bool store_pairs):
index (index),
code_size(index->code_size),
nbit(index->nbit),
store_pairs(store_pairs),
period(index->period), freq(2.0 / index->period),
q(nbit), zero(nbit), qcode(code_size),
hc(qcode.data(), code_size)
{
}
void set_query (const float *query) override {
FAISS_THROW_IF_NOT(query);
FAISS_THROW_IF_NOT(q.size() == nbit);
index->vt->apply_noalloc (1, query, q.data());
if (index->threshold_type ==
IndexIVFSpectralHash::Thresh_global) {
binarize_with_freq
(nbit, freq, q.data(), zero.data(), qcode.data());
hc.set (qcode.data(), code_size);
}
}
idx_t list_no;
void set_list (idx_t list_no, float /*coarse_dis*/) override {
this->list_no = list_no;
if (index->threshold_type != IndexIVFSpectralHash::Thresh_global) {
const float *c = index->trained.data() + list_no * nbit;
binarize_with_freq (nbit, freq, q.data(), c, qcode.data());
hc.set (qcode.data(), code_size);
}
}
float distance_to_code (const uint8_t *code) const final {
return hc.hamming (code);
}
size_t scan_codes (size_t list_size,
const uint8_t *codes,
const idx_t *ids,
float *simi, idx_t *idxi,
size_t k) const override
{
size_t nup = 0;
for (size_t j = 0; j < list_size; j++) {
float dis = hc.hamming (codes);
if (dis < simi [0]) {
maxheap_pop (k, simi, idxi);
long id = store_pairs ? (list_no << 32 | j) : ids[j];
maxheap_push (k, simi, idxi, dis, id);
nup++;
}
codes += code_size;
}
return nup;
}
void scan_codes_range (size_t list_size,
const uint8_t *codes,
const idx_t *ids,
float radius,
RangeQueryResult & res) const override
{
for (size_t j = 0; j < list_size; j++) {
float dis = hc.hamming (codes);
if (dis < radius) {
long id = store_pairs ? (list_no << 32 | j) : ids[j];
res.add (dis, id);
}
codes += code_size;
}
}
};
} // anonymous namespace
InvertedListScanner* IndexIVFSpectralHash::get_InvertedListScanner
(bool store_pairs) const
{
switch (code_size) {
#define HANDLE_CODE_SIZE(cs) \
case cs: \
return new IVFScanner<HammingComputer ## cs> (this, store_pairs)
HANDLE_CODE_SIZE(4);
HANDLE_CODE_SIZE(8);
HANDLE_CODE_SIZE(16);
HANDLE_CODE_SIZE(20);
HANDLE_CODE_SIZE(32);
HANDLE_CODE_SIZE(64);
#undef HANDLE_CODE_SIZE
default:
if (code_size % 8 == 0) {
return new IVFScanner<HammingComputerM8>(this, store_pairs);
} else if (code_size % 4 == 0) {
return new IVFScanner<HammingComputerM4>(this, store_pairs);
} else {
FAISS_THROW_MSG("not supported");
}
}
}
} // namespace faiss