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/data/users/matthijs/github_faiss/faiss/PolysemousTraining.cpp
/**
* Copyright (c) 2015-present, Facebook, Inc.
* All rights reserved.
*
* This source code is licensed under the BSD+Patents license found in the
* LICENSE file in the root directory of this source tree.
*/
#include "PolysemousTraining.h"
#include <cstdlib>
#include <cmath>
#include <cstring>
#include <algorithm>
#include "utils.h"
#include "hamming.h"
#include "FaissAssert.h"
/*****************************************
* Mixed PQ / Hamming
******************************************/
namespace faiss {
/****************************************************
* Optimization code
****************************************************/
SimulatedAnnealingParameters::SimulatedAnnealingParameters ()
{
// set some reasonable defaults for the optimization
init_temperature = 0.7;
temperature_decay = pow (0.9, 1/500.);
// reduce by a factor 0.9 every 500 it
n_iter = 500000;
n_redo = 2;
seed = 123;
verbose = 0;
only_bit_flips = false;
init_random = false;
}
// what would the cost update be if iw and jw were swapped?
// default implementation just computes both and computes the difference
double PermutationObjective::cost_update (
const int *perm, int iw, int jw) const
{
double orig_cost = compute_cost (perm);
std::vector<int> perm2 (n);
for (int i = 0; i < n; i++)
perm2[i] = perm[i];
perm2[iw] = perm[jw];
perm2[jw] = perm[iw];
double new_cost = compute_cost (perm2.data());
return new_cost - orig_cost;
}
SimulatedAnnealingOptimizer::SimulatedAnnealingOptimizer (
PermutationObjective *obj,
const SimulatedAnnealingParameters &p):
SimulatedAnnealingParameters (p),
obj (obj),
n(obj->n),
logfile (nullptr)
{
rnd = new RandomGenerator (p.seed);
FAISS_THROW_IF_NOT (n < 100000 && n >=0 );
}
SimulatedAnnealingOptimizer::~SimulatedAnnealingOptimizer ()
{
delete rnd;
}
// run the optimization and return the best result in best_perm
double SimulatedAnnealingOptimizer::run_optimization (int * best_perm)
{
double min_cost = 1e30;
// just do a few runs of the annealing and keep the lowest output cost
for (int it = 0; it < n_redo; it++) {
std::vector<int> perm(n);
for (int i = 0; i < n; i++)
perm[i] = i;
if (init_random) {
for (int i = 0; i < n; i++) {
int j = i + rnd->rand_int (n - i);
std::swap (perm[i], perm[j]);
}
}
float cost = optimize (perm.data());
if (logfile) fprintf (logfile, "\n");
if(verbose > 1) {
printf (" optimization run %d: cost=%g %s\n",
it, cost, cost < min_cost ? "keep" : "");
}
if (cost < min_cost) {
memcpy (best_perm, perm.data(), sizeof(perm[0]) * n);
min_cost = cost;
}
}
return min_cost;
}
// perform the optimization loop, starting from and modifying
// permutation in-place
double SimulatedAnnealingOptimizer::optimize (int *perm)
{
double cost = init_cost = obj->compute_cost (perm);
int log2n = 0;
while (!(n <= (1 << log2n))) log2n++;
double temperature = init_temperature;
int n_swap = 0, n_hot = 0;
for (int it = 0; it < n_iter; it++) {
temperature = temperature * temperature_decay;
int iw, jw;
if (only_bit_flips) {
iw = rnd->rand_int (n);
jw = iw ^ (1 << rnd->rand_int (log2n));
} else {
iw = rnd->rand_int (n);
jw = rnd->rand_int (n - 1);
if (jw == iw) jw++;
}
double delta_cost = obj->cost_update (perm, iw, jw);
if (delta_cost < 0 || rnd->rand_float () < temperature) {
std::swap (perm[iw], perm[jw]);
cost += delta_cost;
n_swap++;
if (delta_cost >= 0) n_hot++;
}
if (verbose > 2 || (verbose > 1 && it % 10000 == 0)) {
printf (" iteration %d cost %g temp %g n_swap %d "
"(%d hot) \r",
it, cost, temperature, n_swap, n_hot);
fflush(stdout);
}
if (logfile) {
fprintf (logfile, "%d %g %g %d %d\n",
it, cost, temperature, n_swap, n_hot);
}
}
if (verbose > 1) printf("\n");
return cost;
}
/****************************************************
* Cost functions: ReproduceDistanceTable
****************************************************/
static inline int hamming_dis (uint64_t a, uint64_t b)
{
return __builtin_popcountl (a ^ b);
}
namespace {
/// optimize permutation to reproduce a distance table with Hamming distances
struct ReproduceWithHammingObjective : PermutationObjective {
int nbits;
double dis_weight_factor;
static double sqr (double x) { return x * x; }
// weihgting of distances: it is more important to reproduce small
// distances well
double dis_weight (double x) const
{
return exp (-dis_weight_factor * x);
}
std::vector<double> target_dis; // wanted distances (size n^2)
std::vector<double> weights; // weights for each distance (size n^2)
// cost = quadratic difference between actual distance and Hamming distance
double compute_cost(const int* perm) const override {
double cost = 0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
double wanted = target_dis[i * n + j];
double w = weights[i * n + j];
double actual = hamming_dis(perm[i], perm[j]);
cost += w * sqr(wanted - actual);
}
}
return cost;
}
// what would the cost update be if iw and jw were swapped?
// computed in O(n) instead of O(n^2) for the full re-computation
double cost_update(const int* perm, int iw, int jw) const override {
double delta_cost = 0;
for (int i = 0; i < n; i++) {
if (i == iw) {
for (int j = 0; j < n; j++) {
double wanted = target_dis[i * n + j], w = weights[i * n + j];
double actual = hamming_dis(perm[i], perm[j]);
delta_cost -= w * sqr(wanted - actual);
double new_actual =
hamming_dis(perm[jw], perm[j == iw ? jw : j == jw ? iw : j]);
delta_cost += w * sqr(wanted - new_actual);
}
} else if (i == jw) {
for (int j = 0; j < n; j++) {
double wanted = target_dis[i * n + j], w = weights[i * n + j];
double actual = hamming_dis(perm[i], perm[j]);
delta_cost -= w * sqr(wanted - actual);
double new_actual =
hamming_dis(perm[iw], perm[j == iw ? jw : j == jw ? iw : j]);
delta_cost += w * sqr(wanted - new_actual);
}
} else {
int j = iw;
{
double wanted = target_dis[i * n + j], w = weights[i * n + j];
double actual = hamming_dis(perm[i], perm[j]);
delta_cost -= w * sqr(wanted - actual);
double new_actual = hamming_dis(perm[i], perm[jw]);
delta_cost += w * sqr(wanted - new_actual);
}
j = jw;
{
double wanted = target_dis[i * n + j], w = weights[i * n + j];
double actual = hamming_dis(perm[i], perm[j]);
delta_cost -= w * sqr(wanted - actual);
double new_actual = hamming_dis(perm[i], perm[iw]);
delta_cost += w * sqr(wanted - new_actual);
}
}
}
return delta_cost;
}
ReproduceWithHammingObjective (
int nbits,
const std::vector<double> & dis_table,
double dis_weight_factor):
nbits (nbits), dis_weight_factor (dis_weight_factor)
{
n = 1 << nbits;
FAISS_THROW_IF_NOT (dis_table.size() == n * n);
set_affine_target_dis (dis_table);
}
void set_affine_target_dis (const std::vector<double> & dis_table)
{
double sum = 0, sum2 = 0;
int n2 = n * n;
for (int i = 0; i < n2; i++) {
sum += dis_table [i];
sum2 += dis_table [i] * dis_table [i];
}
double mean = sum / n2;
double stddev = sqrt(sum2 / n2 - (sum / n2) * (sum / n2));
target_dis.resize (n2);
for (int i = 0; i < n2; i++) {
// the mapping function
double td = (dis_table [i] - mean) / stddev * sqrt(nbits / 4) +
nbits / 2;
target_dis[i] = td;
// compute a weight
weights.push_back (dis_weight (td));
}
}
~ReproduceWithHammingObjective() override {}
};
} // anonymous namespace
// weihgting of distances: it is more important to reproduce small
// distances well
double ReproduceDistancesObjective::dis_weight (double x) const
{
return exp (-dis_weight_factor * x);
}
double ReproduceDistancesObjective::get_source_dis (int i, int j) const
{
return source_dis [i * n + j];
}
// cost = quadratic difference between actual distance and Hamming distance
double ReproduceDistancesObjective::compute_cost (const int *perm) const
{
double cost = 0;
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
double wanted = target_dis [i * n + j];
double w = weights [i * n + j];
double actual = get_source_dis (perm[i], perm[j]);
cost += w * sqr (wanted - actual);
}
}
return cost;
}
// what would the cost update be if iw and jw were swapped?
// computed in O(n) instead of O(n^2) for the full re-computation
double ReproduceDistancesObjective::cost_update(
const int *perm, int iw, int jw) const
{
double delta_cost = 0;
for (int i = 0; i < n; i++) {
if (i == iw) {
for (int j = 0; j < n; j++) {
double wanted = target_dis [i * n + j],
w = weights [i * n + j];
double actual = get_source_dis (perm[i], perm[j]);
delta_cost -= w * sqr (wanted - actual);
double new_actual = get_source_dis (
perm[jw],
perm[j == iw ? jw : j == jw ? iw : j]);
delta_cost += w * sqr (wanted - new_actual);
}
} else if (i == jw) {
for (int j = 0; j < n; j++) {
double wanted = target_dis [i * n + j],
w = weights [i * n + j];
double actual = get_source_dis (perm[i], perm[j]);
delta_cost -= w * sqr (wanted - actual);
double new_actual = get_source_dis (
perm[iw],
perm[j == iw ? jw : j == jw ? iw : j]);
delta_cost += w * sqr (wanted - new_actual);
}
} else {
int j = iw;
{
double wanted = target_dis [i * n + j],
w = weights [i * n + j];
double actual = get_source_dis (perm[i], perm[j]);
delta_cost -= w * sqr (wanted - actual);
double new_actual = get_source_dis (perm[i], perm[jw]);
delta_cost += w * sqr (wanted - new_actual);
}
j = jw;
{
double wanted = target_dis [i * n + j],
w = weights [i * n + j];
double actual = get_source_dis (perm[i], perm[j]);
delta_cost -= w * sqr (wanted - actual);
double new_actual = get_source_dis (perm[i], perm[iw]);
delta_cost += w * sqr (wanted - new_actual);
}
}
}
return delta_cost;
}
ReproduceDistancesObjective::ReproduceDistancesObjective (
int n,
const double *source_dis_in,
const double *target_dis_in,
double dis_weight_factor):
dis_weight_factor (dis_weight_factor),
target_dis (target_dis_in)
{
this->n = n;
set_affine_target_dis (source_dis_in);
}
void ReproduceDistancesObjective::compute_mean_stdev (
const double *tab, size_t n2,
double *mean_out, double *stddev_out)
{
double sum = 0, sum2 = 0;
for (int i = 0; i < n2; i++) {
sum += tab [i];
sum2 += tab [i] * tab [i];
}
double mean = sum / n2;
double stddev = sqrt(sum2 / n2 - (sum / n2) * (sum / n2));
*mean_out = mean;
*stddev_out = stddev;
}
void ReproduceDistancesObjective::set_affine_target_dis (
const double *source_dis_in)
{
int n2 = n * n;
double mean_src, stddev_src;
compute_mean_stdev (source_dis_in, n2, &mean_src, &stddev_src);
double mean_target, stddev_target;
compute_mean_stdev (target_dis, n2, &mean_target, &stddev_target);
printf ("map mean %g std %g -> mean %g std %g\n",
mean_src, stddev_src, mean_target, stddev_target);
source_dis.resize (n2);
weights.resize (n2);
for (int i = 0; i < n2; i++) {
// the mapping function
source_dis[i] = (source_dis_in[i] - mean_src) / stddev_src
* stddev_target + mean_target;
// compute a weight
weights [i] = dis_weight (target_dis[i]);
}
}
/****************************************************
* Cost functions: RankingScore
****************************************************/
/// Maintains a 3D table of elementary costs.
/// Accumulates elements based on Hamming distance comparisons
template <typename Ttab, typename Taccu>
struct Score3Computer: PermutationObjective {
int nc;
// cost matrix of size nc * nc *nc
// n_gt (i,j,k) = count of d_gt(x, y-) < d_gt(x, y+)
// where x has PQ code i, y- PQ code j and y+ PQ code k
std::vector<Ttab> n_gt;
/// the cost is a triple loop on the nc * nc * nc matrix of entries.
///
Taccu compute (const int * perm) const
{
Taccu accu = 0;
const Ttab *p = n_gt.data();
for (int i = 0; i < nc; i++) {
int ip = perm [i];
for (int j = 0; j < nc; j++) {
int jp = perm [j];
for (int k = 0; k < nc; k++) {
int kp = perm [k];
if (hamming_dis (ip, jp) <
hamming_dis (ip, kp)) {
accu += *p; // n_gt [ ( i * nc + j) * nc + k];
}
p++;
}
}
}
return accu;
}
/** cost update if entries iw and jw of the permutation would be
* swapped.
*
* The computation is optimized by avoiding elements in the
* nc*nc*nc cube that are known not to change. For nc=256, this
* reduces the nb of cells to visit to about 6/256 th of the
* cells. Practical speedup is about 8x, and the code is quite
* complex :-/
*/
Taccu compute_update (const int *perm, int iw, int jw) const
{
assert (iw != jw);
if (iw > jw) std::swap (iw, jw);
Taccu accu = 0;
const Ttab * n_gt_i = n_gt.data();
for (int i = 0; i < nc; i++) {
int ip0 = perm [i];
int ip = perm [i == iw ? jw : i == jw ? iw : i];
//accu += update_i (perm, iw, jw, ip0, ip, n_gt_i);
accu += update_i_cross (perm, iw, jw,
ip0, ip, n_gt_i);
if (ip != ip0)
accu += update_i_plane (perm, iw, jw,
ip0, ip, n_gt_i);
n_gt_i += nc * nc;
}
return accu;
}
Taccu update_i (const int *perm, int iw, int jw,
int ip0, int ip, const Ttab * n_gt_i) const
{
Taccu accu = 0;
const Ttab *n_gt_ij = n_gt_i;
for (int j = 0; j < nc; j++) {
int jp0 = perm[j];
int jp = perm [j == iw ? jw : j == jw ? iw : j];
for (int k = 0; k < nc; k++) {
int kp0 = perm [k];
int kp = perm [k == iw ? jw : k == jw ? iw : k];
int ng = n_gt_ij [k];
if (hamming_dis (ip, jp) < hamming_dis (ip, kp)) {
accu += ng;
}
if (hamming_dis (ip0, jp0) < hamming_dis (ip0, kp0)) {
accu -= ng;
}
}
n_gt_ij += nc;
}
return accu;
}
// 2 inner loops for the case ip0 != ip
Taccu update_i_plane (const int *perm, int iw, int jw,
int ip0, int ip, const Ttab * n_gt_i) const
{
Taccu accu = 0;
const Ttab *n_gt_ij = n_gt_i;
for (int j = 0; j < nc; j++) {
if (j != iw && j != jw) {
int jp = perm[j];
for (int k = 0; k < nc; k++) {
if (k != iw && k != jw) {
int kp = perm [k];
Ttab ng = n_gt_ij [k];
if (hamming_dis (ip, jp) < hamming_dis (ip, kp)) {
accu += ng;
}
if (hamming_dis (ip0, jp) < hamming_dis (ip0, kp)) {
accu -= ng;
}
}
}
}
n_gt_ij += nc;
}
return accu;
}
/// used for the 8 cells were the 3 indices are swapped
inline Taccu update_k (const int *perm, int iw, int jw,
int ip0, int ip, int jp0, int jp,
int k,
const Ttab * n_gt_ij) const
{
Taccu accu = 0;
int kp0 = perm [k];
int kp = perm [k == iw ? jw : k == jw ? iw : k];
Ttab ng = n_gt_ij [k];
if (hamming_dis (ip, jp) < hamming_dis (ip, kp)) {
accu += ng;
}
if (hamming_dis (ip0, jp0) < hamming_dis (ip0, kp0)) {
accu -= ng;
}
return accu;
}
/// compute update on a line of k's, where i and j are swapped
Taccu update_j_line (const int *perm, int iw, int jw,
int ip0, int ip, int jp0, int jp,
const Ttab * n_gt_ij) const
{
Taccu accu = 0;
for (int k = 0; k < nc; k++) {
if (k == iw || k == jw) continue;
int kp = perm [k];
Ttab ng = n_gt_ij [k];
if (hamming_dis (ip, jp) < hamming_dis (ip, kp)) {
accu += ng;
}
if (hamming_dis (ip0, jp0) < hamming_dis (ip0, kp)) {
accu -= ng;
}
}
return accu;
}
/// considers the 2 pairs of crossing lines j=iw or jw and k = iw or kw
Taccu update_i_cross (const int *perm, int iw, int jw,
int ip0, int ip, const Ttab * n_gt_i) const
{
Taccu accu = 0;
const Ttab *n_gt_ij = n_gt_i;
for (int j = 0; j < nc; j++) {
int jp0 = perm[j];
int jp = perm [j == iw ? jw : j == jw ? iw : j];
accu += update_k (perm, iw, jw, ip0, ip, jp0, jp, iw, n_gt_ij);
accu += update_k (perm, iw, jw, ip0, ip, jp0, jp, jw, n_gt_ij);
if (jp != jp0)
accu += update_j_line (perm, iw, jw, ip0, ip, jp0, jp, n_gt_ij);
n_gt_ij += nc;
}
return accu;
}
/// PermutationObjective implementeation (just negates the scores
/// for minimization)
double compute_cost(const int* perm) const override {
return -compute(perm);
}
double cost_update(const int* perm, int iw, int jw) const override {
double ret = -compute_update(perm, iw, jw);
return ret;
}
~Score3Computer() override {}
};
struct IndirectSort {
const float *tab;
bool operator () (int a, int b) {return tab[a] < tab[b]; }
};
struct RankingScore2: Score3Computer<float, double> {
int nbits;
int nq, nb;
const uint32_t *qcodes, *bcodes;
const float *gt_distances;
RankingScore2 (int nbits, int nq, int nb,
const uint32_t *qcodes, const uint32_t *bcodes,
const float *gt_distances):
nbits(nbits), nq(nq), nb(nb), qcodes(qcodes),
bcodes(bcodes), gt_distances(gt_distances)
{
n = nc = 1 << nbits;
n_gt.resize (nc * nc * nc);
init_n_gt ();
}
double rank_weight (int r)
{
return 1.0 / (r + 1);
}
/// count nb of i, j in a x b st. i < j
/// a and b should be sorted on input
/// they are the ranks of j and k respectively.
/// specific version for diff-of-rank weighting, cannot optimized
/// with a cumulative table
double accum_gt_weight_diff (const std::vector<int> & a,
const std::vector<int> & b)
{
int nb = b.size(), na = a.size();
double accu = 0;
int j = 0;
for (int i = 0; i < na; i++) {
int ai = a[i];
while (j < nb && ai >= b[j]) j++;
double accu_i = 0;
for (int k = j; k < b.size(); k++)
accu_i += rank_weight (b[k] - ai);
accu += rank_weight (ai) * accu_i;
}
return accu;
}
void init_n_gt ()
{
for (int q = 0; q < nq; q++) {
const float *gtd = gt_distances + q * nb;
const uint32_t *cb = bcodes;// all same codes
float * n_gt_q = & n_gt [qcodes[q] * nc * nc];
printf("init gt for q=%d/%d \r", q, nq); fflush(stdout);
std::vector<int> rankv (nb);
int * ranks = rankv.data();
// elements in each code bin, ordered by rank within each bin
std::vector<std::vector<int> > tab (nc);
{ // build rank table
IndirectSort s = {gtd};
for (int j = 0; j < nb; j++) ranks[j] = j;
std::sort (ranks, ranks + nb, s);
}
for (int rank = 0; rank < nb; rank++) {
int i = ranks [rank];
tab [cb[i]].push_back (rank);
}
// this is very expensive. Any suggestion for improvement
// welcome.
for (int i = 0; i < nc; i++) {
std::vector<int> & di = tab[i];
for (int j = 0; j < nc; j++) {
std::vector<int> & dj = tab[j];
n_gt_q [i * nc + j] += accum_gt_weight_diff (di, dj);
}
}
}
}
};
/*****************************************
* PolysemousTraining
******************************************/
PolysemousTraining::PolysemousTraining ()
{
optimization_type = OT_ReproduceDistances_affine;
ntrain_permutation = 0;
dis_weight_factor = log(2);
}
void PolysemousTraining::optimize_reproduce_distances (
ProductQuantizer &pq) const
{
int dsub = pq.dsub;
int n = pq.ksub;
int nbits = pq.nbits;
#pragma omp parallel for
for (int m = 0; m < pq.M; m++) {
std::vector<double> dis_table;
// printf ("Optimizing quantizer %d\n", m);
float * centroids = pq.get_centroids (m, 0);
for (int i = 0; i < n; i++) {
for (int j = 0; j < n; j++) {
dis_table.push_back (fvec_L2sqr (centroids + i * dsub,
centroids + j * dsub,
dsub));
}
}
std::vector<int> perm (n);
ReproduceWithHammingObjective obj (
nbits, dis_table,
dis_weight_factor);
SimulatedAnnealingOptimizer optim (&obj, *this);
if (log_pattern.size()) {
char fname[256];
snprintf (fname, 256, log_pattern.c_str(), m);
printf ("opening log file %s\n", fname);
optim.logfile = fopen (fname, "w");
FAISS_THROW_IF_NOT_MSG (optim.logfile, "could not open logfile");
}
double final_cost = optim.run_optimization (perm.data());
if (verbose > 0) {
printf ("SimulatedAnnealingOptimizer for m=%d: %g -> %g\n",
m, optim.init_cost, final_cost);
}
if (log_pattern.size()) fclose (optim.logfile);
std::vector<float> centroids_copy;
for (int i = 0; i < dsub * n; i++)
centroids_copy.push_back (centroids[i]);
for (int i = 0; i < n; i++)
memcpy (centroids + perm[i] * dsub,
centroids_copy.data() + i * dsub,
dsub * sizeof(centroids[0]));
}
}
void PolysemousTraining::optimize_ranking (
ProductQuantizer &pq, size_t n, const float *x) const
{
int dsub = pq.dsub;
int nbits = pq.nbits;
std::vector<uint8_t> all_codes (pq.code_size * n);
pq.compute_codes (x, all_codes.data(), n);
FAISS_THROW_IF_NOT (pq.byte_per_idx == 1);
if (n == 0)
pq.compute_sdc_table ();
#pragma omp parallel for
for (int m = 0; m < pq.M; m++) {
size_t nq, nb;
std::vector <uint32_t> codes; // query codes, then db codes
std::vector <float> gt_distances; // nq * nb matrix of distances
if (n > 0) {
std::vector<float> xtrain (n * dsub);
for (int i = 0; i < n; i++)
memcpy (xtrain.data() + i * dsub,
x + i * pq.d + m * dsub,
sizeof(float) * dsub);
codes.resize (n);
for (int i = 0; i < n; i++)
codes [i] = all_codes [i * pq.code_size + m];
nq = n / 4; nb = n - nq;
const float *xq = xtrain.data();
const float *xb = xq + nq * dsub;
gt_distances.resize (nq * nb);
pairwise_L2sqr (dsub,
nq, xq,
nb, xb,
gt_distances.data());
} else {
nq = nb = pq.ksub;
codes.resize (2 * nq);
for (int i = 0; i < nq; i++)
codes[i] = codes [i + nq] = i;
gt_distances.resize (nq * nb);
memcpy (gt_distances.data (),
pq.sdc_table.data () + m * nq * nb,
sizeof (float) * nq * nb);
}
double t0 = getmillisecs ();
PermutationObjective *obj = new RankingScore2 (
nbits, nq, nb,
codes.data(), codes.data() + nq,
gt_distances.data ());
ScopeDeleter1<PermutationObjective> del (obj);
if (verbose > 0) {
printf(" m=%d, nq=%ld, nb=%ld, intialize RankingScore "
"in %.3f ms\n",
m, nq, nb, getmillisecs () - t0);
}
SimulatedAnnealingOptimizer optim (obj, *this);
if (log_pattern.size()) {
char fname[256];
snprintf (fname, 256, log_pattern.c_str(), m);
printf ("opening log file %s\n", fname);
optim.logfile = fopen (fname, "w");
FAISS_THROW_IF_NOT_FMT (optim.logfile,
"could not open logfile %s", fname);
}
std::vector<int> perm (pq.ksub);
double final_cost = optim.run_optimization (perm.data());
printf ("SimulatedAnnealingOptimizer for m=%d: %g -> %g\n",
m, optim.init_cost, final_cost);
if (log_pattern.size()) fclose (optim.logfile);
float * centroids = pq.get_centroids (m, 0);
std::vector<float> centroids_copy;
for (int i = 0; i < dsub * pq.ksub; i++)
centroids_copy.push_back (centroids[i]);
for (int i = 0; i < pq.ksub; i++)
memcpy (centroids + perm[i] * dsub,
centroids_copy.data() + i * dsub,
dsub * sizeof(centroids[0]));
}
}
void PolysemousTraining::optimize_pq_for_hamming (ProductQuantizer &pq,
size_t n, const float *x) const
{
if (optimization_type == OT_None) {
} else if (optimization_type == OT_ReproduceDistances_affine) {
optimize_reproduce_distances (pq);
} else {
optimize_ranking (pq, n, x);
}
pq.compute_sdc_table ();
}
} // namespace faiss
faiss::RandomGenerator
random generator that can be used in multithreaded contexts
Definition: utils.h:48
faiss::ProductQuantizer::nbits
size_t nbits
number of bits per quantization index
Definition: ProductQuantizer.h:29
faiss::fvec_L2sqr
float fvec_L2sqr(const float *x, const float *y, size_t d)
Squared L2 distance between two vectors.
Definition: utils.cpp:574
faiss::ProductQuantizer::byte_per_idx
size_t byte_per_idx
nb bytes per code component (1 or 2)
Definition: ProductQuantizer.h:33
faiss::Score3Computer::compute_update
Taccu compute_update(const int *perm, int iw, int jw) const
Definition: PolysemousTraining.cpp:486
faiss::ProductQuantizer::sdc_table
std::vector< float > sdc_table
Symmetric Distance Table.
Definition: ProductQuantizer.h:159
faiss::SimulatedAnnealingOptimizer::SimulatedAnnealingOptimizer
SimulatedAnnealingOptimizer(PermutationObjective *obj, const SimulatedAnnealingParameters &p)
logs values of the cost function
Definition: PolysemousTraining.cpp:67
faiss::ScopeDeleter1
Definition: FaissException.h:52
faiss::SimulatedAnnealingOptimizer::n
int n
size of the permutation
Definition: PolysemousTraining.h:101
faiss::Score3Computer::compute
Taccu compute(const int *perm) const
Definition: PolysemousTraining.cpp:455
faiss::ProductQuantizer::dsub
size_t dsub
dimensionality of each subvector
Definition: ProductQuantizer.h:32
faiss::ProductQuantizer::compute_codes
void compute_codes(const float *x, uint8_t *codes, size_t n) const
same as compute_code for several vectors
Definition: ProductQuantizer.cpp:386
faiss::Score3Computer::update_j_line
Taccu update_j_line(const int *perm, int iw, int jw, int ip0, int ip, int jp0, int jp, const Ttab *n_gt_ij) const
compute update on a line of k&#39;s, where i and j are swapped
Definition: PolysemousTraining.cpp:585
faiss::ReproduceDistancesObjective::target_dis
const double * target_dis
wanted distances (size n^2)
Definition: PolysemousTraining.h:69
faiss::ProductQuantizer::code_size
size_t code_size
byte per indexed vector
Definition: ProductQuantizer.h:34
faiss::SimulatedAnnealingOptimizer::init_cost
double init_cost
remember intial cost of optimization
Definition: PolysemousTraining.h:109
faiss::RandomGenerator::rand_int
int rand_int()
random 31-bit positive integer
faiss::ProductQuantizer::ksub
size_t ksub
number of centroids for each subquantizer
Definition: ProductQuantizer.h:35
faiss::PolysemousTraining::optimize_ranking
void optimize_ranking(ProductQuantizer &pq, size_t n, const float *x) const
called by optimize_pq_for_hamming
Definition: PolysemousTraining.cpp:826
faiss::pairwise_L2sqr
void pairwise_L2sqr(long d, long nq, const float *xq, long nb, const float *xb, float *dis, long ldq, long ldb, long ldd)
Definition: utils.cpp:1344
faiss::Score3Computer::compute_cost
double compute_cost(const int *perm) const override
Definition: PolysemousTraining.cpp:631
faiss::Score3Computer
Definition: PolysemousTraining.cpp:443
faiss::getmillisecs
double getmillisecs()
ms elapsed since some arbitrary epoch
Definition: utils.cpp:74
faiss::ReproduceDistancesObjective::weights
std::vector< double > weights
weights for each distance (size n^2)
Definition: PolysemousTraining.h:70
faiss::RankingScore2::accum_gt_weight_diff
double accum_gt_weight_diff(const std::vector< int > &a, const std::vector< int > &b)
Definition: PolysemousTraining.cpp:682
faiss::SimulatedAnnealingParameters
parameters used for the simulated annealing method
Definition: PolysemousTraining.h:25
faiss::IndirectSort
Definition: PolysemousTraining.cpp:647
faiss::Score3Computer::update_i_cross
Taccu update_i_cross(const int *perm, int iw, int jw, int ip0, int ip, const Ttab *n_gt_i) const
considers the 2 pairs of crossing lines j=iw or jw and k = iw or kw
Definition: PolysemousTraining.cpp:606
faiss::ProductQuantizer::M
size_t M
number of subquantizers
Definition: ProductQuantizer.h:28
faiss::PolysemousTraining::OT_ReproduceDistances_affine
default
Definition: PolysemousTraining.h:129
faiss::PermutationObjective
abstract class for the loss function
Definition: PolysemousTraining.h:44
faiss::ProductQuantizer
Definition: ProductQuantizer.h:25
faiss::Score3Computer::update_k
Taccu update_k(const int *perm, int iw, int jw, int ip0, int ip, int jp0, int jp, int k, const Ttab *n_gt_ij) const
used for the 8 cells were the 3 indices are swapped
Definition: PolysemousTraining.cpp:566
faiss::ReproduceDistancesObjective::source_dis
std::vector< double > source_dis
&quot;real&quot; corrected distances (size n^2)
Definition: PolysemousTraining.h:68
faiss::ProductQuantizer::get_centroids
float * get_centroids(size_t m, size_t i)
return the centroids associated with subvector m
Definition: ProductQuantizer.h:59
faiss::PolysemousTraining::optimize_reproduce_distances
void optimize_reproduce_distances(ProductQuantizer &pq) const
called by optimize_pq_for_hamming
Definition: PolysemousTraining.cpp:763
faiss::PolysemousTraining::optimize_pq_for_hamming
void optimize_pq_for_hamming(ProductQuantizer &pq, size_t n, const float *x) const
Definition: PolysemousTraining.cpp:933
faiss::ProductQuantizer::d
size_t d
size of the input vectors
Definition: ProductQuantizer.h:27
faiss::RankingScore2
Definition: PolysemousTraining.cpp:654
faiss::SimulatedAnnealingOptimizer
Simulated annealing optimization algorithm for permutations.
Definition: PolysemousTraining.h:98