-
Notifications
You must be signed in to change notification settings - Fork 144
/
knnbench.cpp
396 lines (347 loc) · 14.1 KB
/
knnbench.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
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
/*
Copyright (c) 2010--2011, Stephane Magnenat, ASL, ETHZ, Switzerland
You can contact the author at <stephane at magnenat dot net>
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
* Neither the name of the <organization> nor the
names of its contributors may be used to endorse or promote products
derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL ETH-ASL BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
// currently disable FLANN
#undef HAVE_FLANN
#include "nabo/nabo.h"
#include "experimental/nabo_experimental.h"
#include "helpers.h"
#ifdef HAVE_ANN
#include "ANN.h"
#endif // HAVE_ANN
#ifdef HAVE_FLANN
#include "flann/flann.hpp"
#endif // HAVE_FLANN
#include <iostream>
#include <fstream>
#include <stdexcept>
using namespace std;
using namespace Nabo;
typedef Nabo::NearestNeighbourSearch<double>::Matrix MatrixD;
typedef Nabo::NearestNeighbourSearch<double>::Vector VectorD;
typedef Nabo::NearestNeighbourSearch<double>::Index IndexD;
typedef Nabo::NearestNeighbourSearch<double>::IndexVector IndexVectorD;
typedef Nabo::NearestNeighbourSearch<float>::Matrix MatrixF;
typedef Nabo::NearestNeighbourSearch<float>::Vector VectorF;
typedef Nabo::NearestNeighbourSearch<float>::Index IndexF;
typedef Nabo::NearestNeighbourSearch<float>::IndexVector IndexVectorF;
typedef Nabo::BruteForceSearch<double> BFSD;
typedef Nabo::BruteForceSearch<float> BFSF;
// typedef Nabo::KDTreeBalancedPtInNodesPQ<double> KDTD1;
// typedef Nabo::KDTreeBalancedPtInNodesStack<double> KDTD2;
// struct KDTD3: public Nabo::KDTreeBalancedPtInLeavesStack<double>
// {
// KDTD3(const Matrix& cloud):
// Nabo::KDTreeBalancedPtInLeavesStack<double>(cloud, true)
// {}
// };
// struct KDTD4: public Nabo::KDTreeBalancedPtInLeavesStack<double>
// {
// KDTD4(const Matrix& cloud):
// Nabo::KDTreeBalancedPtInLeavesStack<double>(cloud, false)
// {}
// };
// typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStack<double,IndexHeapSTL<int,double>> KDTD5A;
// typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStack<double,IndexHeapBruteForceVector<int,double>> KDTD5B;
// typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapBruteForceVector<int,double>> KDTD5OB;
// typedef Nabo::KDTreeUnbalancedPtInLeavesImplicitBoundsStackOpt<double,IndexHeapSTL<int,double>> KDTD5OA;
// typedef Nabo::KDTreeUnbalancedPtInLeavesExplicitBoundsStack<double> KDTD6;
struct BenchResult
{
double creationDuration;
double executionDuration;
double visitCount;
double totalCount;
BenchResult():
creationDuration(0),
executionDuration(0),
visitCount(0),
totalCount(0)
{}
void operator +=(const BenchResult& that)
{
creationDuration += that.creationDuration;
executionDuration += that.executionDuration;
visitCount += that.visitCount;
totalCount += that.totalCount;
}
void operator /=(const double factor)
{
creationDuration /= factor;
executionDuration /= factor;
visitCount /= factor;
totalCount /= factor;
}
};
typedef vector<BenchResult> BenchResults;
// template<typename T>
// BenchResult doBench(const Matrix& d, const Matrix& q, const Index K, const int itCount)
// {
// BenchResult result;
// boost::timer t;
// T nns(d);
// result.creationDuration = t.elapsed();
//
// t.restart();
// nns.knnM(q, K, 0, 0);
// result.executionDuration = t.elapsed();
//
// result.visitCount = double(nns.getStatistics().totalVisitCount);
// result.totalCount = double(itCount) * double(d.cols());
//
// return result;
// }
template<typename T>
BenchResult doBenchType(const typename NearestNeighbourSearch<T>::SearchType type,
const unsigned creationOptionFlags,
const typename NearestNeighbourSearch<T>::Matrix& d,
const typename NearestNeighbourSearch<T>::Matrix& q,
const int K,
const int itCount,
const int searchCount)
{
typedef NearestNeighbourSearch<T> nnsT;
typedef typename NearestNeighbourSearch<T>::Matrix Matrix;
typedef typename NearestNeighbourSearch<T>::IndexMatrix IndexMatrix;
BenchResult result;
boost::timer t;
nnsT* nns(nnsT::create(d, d.rows(), type, creationOptionFlags));
result.creationDuration = t.elapsed();
for (int s = 0; s < searchCount; ++s)
{
t.restart();
IndexMatrix indices(K, q.cols());
Matrix dists2(K, q.cols());
const unsigned long visitCount = nns->knn(q, indices, dists2, K, 0, 0);
result.executionDuration += t.elapsed();
result.visitCount += double(visitCount);
}
result.executionDuration /= double(searchCount);
result.visitCount /= double(searchCount);
delete nns;
result.totalCount = double(q.cols()) * double(d.cols());
return result;
}
#ifdef HAVE_ANN
BenchResult doBenchANNStack(const MatrixD& d, const MatrixD& q, const int K, const int itCount, const int searchCount)
{
BenchResult result;
boost::timer t;
const int ptCount(d.cols());
const double **pa = new const double *[d.cols()];
for (int i = 0; i < ptCount; ++i)
pa[i] = &d.coeff(0, i);
ANNkd_tree* ann_kdt = new ANNkd_tree(const_cast<double**>(pa), ptCount, d.rows(), 8);
result.creationDuration = t.elapsed();
for (int s = 0; s < searchCount; ++s)
{
t.restart();
ANNidx nnIdx[K];
ANNdist dists[K];
for (int i = 0; i < itCount; ++i)
{
const VectorD& tq(q.col(i));
ANNpoint queryPt(const_cast<double*>(&tq.coeff(0)));
ann_kdt->annkSearch( // search
queryPt, // query point
K, // number of near neighbours
nnIdx, // nearest neighbours (returned)
dists, // distance (returned)
0); // error bound
}
result.executionDuration += t.elapsed();
}
result.executionDuration /= double(searchCount);
return result;
}
BenchResult doBenchANNPriority(const MatrixD& d, const MatrixD& q, const int K, const int itCount, const int searchCount)
{
BenchResult result;
boost::timer t;
const int ptCount(d.cols());
const double **pa = new const double *[d.cols()];
for (int i = 0; i < ptCount; ++i)
pa[i] = &d.coeff(0, i);
ANNkd_tree* ann_kdt = new ANNkd_tree(const_cast<double**>(pa), ptCount, d.rows(), 8);
result.creationDuration = t.elapsed();
for (int s = 0; s < searchCount; ++s)
{
t.restart();
ANNidx nnIdx[K];
ANNdist dists[K];
for (int i = 0; i < itCount; ++i)
{
const VectorD& tq(q.col(i));
ANNpoint queryPt(const_cast<double*>(&tq.coeff(0)));
ann_kdt->annkPriSearch( // search
queryPt, // query point
K, // number of near neighbours
nnIdx, // nearest neighbours (returned)
dists, // distance (returned)
0); // error bound
}
result.executionDuration += t.elapsed();
}
result.executionDuration /= double(searchCount);
return result;
}
#endif // HAVE_ANN
#ifdef HAVE_FLANN
template<typename T>
BenchResult doBenchFLANN(const Matrix& d, const Matrix& q, const Index K, const int itCount)
{
BenchResult result;
const int dimCount(d.rows());
const int dPtCount(d.cols());
const int qPtCount(itCount);
flann::Matrix<T> dataset(new T[dPtCount*dimCount], dPtCount, dimCount);
for (int point = 0; point < dPtCount; ++point)
for (int dim = 0; dim < dimCount; ++dim)
dataset[point][dim] = d(dim, point);
flann::Matrix<T> query(new T[qPtCount*dimCount], qPtCount, dimCount);
for (int point = 0; point < qPtCount; ++point)
for (int dim = 0; dim < dimCount; ++dim)
query[point][dim] = q(dim, point);
flann::Matrix<int> indices(new int[query.rows*K], query.rows, K);
flann::Matrix<float> dists(new float[query.rows*K], query.rows, K);
// construct an randomized kd-tree index using 4 kd-trees
boost::timer t;
flann::Index<T> index(dataset, flann::KDTreeIndexParams(4) /*flann::AutotunedIndexParams(0.9)*/); // exact search
index.buildIndex();
result.creationDuration = t.elapsed();
t.restart();
// do a knn search, using 128 checks
index.knnSearch(query, indices, dists, int(K), flann::SearchParams(128)); // last parameter ignored because of autotuned
result.executionDuration = t.elapsed();
dataset.free();
query.free();
indices.free();
dists.free();
return result;
}
#endif // HAVE_FLANN
int main(int argc, char* argv[])
{
if (argc != 6)
{
cerr << "Usage " << argv[0] << " DATA K METHOD RUN_COUNT SEARCH_COUNT" << endl;
return 1;
}
const MatrixD dD(load<double>(argv[1]));
const MatrixF dF(load<float>(argv[1]));
const int K(atoi(argv[2]));
const int method(atoi(argv[3]));
const int itCount(method >= 0 ? method : dD.cols() * 2);
const int runCount(atoi(argv[4]));
const int searchCount(atoi(argv[5]));
// compare KDTree with brute force search
if (K >= dD.cols())
{
cerr << "Requested more nearest neighbour than points in the data set" << endl;
return 2;
}
// create queries
MatrixD qD(createQuery<double>(dD, itCount, method));
MatrixF qF(createQuery<float>(dF, itCount, method));
const char* benchLabels[] =
{
//doBench<KDTD1>("Nabo, pt in nodes, priority, balance variance",
//doBench<KDTD2>("Nabo, pt in nodes, stack, balance variance",
//doBench<KDTD3>("Nabo, balanced, stack, pt in leaves only, balance variance",
//"Nabo, balanced, stack, pt in leaves only, balance cell aspect ratio",
//"Nabo, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap",
//"Nabo, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap",
"Nabo, double, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap, opt",
"Nabo, double, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt",
"Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, brute-force vector heap, opt",
"Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt",
"Nabo, float, unbalanced, stack, pt in leaves only, implicit bounds, ANN_KD_SL_MIDPT, STL heap, opt, stats",
#ifdef HAVE_OPENCL
"Nabo, float, OpenCL, GPU, balanced, points in nodes, stack, implicit bounds, balance aspect ratio, stats",
"Nabo, float, OpenCL, GPU, balanced, points in leaves, stack, implicit bounds, balance aspect ratio, stats",
//"Nabo, float, OpenCL, GPU, brute force",
#endif // HAVE_OPENCL
//"Nabo, unbalanced, points in leaves, stack, explicit bounds, ANN_KD_SL_MIDPT",
#ifdef HAVE_ANN
"ANN stack, double",
//"ANN priority",
#endif // HAVE_ANN
#ifdef HAVE_FLANN
"FLANN, double",
"FLANN, float",
#endif // HAVE_FLANN
};
// do bench themselves, accumulate over several times
size_t benchCount(sizeof(benchLabels) / sizeof(const char *));
cout << "Doing " << benchCount << " different benches " << runCount << " times, with " << searchCount << " query per run" << endl;
BenchResults results(benchCount);
for (int run = 0; run < runCount; ++run)
{
size_t i = 0;
//results.at(i++) += doBench<KDTD1>(d, q, K, itCount, searchCount);
//results.at(i++) += doBench<KDTD2>(d, q, K, itCount, searchCount);
//results.at(i++) += doBench<KDTD3>(d, q, K, itCount, searchCount);
//results.at(i++) += doBench<KDTD4>(d, q, K, itCount, searchCount);
//results.at(i++) += doBench<KDTD5A>(d, q, K, itCount, searchCount);
//results.at(i++) += doBench<KDTD5B>(d, q, K, itCount, searchCount);
results.at(i++) += doBenchType<double>(NNSearchD::KDTREE_LINEAR_HEAP, 0, dD, qD, K, itCount, searchCount);
results.at(i++) += doBenchType<double>(NNSearchD::KDTREE_TREE_HEAP, 0, dD, qD, K, itCount, searchCount);
results.at(i++) += doBenchType<float>(NNSearchF::KDTREE_LINEAR_HEAP, 0, dF, qF, K, itCount, searchCount);
results.at(i++) += doBenchType<float>(NNSearchF::KDTREE_TREE_HEAP, 0, dF, qF, K, itCount, searchCount);
results.at(i++) += doBenchType<float>(NNSearchF::KDTREE_TREE_HEAP, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount);
#ifdef HAVE_OPENCL
results.at(i++) += doBenchType<float>(NNSearchF::KDTREE_CL_PT_IN_NODES, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount);
results.at(i++) += doBenchType<float>(NNSearchF::KDTREE_CL_PT_IN_LEAVES, NNSearchF::TOUCH_STATISTICS, dF, qF, K, itCount, searchCount);
//results.at(i++) += doBenchType<float>(NNSearchF::BRUTE_FORCE_CL, dF, qF, K, itCount, searchCount);
#endif // HAVE_OPENCL
#ifdef HAVE_ANN
results.at(i++) += doBenchANNStack(dD, qD, K, itCount, searchCount);
//results.at(i++) += doBenchANNPriority(d, q, K, itCount);
#endif // HAVE_ANN
#ifdef HAVE_FLANN
results.at(i++) += doBenchFLANN<double>(dD, qD, K, itCount, searchCount);
results.at(i++) += doBenchFLANN<float>(dF, qF, K, itCount, searchCount);
#endif // HAVE_FLANN
}
// print results
cout << "Showing average over " << runCount << " runs\n\n";
for (size_t i = 0; i < benchCount; ++i)
{
results[i] /= double(runCount);
cout << "Method " << benchLabels[i] << ":\n";
cout << " creation duration: " << results[i].creationDuration << "\n";
cout << " execution duration: " << results[i].executionDuration << "\n";
if (results[i].totalCount != 0)
{
cout << " visit count: " << results[i].visitCount << "\n";
cout << " total count: " << results[i].totalCount << "\n";
cout << " precentage visit: " << (results[i].visitCount * 100.) / results[i].totalCount << "\n";
}
else
cout << " no stats for visits\n";
cout << endl;
}
return 0;
}