mirror of
https://github.com/opencv/opencv.git
synced 2026-07-21 19:33:03 +04:00
Tonu Samuel
a015333f04
SVM::predict is dominated by the per-feature kernel reduction over the support vectors. Vectorize the four reduction kernels in svm.cpp with universal intrinsics (two independent accumulators + scalar tail): calc_non_rbf_base (dot product), calc_rbf (squared distance), calc_intersec (min-sum) and calc_chi2. Same approach as the KNN findNearest reduction in #29380. Adds modules/ml/perf/perf_svm.cpp covering the RBF/POLY/INTER/CHI2 kernels.
49 lines
1.7 KiB
C++
49 lines
1.7 KiB
C++
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html.
|
|
#include "perf_precomp.hpp"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
// SVM::predict is dominated by the per-feature kernel reduction over the support
|
|
// vectors: calc_non_rbf_base (LINEAR/POLY/SIGMOID dot product), calc_rbf
|
|
// (squared distance), calc_intersec (min-sum) and calc_chi2. Train data is kept
|
|
// non-negative so the INTER and CHI2 kernels stay in their valid domain.
|
|
typedef TestBaseWithParam< tuple<int, int, int> > SVMPredict; // (samples, dims, kernelType)
|
|
|
|
PERF_TEST_P(SVMPredict, kernels, testing::Combine(
|
|
testing::Values(1500),
|
|
testing::Values(512),
|
|
testing::Values((int)SVM::RBF, (int)SVM::POLY, (int)SVM::INTER, (int)SVM::CHI2)))
|
|
{
|
|
const int nsamples = get<0>(GetParam());
|
|
const int dims = get<1>(GetParam());
|
|
const int kernel = get<2>(GetParam());
|
|
const int nquery = 1000;
|
|
|
|
Mat train(nsamples, dims, CV_32F), query(nquery, dims, CV_32F);
|
|
Mat responses(nsamples, 1, CV_32S);
|
|
RNG& rng = theRNG();
|
|
rng.fill(train, RNG::UNIFORM, 0.f, 1.f);
|
|
rng.fill(query, RNG::UNIFORM, 0.f, 1.f);
|
|
for (int i = 0; i < nsamples; i++)
|
|
responses.at<int>(i) = i & 1;
|
|
|
|
Ptr<SVM> svm = SVM::create();
|
|
svm->setType(SVM::C_SVC);
|
|
svm->setKernel(kernel);
|
|
svm->setC(1);
|
|
svm->setGamma(0.1);
|
|
svm->setDegree(3); // POLY
|
|
svm->setCoef0(1); // POLY
|
|
svm->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 1000, 1e-3));
|
|
svm->train(train, ROW_SAMPLE, responses);
|
|
|
|
Mat results;
|
|
TEST_CYCLE() svm->predict(query, results);
|
|
|
|
SANITY_CHECK_NOTHING();
|
|
}
|
|
|
|
}} // namespace
|