Added knearest neighbor of OpenCL version.
It includes the accuracy/performance test and the implementation of KNN.
This commit is contained in:
Jin Ma
parent
06c33df307
commit
1bfe39f485
109
modules/ocl/perf/perf_ml.cpp
Normal file
109
modules/ocl/perf/perf_ml.cpp
Normal file
@ -0,0 +1,109 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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||||
// Third party copyrights are property of their respective owners.
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||||
//
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// @Authors
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// Jin Ma, jin@multicorewareinc.com
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// Xiaopeng Fu, fuxiaopeng2222@163.com
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other oclMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not 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 the Intel Corporation or contributors 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
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "perf_precomp.hpp"
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using namespace perf;
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using namespace std;
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using namespace cv::ocl;
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using namespace cv;
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using std::tr1::tuple;
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using std::tr1::get;
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////////////////////////////////// K-NEAREST NEIGHBOR ////////////////////////////////////
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static void genData(Mat& trainData, Size size, Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
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{
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trainData.create(size, CV_32FC1);
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randu(trainData, 1.0, 100.0);
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if(nClasses != 0)
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{
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trainLabel.create(size.height, 1, CV_8UC1);
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randu(trainLabel, 0, nClasses - 1);
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trainLabel.convertTo(trainLabel, CV_32FC1);
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}
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}
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typedef tuple<int> KNNParamType;
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typedef TestBaseWithParam<KNNParamType> KNNFixture;
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PERF_TEST_P(KNNFixture, KNN,
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testing::Values(1000, 2000, 4000))
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{
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KNNParamType params = GetParam();
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const int rows = get<0>(params);
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int columns = 100;
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int k = rows/250;
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Mat trainData, trainLabels;
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Size size(columns, rows);
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genData(trainData, size, trainLabels, 3);
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Mat testData;
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genData(testData, size);
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Mat best_label;
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if(RUN_PLAIN_IMPL)
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{
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TEST_CYCLE()
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{
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CvKNearest knn_cpu;
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knn_cpu.train(trainData, trainLabels);
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knn_cpu.find_nearest(testData, k, &best_label);
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}
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}else if(RUN_OCL_IMPL)
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{
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cv::ocl::oclMat best_label_ocl;
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cv::ocl::oclMat testdata;
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testdata.upload(testData);
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OCL_TEST_CYCLE()
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{
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cv::ocl::KNearestNeighbour knn_ocl;
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knn_ocl.train(trainData, trainLabels);
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knn_ocl.find_nearest(testdata, k, best_label_ocl);
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}
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best_label_ocl.download(best_label);
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}else
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OCL_PERF_ELSE
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SANITY_CHECK(best_label);
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}
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163
modules/ocl/src/knearest.cpp
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163
modules/ocl/src/knearest.cpp
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@ -0,0 +1,163 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Jin Ma, jin@multicorewareinc.com
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//
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// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other oclMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not 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 the Intel Corporation or contributors 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
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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using namespace cv;
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using namespace cv::ocl;
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namespace cv
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{
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namespace ocl
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{
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extern const char* knearest;//knearest
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}
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}
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KNearestNeighbour::KNearestNeighbour()
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{
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clear();
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}
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KNearestNeighbour::~KNearestNeighbour()
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{
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clear();
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}
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KNearestNeighbour::KNearestNeighbour(const Mat& train_data, const Mat& responses,
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const Mat& sample_idx, bool is_regression, int max_k)
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{
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max_k = max_k;
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CvKNearest::train(train_data, responses, sample_idx, is_regression, max_k);
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}
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void KNearestNeighbour::clear()
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{
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CvKNearest::clear();
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}
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bool KNearestNeighbour::train(const Mat& trainData, Mat& labels, Mat& sampleIdx,
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bool isRegression, int _max_k, bool updateBase)
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{
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max_k = _max_k;
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bool cv_knn_train = CvKNearest::train(trainData, labels, sampleIdx, isRegression, max_k, updateBase);
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CvVectors* s = CvKNearest::samples;
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cv::Mat samples_mat(s->count, CvKNearest::var_count + 1, s->type);
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float* s1 = (float*)(s + 1);
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for(int i = 0; i < s->count; i++)
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{
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float* t1 = s->data.fl[i];
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for(int j = 0; j < CvKNearest::var_count; j++)
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{
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Point pos(j, i);
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samples_mat.at<float>(pos) = t1[j];
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}
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Point pos_label(CvKNearest::var_count, i);
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samples_mat.at<float>(pos_label) = s1[i];
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}
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samples_ocl = samples_mat;
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return cv_knn_train;
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}
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void KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lables)
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{
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CV_Assert(!samples_ocl.empty());
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lables.create(samples.rows, 1, CV_32FC1);
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CV_Assert(samples.cols == CvKNearest::var_count);
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CV_Assert(samples.type() == CV_32FC1);
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CV_Assert(k >= 1 && k <= max_k);
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int k1 = KNearest::get_sample_count();
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k1 = MIN( k1, k );
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String kernel_name = "knn_find_nearest";
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cl_ulong local_memory_size = queryLocalMemInfo();
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int nThreads = local_memory_size / (2 * k * 4);
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if(nThreads >= 256)
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nThreads = 256;
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int smem_size = nThreads * k * 4 * 2;
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size_t local_thread[] = {1, nThreads, 1};
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size_t global_thread[] = {1, samples.rows, 1};
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char build_option[50];
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if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE))
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{
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sprintf(build_option, " ");
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}else
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sprintf(build_option, "-D DOUBLE_SUPPORT");
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std::vector< std::pair<size_t, const void*> > args;
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int samples_ocl_step = samples_ocl.step/samples_ocl.elemSize();
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int samples_step = samples.step/samples.elemSize();
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int lables_step = lables.step/lables.elemSize();
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int _regression = 0;
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if(CvKNearest::regression)
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_regression = 1;
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args.push_back(make_pair(sizeof(cl_mem), (void*)&samples.data));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples.rows));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples.cols));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples_step));
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args.push_back(make_pair(sizeof(cl_int), (void*)&k));
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args.push_back(make_pair(sizeof(cl_mem), (void*)&samples_ocl.data));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.rows));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl_step));
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args.push_back(make_pair(sizeof(cl_mem), (void*)&lables.data));
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args.push_back(make_pair(sizeof(cl_int), (void*)&lables_step));
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args.push_back(make_pair(sizeof(cl_int), (void*)&_regression));
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args.push_back(make_pair(sizeof(cl_int), (void*)&k1));
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args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.cols));
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args.push_back(make_pair(sizeof(cl_int), (void*)&nThreads));
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args.push_back(make_pair(smem_size, (void*)NULL));
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openCLExecuteKernel(Context::getContext(), &knearest, kernel_name, global_thread, local_thread, args, -1, -1, build_option);
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}
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186
modules/ocl/src/opencl/knearest.cl
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186
modules/ocl/src/opencl/knearest.cl
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@ -0,0 +1,186 @@
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
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// Jin Ma, jin@multicorewareinc.com
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//
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// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other oclMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not 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 the Intel Corporation or contributors 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.
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//
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//M*/
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#if defined (DOUBLE_SUPPORT)
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#pragma OPENCL EXTENSION cl_khr_fp64:enable
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#define TYPE double
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#else
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#define TYPE float
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#endif
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#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
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///////////////////////////////////// find_nearest //////////////////////////////////////
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__kernel void knn_find_nearest(__global float* sample, int sample_row, int sample_col, int sample_step,
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int k, __global float* samples_ocl, int sample_ocl_row, int sample_ocl_step,
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__global float* _results, int _results_step, int _regression, int K1,
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int sample_ocl_col, int nThreads, __local float* nr)
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{
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int k1 = 0;
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int k2 = 0;
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bool regression = false;
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if(_regression)
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regression = true;
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TYPE inv_scale;
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#ifdef DOUBLE_SUPPORT
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inv_scale = 1.0/K1;
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#else
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inv_scale = 1.0f/K1;
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#endif
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int y = get_global_id(1);
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int j, j1;
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int threadY = (y % nThreads);
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__local float* dd = nr + nThreads * k;
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if(y >= sample_row)
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{
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return;
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}
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for(j = 0; j < sample_ocl_row; j++)
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{
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TYPE sum;
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#ifdef DOUBLE_SUPPORT
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sum = 0.0;
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#else
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sum = 0.0f;
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#endif
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float si;
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int t, ii, ii1;
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for(t = 0; t < sample_col - 16; t += 16)
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{
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float16 t0 = vload16(0, sample + y * sample_step + t) - vload16(0, samples_ocl + j * sample_ocl_step + t);
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t0 *= t0;
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sum += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
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t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
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}
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for(; t < sample_col; t++)
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{
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#ifdef DOUBLE_SUPPORT
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double t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
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#else
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float t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
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#endif
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sum = sum + t0 * t0;
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}
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si = (float)sum;
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for(ii = k1 - 1; ii >= 0; ii--)
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{
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if(as_int(si) > as_int(dd[ii * nThreads + threadY]))
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break;
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}
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if(ii < k - 1)
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{
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for(ii1 = k2 - 1; ii1 > ii; ii1--)
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{
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dd[(ii1 + 1) * nThreads + threadY] = dd[ii1 * nThreads + threadY];
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nr[(ii1 + 1) * nThreads + threadY] = nr[ii1 * nThreads + threadY];
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}
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dd[(ii + 1) * nThreads + threadY] = si;
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nr[(ii + 1) * nThreads + threadY] = samples_ocl[sample_col + j * sample_ocl_step];
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}
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k1 = (k1 + 1) < k ? (k1 + 1) : k;
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k2 = k1 < (k - 1) ? k1 : (k - 1);
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}
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/*! find_nearest_neighbor done!*/
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/*! write_results start!*/
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switch (regression)
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{
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case true:
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{
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TYPE s;
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#ifdef DOUBLE_SUPPORT
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s = 0.0;
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#else
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s = 0.0f;
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#endif
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for(j = 0; j < K1; j++)
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s += nr[j * nThreads + threadY];
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_results[y * _results_step] = (float)(s * inv_scale);
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}
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break;
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case false:
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{
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int prev_start = 0, best_count = 0, cur_count;
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float best_val;
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for(j = K1 - 1; j > 0; j--)
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{
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bool swap_f1 = false;
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for(j1 = 0; j1 < j; j1++)
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{
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if(nr[j1 * nThreads + threadY] > nr[(j1 + 1) * nThreads + threadY])
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{
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int t;
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CV_SWAP(nr[j1 * nThreads + threadY], nr[(j1 + 1) * nThreads + threadY], t);
|
||||
swap_f1 = true;
|
||||
}
|
||||
}
|
||||
if(!swap_f1)
|
||||
break;
|
||||
}
|
||||
|
||||
best_val = 0;
|
||||
for(j = 1; j <= K1; j++)
|
||||
if(j == K1 || nr[j * nThreads + threadY] != nr[(j - 1) * nThreads + threadY])
|
||||
{
|
||||
cur_count = j - prev_start;
|
||||
if(best_count < cur_count)
|
||||
{
|
||||
best_count = cur_count;
|
||||
best_val = nr[(j - 1) * nThreads + threadY];
|
||||
}
|
||||
prev_start = j;
|
||||
}
|
||||
_results[y * _results_step] = best_val;
|
||||
}
|
||||
break;
|
||||
}
|
||||
///*! write_results done!*/
|
||||
}
|
||||
124
modules/ocl/test/test_ml.cpp
Normal file
124
modules/ocl/test/test_ml.cpp
Normal file
@ -0,0 +1,124 @@
|
||||
///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
// Copyright (C) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// @Authors
|
||||
// Jin Ma, jin@multicorewareinc.com
|
||||
// Xiaopeng Fu, fuxiaopeng2222@163.com
|
||||
// Erping Pang, pang_er_ping@163.com
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other oclMaterials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not 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 the Intel Corporation or contributors 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.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "test_precomp.hpp"
|
||||
#ifdef HAVE_OPENCL
|
||||
using namespace cv;
|
||||
using namespace cv::ocl;
|
||||
using namespace cvtest;
|
||||
using namespace testing;
|
||||
///////K-NEAREST NEIGHBOR//////////////////////////
|
||||
static void genTrainData(Mat& trainData, int trainDataRow, int trainDataCol,
|
||||
Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
|
||||
{
|
||||
cv::RNG &rng = TS::ptr()->get_rng();
|
||||
cv::Size size(trainDataCol, trainDataRow);
|
||||
trainData = randomMat(rng, size, CV_32FC1, 1.0, 1000.0, false);
|
||||
if(nClasses != 0)
|
||||
{
|
||||
cv::Size size1(trainDataRow, 1);
|
||||
trainLabel = randomMat(rng, size1, CV_8UC1, 0, nClasses - 1, false);
|
||||
trainLabel.convertTo(trainLabel, CV_32FC1);
|
||||
}
|
||||
}
|
||||
|
||||
PARAM_TEST_CASE(KNN, int, Size, int, bool)
|
||||
{
|
||||
int k;
|
||||
int trainDataCol;
|
||||
int testDataRow;
|
||||
int nClass;
|
||||
bool regression;
|
||||
virtual void SetUp()
|
||||
{
|
||||
k = GET_PARAM(0);
|
||||
nClass = GET_PARAM(2);
|
||||
trainDataCol = GET_PARAM(1).width;
|
||||
testDataRow = GET_PARAM(1).height;
|
||||
regression = GET_PARAM(3);
|
||||
}
|
||||
};
|
||||
|
||||
TEST_P(KNN, Accuracy)
|
||||
{
|
||||
Mat trainData, trainLabels;
|
||||
const int trainDataRow = 500;
|
||||
genTrainData(trainData, trainDataRow, trainDataCol, trainLabels, nClass);
|
||||
|
||||
Mat testData, testLabels;
|
||||
genTrainData(testData, testDataRow, trainDataCol);
|
||||
|
||||
KNearestNeighbour knn_ocl;
|
||||
CvKNearest knn_cpu;
|
||||
Mat best_label_cpu;
|
||||
oclMat best_label_ocl;
|
||||
|
||||
/*ocl k-Nearest_Neighbor start*/
|
||||
oclMat trainData_ocl;
|
||||
trainData_ocl.upload(trainData);
|
||||
Mat simpleIdx;
|
||||
knn_ocl.train(trainData, trainLabels, simpleIdx, regression);
|
||||
|
||||
oclMat testdata;
|
||||
testdata.upload(testData);
|
||||
knn_ocl.find_nearest(testdata, k, best_label_ocl);
|
||||
/*ocl k-Nearest_Neighbor end*/
|
||||
|
||||
/*cpu k-Nearest_Neighbor start*/
|
||||
knn_cpu.train(trainData, trainLabels, simpleIdx, regression);
|
||||
knn_cpu.find_nearest(testData, k, &best_label_cpu);
|
||||
/*cpu k-Nearest_Neighbor end*/
|
||||
if(regression)
|
||||
{
|
||||
EXPECT_MAT_SIMILAR(Mat(best_label_ocl), best_label_cpu, 1e-5);
|
||||
}
|
||||
else
|
||||
{
|
||||
EXPECT_MAT_NEAR(Mat(best_label_ocl), best_label_cpu, 0.0);
|
||||
}
|
||||
}
|
||||
INSTANTIATE_TEST_CASE_P(OCL_ML, KNN, Combine(Values(6, 5), Values(Size(200, 400), Size(300, 600)),
|
||||
Values(4, 3), Values(false, true)));
|
||||
#endif // HAVE_OPENCL
|
||||
Loading…
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Reference in New Issue
Block a user