Added knearest neighbor of OpenCL version.

It includes the accuracy/performance test and the implementation of KNN.

modules/ocl/perf/perf_ml.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  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
+// 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 "perf_precomp.hpp"
+using namespace perf;
+using namespace std;
+using namespace cv::ocl;
+using namespace cv;
+using std::tr1::tuple;
+using std::tr1::get;
+////////////////////////////////// K-NEAREST NEIGHBOR ////////////////////////////////////
+static void genData(Mat& trainData, Size size, Mat& trainLabel = Mat().setTo(Scalar::all(0)), int nClasses = 0)
+{
+    trainData.create(size, CV_32FC1);
+    randu(trainData, 1.0, 100.0);
+
+    if(nClasses != 0)
+    {
+        trainLabel.create(size.height, 1, CV_8UC1);
+        randu(trainLabel, 0, nClasses - 1);
+        trainLabel.convertTo(trainLabel, CV_32FC1);
+    }
+}
+
+typedef tuple<int> KNNParamType;
+typedef TestBaseWithParam<KNNParamType> KNNFixture;
+
+PERF_TEST_P(KNNFixture, KNN,
+            testing::Values(1000, 2000, 4000))
+{
+    KNNParamType params = GetParam();
+    const int rows = get<0>(params);
+    int columns = 100;
+    int k = rows/250;
+
+    Mat trainData, trainLabels;
+    Size size(columns, rows);
+    genData(trainData, size, trainLabels, 3);
+
+    Mat testData;
+    genData(testData, size);
+    Mat best_label;
+
+    if(RUN_PLAIN_IMPL)
+    {
+        TEST_CYCLE()
+        {
+            CvKNearest knn_cpu;
+            knn_cpu.train(trainData, trainLabels);
+            knn_cpu.find_nearest(testData, k, &best_label);
+        }
+    }else if(RUN_OCL_IMPL)
+    {
+        cv::ocl::oclMat best_label_ocl;
+        cv::ocl::oclMat testdata;
+        testdata.upload(testData);
+
+        OCL_TEST_CYCLE()
+        {
+            cv::ocl::KNearestNeighbour knn_ocl;
+            knn_ocl.train(trainData, trainLabels);
+            knn_ocl.find_nearest(testdata, k, best_label_ocl);
+        }
+        best_label_ocl.download(best_label);
+    }else
+        OCL_PERF_ELSE
+    SANITY_CHECK(best_label);
+}
\ No newline at end of file

modules/ocl/src/knearest.cpp

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  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, 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.
+//
+// @Authors
+//    Jin Ma, jin@multicorewareinc.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 "precomp.hpp"
+using namespace cv;
+using namespace cv::ocl;
+
+namespace cv
+{
+    namespace ocl
+    {
+        extern const char* knearest;//knearest
+    }
+}
+
+KNearestNeighbour::KNearestNeighbour()
+{
+    clear();
+}
+
+KNearestNeighbour::~KNearestNeighbour()
+{
+    clear();
+}
+
+KNearestNeighbour::KNearestNeighbour(const Mat& train_data, const Mat& responses,
+                                     const Mat& sample_idx, bool is_regression, int max_k)
+{
+    max_k = max_k;
+    CvKNearest::train(train_data, responses, sample_idx, is_regression, max_k);
+}
+
+void KNearestNeighbour::clear()
+{
+    CvKNearest::clear();
+}
+
+bool KNearestNeighbour::train(const Mat& trainData, Mat& labels, Mat& sampleIdx,
+                              bool isRegression, int _max_k, bool updateBase)
+{
+    max_k = _max_k;
+    bool cv_knn_train = CvKNearest::train(trainData, labels, sampleIdx, isRegression, max_k, updateBase);
+
+    CvVectors* s = CvKNearest::samples;
+
+    cv::Mat samples_mat(s->count, CvKNearest::var_count + 1, s->type);
+
+    float* s1 = (float*)(s + 1);
+    for(int i = 0; i < s->count; i++)
+    {
+        float* t1 = s->data.fl[i];
+        for(int j = 0; j < CvKNearest::var_count; j++)
+        {
+            Point pos(j, i);
+            samples_mat.at<float>(pos) = t1[j];
+        }
+
+        Point pos_label(CvKNearest::var_count, i);
+        samples_mat.at<float>(pos_label) = s1[i];
+    }
+
+    samples_ocl = samples_mat;
+    return cv_knn_train;
+}
+
+void KNearestNeighbour::find_nearest(const oclMat& samples, int k, oclMat& lables)
+{
+    CV_Assert(!samples_ocl.empty());
+    lables.create(samples.rows, 1, CV_32FC1);
+
+    CV_Assert(samples.cols == CvKNearest::var_count);
+    CV_Assert(samples.type() == CV_32FC1);
+    CV_Assert(k >= 1 && k <= max_k);
+
+    int k1 = KNearest::get_sample_count();
+    k1 = MIN( k1, k );
+
+    String kernel_name = "knn_find_nearest";
+    cl_ulong local_memory_size = queryLocalMemInfo();
+    int nThreads = local_memory_size / (2 * k * 4);
+    if(nThreads >= 256)
+        nThreads = 256;
+
+    int smem_size = nThreads * k * 4 * 2;
+    size_t local_thread[] = {1, nThreads, 1};
+    size_t global_thread[] = {1, samples.rows, 1};
+
+    char build_option[50];
+    if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE))
+    {
+        sprintf(build_option, " ");
+    }else
+        sprintf(build_option, "-D DOUBLE_SUPPORT");
+
+    std::vector< std::pair<size_t, const void*> > args;
+
+    int samples_ocl_step = samples_ocl.step/samples_ocl.elemSize();
+    int samples_step = samples.step/samples.elemSize();
+    int lables_step = lables.step/lables.elemSize();
+
+    int _regression = 0;
+    if(CvKNearest::regression)
+        _regression = 1;
+
+    args.push_back(make_pair(sizeof(cl_mem), (void*)&samples.data));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples.rows));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples.cols));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples_step));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&k));
+    args.push_back(make_pair(sizeof(cl_mem), (void*)&samples_ocl.data));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.rows));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl_step));
+    args.push_back(make_pair(sizeof(cl_mem), (void*)&lables.data));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&lables_step));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&_regression));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&k1));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&samples_ocl.cols));
+    args.push_back(make_pair(sizeof(cl_int), (void*)&nThreads));
+    args.push_back(make_pair(smem_size, (void*)NULL));
+    openCLExecuteKernel(Context::getContext(), &knearest, kernel_name, global_thread, local_thread, args, -1, -1, build_option);
+}
\ No newline at end of file

modules/ocl/src/opencl/knearest.cl

+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  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
+//
+// 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*/
+#if defined (DOUBLE_SUPPORT)
+#pragma OPENCL EXTENSION cl_khr_fp64:enable
+#define TYPE double
+#else
+#define TYPE float
+#endif
+
+#define CV_SWAP(a,b,t) ((t) = (a), (a) = (b), (b) = (t))
+///////////////////////////////////// find_nearest //////////////////////////////////////
+__kernel void knn_find_nearest(__global float* sample, int sample_row, int sample_col, int sample_step,
+                               int k, __global float* samples_ocl, int sample_ocl_row, int sample_ocl_step,
+                               __global float* _results, int _results_step, int _regression, int K1,
+                               int sample_ocl_col, int nThreads, __local float* nr)
+{
+    int k1 = 0;
+    int k2 = 0;
+
+    bool regression = false;
+
+    if(_regression)
+        regression = true;
+
+    TYPE inv_scale;
+#ifdef DOUBLE_SUPPORT
+    inv_scale = 1.0/K1;
+#else
+    inv_scale = 1.0f/K1;
+#endif
+
+    int y = get_global_id(1);
+    int j, j1;
+    int threadY = (y % nThreads);
+    __local float* dd = nr + nThreads * k;
+    if(y >= sample_row)
+    {
+        return;
+    }
+    for(j = 0; j < sample_ocl_row; j++)
+    {
+        TYPE sum;
+#ifdef DOUBLE_SUPPORT
+        sum = 0.0;
+#else
+        sum = 0.0f;
+#endif
+        float si;
+        int t, ii, ii1;
+        for(t = 0; t < sample_col - 16; t += 16)
+        {
+            float16 t0 = vload16(0, sample + y * sample_step + t) - vload16(0, samples_ocl + j * sample_ocl_step + t);
+            t0 *= t0;
+            sum += t0.s0 + t0.s1 + t0.s2 + t0.s3 + t0.s4 + t0.s5 + t0.s6 + t0.s7 +
+                t0.s8 + t0.s9 + t0.sa + t0.sb + t0.sc + t0.sd + t0.se + t0.sf;
+        }
+
+        for(; t < sample_col; t++)
+        {
+#ifdef DOUBLE_SUPPORT
+            double t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
+#else
+            float t0 = sample[y * sample_step + t] - samples_ocl[j * sample_ocl_step + t];
+#endif
+            sum = sum + t0 * t0;
+        }
+
+        si = (float)sum;
+        for(ii = k1 - 1; ii >= 0; ii--)
+        {
+            if(as_int(si) > as_int(dd[ii * nThreads + threadY]))
+                break;
+        }
+        if(ii < k - 1)
+        {
+            for(ii1 = k2 - 1; ii1 > ii; ii1--)
+            {
+                dd[(ii1 + 1) * nThreads + threadY] = dd[ii1 * nThreads + threadY];
+                nr[(ii1 + 1) * nThreads + threadY] = nr[ii1 * nThreads + threadY];
+            }
+
+            dd[(ii + 1) * nThreads + threadY] = si;
+            nr[(ii + 1) * nThreads + threadY] = samples_ocl[sample_col + j * sample_ocl_step];
+        }
+        k1 = (k1 + 1) < k ? (k1 + 1) : k;
+        k2 = k1 < (k - 1) ? k1 : (k - 1);
+    }
+    /*! find_nearest_neighbor done!*/
+    /*! write_results start!*/
+    switch (regression)
+    {
+    case true:
+        {
+            TYPE s;
+#ifdef DOUBLE_SUPPORT
+            s = 0.0;
+#else
+            s = 0.0f;
+#endif
+            for(j = 0; j < K1; j++)
+                s += nr[j * nThreads + threadY];
+
+            _results[y * _results_step] = (float)(s * inv_scale);
+        }
+        break;
+    case false:
+        {
+            int prev_start = 0, best_count = 0, cur_count;
+            float best_val;
+
+            for(j = K1 - 1; j > 0; j--)
+            {
+                bool swap_f1 = false;
+                for(j1 = 0; j1 < j; j1++)
+                {
+                    if(nr[j1 * nThreads + threadY] > nr[(j1 + 1) * nThreads + threadY])
+                    {
+                        int t;
+                        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!*/
+}

modules/ocl/test/test_ml.cpp

+///////////////////////////////////////////////////////////////////////////////////////
+//
+//  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
\ No newline at end of file