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Added knearest neighbor of OpenCL version.

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It includes the accuracy/performance test and the implementation of KNN.
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Jin Ma
2013-09-22 10:23:54 +08:00
parent 06c33df307
commit 1bfe39f485
4 changed files with 582 additions and 0 deletions
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modules/ocl/src/knearest.cpp Normal file
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/*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:
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// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// * 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.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
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//
//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);
}