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Merge pull request #1457 from pengx17:2.4_oclsvm

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Roman Donchenko
2013-09-30 17:50:29 +04:00
committed by OpenCV Buildbot
parent 54c902dccd 315c054379
commit e35bc11504
4 changed files with 1607 additions and 1 deletions
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20
modules/ocl/include/opencv2/ocl/ocl.hpp
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@@ -1900,6 +1900,26 @@ namespace cv
private:
oclMat samples_ocl;
};
/*!*************** SVM *************!*/
class CV_EXPORTS CvSVM_OCL : public CvSVM
{
public:
CvSVM_OCL();
CvSVM_OCL(const cv::Mat& trainData, const cv::Mat& responses,
const cv::Mat& varIdx=cv::Mat(), const cv::Mat& sampleIdx=cv::Mat(),
CvSVMParams params=CvSVMParams());
CV_WRAP float predict( const int row_index, Mat& src, bool returnDFVal=false ) const;
CV_WRAP void predict( cv::InputArray samples, cv::OutputArray results ) const;
CV_WRAP float predict( const cv::Mat& sample, bool returnDFVal=false ) const;
float predict( const CvMat* samples, CV_OUT CvMat* results ) const;
protected:
float predict( const int row_index, int row_len, Mat& src, bool returnDFVal=false ) const;
void create_kernel();
void create_solver();
};
/*!*************** END *************!*/
}
}
#if defined _MSC_VER && _MSC_VER >= 1200

209
modules/ocl/src/opencl/svm.cl Normal file
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@@ -0,0 +1,209 @@
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2010-2013, Institute Of Software Chinese Academy Of Science, all rights reserved.
// Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// @Authors
// Erping Pang, erping@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.
//
//
#if defined (DOUBLE_SUPPORT)
#ifdef cl_khr_fp64
#pragma OPENCL EXTENSION cl_khr_fp64:enable
#elif defined (cl_amd_fp64)
#pragma OPENCL EXTENSION cl_amd_fp64:enable
#endif
#define TYPE double
#else
#define TYPE float
#endif
#if defined ADDEXP
#define EXP(X) exp(X)
#else
#define EXP(X) X
#endif
#if defined ADDPOW
#define POW(X,Y) pow(fabs(X),(Y))
#else
#define POW(X,Y) X
#endif
#define FLT_MAX 3.402823466e+38F
#define MAX_VAL (FLT_MAX*1e-3)
__kernel void svm_linear(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta)
{
const int col = get_global_id(0);
const int row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - 16; t += 16)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += 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 < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE temp1 = (TYPE) (temp * alpha + beta);
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_sigmod(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta)
{
const int col = get_global_id(0);
const int row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - 16; t += 16)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += 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 < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE tp = (TYPE) (temp * alpha + beta);
TYPE e = exp(-fabs(tp));
TYPE temp1;
if(tp > 0)
{
temp1 = (TYPE)((1. - e) / (1. + e));
}
else
{
temp1 = (TYPE)((e - 1.) / (e + 1.));
}
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_poly(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE alpha, TYPE beta, TYPE degree)
{
const int col = get_global_id(0);
const int row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - 16; t += 16)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 *= t1;
temp += 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 < width; t++)
{
temp += src[row * src_step + t] * src2[col * src2_step + t];
}
TYPE temp1 = (TYPE)(POW((temp * alpha + beta), degree));
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}
__kernel void svm_rbf(__global float* src, int src_step, __global float* src2, int src2_step, __global TYPE* dst, int dst_step, int src_rows, int src2_cols,
int width, TYPE gamma)
{
const int col = get_global_id(0);
const int row = get_global_id(1);
if(row < src_rows && col < src2_cols)
{
int t = 0;
TYPE temp = 0.0;
for(t = 0; t < width - 16; t += 16)
{
float16 t0 = vload16(0, src + row * src_step + t);
float16 t1 = vload16(0, src2 + col * src2_step + t);
t0 = (t0 - t1) * (t0 - t1);
temp += 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 < width; t++)
{
temp += (src[row * src_step + t] - src2[col * src2_step + t]) * (src[row * src_step + t] - src2[col * src2_step + t]);
}
TYPE temp1 = EXP((TYPE)(temp * gamma));
if( temp1 > MAX_VAL )
{
dst[row * dst_step + col] = MAX_VAL;
}
else
{
dst[row * dst_step + col] = temp1;
}
}
}

1201
modules/ocl/src/svm.cpp Normal file
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178
modules/ocl/test/test_ml.cpp
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@@ -121,4 +121,180 @@ TEST_P(KNN, Accuracy)
}
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
////////////////////////////////SVM/////////////////////////////////////////////////
PARAM_TEST_CASE(SVM_OCL, int, int, int)
{
cv::Size size;
int kernel_type;
int svm_type;
Mat src, labels, samples, labels_predict;
int K;
cv::RNG rng ;
virtual void SetUp()
{
kernel_type = GET_PARAM(0);
svm_type = GET_PARAM(1);
K = GET_PARAM(2);
rng = TS::ptr()->get_rng();
cv::Size size = cv::Size(MWIDTH, MHEIGHT);
src.create(size, CV_32FC1);
labels.create(1, size.height, CV_32SC1);
int row_idx = 0;
const int max_number = size.height / K - 1;
CV_Assert(K <= size.height);
for(int i = 0; i < K; i++ )
{
Mat center_row_header = src.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number) ||
(i == K - 1 && j < max_number + size.height % K); j ++)
{
Mat cur_row_header = src.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number;
}
labels.convertTo(labels, CV_32FC1);
cv::Size test_size = cv::Size(MWIDTH, 100);
samples.create(test_size, CV_32FC1);
labels_predict.create(1, test_size.height, CV_32SC1);
const int max_number_test = test_size.height / K - 1;
row_idx = 0;
for(int i = 0; i < K; i++ )
{
Mat center_row_header = samples.row(row_idx);
center_row_header.setTo(0);
int nchannel = center_row_header.channels();
for(int j = 0; j < nchannel; j++)
{
center_row_header.at<float>(0, i * nchannel + j) = 500.0;
}
labels_predict.at<int>(0, row_idx) = i;
for(int j = 0; (j < max_number_test) ||
(i == K - 1 && j < max_number_test + test_size.height % K); j ++)
{
Mat cur_row_header = samples.row(row_idx + 1 + j);
center_row_header.copyTo(cur_row_header);
Mat tmpmat = randomMat(rng, cur_row_header.size(), cur_row_header.type(), 1, 100, false);
cur_row_header += tmpmat;
labels_predict.at<int>(0, row_idx + 1 + j) = i;
}
row_idx += 1 + max_number_test;
}
labels_predict.convertTo(labels_predict, CV_32FC1);
}
};
TEST_P(SVM_OCL, Accuracy)
{
CvSVMParams params;
params.degree = 0.4;
params.gamma = 1;
params.coef0 = 1;
params.C = 1;
params.nu = 0.5;
params.p = 1;
params.svm_type = svm_type;
params.kernel_type = kernel_type;
params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER, 1000, 0.001);
CvSVM SVM;
SVM.train(src, labels, Mat(), Mat(), params);
cv::ocl::CvSVM_OCL SVM_OCL;
SVM_OCL.train(src, labels, Mat(), Mat(), params);
int c = SVM.get_support_vector_count();
int c1 = SVM_OCL.get_support_vector_count();
Mat sv(c, MHEIGHT, CV_32FC1);
Mat sv_ocl(c1, MHEIGHT, CV_32FC1);
for(int i = 0; i < c; i++)
{
const float* v = SVM.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv.at<float>(i, j) = v[j];
}
}
for(int i = 0; i < c1; i++)
{
const float* v_ocl = SVM_OCL.get_support_vector(i);
for(int j = 0; j < MHEIGHT; j++)
{
sv_ocl.at<float>(i, j) = v_ocl[j];
}
}
cv::BFMatcher matcher(cv::NORM_L2);
std::vector<cv::DMatch> matches;
matcher.match(sv, sv_ocl, matches);
int count = 0;
for(std::vector<cv::DMatch>::iterator itr = matches.begin(); itr != matches.end(); itr++)
{
if((*itr).distance < 0.1)
{
count ++;
}
}
if(c != 0)
{
float matchedRatio = (float)count / c;
EXPECT_GT(matchedRatio, 0.95);
}
if(c != 0)
{
CvMat *result = cvCreateMat(1, samples.rows, CV_32FC1);
CvMat test_samples = samples;
CvMat *result_ocl = cvCreateMat(1, samples.rows, CV_32FC1);
SVM.predict(&test_samples, result);
SVM_OCL.predict(&test_samples, result_ocl);
int true_resp = 0, true_resp_ocl = 0;
for (int i = 0; i < samples.rows; i++)
{
if (result->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp++;
}
}
float matchedRatio = (float)true_resp / samples.rows;
for (int i = 0; i < samples.rows; i++)
{
if (result_ocl->data.fl[i] == labels_predict.at<float>(0, i))
{
true_resp_ocl++;
}
}
float matchedRatio_ocl = (float)true_resp_ocl / samples.rows;
if(matchedRatio != 0 && true_resp_ocl < true_resp)
{
EXPECT_NEAR(matchedRatio_ocl, matchedRatio, 0.03);
}
}
}
INSTANTIATE_TEST_CASE_P(OCL_ML, SVM_OCL, testing::Combine(
Values(CvSVM::LINEAR, CvSVM::POLY, CvSVM::RBF, CvSVM::SIGMOID),
Values(CvSVM::C_SVC, CvSVM::NU_SVC, CvSVM::ONE_CLASS, CvSVM::EPS_SVR, CvSVM::NU_SVR),
Values(2, 3, 4)
));
#endif // HAVE_OPENCL