diff --git a/modules/ocl/include/opencv2/ocl/ocl.hpp b/modules/ocl/include/opencv2/ocl/ocl.hpp index d3dbded34dbae06a8b8b94760684837a037712d7..ab12dc2cbf54ad28ee1901eb24f89b12e74a8cef 100644 --- a/modules/ocl/include/opencv2/ocl/ocl.hpp +++ b/modules/ocl/include/opencv2/ocl/ocl.hpp @@ -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 diff --git a/modules/ocl/src/opencl/svm.cl b/modules/ocl/src/opencl/svm.cl new file mode 100644 index 0000000000000000000000000000000000000000..074ceb05982272ed655122b6e36f820171481459 --- /dev/null +++ b/modules/ocl/src/opencl/svm.cl @@ -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; + } + } +} \ No newline at end of file diff --git a/modules/ocl/src/svm.cpp b/modules/ocl/src/svm.cpp new file mode 100644 index 0000000000000000000000000000000000000000..212c0d56af735bed1f6aec95e8ba8816d6abe05b --- /dev/null +++ b/modules/ocl/src/svm.cpp @@ -0,0 +1,1197 @@ +/*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) 2010-2013, 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. +// +//M*/ +#include "precomp.hpp" +using namespace cv; +using namespace ocl; + +#if 1 +typedef float Qfloat; +#define QFLOAT_TYPE CV_32F +#else +typedef double Qfloat; +#define QFLOAT_TYPE CV_64F +#endif + +namespace cv +{ +namespace ocl +{ +///////////////////////////OpenCL kernel strings/////////////////////////// +extern const char *svm; +} +} +class CvSVMKernel_ocl: public CvSVMKernel +{ +public: + typedef void (CvSVMKernel_ocl::*Calc_ocl)( int vec_count, const int row_idx, Qfloat* results, Mat& src); + CvSVMKernel_ocl(const CvSVMParams* params, Calc_ocl _calc_func , Calc _calc_func1); + + Calc_ocl calc_func_ocl; + bool create( const CvSVMParams* params, Calc_ocl _calc_func, Calc _calc_func1); + + void calc( int vcount, const int row_idx, Qfloat* results, Mat& src); + void calc_linear( int vec_count, const int row_idx, Qfloat* results, Mat& src); + + void calc_poly( int vec_count, const int row_idx, Qfloat* results, Mat& src); + void calc_sigmoid( int vec_count, const int row_idx, Qfloat* results, Mat& src); + void calc_non_rbf_base( int vec_count, const int row_idx, Qfloat* results, Mat& src); + void calc_rbf( int vec_count, const int row_idx, Qfloat* results, Mat& src); +}; +class CvSVMSolver_ocl: public CvSVMSolver +{ +public: + CvSVMSolver_ocl(); + CvSVMSolver_ocl(const CvSVMParams *); + float* get_row_base( int i, bool* _existed, Mat& src); + bool solve_generic( CvSVMSolutionInfo& si ); + float* get_row( int i, float* dst, Mat& src); +}; + +typedef struct CvSparseVecElem32f +{ + int idx; + float val; +} CvSparseVecElem32f; +static int icvCmpSparseVecElems( const void* a, const void* b ) +{ + return ((CvSparseVecElem32f*)a)->idx - ((CvSparseVecElem32f*)b)->idx; +} +void cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx, + int class_count, const CvMat* prob, float** row_sample, + int as_sparse CV_DEFAULT(0) ); +void cvPreparePredictData( const CvArr* _sample, int dims_all, + const CvMat* comp_idx, int class_count, + const CvMat* prob, float** _row_sample, + int as_sparse ) +{ + float* row_sample = 0; + int* inverse_comp_idx = 0; + + CV_FUNCNAME( "cvPreparePredictData" ); + + __BEGIN__; + + const CvMat* sample = (const CvMat*)_sample; + float* sample_data; + int sample_step; + int is_sparse = CV_IS_SPARSE_MAT(sample); + int d, sizes[CV_MAX_DIM]; + int i, dims_selected; + int vec_size; + + if( !is_sparse && !CV_IS_MAT(sample) ) + { + CV_ERROR( !sample ? CV_StsNullPtr : CV_StsBadArg, "The sample is not a valid vector" ); + } + + if( cvGetElemType( sample ) != CV_32FC1 ) + { + CV_ERROR( CV_StsUnsupportedFormat, "Input sample must have 32fC1 type" ); + } + + CV_CALL( d = cvGetDims( sample, sizes )); + + if( !((is_sparse && d == 1) || (!is_sparse && d == 2 && (sample->rows == 1 || sample->cols == 1))) ) + { + CV_ERROR( CV_StsBadSize, "Input sample must be 1-dimensional vector" ); + } + + if( d == 1 ) + { + sizes[1] = 1; + } + + if( sizes[0] + sizes[1] - 1 != dims_all ) + CV_ERROR( CV_StsUnmatchedSizes, + "The sample size is different from what has been used for training" ); + + if( !_row_sample ) + { + CV_ERROR( CV_StsNullPtr, "INTERNAL ERROR: The row_sample pointer is NULL" ); + } + + if( comp_idx && (!CV_IS_MAT(comp_idx) || comp_idx->rows != 1 || + CV_MAT_TYPE(comp_idx->type) != CV_32SC1) ) + { + CV_ERROR( CV_StsBadArg, "INTERNAL ERROR: invalid comp_idx" ); + } + + dims_selected = comp_idx ? comp_idx->cols : dims_all; + + if( prob ) + { + if( !CV_IS_MAT(prob) ) + { + CV_ERROR( CV_StsBadArg, "The output matrix of probabilities is invalid" ); + } + + if( (prob->rows != 1 && prob->cols != 1) || + (CV_MAT_TYPE(prob->type) != CV_32FC1 && + CV_MAT_TYPE(prob->type) != CV_64FC1) ) + CV_ERROR( CV_StsBadSize, + "The matrix of probabilities must be 1-dimensional vector of 32fC1 type" ); + + if( prob->rows + prob->cols - 1 != class_count ) + CV_ERROR( CV_StsUnmatchedSizes, + "The vector of probabilities must contain as many elements as " + "the number of classes in the training set" ); + } + + vec_size = !as_sparse ? dims_selected * sizeof(row_sample[0]) : + (dims_selected + 1) * sizeof(CvSparseVecElem32f); + + if( CV_IS_MAT(sample) ) + { + sample_data = sample->data.fl; + sample_step = CV_IS_MAT_CONT(sample->type) ? 1 : sample->step / sizeof(row_sample[0]); + + if( !comp_idx && CV_IS_MAT_CONT(sample->type) && !as_sparse ) + { + *_row_sample = sample_data; + } + else + { + CV_CALL( row_sample = (float*)cvAlloc( vec_size )); + + if( !comp_idx ) + for( i = 0; i < dims_selected; i++ ) + { + row_sample[i] = sample_data[sample_step * i]; + } + else + { + int* comp = comp_idx->data.i; + for( i = 0; i < dims_selected; i++ ) + { + row_sample[i] = sample_data[sample_step * comp[i]]; + } + } + + *_row_sample = row_sample; + } + + if( as_sparse ) + { + const float* src = (const float*)row_sample; + CvSparseVecElem32f* dst = (CvSparseVecElem32f*)row_sample; + + dst[dims_selected].idx = -1; + for( i = dims_selected - 1; i >= 0; i-- ) + { + dst[i].idx = i; + dst[i].val = src[i]; + } + } + } + else + { + CvSparseNode* node; + CvSparseMatIterator mat_iterator; + const CvSparseMat* sparse = (const CvSparseMat*)sample; + assert( is_sparse ); + + node = cvInitSparseMatIterator( sparse, &mat_iterator ); + CV_CALL( row_sample = (float*)cvAlloc( vec_size )); + + if( comp_idx ) + { + CV_CALL( inverse_comp_idx = (int*)cvAlloc( dims_all * sizeof(int) )); + memset( inverse_comp_idx, -1, dims_all * sizeof(int) ); + for( i = 0; i < dims_selected; i++ ) + { + inverse_comp_idx[comp_idx->data.i[i]] = i; + } + } + + if( !as_sparse ) + { + memset( row_sample, 0, vec_size ); + + for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) ) + { + int idx = *CV_NODE_IDX( sparse, node ); + if( inverse_comp_idx ) + { + idx = inverse_comp_idx[idx]; + if( idx < 0 ) + { + continue; + } + } + row_sample[idx] = *(float*)CV_NODE_VAL( sparse, node ); + } + } + else + { + CvSparseVecElem32f* ptr = (CvSparseVecElem32f*)row_sample; + + for( ; node != 0; node = cvGetNextSparseNode(&mat_iterator) ) + { + int idx = *CV_NODE_IDX( sparse, node ); + if( inverse_comp_idx ) + { + idx = inverse_comp_idx[idx]; + if( idx < 0 ) + { + continue; + } + } + ptr->idx = idx; + ptr->val = *(float*)CV_NODE_VAL( sparse, node ); + ptr++; + } + + qsort( row_sample, ptr - (CvSparseVecElem32f*)row_sample, + sizeof(ptr[0]), icvCmpSparseVecElems ); + ptr->idx = -1; + } + + *_row_sample = row_sample; + } + + __END__; + + if( inverse_comp_idx ) + { + cvFree( &inverse_comp_idx ); + } + + if( cvGetErrStatus() < 0 && _row_sample ) + { + cvFree( &row_sample ); + *_row_sample = 0; + } +} +float CvSVM_OCL::predict( const int row_index, int row_len, Mat& src, bool returnDFVal ) const +{ + assert( kernel ); + + (void)row_len; + + int class_count = class_labels ? class_labels->cols : + params.svm_type == ONE_CLASS ? 1 : 0; + + float result = 0; + cv::AutoBuffer _buffer(sv_total + (class_count + 1) * 2); + float* buffer = _buffer; + + if( params.svm_type == EPS_SVR || + params.svm_type == NU_SVR || + params.svm_type == ONE_CLASS ) + { + CvSVMDecisionFunc* df = (CvSVMDecisionFunc*)decision_func; + int i, sv_count = df->sv_count; + double sum = -df->rho; + + ((CvSVMKernel_ocl*)kernel)->calc( sv_count, row_index, buffer, src); + for( i = 0; i < sv_count; i++ ) + { + sum += buffer[i] * df->alpha[i]; + } + + result = params.svm_type == ONE_CLASS ? (float)(sum > 0) : (float)sum; + } + else if( params.svm_type == C_SVC || + params.svm_type == NU_SVC ) + { + CvSVMDecisionFunc* df = (CvSVMDecisionFunc*)decision_func; + int* vote = (int*)(buffer + sv_total); + int i, j, k; + + memset( vote, 0, class_count * sizeof(vote[0])); + ((CvSVMKernel_ocl*)kernel)->calc( sv_total, row_index, buffer, src); + double sum = 0.; + + for( i = 0; i < class_count; i++ ) + { + for( j = i + 1; j < class_count; j++, df++ ) + { + sum = -df->rho; + int sv_count = df->sv_count; + for( k = 0; k < sv_count; k++ ) + { + sum += df->alpha[k] * buffer[df->sv_index[k]]; + } + + vote[sum > 0 ? i : j]++; + } + } + + for( i = 1, k = 0; i < class_count; i++ ) + { + if( vote[i] > vote[k] ) + { + k = i; + } + } + result = returnDFVal && class_count == 2 ? (float)sum : (float)(class_labels->data.i[k]); + } + else + CV_Error( CV_StsBadArg, "INTERNAL ERROR: Unknown SVM type, " + "the SVM structure is probably corrupted" ); + + return result; +} +float CvSVM_OCL::predict( const Mat& _sample, bool returnDFVal ) const +{ + CvMat sample = _sample; + return CvSVM::predict(&sample, returnDFVal); +} +float CvSVM_OCL::predict( const int row_index, Mat& src, bool returnDFVal) const +{ + float result = 0; + + result = predict( row_index, get_var_count(), src, returnDFVal); + + return result; +} +#undef get_C +#define get_C(i) (C[y[i]>0]) +#undef is_upper_bound +#define is_upper_bound(i) (alpha_status[i] > 0) +#undef is_lower_bound +#define is_lower_bound(i) (alpha_status[i] < 0) +#undef update_alpha_status +#define update_alpha_status(i) \ + alpha_status[i] = (schar)(alpha[i] >= get_C(i) ? 1 : alpha[i] <= 0 ? -1 : 0) + +CvSVMSolver_ocl::CvSVMSolver_ocl(const CvSVMParams* _params) +{ + params = _params; +} +float* CvSVMSolver_ocl::get_row( int i, float* dst, Mat& src ) +{ + bool existed = false; + float* row = get_row_base( i, &existed, src); + return (this->*get_row_func)( i, row, dst, existed ); +} +float* CvSVMSolver_ocl::get_row_base( int i, bool* _existed, Mat& src ) +{ + int i1 = i < sample_count ? i : i - sample_count; + CvSVMKernelRow* row = rows + i1; + bool existed = row->data != 0; + Qfloat* data; + + if( existed || cache_size <= 0 ) + { + CvSVMKernelRow* del_row = existed ? row : lru_list.prev; + data = del_row->data; + assert( data != 0 ); + + // delete row from the LRU list + del_row->data = 0; + del_row->prev->next = del_row->next; + del_row->next->prev = del_row->prev; + } + else + { + data = (Qfloat*)cvMemStorageAlloc( storage, cache_line_size ); + cache_size -= cache_line_size; + } + + // insert row into the LRU list + row->data = data; + row->prev = &lru_list; + row->next = lru_list.next; + row->prev->next = row->next->prev = row; + + if( !existed ) + { + ((CvSVMKernel_ocl*)kernel)->calc( sample_count, i1, row->data, src); + } + + if( _existed ) + { + *_existed = existed; + } + + return row->data; +} + +void matmul_sigmod(oclMat & src, oclMat & src2, oclMat & dst, int src_rows, int src2_cols, int var_count, double alpha1, double beta1) +{ + Context *clCxt = Context::getContext(); + string kernelName = "svm_sigmod"; + int src_step = (int)src.step / src.elemSize(); + int src2_step = (int)src2.step / src2.elemSize(); + int dst_step = (int)dst.step / dst.elemSize(); + int x = MIN(16, src_rows); + int y = MIN(16, src2_cols); + size_t localThreads[] = {x, y, 1}; + size_t globalThreads[] = {src2_cols, src_rows, 1}; + int width = var_count; + + vector< pair > args; + args.push_back(make_pair(sizeof(cl_mem), (void* )&src.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&src2.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&dst.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&dst_step)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_rows)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_cols)); + args.push_back(make_pair(sizeof(cl_int), (void* )&width)); + + float alpha = 0.0f, beta = 0.0f; + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + alpha = (float)alpha1; + beta = (float)beta1; + args.push_back(make_pair(sizeof(cl_float), (void* )&alpha)); + args.push_back(make_pair(sizeof(cl_float), (void* )&beta)); + } + else + { + args.push_back(make_pair(sizeof(cl_double), (void* )&alpha1)); + args.push_back(make_pair(sizeof(cl_double), (void* )&beta1)); + } + openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1); +} +void matmul_poly(oclMat & src, oclMat & src2, oclMat & dst, int src_rows, int src2_cols, int var_count, double alpha1, double beta1, double degree1, bool flag) +{ + Context *clCxt = Context::getContext(); + string kernelName = "svm_poly"; + int src_step = (int)src.step / src.elemSize(); + int src2_step = (int)src2.step / src2.elemSize(); + int dst_step = (int)dst.step / dst.elemSize(); + int x = MIN(16, src_rows); + int y = MIN(16, src2_cols); + size_t localThreads[] = {x, y, 1}; + size_t globalThreads[] = {src2_cols, src_rows, 1}; + int width = var_count; + + char build_options[50]; + + if(flag) + { + sprintf(build_options, "-D ADDPOW"); + } + vector< pair > args; + args.push_back(make_pair(sizeof(cl_mem), (void* )&src.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&src2.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&dst.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&dst_step)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_rows)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_cols)); + args.push_back(make_pair(sizeof(cl_int), (void* )&width)); + + float alpha = 0.0f, beta = 0.0f, degree = 0.0f; + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + alpha = (float)alpha1; + beta = (float)beta1; + degree = (float)degree1; + args.push_back(make_pair(sizeof(cl_float), (void* )&alpha)); + args.push_back(make_pair(sizeof(cl_float), (void* )&beta)); + args.push_back(make_pair(sizeof(cl_float), (void* )°ree)); + } + else + { + args.push_back(make_pair(sizeof(cl_double), (void* )&alpha1)); + args.push_back(make_pair(sizeof(cl_double), (void* )&beta1)); + args.push_back(make_pair(sizeof(cl_double), (void* )°ree1)); + } + openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1, build_options); +} +void matmul_linear(oclMat & src, oclMat & src2, oclMat & dst, int src_rows, int src2_cols, int var_count, double alpha1, double beta1) +{ + Context *clCxt = Context::getContext(); + string kernelName = "svm_linear"; + int src_step = (int)src.step / src.elemSize(); + int src2_step = (int)src2.step / src2.elemSize(); + int dst_step = (int)dst.step / dst.elemSize(); + int x = MIN(16, src_rows); + int y = MIN(16, src2_cols); + size_t localThreads[] = {x, y, 1}; + size_t globalThreads[] = {src2_cols, src_rows, 1}; + int width = var_count; + + vector< pair > args; + args.push_back(make_pair(sizeof(cl_mem), (void* )&src.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&src2.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&dst.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&dst_step)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_rows)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_cols)); + args.push_back(make_pair(sizeof(cl_int), (void* )&width)); + + float alpha = 0.0f, beta = 0.0f; + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + alpha = (float)alpha1; + beta = (float)beta1; + args.push_back(make_pair(sizeof(cl_float), (void* )&alpha)); + args.push_back(make_pair(sizeof(cl_float), (void* )&beta)); + } + else + { + args.push_back(make_pair(sizeof(cl_double), (void* )&alpha1)); + args.push_back(make_pair(sizeof(cl_double), (void* )&beta1)); + } + openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1); +} +void matmul_rbf(oclMat& src, oclMat& src_e, oclMat& dst, int src_rows, int src2_cols, int var_count, double gamma1, bool flag) +{ + + Context *clCxt = Context::getContext(); + + string kernelName = "svm_rbf"; + + int width = var_count; + int src_step = (int)src.step / src.elemSize(); + int src_e_step = (int)src_e.step / src_e.elemSize(); + int dst_step = (int)dst.step / dst.elemSize(); + + int x = MIN(16, src_rows); + int y = MIN(16, src2_cols); + size_t localThreads[] = {x, y, 1}; + size_t globalThreads[] = {src2_cols, src_rows, 1}; + char build_options[50]; + + if(flag) + { + sprintf(build_options, "-D ADDEXP"); + } + vector< pair > args; + args.push_back(make_pair(sizeof(cl_mem), (void* )&src.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&src_e.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_e_step)); + args.push_back(make_pair(sizeof(cl_mem), (void* )&dst.data)); + args.push_back(make_pair(sizeof(cl_int), (void* )&dst_step)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src_rows)); + args.push_back(make_pair(sizeof(cl_int), (void* )&src2_cols)); + args.push_back(make_pair(sizeof(cl_int), (void* )&width)); + float gamma = 0.0f; + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + gamma = (float)gamma1; + args.push_back(make_pair(sizeof(cl_float), (void* )&gamma)); + } + else + { + args.push_back(make_pair(sizeof(cl_double), (void* )&gamma1)); + } + + openCLExecuteKernel(clCxt, &svm, kernelName, globalThreads, localThreads, args, -1, -1, build_options); +} +float CvSVM_OCL::predict(const CvMat* samples, CV_OUT CvMat* results) const +{ + int var_count = get_var_count(); + int sample_count = samples->rows; + + //float* row_sample = 0; + Mat src_temp = Mat(sample_count, var_count, CV_32FC1); + CV_FUNCNAME( "CvSVM::predict" ); + + + for(int i = 0; i < samples->rows; i++) + { + __BEGIN__; + CvMat sample; + float* row_sample = 0; + cvGetRow( samples, &sample, i ); + int class_count; + if( !kernel ) + { + CV_ERROR( CV_StsBadArg, "The SVM should be trained first" ); + } + + class_count = class_labels ? class_labels->cols : + params.svm_type == ONE_CLASS ? 1 : 0; + + CV_CALL( cvPreparePredictData(&sample, var_all, var_idx, + class_count, 0, &row_sample )); + for(int j = 0; j < var_count; ++j) + { + src_temp.at(i, j) = row_sample[j]; + } + __END__; + } + + Mat dst1; + double alpha1 = 0.0, beta1 = 0.0, gamma1 = 0.0, degree1 = 0.0; + if(params.kernel_type == CvSVM::LINEAR) + { + alpha1 = 1; + beta1 = 0; + } + if(params.kernel_type == CvSVM::POLY) + { + alpha1 = params.gamma; + beta1 = params.coef0; + degree1 = params.degree; + } + if(params.kernel_type == CvSVM::SIGMOID) + { + alpha1 = - 2 * params.gamma; + beta1 = - 2 * params.coef0; + } + if(params.kernel_type == CvSVM::RBF) + { + gamma1 = - params.gamma; + } + + Mat sv_temp = Mat(sv_total, var_count, CV_32FC1, Scalar::all(0)); + + + for(int i = 0; i < sv_total; ++i) + { + for(int j = 0; j < var_count; ++j) + { + sv_temp.at(i, j) = sv[i][j]; + } + } + oclMat src(sample_count, var_count, CV_32FC1, Scalar::all(0)); + oclMat sv_; + + src.upload(src_temp); + oclMat dst; + +#if defined HAVE_CLAMDBLAS + + dst = oclMat(sample_count, sv_total, CV_32FC1); + oclMat src3(sample_count, sv_total, CV_32FC1, Scalar::all(1)); + if(params.kernel_type != CvSVM::RBF) + { + Mat sv_temp1; + transpose(sv_temp, sv_temp1); + sv_.upload(sv_temp1); + gemm(src, sv_, alpha1, src3, beta1, dst); + } + +#else + + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + dst = oclMat(sample_count, sv_total, CV_32FC1); + } + else + { + dst = oclMat(sample_count, sv_total, CV_64FC1); + } + if(params.kernel_type == CvSVM::LINEAR) + { + sv_.upload(sv_temp); + matmul_linear(src, sv_, dst, sample_count, sv_total, var_count, alpha1, beta1); + } + if( params.kernel_type == CvSVM::SIGMOID) + { + sv_.upload(sv_temp); + matmul_sigmod(src, sv_, dst, sample_count, sv_total, var_count, alpha1, beta1); + } + + if(params.kernel_type == CvSVM::POLY) + { + sv_.upload(sv_temp); + if(sample_count > 0) + { + matmul_poly(src, sv_, dst, sample_count, sv_total, var_count, alpha1, beta1, degree1, true); + } + else + { + matmul_poly(src, sv_, dst, sample_count, sv_total, var_count, alpha1, beta1, degree1, false); + } + } +#endif + + if(params.kernel_type == CvSVM::RBF) + { + sv_.upload(sv_temp); + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + dst = oclMat(sample_count, sv_total, CV_32FC1); + } + else + { + dst = oclMat(sample_count, sv_total, CV_64FC1); + } + if(sample_count > 0) + { + matmul_rbf(src, sv_, dst, sample_count, sv_total, var_count, gamma1, true); + } + else + { + matmul_rbf(src, sv_, dst, sample_count, sv_total, var_count, gamma1, false); + } + } + dst.download(dst1); + + float result = 0; + for(int i = 0; i < samples->rows; i++ ) + { + int r = (int)this->predict(i, dst1); + if (results) + { + results->data.fl[i] = (float)r; + } + if (i == 0) + { + result = (float)r; + } + } + return result; +} +void CvSVM_OCL::predict( cv::InputArray _samples, cv::OutputArray _results ) const +{ + _results.create(_samples.size().height, 1, CV_32F); + CvMat samples = _samples.getMat(), results = _results.getMat(); + predict(&samples, &results); +} +bool CvSVMSolver_ocl::solve_generic( CvSVMSolutionInfo& si ) +{ + int iter = 0; + int i, j, k; + + // 1. initialize gradient and alpha status + for( i = 0; i < alpha_count; i++ ) + { + update_alpha_status(i); + G[i] = b[i]; + if( fabs(G[i]) > 1e200 ) + { + return false; + } + } + Mat dst1; + double alpha1 = 0.0, beta1 = 0.0, gamma1 = 0.0, degree1 = 0.0; + if(params->kernel_type == CvSVM::LINEAR) + { + alpha1 = 1; + beta1 = 0; + } + if(params->kernel_type == CvSVM::POLY) + { + alpha1 = params->gamma; + beta1 = params->coef0; + degree1 = params->degree; + } + if(params->kernel_type == CvSVM::SIGMOID) + { + alpha1 = -2 * params->gamma; + beta1 = -2 * params->coef0; + } + if(params->kernel_type == CvSVM::RBF) + { + gamma1 = -params->gamma; + } + Mat src1 = Mat(sample_count, var_count, CV_32FC1); + + for(int i = 0; i < sample_count; ++i) + { + for(int j = 0; j < var_count; ++j) + { + src1.at(i, j) = samples[i][j]; + } + } + oclMat src, src_e; + src.upload(src1); + oclMat dst; + +#if defined HAVE_CLAMDBLAS + + dst = oclMat(sample_count, sample_count, CV_32FC1); + oclMat src3(sample_count, sample_count, CV_32FC1, Scalar::all(1)); + if(params->kernel_type != CvSVM::RBF) + { + ocl::transpose(src, src_e); + gemm(src, src_e, alpha1, src3, beta1, dst); + } + +#else + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + dst = oclMat(sample_count, sample_count, CV_32FC1); + } + else + { + dst = oclMat(sample_count, sample_count, CV_64FC1); + } + if(params->kernel_type == CvSVM::LINEAR ) + { + src_e = src; + matmul_linear(src, src_e, dst, sample_count, sample_count, var_count, alpha1, beta1); + } + if( params->kernel_type == CvSVM::SIGMOID) + { + src_e = src; + matmul_sigmod(src, src_e, dst, sample_count, sample_count, var_count, alpha1, beta1); + } + + if(params->kernel_type == CvSVM::POLY) + { + src_e = src; + if(sample_count > 0) + { + matmul_poly(src, src_e, dst, sample_count, sample_count, var_count, alpha1, beta1, degree1, true); + } + else + { + matmul_poly(src, src_e, dst, sample_count, sample_count, var_count, alpha1, beta1, degree1, false); + } + } + +#endif + + if(params->kernel_type == CvSVM::RBF) + { + src_e = src; + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + dst = oclMat(sample_count, sample_count, CV_32FC1); + } + else + { + dst = oclMat(sample_count, sample_count, CV_64FC1); + } + if(sample_count > 0) + { + matmul_rbf(src, src_e, dst, sample_count, sample_count, var_count, gamma1, true); + } + else + { + matmul_rbf(src, src_e, dst, sample_count, sample_count, var_count, gamma1, false); + } + } + dst.download(dst1); + for( i = 0; i < alpha_count; i++ ) + { + if( !is_lower_bound(i) ) + { + const Qfloat *Q_i = CvSVMSolver::get_row( i, buf[0]); + double alpha_i = alpha[i]; + + for( j = 0; j < alpha_count; j++ ) + { + G[j] += alpha_i * Q_i[j]; + } + } + } + + // 2. optimization loop + for(;;) + { + const Qfloat *Q_i, *Q_j; + double C_i, C_j; + double old_alpha_i, old_alpha_j, alpha_i, alpha_j; + double delta_alpha_i, delta_alpha_j; + +#ifdef _DEBUG + for( i = 0; i < alpha_count; i++ ) + { + if( fabs(G[i]) > 1e+300 ) + { + return false; + } + + if( fabs(alpha[i]) > 1e16 ) + { + return false; + } + } +#endif + + if( (this->*select_working_set_func)( i, j ) != 0 || iter++ >= max_iter ) + { + break; + } + Q_i = get_row( i, buf[0], dst1); + Q_j = get_row( j, buf[1], dst1); + + C_i = get_C(i); + C_j = get_C(j); + + alpha_i = old_alpha_i = alpha[i]; + alpha_j = old_alpha_j = alpha[j]; + + if( y[i] != y[j] ) + { + double denom = Q_i[i] + Q_j[j] + 2 * Q_i[j]; + double delta = (-G[i] - G[j]) / MAX(fabs(denom), FLT_EPSILON); + double diff = alpha_i - alpha_j; + alpha_i += delta; + alpha_j += delta; + + if( diff > 0 && alpha_j < 0 ) + { + alpha_j = 0; + alpha_i = diff; + } + else if( diff <= 0 && alpha_i < 0 ) + { + alpha_i = 0; + alpha_j = -diff; + } + + if( diff > C_i - C_j && alpha_i > C_i ) + { + alpha_i = C_i; + alpha_j = C_i - diff; + } + else if( diff <= C_i - C_j && alpha_j > C_j ) + { + alpha_j = C_j; + alpha_i = C_j + diff; + } + } + else + { + double denom = Q_i[i] + Q_j[j] - 2 * Q_i[j]; + double delta = (G[i] - G[j]) / MAX(fabs(denom), FLT_EPSILON); + double sum = alpha_i + alpha_j; + alpha_i -= delta; + alpha_j += delta; + + if( sum > C_i && alpha_i > C_i ) + { + alpha_i = C_i; + alpha_j = sum - C_i; + } + else if( sum <= C_i && alpha_j < 0) + { + alpha_j = 0; + alpha_i = sum; + } + + if( sum > C_j && alpha_j > C_j ) + { + alpha_j = C_j; + alpha_i = sum - C_j; + } + else if( sum <= C_j && alpha_i < 0 ) + { + alpha_i = 0; + alpha_j = sum; + } + } + // update alpha + alpha[i] = alpha_i; + alpha[j] = alpha_j; + update_alpha_status(i); + update_alpha_status(j); + + // update G + delta_alpha_i = alpha_i - old_alpha_i; + delta_alpha_j = alpha_j - old_alpha_j; + + for( k = 0; k < alpha_count; k++ ) + { + G[k] += Q_i[k] * delta_alpha_i + Q_j[k] * delta_alpha_j; + } + } + + // calculate rho + (this->*calc_rho_func)( si.rho, si.r ); + + // calculate objective value + for( i = 0, si.obj = 0; i < alpha_count; i++ ) + { + si.obj += alpha[i] * (G[i] + b[i]); + } + + si.obj *= 0.5; + + si.upper_bound_p = C[1]; + si.upper_bound_n = C[0]; + + return true; +} + +void CvSVMKernel_ocl::calc( int vcount, const int row_idx, Qfloat* results, Mat& src) +{ + //const Qfloat max_val = (Qfloat)(FLT_MAX*1e-3); + //int j; + (this->*calc_func_ocl)( vcount, row_idx, results, src); + +#if defined HAVE_CLAMDBLAS + const Qfloat max_val = (Qfloat)(FLT_MAX * 1e-3); + int j; + for( j = 0; j < vcount; j++ ) + { + if( results[j] > max_val ) + { + results[j] = max_val; + } + } +#endif +} +bool CvSVMKernel_ocl::create( const CvSVMParams* _params, Calc_ocl _calc_func, Calc _calc_func1 ) +{ + clear(); + params = _params; + calc_func_ocl = _calc_func; + calc_func = _calc_func1; + if( !calc_func_ocl ) + calc_func_ocl = params->kernel_type == CvSVM::RBF ? &CvSVMKernel_ocl::calc_rbf : + params->kernel_type == CvSVM::POLY ? &CvSVMKernel_ocl::calc_poly : + params->kernel_type == CvSVM::SIGMOID ? &CvSVMKernel_ocl::calc_sigmoid : + &CvSVMKernel_ocl::calc_linear; + if( !calc_func) + calc_func = params->kernel_type == CvSVM::RBF ? &CvSVMKernel::calc_rbf : + params->kernel_type == CvSVM::POLY ? &CvSVMKernel::calc_poly : + params->kernel_type == CvSVM::SIGMOID ? &CvSVMKernel::calc_sigmoid : + &CvSVMKernel::calc_linear; + return true; +} +CvSVMKernel_ocl::CvSVMKernel_ocl(const CvSVMParams* params, CvSVMKernel_ocl::Calc_ocl _calc_func, CvSVMKernel::Calc _calc_func1) +{ + CvSVMKernel::clear(); + CvSVMKernel_ocl::create( params, _calc_func, _calc_func1 ); +} +void CvSVMKernel_ocl::calc_non_rbf_base( int vcount, const int row_idx, Qfloat* results, Mat& src) +{ +#if defined HAVE_CLAMDBLAS + + for(int i = 0; i < vcount; i++) + { + results[i] = (Qfloat) * src.ptr(row_idx, i); + } +#else + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + for(int i = 0; i < vcount; i++) + { + results[i] = (Qfloat) * src.ptr(row_idx, i); + } + } + else + { + for(int i = 0; i < vcount; i++) + { + results[i] = (Qfloat) * src.ptr(row_idx, i); + } + } +#endif +} +void CvSVMKernel_ocl::calc_rbf( int vcount, const int row_idx, Qfloat* results, Mat& src) +{ + if(!Context::getContext()->supportsFeature(Context::CL_DOUBLE)) + { + for(int m = 0; m < vcount; m++) + { + results[m] = (Qfloat) * src.ptr(row_idx, m); + } + } + else + { + for(int m = 0; m < vcount; m++) + { + results[m] = (Qfloat) * src.ptr(row_idx, m); + } + } +} +void CvSVMKernel_ocl::calc_linear( int vcount, const int row_idx, Qfloat* results, Mat& src ) +{ + calc_non_rbf_base( vcount, row_idx, results, src); +} + +void CvSVMKernel_ocl::calc_poly( int vcount, const int row_idx, Qfloat* results, Mat& src) +{ + + calc_non_rbf_base( vcount, row_idx, results, src); + +#if defined HAVE_CLAMDBLAS + + CvMat R = cvMat( 1, vcount, QFLOAT_TYPE, results ); + if( vcount > 0 ) + { + cvPow( &R, &R, params->degree ); + } +#endif +} + + +void CvSVMKernel_ocl::calc_sigmoid( int vcount, const int row_idx, Qfloat* results, Mat& src) +{ + calc_non_rbf_base( vcount, row_idx, results, src); + // TODO: speedup this +#if defined HAVE_CLAMDBLAS + for(int j = 0; j < vcount; j++ ) + { + Qfloat t = results[j]; + double e = exp(-fabs(t)); + if( t > 0 ) + { + results[j] = (Qfloat)((1. - e) / (1. + e)); + } + else + { + results[j] = (Qfloat)((e - 1.) / (e + 1.)); + } + } +#endif +} +CvSVM_OCL::CvSVM_OCL() +{ + CvSVM::CvSVM(); +} + +CvSVM_OCL::CvSVM_OCL( const Mat& _train_data, const Mat& _responses, + const Mat& _var_idx, const Mat& _sample_idx, CvSVMParams _params ) +{ + decision_func = 0; + class_labels = 0; + class_weights = 0; + storage = 0; + var_idx = 0; + kernel = 0; + solver = 0; + default_model_name = "my_svm"; + + train( _train_data, _responses, _var_idx, _sample_idx, _params ); +} + +void CvSVM_OCL::create_kernel() +{ + kernel = new CvSVMKernel_ocl(¶ms, 0, 0); +} +void CvSVM_OCL::create_solver( ) +{ + solver = new CvSVMSolver_ocl(¶ms); +}