diff --git a/modules/ocl/src/brute_force_matcher.cpp b/modules/ocl/src/brute_force_matcher.cpp index e61a9f6330..e8f28b778c 100644 --- a/modules/ocl/src/brute_force_matcher.cpp +++ b/modules/ocl/src/brute_force_matcher.cpp @@ -16,6 +16,7 @@ // // @Authors // Nathan, liujun@multicorewareinc.com +// Peng Xiao, pengxiao@outlook.com // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: @@ -61,6 +62,8 @@ namespace cv } } +static const int OPT_SIZE = 100; + template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ > void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, const oclMat &trainIdx, const oclMat &distance, int distType) @@ -74,6 +77,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat int m_size = MAX_DESC_LEN; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -82,18 +88,15 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); - args.push_back( make_pair( sizeof(cl_int), (void *)&m_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_UnrollMatch"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -115,6 +118,9 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, int block_size = BLOCK_SIZE; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -123,17 +129,15 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_Match"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -157,6 +161,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist int m_size = MAX_DESC_LEN; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -167,8 +174,6 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&nMatches.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); - args.push_back( make_pair( sizeof(cl_int), (void *)&m_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); @@ -176,11 +181,10 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_RadiusUnrollMatch"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -197,6 +201,9 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c int block_size = BLOCK_SIZE; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -207,7 +214,6 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&nMatches.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); @@ -215,11 +221,10 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); args.push_back( make_pair( sizeof(cl_int), (void *)&trainIdx.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_RadiusMatch"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -294,6 +299,9 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl int m_size = MAX_DESC_LEN; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -302,18 +310,15 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); - args.push_back( make_pair( sizeof(cl_int), (void *)&m_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_knnUnrollMatch"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -328,6 +333,9 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, int block_size = BLOCK_SIZE; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size); + if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -336,17 +344,15 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/, args.push_back( make_pair( sizeof(cl_mem), (void *)&trainIdx.data )); args.push_back( make_pair( sizeof(cl_mem), (void *)&distance.data )); args.push_back( make_pair( smemSize, (void *)NULL)); - args.push_back( make_pair( sizeof(cl_int), (void *)&block_size )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_knnMatch"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -361,6 +367,8 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat int m_size = MAX_DESC_LEN; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d", distType); if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -375,11 +383,10 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_calcDistanceUnrolled"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -393,6 +400,8 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask int block_size = BLOCK_SIZE; vector< pair > args; + char opt [OPT_SIZE] = ""; + sprintf(opt, "-D DIST_TYPE=%d", distType); if(globalSize[0] != 0) { args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data )); @@ -406,11 +415,10 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask args.push_back( make_pair( sizeof(cl_int), (void *)&train.rows )); args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols )); args.push_back( make_pair( sizeof(cl_int), (void *)&query.step )); - args.push_back( make_pair( sizeof(cl_int), (void *)&distType )); std::string kernelName = "BruteForceMatch_calcDistance"; - openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth()); + openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt); } } @@ -534,24 +542,23 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat &query, const // match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int int callType = query.depth(); - char cvFuncName[] = "singleMatch"; if (callType != 5) - CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0 || callType != 2 || callType != 4))) { - CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); } CV_Assert(query.channels() == 1 && query.depth() < CV_64F); CV_Assert(train.cols == query.cols && train.type() == query.type()); - trainIdx.create(1, query.rows, CV_32S); - distance.create(1, query.rows, CV_32F); + ensureSizeIsEnough(1, query.rows, CV_32S, trainIdx); + ensureSizeIsEnough(1, query.rows, CV_32F, distance); matchDispatcher(query, train, mask, trainIdx, distance, distType); -exit: + return; } @@ -656,24 +663,26 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, c // match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int int callType = query.depth(); - char cvFuncName[] = "matchCollection"; if (callType != 5) - CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0 || callType != 2 || callType != 4))) { - CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); } CV_Assert(query.channels() == 1 && query.depth() < CV_64F); + + const int nQuery = query.rows; + + ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx); + ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx); + ensureSizeIsEnough(1, nQuery, CV_32F, distance); - trainIdx.create(1, query.rows, CV_32S); - imgIdx.create(1, query.rows, CV_32S); - distance.create(1, query.rows, CV_32F); matchDispatcher(query, (const oclMat *)trainCollection.ptr(), trainCollection.cols, masks, trainIdx, imgIdx, distance, distType); -exit: + return; } @@ -746,35 +755,37 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, co // match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int int callType = query.depth(); - char cvFuncName[] = "knnMatchSingle"; if (callType != 5) - CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0 || callType != 2 || callType != 4))) { - CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); } CV_Assert(query.channels() == 1 && query.depth() < CV_64F); CV_Assert(train.type() == query.type() && train.cols == query.cols); + const int nQuery = query.rows; + const int nTrain = train.rows; + if (k == 2) { - trainIdx.create(1, query.rows, CV_32SC2); - distance.create(1, query.rows, CV_32FC2); + ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx); + ensureSizeIsEnough(1, nQuery, CV_32FC2, distance); } else { - trainIdx.create(query.rows, k, CV_32S); - distance.create(query.rows, k, CV_32F); - allDist.create(query.rows, train.rows, CV_32FC1); + ensureSizeIsEnough(nQuery, k, CV_32S, trainIdx); + ensureSizeIsEnough(nQuery, k, CV_32F, distance); + ensureSizeIsEnough(nQuery, nTrain, CV_32FC1, allDist); } trainIdx.setTo(Scalar::all(-1)); kmatchDispatcher(query, train, k, mask, trainIdx, distance, allDist, distType); -exit: + return; } @@ -873,9 +884,9 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &quer const int nQuery = query.rows; - trainIdx.create(1, nQuery, CV_32SC2); - imgIdx.create(1, nQuery, CV_32SC2); - distance.create(1, nQuery, CV_32SC2); + ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx); + ensureSizeIsEnough(1, nQuery, CV_32SC2, imgIdx); + ensureSizeIsEnough(1, nQuery, CV_32FC2, distance); trainIdx.setTo(Scalar::all(-1)); @@ -1021,31 +1032,34 @@ void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query, // match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int int callType = query.depth(); - char cvFuncName[] = "radiusMatchSingle"; + if (callType != 5) - CV_ERROR(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_FORMAT_ERR, "BruteForceMatch OpenCL only support float type query!\n"); if ((distType == 0 && callType == 1 ) || (distType == 1 && callType != 5) || (distType == 2 && (callType != 0 || callType != 2 || callType != 4))) { - CV_ERROR(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); + CV_Error(CV_UNSUPPORTED_DEPTH_ERR, "BruteForceMatch OpenCL only support float type query!\n"); } + const int nQuery = query.rows; + const int nTrain = train.rows; + CV_Assert(query.channels() == 1 && query.depth() < CV_64F); CV_Assert(train.type() == query.type() && train.cols == query.cols); CV_Assert(trainIdx.empty() || (trainIdx.rows == query.rows && trainIdx.size() == distance.size())); - nMatches.create(1, query.rows, CV_32SC1); + ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches); if (trainIdx.empty()) { - trainIdx.create(query.rows, std::max((train.rows/ 100), 10), CV_32SC1); - distance.create(query.rows, std::max((train.rows/ 100), 10), CV_32FC1); + ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx); + ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance); } nMatches.setTo(Scalar::all(0)); matchDispatcher(query, train, maxDistance, mask, trainIdx, distance, nMatches, distType); -exit: + return; } diff --git a/modules/ocl/src/opencl/brute_force_match.cl b/modules/ocl/src/opencl/brute_force_match.cl index e76fb1d21e..7446c779b0 100644 --- a/modules/ocl/src/opencl/brute_force_match.cl +++ b/modules/ocl/src/opencl/brute_force_match.cl @@ -1,5 +1,58 @@ +/*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 +// Nathan, liujun@multicorewareinc.com +// Peng Xiao, pengxiao@outlook.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*/ + #pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable -#define MAX_FLOAT 1e7f +#define MAX_FLOAT 3.40282e+038f + +#ifndef BLOCK_SIZE +#define BLOCK_SIZE 16 +#endif +#ifndef MAX_DESC_LEN +#define MAX_DESC_LEN 64 +#endif int bit1Count(float x) { @@ -13,83 +66,52 @@ int bit1Count(float x) return (float)c; } +#ifndef DIST_TYPE +#define DIST_TYPE 0 +#endif + +#if (DIST_TYPE == 0) +#define DIST(x, y) fabs((x) - (y)) +#elif (DIST_TYPE == 1) +#define DIST(x, y) (((x) - (y)) * ((x) - (y))) +#elif (DIST_TYPE == 2) +#define DIST(x, y) bit1Count((uint)(x) ^ (uint)(y)) +#endif + + float reduce_block(__local float *s_query, __local float *s_train, - int block_size, int lidx, - int lidy, - int distType + int lidy ) { - /* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to - sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/ float result = 0; - switch(distType) + #pragma unroll + for (int j = 0 ; j < BLOCK_SIZE ; j++) { - case 0: - for (int j = 0 ; j < block_size ; j++) - { - result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]); - } - break; - case 1: - for (int j = 0 ; j < block_size ; j++) - { - float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx]; - result += qr * qr; - } - break; - case 2: - for (int j = 0 ; j < block_size ; j++) - { - result += bit1Count((uint)s_query[lidy * block_size + j] ^ (uint)s_train[(uint)j * block_size + lidx]); - } - break; + result += DIST(s_query[lidy * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]); } return result; } float reduce_multi_block(__local float *s_query, __local float *s_train, - int max_desc_len, - int block_size, int block_index, int lidx, - int lidy, - int distType + int lidy ) { - /* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to - sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/ float result = 0; - switch(distType) + #pragma unroll + for (int j = 0 ; j < BLOCK_SIZE ; j++) { - case 0: - for (int j = 0 ; j < block_size ; j++) - { - result += fabs(s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx]); - } - break; - case 1: - for (int j = 0 ; j < block_size ; j++) - { - float qr = s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx]; - result += qr * qr; - } - break; - case 2: - for (int j = 0 ; j < block_size ; j++) - { - //result += popcount((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]); - result += bit1Count((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]); - } - break; + result += DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]); } return result; } -/* 2dim launch, global size: dim0 is (query rows + block_size - 1) / block_size * block_size, dim1 is block_size -local size: dim0 is block_size, dim1 is block_size. +/* 2dim launch, global size: dim0 is (query rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, dim1 is BLOCK_SIZE +local size: dim0 is BLOCK_SIZE, dim1 is BLOCK_SIZE. */ __kernel void BruteForceMatch_UnrollMatch_D5( __global float *query, @@ -98,29 +120,28 @@ __kernel void BruteForceMatch_UnrollMatch_D5( __global int *bestTrainIdx, __global float *bestDistance, __local float *sharebuffer, - int block_size, - int max_desc_len, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType + int step ) { + const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); __local float *s_query = sharebuffer; - __local float *s_train = sharebuffer + block_size * max_desc_len; + __local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN; - int queryIdx = groupidx * block_size + lidy; + int queryIdx = groupidx * BLOCK_SIZE + lidy; // load the query into local memory. - for (int i = 0 ; i < max_desc_len / block_size; i ++) + #pragma unroll + for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++) { - int loadx = lidx + i * block_size; - s_query[lidy * max_desc_len + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; + int loadx = lidx + i * BLOCK_SIZE; + s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; } float myBestDistance = MAX_FLOAT; @@ -128,24 +149,25 @@ __kernel void BruteForceMatch_UnrollMatch_D5( // loopUnrolledCached to find the best trainIdx and best distance. volatile int imgIdx = 0; - for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++) + for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++) { float result = 0; - for (int i = 0 ; i < max_desc_len / block_size ; i++) + #pragma unroll + for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++) { - //load a block_size * block_size block into local train. - const int loadx = lidx + i * block_size; - s_train[lidx * block_size + lidy] = loadx < train_cols ? train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; + //load a BLOCK_SIZE * BLOCK_SIZE block into local train. + const int loadx = lidx + i * BLOCK_SIZE; + s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; //synchronize to make sure each elem for reduceIteration in share memory is written already. barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType); + result += reduce_multi_block(s_query, s_train, i, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } - int trainIdx = t * block_size + lidx; + int trainIdx = t * BLOCK_SIZE + lidx; if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance/* && mask(queryIdx, trainIdx)*/) { @@ -157,18 +179,19 @@ __kernel void BruteForceMatch_UnrollMatch_D5( barrier(CLK_LOCAL_MEM_FENCE); __local float *s_distance = (__local float*)(sharebuffer); - __local int* s_trainIdx = (__local int *)(sharebuffer + block_size * block_size); + __local int* s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE); //find BestMatch - s_distance += lidy * block_size; - s_trainIdx += lidy * block_size; + s_distance += lidy * BLOCK_SIZE; + s_trainIdx += lidy * BLOCK_SIZE; s_distance[lidx] = myBestDistance; s_trainIdx[lidx] = myBestTrainIdx; barrier(CLK_LOCAL_MEM_FENCE); //reduce -- now all reduce implement in each threads. - for (int k = 0 ; k < block_size; k++) + #pragma unroll + for (int k = 0 ; k < BLOCK_SIZE; k++) { if (myBestDistance > s_distance[k]) { @@ -191,53 +214,51 @@ __kernel void BruteForceMatch_Match_D5( __global int *bestTrainIdx, __global float *bestDistance, __local float *sharebuffer, - int block_size, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType + int step ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); - const int queryIdx = groupidx * block_size + lidy; + const int queryIdx = groupidx * BLOCK_SIZE + lidy; float myBestDistance = MAX_FLOAT; int myBestTrainIdx = -1; __local float *s_query = sharebuffer; - __local float *s_train = sharebuffer + block_size * block_size; + __local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE; // loop - for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++) + for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++) { //Dist dist; float result = 0; - for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; i++) + for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++) { - const int loadx = lidx + i * block_size; + const int loadx = lidx + i * BLOCK_SIZE; //load query and train into local memory - s_query[lidy * block_size + lidx] = 0; - s_train[lidx * block_size + lidy] = 0; + s_query[lidy * BLOCK_SIZE + lidx] = 0; + s_train[lidx * BLOCK_SIZE + lidy] = 0; if (loadx < query_cols) { - s_query[lidy * block_size + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx]; - s_train[lidx * block_size + lidy] = train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx]; + s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx]; + s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx]; } barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType); + result += reduce_block(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } - const int trainIdx = t * block_size + lidx; + const int trainIdx = t * BLOCK_SIZE + lidx; if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/) { @@ -250,18 +271,18 @@ __kernel void BruteForceMatch_Match_D5( barrier(CLK_LOCAL_MEM_FENCE); __local float *s_distance = (__local float *)sharebuffer; - __local int *s_trainIdx = (__local int *)(sharebuffer + block_size * block_size); + __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE); //findBestMatch - s_distance += lidy * block_size; - s_trainIdx += lidy * block_size; + s_distance += lidy * BLOCK_SIZE; + s_trainIdx += lidy * BLOCK_SIZE; s_distance[lidx] = myBestDistance; s_trainIdx[lidx] = myBestTrainIdx; barrier(CLK_LOCAL_MEM_FENCE); //reduce -- now all reduce implement in each threads. - for (int k = 0 ; k < block_size; k++) + for (int k = 0 ; k < BLOCK_SIZE; k++) { if (myBestDistance > s_distance[k]) { @@ -287,16 +308,13 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5( __global float *bestDistance, __global int *nMatches, __local float *sharebuffer, - int block_size, - int max_desc_len, int query_rows, int query_cols, int train_rows, int train_cols, int bestTrainIdx_cols, int step, - int ostep, - int distType + int ostep ) { const int lidx = get_local_id(0); @@ -304,25 +322,25 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5( const int groupidx = get_group_id(0); const int groupidy = get_group_id(1); - const int queryIdx = groupidy * block_size + lidy; - const int trainIdx = groupidx * block_size + lidx; + const int queryIdx = groupidy * BLOCK_SIZE + lidy; + const int trainIdx = groupidx * BLOCK_SIZE + lidx; __local float *s_query = sharebuffer; - __local float *s_train = sharebuffer + block_size * block_size; + __local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE; float result = 0; - for (int i = 0 ; i < max_desc_len / block_size ; ++i) + for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i) { - //load a block_size * block_size block into local train. - const int loadx = lidx + i * block_size; + //load a BLOCK_SIZE * BLOCK_SIZE block into local train. + const int loadx = lidx + i * BLOCK_SIZE; - s_query[lidy * block_size + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; - s_train[lidx * block_size + lidy] = loadx < query_cols ? train[min(groupidx * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; + s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; + s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; //synchronize to make sure each elem for reduceIteration in share memory is written already. barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType); + result += reduce_block(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } @@ -350,15 +368,13 @@ __kernel void BruteForceMatch_RadiusMatch_D5( __global float *bestDistance, __global int *nMatches, __local float *sharebuffer, - int block_size, int query_rows, int query_cols, int train_rows, int train_cols, int bestTrainIdx_cols, int step, - int ostep, - int distType + int ostep ) { const int lidx = get_local_id(0); @@ -366,25 +382,25 @@ __kernel void BruteForceMatch_RadiusMatch_D5( const int groupidx = get_group_id(0); const int groupidy = get_group_id(1); - const int queryIdx = groupidy * block_size + lidy; - const int trainIdx = groupidx * block_size + lidx; + const int queryIdx = groupidy * BLOCK_SIZE + lidy; + const int trainIdx = groupidx * BLOCK_SIZE + lidx; __local float *s_query = sharebuffer; - __local float *s_train = sharebuffer + block_size * block_size; + __local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE; float result = 0; - for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; ++i) + for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i) { - //load a block_size * block_size block into local train. - const int loadx = lidx + i * block_size; + //load a BLOCK_SIZE * BLOCK_SIZE block into local train. + const int loadx = lidx + i * BLOCK_SIZE; - s_query[lidy * block_size + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; - s_train[lidx * block_size + lidy] = loadx < query_cols ? train[min(groupidx * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; + s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; + s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; //synchronize to make sure each elem for reduceIteration in share memory is written already. barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType); + result += reduce_block(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } @@ -410,29 +426,26 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5( __global int2 *bestTrainIdx, __global float2 *bestDistance, __local float *sharebuffer, - int block_size, - int max_desc_len, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType + int step ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); - const int queryIdx = groupidx * block_size + lidy; + const int queryIdx = groupidx * BLOCK_SIZE + lidy; local float *s_query = sharebuffer; - local float *s_train = sharebuffer + block_size * max_desc_len; + local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN; // load the query into local memory. - for (int i = 0 ; i < max_desc_len / block_size; i ++) + for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++) { - int loadx = lidx + i * block_size; - s_query[lidy * max_desc_len + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; + int loadx = lidx + i * BLOCK_SIZE; + s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0; } float myBestDistance1 = MAX_FLOAT; @@ -442,25 +455,25 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5( //loopUnrolledCached volatile int imgIdx = 0; - for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++) + for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++) { float result = 0; - for (int i = 0 ; i < max_desc_len / block_size ; i++) + for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++) { - const int loadX = lidx + i * block_size; - //load a block_size * block_size block into local train. - const int loadx = lidx + i * block_size; - s_train[lidx * block_size + lidy] = loadx < train_cols ? train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; + const int loadX = lidx + i * BLOCK_SIZE; + //load a BLOCK_SIZE * BLOCK_SIZE block into local train. + const int loadx = lidx + i * BLOCK_SIZE; + s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0; //synchronize to make sure each elem for reduceIteration in share memory is written already. barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType); + result += reduce_multi_block(s_query, s_train, i, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } - const int trainIdx = t * block_size + lidx; + const int trainIdx = t * BLOCK_SIZE + lidx; if (queryIdx < query_rows && trainIdx < train_rows) { @@ -482,11 +495,11 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5( barrier(CLK_LOCAL_MEM_FENCE); local float *s_distance = (local float *)sharebuffer; - local int *s_trainIdx = (local int *)(sharebuffer + block_size * block_size); + local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE); // find BestMatch - s_distance += lidy * block_size; - s_trainIdx += lidy * block_size; + s_distance += lidy * BLOCK_SIZE; + s_trainIdx += lidy * BLOCK_SIZE; s_distance[lidx] = myBestDistance1; s_trainIdx[lidx] = myBestTrainIdx1; @@ -499,7 +512,7 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5( if (lidx == 0) { - for (int i = 0 ; i < block_size ; i++) + for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; if (val < bestDistance1) @@ -527,7 +540,7 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5( if (lidx == 0) { - for (int i = 0 ; i < block_size ; i++) + for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; @@ -559,22 +572,20 @@ __kernel void BruteForceMatch_knnMatch_D5( __global int2 *bestTrainIdx, __global float2 *bestDistance, __local float *sharebuffer, - int block_size, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType + int step ) { const int lidx = get_local_id(0); const int lidy = get_local_id(1); const int groupidx = get_group_id(0); - const int queryIdx = groupidx * block_size + lidy; + const int queryIdx = groupidx * BLOCK_SIZE + lidy; local float *s_query = sharebuffer; - local float *s_train = sharebuffer + block_size * block_size; + local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE; float myBestDistance1 = MAX_FLOAT; float myBestDistance2 = MAX_FLOAT; @@ -582,30 +593,30 @@ __kernel void BruteForceMatch_knnMatch_D5( int myBestTrainIdx2 = -1; //loop - for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++) + for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++) { float result = 0.0f; - for (int i = 0 ; i < (query_cols + block_size -1) / block_size ; i++) + for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++) { - const int loadx = lidx + i * block_size; + const int loadx = lidx + i * BLOCK_SIZE; //load query and train into local memory - s_query[lidy * block_size + lidx] = 0; - s_train[lidx * block_size + lidy] = 0; + s_query[lidy * BLOCK_SIZE + lidx] = 0; + s_train[lidx * BLOCK_SIZE + lidy] = 0; if (loadx < query_cols) { - s_query[lidy * block_size + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx]; - s_train[lidx * block_size + lidy] = train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx]; + s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx]; + s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx]; } barrier(CLK_LOCAL_MEM_FENCE); - result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType); + result += reduce_block(s_query, s_train, lidx, lidy); barrier(CLK_LOCAL_MEM_FENCE); } - const int trainIdx = t * block_size + lidx; + const int trainIdx = t * BLOCK_SIZE + lidx; if (queryIdx < query_rows && trainIdx < train_rows /*&& mask(queryIdx, trainIdx)*/) { @@ -627,11 +638,11 @@ __kernel void BruteForceMatch_knnMatch_D5( barrier(CLK_LOCAL_MEM_FENCE); __local float *s_distance = (__local float *)sharebuffer; - __local int *s_trainIdx = (__local int *)(sharebuffer + block_size * block_size); + __local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE); //findBestMatch - s_distance += lidy * block_size; - s_trainIdx += lidy * block_size; + s_distance += lidy * BLOCK_SIZE; + s_trainIdx += lidy * BLOCK_SIZE; s_distance[lidx] = myBestDistance1; s_trainIdx[lidx] = myBestTrainIdx1; @@ -644,7 +655,7 @@ __kernel void BruteForceMatch_knnMatch_D5( if (lidx == 0) { - for (int i = 0 ; i < block_size ; i++) + for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; if (val < bestDistance1) @@ -672,7 +683,7 @@ __kernel void BruteForceMatch_knnMatch_D5( if (lidx == 0) { - for (int i = 0 ; i < block_size ; i++) + for (int i = 0 ; i < BLOCK_SIZE ; i++) { float val = s_distance[i]; @@ -703,14 +714,11 @@ kernel void BruteForceMatch_calcDistanceUnrolled_D5( //__global float *mask, __global float *allDist, __local float *sharebuffer, - int block_size, - int max_desc_len, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType) + int step) { /* Todo */ } @@ -721,13 +729,11 @@ kernel void BruteForceMatch_calcDistance_D5( //__global float *mask, __global float *allDist, __local float *sharebuffer, - int block_size, int query_rows, int query_cols, int train_rows, int train_cols, - int step, - int distType) + int step) { /* Todo */ } @@ -736,8 +742,7 @@ kernel void BruteForceMatch_findBestMatch_D5( __global float *allDist, __global int *bestTrainIdx, __global float *bestDistance, - int k, - int block_size + int k ) { /* Todo */ diff --git a/modules/ocl/test/test_brute_force_matcher.cpp b/modules/ocl/test/test_brute_force_matcher.cpp index 424781fe0a..d658c32d16 100644 --- a/modules/ocl/test/test_brute_force_matcher.cpp +++ b/modules/ocl/test/test_brute_force_matcher.cpp @@ -43,16 +43,14 @@ #ifdef HAVE_OPENCL namespace { - ///////////////////////////////////////////////////////////////////////////////////////////////// // BruteForceMatcher - - CV_ENUM(DistType, cv::ocl::BruteForceMatcher_OCL_base::L1Dist, cv::ocl::BruteForceMatcher_OCL_base::L2Dist, cv::ocl::BruteForceMatcher_OCL_base::HammingDist) + CV_ENUM(DistType, cv::ocl::BruteForceMatcher_OCL_base::L1Dist,\ + cv::ocl::BruteForceMatcher_OCL_base::L2Dist,\ + cv::ocl::BruteForceMatcher_OCL_base::HammingDist) IMPLEMENT_PARAM_CLASS(DescriptorSize, int) - - PARAM_TEST_CASE(BruteForceMatcher/*, NormCode*/, DistType, DescriptorSize) + PARAM_TEST_CASE(BruteForceMatcher, DistType, DescriptorSize) { - //std::vector oclinfo; cv::ocl::BruteForceMatcher_OCL_base::DistType distType; int normCode; int dim; @@ -64,13 +62,9 @@ namespace virtual void SetUp() { - //normCode = GET_PARAM(0); distType = (cv::ocl::BruteForceMatcher_OCL_base::DistType)(int)GET_PARAM(0); dim = GET_PARAM(1); - //int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE); - //CV_Assert(devnums > 0); - queryDescCount = 300; // must be even number because we split train data in some cases in two countFactor = 4; // do not change it @@ -172,49 +166,33 @@ namespace cv::ocl::BruteForceMatcher_OCL_base matcher(distType); - // assume support atomic. - //if (!supportFeature(devInfo, cv::gpu::GLOBAL_ATOMICS)) - //{ - // try - // { - // std::vector< std::vector > matches; - // matcher.radiusMatch(loadMat(query), loadMat(train), matches, radius); - // } - // catch (const cv::Exception& e) - // { - // ASSERT_EQ(CV_StsNotImplemented, e.code); - // } - //} - //else + std::vector< std::vector > matches; + matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius); + + ASSERT_EQ(static_cast(queryDescCount), matches.size()); + + int badCount = 0; + for (size_t i = 0; i < matches.size(); i++) { - std::vector< std::vector > matches; - matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius); - - ASSERT_EQ(static_cast(queryDescCount), matches.size()); - - int badCount = 0; - for (size_t i = 0; i < matches.size(); i++) + if ((int)matches[i].size() != 1) { - if ((int)matches[i].size() != 1) - { - badCount++; - } - else - { - cv::DMatch match = matches[i][0]; - if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0)) - badCount++; - } + badCount++; + } + else + { + cv::DMatch match = matches[i][0]; + if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0)) + badCount++; } - - ASSERT_EQ(0, badCount); } + + ASSERT_EQ(0, badCount); } - INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine( - //ALL_DEVICES, - testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)), - testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)))); + INSTANTIATE_TEST_CASE_P(OCL_Features2D, BruteForceMatcher, + testing::Combine( + testing::Values(DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist), DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)), + testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)))); } // namespace #endif