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Merge pull request #807 from pengx17:2.4_ocl_bfm_opt

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pull/804/merge
Vadim Pisarevsky 12 years ago committed by OpenCV Buildbot
parent 3d39087a67 2338a895f5
commit 03e2a52e2c
3 changed files with 267 additions and 270 deletions
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  1. 128
      modules/ocl/src/brute_force_matcher.cpp
  2. 341
      modules/ocl/src/opencl/brute_force_match.cl
  3. 68
      modules/ocl/test/test_brute_force_matcher.cpp

128
modules/ocl/src/brute_force_matcher.cpp
Unescape View File

@ -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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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<size_t, const void *> > 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;
}

341
modules/ocl/src/opencl/brute_force_match.cl
Unescape View File

@ -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 */

68
modules/ocl/test/test_brute_force_matcher.cpp
Unescape View File

@ -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<cv::ocl::Info> 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<cv::DMatch> > 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<cv::DMatch> > matches;
matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius);
std::vector< std::vector<cv::DMatch> > matches;
matcher.radiusMatch(cv::ocl::oclMat(query), cv::ocl::oclMat(train), matches, radius);
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
int badCount = 0;
for (size_t i = 0; i < matches.size(); i++)
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++;
}
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

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