Merge pull request #807 from pengx17:2.4_ocl_bfm_opt

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<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);
 	
-    trainIdx.create(1, query.rows, CV_32S);
-    imgIdx.create(1, query.rows, CV_32S);
-    distance.create(1, query.rows, CV_32F);
+	const int nQuery = query.rows;
+
+    ensureSizeIsEnough(1, nQuery, CV_32S, trainIdx);
+    ensureSizeIsEnough(1, nQuery, CV_32S, imgIdx);
+    ensureSizeIsEnough(1, nQuery, CV_32F, distance);
+
 
     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;
 }
 

modules/ocl/src/opencl/brute_force_match.cl

+/*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)
-    {
-    case 0:
-        for (int j = 0 ; j < block_size ; j++)
+    #pragma unroll
+    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)
-    {
-    case 0:
-        for (int j = 0 ; j < block_size ; j++)
+    #pragma unroll
+    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 */

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<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
 
@@ -172,21 +166,6 @@ 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);
 
@@ -209,10 +188,9 @@ namespace
 
         ASSERT_EQ(0, badCount);
     }
-    }
 
-    INSTANTIATE_TEST_CASE_P(GPU_Features2D, BruteForceMatcher, testing::Combine(
-                                //ALL_DEVICES,
+    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))));