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Merge pull request #811 from pengx17:2.4_ocl_bfmatcher_newtype

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This commit is contained in:
Andrey Kamaev 2013-04-15 12:12:17 +04:00 committed by OpenCV Buildbot
commit 0df6dc16a5
3 changed files with 178 additions and 194 deletions
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142
modules/ocl/src/brute_force_matcher.cpp
View File

@ -64,11 +64,19 @@ namespace cv
static const int OPT_SIZE = 100;
static const char * T_ARR [] = {
"uchar",
"char",
"ushort",
"short",
"int",
"float -D T_FLOAT",
"double"};
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)
{
assert(query.type() == CV_32F);
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
@ -78,7 +86,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
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);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -96,7 +106,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
std::string kernelName = "BruteForceMatch_UnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -110,7 +120,6 @@ template < int BLOCK_SIZE/*, typename Mask*/ >
void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, int distType)
{
assert(query.type() == CV_32F);
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
@ -119,8 +128,9 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
@ -137,7 +147,7 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
std::string kernelName = "BruteForceMatch_Match";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -152,7 +162,6 @@ template < int BLOCK_SIZE, int MAX_DESC_LEN/*, typename Mask*/ >
void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
assert(query.type() == CV_32F);
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
@ -162,7 +171,9 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
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);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -184,7 +195,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
std::string kernelName = "BruteForceMatch_RadiusUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -193,7 +204,6 @@ template < int BLOCK_SIZE/*, typename Mask*/ >
void radius_match(const oclMat &query, const oclMat &train, float maxDistance, const oclMat &/*mask*/,
const oclMat &trainIdx, const oclMat &distance, const oclMat &nMatches, int distType)
{
assert(query.type() == CV_32F);
cv::ocl::Context *ctx = query.clCxt;
size_t globalSize[] = {(train.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (query.rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
@ -202,7 +212,9 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
@ -224,7 +236,7 @@ void radius_match(const oclMat &query, const oclMat &train, float maxDistance, c
std::string kernelName = "BruteForceMatch_RadiusMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -300,7 +312,9 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl
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);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -318,7 +332,7 @@ void knn_matchUnrolledCached(const oclMat &query, const oclMat &train, const ocl
std::string kernelName = "BruteForceMatch_knnUnrollMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -334,7 +348,9 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
@ -352,7 +368,7 @@ void knn_match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
std::string kernelName = "BruteForceMatch_knnMatch";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -368,7 +384,10 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d", distType);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
T_ARR[query.depth()], distType, block_size, m_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
@ -386,7 +405,7 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat
std::string kernelName = "BruteForceMatch_calcDistanceUnrolled";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -401,7 +420,10 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D DIST_TYPE=%d", distType);
sprintf(opt,
"-D T=%s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
T_ARR[query.depth()], distType, block_size);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
@ -418,7 +440,7 @@ void calcDistance(const oclMat &query, const oclMat &train, const oclMat &/*mask
std::string kernelName = "BruteForceMatch_calcDistance";
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, query.depth(), opt);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1, opt);
}
}
@ -480,7 +502,7 @@ void findKnnMatch(int k, const oclMat &trainIdx, const oclMat &distance, const o
//args.push_back( make_pair( sizeof(cl_int), (void *)&train.cols ));
//args.push_back( make_pair( sizeof(cl_int), (void *)&query.step ));
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, trainIdx.depth(), -1);
openCLExecuteKernel(ctx, &brute_force_match, kernelName, globalSize, localSize, args, -1, -1);
}
}
@ -540,17 +562,6 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchSingle(const oclMat &query, const
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
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_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.cols == query.cols && train.type() == query.type());
@ -605,7 +616,7 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchConvert(const Mat &trainIdx, cons
void cv::ocl::BruteForceMatcher_OCL_base::match(const oclMat &query, const oclMat &train, vector<DMatch> &matches, const oclMat &mask)
{
assert(mask.empty()); // mask is not supported at the moment
assert(mask.empty()); // mask is not supported at the moment
oclMat trainIdx, distance;
matchSingle(query, train, trainIdx, distance, mask);
matchDownload(trainIdx, distance, matches);
@ -661,26 +672,14 @@ void cv::ocl::BruteForceMatcher_OCL_base::matchCollection(const oclMat &query, c
if (query.empty() || trainCollection.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
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_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
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);
return;
@ -752,18 +751,6 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatchSingle(const oclMat &query, co
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
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_Assert(query.channels() == 1 && query.depth() < CV_64F);
CV_Assert(train.type() == query.type() && train.cols == query.cols);
@ -860,26 +847,7 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch2Collection(const oclMat &quer
typedef void (*caller_t)(const oclMat & query, const oclMat & trains, const oclMat & masks,
const oclMat & trainIdx, const oclMat & imgIdx, const oclMat & distance);
#if 0
static const caller_t callers[3][6] =
{
{
ocl_match2L1_gpu<unsigned char>, 0/*match2L1_gpu<signed char>*/,
ocl_match2L1_gpu<unsigned short>, ocl_match2L1_gpu<short>,
ocl_match2L1_gpu<int>, ocl_match2L1_gpu<float>
},
{
0/*match2L2_gpu<unsigned char>*/, 0/*match2L2_gpu<signed char>*/,
0/*match2L2_gpu<unsigned short>*/, 0/*match2L2_gpu<short>*/,
0/*match2L2_gpu<int>*/, ocl_match2L2_gpu<float>
},
{
ocl_match2Hamming_gpu<unsigned char>, 0/*match2Hamming_gpu<signed char>*/,
ocl_match2Hamming_gpu<unsigned short>, 0/*match2Hamming_gpu<short>*/,
ocl_match2Hamming_gpu<int>, 0/*match2Hamming_gpu<float>*/
}
};
#endif
CV_Assert(query.channels() == 1 && query.depth() < CV_64F);
const int nQuery = query.rows;
@ -1025,23 +993,11 @@ void cv::ocl::BruteForceMatcher_OCL_base::knnMatch(const oclMat &query, vector<
// radiusMatchSingle
void cv::ocl::BruteForceMatcher_OCL_base::radiusMatchSingle(const oclMat &query, const oclMat &train,
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
oclMat &trainIdx, oclMat &distance, oclMat &nMatches, float maxDistance, const oclMat &mask)
{
if (query.empty() || train.empty())
return;
// match1 doesn't support signed char type, match2 only support float, hamming support uchar, ushort and int
int callType = query.depth();
if (callType != 5)
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");
}
const int nQuery = query.rows;
const int nTrain = train.rows;

206
modules/ocl/src/opencl/brute_force_match.cl
View File

@ -47,6 +47,10 @@
#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
#define MAX_FLOAT 3.40282e+038f
#ifndef T
#define T float
#endif
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
@ -54,68 +58,85 @@
#define MAX_DESC_LEN 64
#endif
int bit1Count(float x)
{
int c = 0;
int ix = (int)x;
for (int i = 0 ; i < 32 ; i++)
{
c += ix & 0x1;
ix >>= 1;
}
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 lidx,
int lidy
)
//http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
int bit1Count(int v)
{
float result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(s_query[lidy * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
}
return result;
v = v - ((v >> 1) & 0x55555555); // reuse input as temporary
v = (v & 0x33333333) + ((v >> 2) & 0x33333333); // temp
return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24; // count
}
float reduce_multi_block(__local float *s_query,
__local float *s_train,
int block_index,
int lidx,
int lidy
)
// dirty fix for non-template support
#if (DIST_TYPE == 0) // L1Dist
# ifdef T_FLOAT
# define DIST(x, y) fabs((x) - (y))
typedef float value_type;
typedef float result_type;
# else
# define DIST(x, y) abs((x) - (y))
typedef int value_type;
typedef int result_type;
# endif
#define DIST_RES(x) (x)
#elif (DIST_TYPE == 1) // L2Dist
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
typedef float value_type;
typedef float result_type;
#define DIST_RES(x) sqrt(x)
#elif (DIST_TYPE == 2) // Hamming
#define DIST(x, y) bit1Count( (x) ^ (y) )
typedef int value_type;
typedef int result_type;
#define DIST_RES(x) (x)
#endif
result_type reduce_block(
__local value_type *s_query,
__local value_type *s_train,
int lidx,
int lidy
)
{
float result = 0;
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j], s_train[j * BLOCK_SIZE + lidx]);
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return result;
return DIST_RES(result);
}
result_type reduce_multi_block(
__local value_type *s_query,
__local value_type *s_train,
int block_index,
int lidx,
int lidy
)
{
result_type result = 0;
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return DIST_RES(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.
*/
__kernel void BruteForceMatch_UnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_UnrollMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -127,13 +148,12 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
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 value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
int queryIdx = groupidx * BLOCK_SIZE + lidy;
// load the query into local memory.
@ -151,7 +171,7 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
volatile int imgIdx = 0;
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
float result = 0;
result_type result = 0;
#pragma unroll
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
@ -207,9 +227,9 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
}
}
__kernel void BruteForceMatch_Match_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -230,14 +250,13 @@ __kernel void BruteForceMatch_Match_D5(
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
// loop
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
//Dist dist;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
@ -299,9 +318,9 @@ __kernel void BruteForceMatch_Match_D5(
}
//radius_unrollmatch
__kernel void BruteForceMatch_RadiusUnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_RadiusUnrollMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
@ -325,10 +344,10 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
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 value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i)
{
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
@ -345,7 +364,8 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
@ -359,9 +379,9 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
}
//radius_match
__kernel void BruteForceMatch_RadiusMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_RadiusMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
@ -385,10 +405,10 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
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 value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
{
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
@ -405,7 +425,8 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx);
@ -419,9 +440,9 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
}
__kernel void BruteForceMatch_knnUnrollMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_knnUnrollMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
@ -438,8 +459,8 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
// load the query into local memory.
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
@ -457,10 +478,9 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
volatile int imgIdx = 0;
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0;
result_type result = 0;
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;
@ -494,8 +514,8 @@ __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 float *s_distance = (local float *)sharebuffer;
__local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
// find BestMatch
s_distance += lidy * BLOCK_SIZE;
@ -565,9 +585,9 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
}
}
__kernel void BruteForceMatch_knnMatch_D5(
__global float *query,
__global float *train,
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
@ -584,8 +604,8 @@ __kernel void BruteForceMatch_knnMatch_D5(
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
@ -595,7 +615,7 @@ __kernel void BruteForceMatch_knnMatch_D5(
//loop
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0.0f;
result_type result = 0.0f;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
@ -708,9 +728,9 @@ __kernel void BruteForceMatch_knnMatch_D5(
}
}
kernel void BruteForceMatch_calcDistanceUnrolled_D5(
__global float *query,
__global float *train,
kernel void BruteForceMatch_calcDistanceUnrolled(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
@ -723,9 +743,9 @@ kernel void BruteForceMatch_calcDistanceUnrolled_D5(
/* Todo */
}
kernel void BruteForceMatch_calcDistance_D5(
__global float *query,
__global float *train,
kernel void BruteForceMatch_calcDistance(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
@ -738,7 +758,7 @@ kernel void BruteForceMatch_calcDistance_D5(
/* Todo */
}
kernel void BruteForceMatch_findBestMatch_D5(
kernel void BruteForceMatch_findBestMatch(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
@ -746,4 +766,4 @@ kernel void BruteForceMatch_findBestMatch_D5(
)
{
/* Todo */
}
}

24
modules/ocl/test/test_brute_force_matcher.cpp
View File

@ -158,11 +158,7 @@ namespace
TEST_P(BruteForceMatcher, RadiusMatch_Single)
{
float radius;
if(distType == cv::ocl::BruteForceMatcher_OCL_base::L2Dist)
radius = 1.f / countFactor / countFactor;
else
radius = 1.f / countFactor;
float radius = 1.f / countFactor;
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
@ -191,8 +187,20 @@ namespace
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))));
testing::Values(
DistType(cv::ocl::BruteForceMatcher_OCL_base::L1Dist),
DistType(cv::ocl::BruteForceMatcher_OCL_base::L2Dist)/*,
DistType(cv::ocl::BruteForceMatcher_OCL_base::HammingDist)*/
),
testing::Values(
DescriptorSize(57),
DescriptorSize(64),
DescriptorSize(83),
DescriptorSize(128),
DescriptorSize(179),
DescriptorSize(256),
DescriptorSize(304))
)
);
} // namespace
#endif