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Optimize OpenCL version of BFMatcher

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vbystricky
2014-09-23 15:13:46 +04:00
committed by vbystricky
parent 22ff1e8826
commit 3787388eac
2 changed files with 296 additions and 723 deletions
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modules/features2d/src/matchers.cpp
+130 -316
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@@ -60,113 +60,58 @@ static void ensureSizeIsEnough(int rows, int cols, int type, UMat &m)
m.create(rows, cols, type);
}
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = cv::format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE, (int)MAX_DESC_LEN );
ocl::Kernel k("BruteForceMatch_UnrollMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= BLOCK_SIZE ? MAX_DESC_LEN : BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, (void *)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
template < int BLOCK_SIZE >
static bool ocl_match(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = cv::format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE);
ocl::Kernel k("BruteForceMatch_Match", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, (void *)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
static bool ocl_matchDispatcher(InputArray query, InputArray train,
const UMat &trainIdx, const UMat &distance, int distType)
{
int query_cols = query.size().width;
bool is_cpu = ocl::Device::getDefault().type() == ocl::Device::TYPE_CPU;
if (query_cols <= 64)
{
if(!ocl_matchUnrolledCached<16, 64>(query, train, trainIdx, distance, distType)) return false;
}
else if (query_cols <= 128 && !is_cpu)
{
if(!ocl_matchUnrolledCached<16, 128>(query, train, trainIdx, distance, distType)) return false;
}
else
{
if(!ocl_match<16>(query, train, trainIdx, distance, distType)) return false;
}
return true;
}
static bool ocl_matchSingle(InputArray query, InputArray train,
UMat &trainIdx, UMat &distance, int dstType)
UMat &trainIdx, UMat &distance, int distType)
{
if (query.empty() || train.empty())
return false;
int query_rows = query.size().height;
const int query_rows = query.rows();
const int query_cols = query.cols();
ensureSizeIsEnough(1, query_rows, CV_32S, trainIdx);
ensureSizeIsEnough(1, query_rows, CV_32F, distance);
return ocl_matchDispatcher(query, train, trainIdx, distance, dstType);
ocl::Device devDef = ocl::Device::getDefault();
UMat uquery = query.getUMat(), utrain = train.getUMat();
int kercn = 1;
if (devDef.isIntel() &&
(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
kercn = 4;
int block_size = 16;
int max_desc_len = 0;
bool is_cpu = devDef.type() == ocl::Device::TYPE_CPU;
if (query_cols <= 64)
max_desc_len = 64 / kercn;
else if (query_cols <= 128 && !is_cpu)
max_desc_len = 128 / kercn;
int depth = query.depth();
cv::String opts;
opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size, max_desc_len);
ocl::Kernel k("BruteForceMatch_Match", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(query.size().height + block_size - 1) / block_size * block_size, block_size};
size_t localSize[] = {block_size, block_size};
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, uquery.rows);
idx = k.set(idx, uquery.cols);
idx = k.set(idx, utrain.rows);
idx = k.set(idx, utrain.cols);
idx = k.set(idx, (int)(uquery.step / sizeof(float)));
return k.run(2, globalSize, localSize, false);
}
static bool ocl_matchConvert(const Mat &trainIdx, const Mat &distance, std::vector< std::vector<DMatch> > &matches)
@@ -213,121 +158,60 @@ static bool ocl_matchDownload(const UMat &trainIdx, const UMat &distance, std::v
return ocl_matchConvert(trainIdxCPU, distanceCPU, matches);
}
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_knn_matchUnrolledCached(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = cv::format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE, (int)MAX_DESC_LEN );
ocl::Kernel k("BruteForceMatch_knnUnrollMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (BLOCK_SIZE * (MAX_DESC_LEN >= BLOCK_SIZE ? MAX_DESC_LEN : BLOCK_SIZE) + BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, (void *)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
template < int BLOCK_SIZE >
static bool ocl_knn_match(InputArray _query, InputArray _train,
const UMat &trainIdx, const UMat &distance, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE);
ocl::Kernel k("BruteForceMatch_knnMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, (void*)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, (int)query.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
static bool ocl_match2Dispatcher(InputArray query, InputArray train, const UMat &trainIdx, const UMat &distance, int distType)
{
bool is_cpu = ocl::Device::getDefault().type() == ocl::Device::TYPE_CPU;
if (query.size().width <= 64)
{
if(!ocl_knn_matchUnrolledCached<16, 64>(query, train, trainIdx, distance, distType))
return false;
}
else if (query.size().width <= 128 && !is_cpu)
{
if(!ocl_knn_matchUnrolledCached<16, 128>(query, train, trainIdx, distance, distType))
return false;
}
else
{
if(!ocl_knn_match<16>(query, train, trainIdx, distance, distType))
return false;
}
return true;
}
static bool ocl_kmatchDispatcher(InputArray query, InputArray train, const UMat &trainIdx,
const UMat &distance, int distType)
{
return ocl_match2Dispatcher(query, train, trainIdx, distance, distType);
}
static bool ocl_knnMatchSingle(InputArray query, InputArray train, UMat &trainIdx,
UMat &distance, int dstType)
UMat &distance, int distType)
{
if (query.empty() || train.empty())
return false;
const int nQuery = query.size().height;
const int query_rows = query.rows();
const int query_cols = query.cols();
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
ensureSizeIsEnough(1, query_rows, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, query_rows, CV_32FC2, distance);
trainIdx.setTo(Scalar::all(-1));
return ocl_kmatchDispatcher(query, train, trainIdx, distance, dstType);
ocl::Device devDef = ocl::Device::getDefault();
UMat uquery = query.getUMat(), utrain = train.getUMat();
int kercn = 1;
if (devDef.isIntel() &&
(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
kercn = 4;
int block_size = 16;
int max_desc_len = 0;
bool is_cpu = devDef.type() == ocl::Device::TYPE_CPU;
if (query_cols <= 64)
max_desc_len = 64 / kercn;
else if (query_cols <= 128 && !is_cpu)
max_desc_len = 128 / kercn;
int depth = query.depth();
cv::String opts;
opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size, max_desc_len);
ocl::Kernel k("BruteForceMatch_knnMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(query_rows + block_size - 1) / block_size * block_size, block_size};
size_t localSize[] = {block_size, block_size};
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, uquery.rows);
idx = k.set(idx, uquery.cols);
idx = k.set(idx, utrain.rows);
idx = k.set(idx, utrain.cols);
idx = k.set(idx, (int)(uquery.step / sizeof(float)));
return k.run(2, globalSize, localSize, false);
}
static bool ocl_knnMatchConvert(const Mat &trainIdx, const Mat &distance, std::vector< std::vector<DMatch> > &matches, bool compactResult)
@@ -383,134 +267,64 @@ static bool ocl_knnMatchDownload(const UMat &trainIdx, const UMat &distance, std
Mat trainIdxCPU = trainIdx.getMat(ACCESS_READ);
Mat distanceCPU = distance.getMat(ACCESS_READ);
if (ocl_knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult) )
return true;
return false;
return ocl_knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
template < int BLOCK_SIZE, int MAX_DESC_LEN >
static bool ocl_matchUnrolledCached(InputArray _query, InputArray _train, float maxDistance,
const UMat &trainIdx, const UMat &distance, const UMat &nMatches, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d",
ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE, (int)MAX_DESC_LEN);
ocl::Kernel k("BruteForceMatch_RadiusUnrollMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_train.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, maxDistance);
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(nMatches));
idx = k.set(idx, (void*)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, trainIdx.cols);
idx = k.set(idx, (int)query.step);
idx = k.set(idx, (int)trainIdx.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
//radius_match
template < int BLOCK_SIZE >
static bool ocl_radius_match(InputArray _query, InputArray _train, float maxDistance,
const UMat &trainIdx, const UMat &distance, const UMat &nMatches, int distType)
{
int depth = _query.depth();
cv::String opts;
opts = format("-D T=%s %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d", ocl::typeToStr(depth), depth == CV_32F ? "-D T_FLOAT" : "", distType, (int)BLOCK_SIZE);
ocl::Kernel k("BruteForceMatch_RadiusMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if(k.empty())
return false;
size_t globalSize[] = {(_train.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, (_query.size().height + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, 1};
size_t localSize[] = {BLOCK_SIZE, BLOCK_SIZE, 1};
const size_t smemSize = (2 * BLOCK_SIZE * BLOCK_SIZE) * sizeof(int);
if(globalSize[0] != 0)
{
UMat query = _query.getUMat(), train = _train.getUMat();
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(query));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(train));
idx = k.set(idx, maxDistance);
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(nMatches));
idx = k.set(idx, (void*)NULL, smemSize);
idx = k.set(idx, query.rows);
idx = k.set(idx, query.cols);
idx = k.set(idx, train.rows);
idx = k.set(idx, train.cols);
idx = k.set(idx, trainIdx.cols);
idx = k.set(idx, (int)query.step);
idx = k.set(idx, (int)trainIdx.step);
return k.run(2, globalSize, localSize, false);
}
return true;
}
static bool ocl_rmatchDispatcher(InputArray query, InputArray train,
UMat &trainIdx, UMat &distance, UMat &nMatches, float maxDistance, int distType)
{
bool is_cpu = ocl::Device::getDefault().type() == ocl::Device::TYPE_CPU;
int query_cols = query.size().width;
if (query_cols <= 64)
{
if(!ocl_matchUnrolledCached<16, 64>(query, train, maxDistance, trainIdx, distance, nMatches, distType)) return false;
}
else if (query_cols <= 128 && !is_cpu)
{
if(!ocl_matchUnrolledCached<16, 128>(query, train, maxDistance, trainIdx, distance, nMatches, distType)) return false;
}
else
{
if(!ocl_radius_match<16>(query, train, maxDistance, trainIdx, distance, nMatches, distType)) return false;
}
return true;
}
static bool ocl_radiusMatchSingle(InputArray query, InputArray train,
UMat &trainIdx, UMat &distance, UMat &nMatches, float maxDistance, int distType)
{
if (query.empty() || train.empty())
return false;
const int nQuery = query.size().height;
const int nTrain = train.size().height;
const int query_rows = query.rows();
const int train_rows = train.rows();
ensureSizeIsEnough(1, nQuery, CV_32SC1, nMatches);
ensureSizeIsEnough(1, query_rows, CV_32SC1, nMatches);
if (trainIdx.empty())
{
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(nQuery, std::max((nTrain / 100), 10), CV_32FC1, distance);
ensureSizeIsEnough(query_rows, std::max((train_rows / 100), 10), CV_32SC1, trainIdx);
ensureSizeIsEnough(query_rows, std::max((train_rows / 100), 10), CV_32FC1, distance);
}
nMatches.setTo(Scalar::all(0));
return ocl_rmatchDispatcher(query, train, trainIdx, distance, nMatches, maxDistance, distType);
ocl::Device devDef = ocl::Device::getDefault();
UMat uquery = query.getUMat(), utrain = train.getUMat();
int kercn = 1;
if (devDef.isIntel() &&
(0 == (uquery.step % 4)) && (0 == (uquery.cols % 4)) && (0 == (uquery.offset % 4)) &&
(0 == (utrain.step % 4)) && (0 == (utrain.cols % 4)) && (0 == (utrain.offset % 4)))
kercn = 4;
int block_size = 16;
int depth = query.depth();
cv::String opts;
opts = cv::format("-D T=%s -D TN=%s -D kercn=%d %s -D DIST_TYPE=%d -D BLOCK_SIZE=%d",
ocl::typeToStr(depth), ocl::typeToStr(CV_MAKETYPE(depth, kercn)), kercn, depth == CV_32F ? "-D T_FLOAT" : "", distType, block_size);
ocl::Kernel k("BruteForceMatch_RadiusMatch", ocl::features2d::brute_force_match_oclsrc, opts);
if (k.empty())
return false;
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};
int idx = 0;
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(uquery));
idx = k.set(idx, ocl::KernelArg::PtrReadOnly(utrain));
idx = k.set(idx, maxDistance);
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(trainIdx));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(distance));
idx = k.set(idx, ocl::KernelArg::PtrWriteOnly(nMatches));
idx = k.set(idx, uquery.rows);
idx = k.set(idx, uquery.cols);
idx = k.set(idx, utrain.rows);
idx = k.set(idx, utrain.cols);
idx = k.set(idx, trainIdx.cols);
idx = k.set(idx, (int)(uquery.step / sizeof(float)));
idx = k.set(idx, (int)(trainIdx.step / sizeof(int)));
return k.run(2, globalSize, localSize, false);
}
static bool ocl_radiusMatchConvert(const Mat &trainIdx, const Mat &distance, const Mat &_nMatches,