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Commit 3787388e authored by vbystricky's avatar vbystricky Committed by vbystricky
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Optimize OpenCL version of BFMatcher

parent 22ff1e88
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Showing with 279 additions and 705 deletions
modules/features2d/src/matchers.cpp
View file @ 3787388e
......@@ -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)
static bool ocl_matchSingle(InputArray query, InputArray train,
UMat &trainIdx, 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())
if (query.empty() || train.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;
}
const int query_rows = query.rows();
const int query_cols = query.cols();
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;
ensureSizeIsEnough(1, query_rows, CV_32S, trainIdx);
ensureSizeIsEnough(1, query_rows, CV_32F, distance);
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);
ocl::Device devDef = ocl::Device::getDefault();
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;
}
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;
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;
int block_size = 16;
int max_desc_len = 0;
bool is_cpu = devDef.type() == ocl::Device::TYPE_CPU;
if (query_cols <= 64)
{
if(!ocl_matchUnrolledCached<16, 64>(query, train, trainIdx, distance, distType)) return false;
}
max_desc_len = 64 / kercn;
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;
}
max_desc_len = 128 / kercn;
static bool ocl_matchSingle(InputArray query, InputArray train,
UMat &trainIdx, UMat &distance, int dstType)
{
if (query.empty() || train.empty())
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;
int query_rows = query.size().height;
ensureSizeIsEnough(1, query_rows, CV_32S, trainIdx);
ensureSizeIsEnough(1, query_rows, CV_32F, distance);
return ocl_matchDispatcher(query, train, trainIdx, distance, dstType);
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)
static bool ocl_knnMatchSingle(InputArray query, InputArray train, UMat &trainIdx,
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())
if (query.empty() || train.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;
}
const int query_rows = query.rows();
const int query_cols = query.cols();
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;
ensureSizeIsEnough(1, query_rows, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, query_rows, CV_32FC2, distance);
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);
trainIdx.setTo(Scalar::all(-1));
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;
}
ocl::Device devDef = ocl::Device::getDefault();
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;
}
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;
static bool ocl_kmatchDispatcher(InputArray query, InputArray train, const UMat &trainIdx,
const UMat &distance, int distType)
{
return ocl_match2Dispatcher(query, train, trainIdx, distance, distType);
}
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;
static bool ocl_knnMatchSingle(InputArray query, InputArray train, UMat &trainIdx,
UMat &distance, int dstType)
{
if (query.empty() || train.empty())
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;
const int nQuery = query.size().height;
ensureSizeIsEnough(1, nQuery, CV_32SC2, trainIdx);
ensureSizeIsEnough(1, nQuery, CV_32FC2, distance);
trainIdx.setTo(Scalar::all(-1));
return ocl_kmatchDispatcher(query, train, trainIdx, distance, dstType);
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;
}
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;
return ocl_knnMatchConvert(trainIdxCPU, distanceCPU, matches, compactResult);
}
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,
......
modules/features2d/src/opencl/brute_force_match.cl
View file @ 3787388e
......@@ -59,39 +59,71 @@
#define MAX_DESC_LEN 64
#endif
#define BLOCK_SIZE_ODD (BLOCK_SIZE + 1)
#ifndef SHARED_MEM_SZ
# if (BLOCK_SIZE < MAX_DESC_LEN)
# define SHARED_MEM_SZ (kercn * (BLOCK_SIZE * MAX_DESC_LEN + BLOCK_SIZE * BLOCK_SIZE))
# else
# define SHARED_MEM_SZ (kercn * 2 * BLOCK_SIZE_ODD * BLOCK_SIZE)
# endif
#endif
#ifndef DIST_TYPE
#define DIST_TYPE 2
#endif
// dirty fix for non-template support
#if (DIST_TYPE == 2) // L1Dist
#if (DIST_TYPE == 2) // L1Dist
# ifdef T_FLOAT
# define DIST(x, y) fabs((x) - (y))
typedef float value_type;
typedef float result_type;
# if (8 == kercn)
typedef float8 value_type;
# define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
# elif (4 == kercn)
typedef float4 value_type;
# define DIST(x, y) {value_type d = fabs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
# else
typedef float value_type;
# define DIST(x, y) result += fabs((x) - (y))
# endif
# else
# define DIST(x, y) abs((x) - (y))
typedef int value_type;
typedef int result_type;
# if (8 == kercn)
typedef int8 value_type;
# define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3 + d.s4 + d.s5 + d.s6 + d.s7;}
# elif (4 == kercn)
typedef int4 value_type;
# define DIST(x, y) {value_type d = abs((x) - (y)); result += d.s0 + d.s1 + d.s2 + d.s3;}
# else
typedef int value_type;
# define DIST(x, y) result += abs((x) - (y))
# endif
# endif
#define DIST_RES(x) (x)
# define DIST_RES(x) (x)
#elif (DIST_TYPE == 4) // L2Dist
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
typedef float value_type;
typedef float result_type;
#define DIST_RES(x) sqrt(x)
typedef float result_type;
# if (8 == kercn)
typedef float8 value_type;
# define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d.s0123, d.s0123) + dot(d.s4567, d.s4567);}
# elif (4 == kercn)
typedef float4 value_type;
# define DIST(x, y) {value_type d = ((x) - (y)); result += dot(d, d);}
# else
typedef float value_type;
# define DIST(x, y) {value_type d = ((x) - (y)); result = mad(d, d, result);}
# endif
# define DIST_RES(x) sqrt(x)
#elif (DIST_TYPE == 6) // Hamming
//http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
inline int bit1Count(int v)
{
v = v - ((v >> 1) & 0x55555555); // reuse input as temporary
v = (v & 0x33333333) + ((v >> 2) & 0x33333333); // temp
return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24; // count
}
#define DIST(x, y) bit1Count( (x) ^ (y) )
typedef int value_type;
typedef int result_type;
#define DIST_RES(x) (x)
# if (8 == kercn)
typedef int8 value_type;
# elif (4 == kercn)
typedef int4 value_type;
# else
typedef int value_type;
# endif
typedef int result_type;
# define DIST(x, y) result += popcount( (x) ^ (y) )
# define DIST_RES(x) (x)
#endif
inline result_type reduce_block(
......@@ -105,9 +137,7 @@ inline result_type reduce_block(
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
}
return DIST_RES(result);
}
......@@ -123,11 +153,9 @@ inline result_type reduce_block_match(
#pragma unroll
for (int j = 0 ; j < BLOCK_SIZE ; j++)
{
result += DIST(
s_query[lidy * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
DIST(s_query[lidy * BLOCK_SIZE_ODD + j], s_train[j * BLOCK_SIZE_ODD + lidx]);
}
return (result);
return result;
}
inline result_type reduce_multi_block(
......@@ -142,23 +170,16 @@ inline result_type reduce_multi_block(
#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]);
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.
*/
__kernel void BruteForceMatch_UnrollMatch(
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
......@@ -170,17 +191,26 @@ __kernel void BruteForceMatch_UnrollMatch(
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__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;
#if 0 < MAX_DESC_LEN
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
// load the query into local memory.
#pragma unroll
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;
const int loadx = mad24(BLOCK_SIZE, i, lidx);
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
}
#else
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
#endif
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
......@@ -189,12 +219,16 @@ __kernel void BruteForceMatch_UnrollMatch(
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
result_type result = 0;
const int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
#if 0 < MAX_DESC_LEN
#pragma unroll
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;
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 = mad24(BLOCK_SIZE, i, lidx);
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -203,89 +237,18 @@ __kernel void BruteForceMatch_UnrollMatch(
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance/* && mask(queryIdx, trainIdx)*/)
{
myBestDistance = result;
myBestTrainIdx = trainIdx;
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__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;
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.
#pragma unroll
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
#else
for (int i = 0, endq = (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE; i < endq; i++)
{
myBestDistance = s_distance[k];
myBestTrainIdx = s_trainIdx[k];
}
}
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = myBestTrainIdx;
bestDistance[queryIdx] = myBestDistance;
}
}
__kernel void BruteForceMatch_Match(
__global T *query,
__global T *train,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
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;
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
__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++)
{
result_type result = 0;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
const int loadx = mad24(i, BLOCK_SIZE, lidx);
//load query and train into local memory
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
s_query[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = 0;
s_train[mad24(BLOCK_SIZE_ODD, lidx, 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[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = query_vec[loadx];
s_train[mad24(BLOCK_SIZE_ODD, lidx, lidy)] = train_vec[loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -294,10 +257,10 @@ __kernel void BruteForceMatch_Match(
barrier(CLK_LOCAL_MEM_FENCE);
}
#endif
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/)
{
......@@ -309,17 +272,18 @@ __kernel void BruteForceMatch_Match(
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_ODD * BLOCK_SIZE);
//findBestMatch
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance += lidy * BLOCK_SIZE_ODD;
s_trainIdx += lidy * BLOCK_SIZE_ODD;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
barrier(CLK_LOCAL_MEM_FENCE);
//reduce -- now all reduce implement in each threads.
#pragma unroll
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
......@@ -336,76 +300,14 @@ __kernel void BruteForceMatch_Match(
}
}
//radius_unrollmatch
__kernel void BruteForceMatch_RadiusUnrollMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
__global int *nMatches,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int bestTrainIdx_cols,
int step,
int ostep
)
{
const int lidx = get_local_id(0);
const int lidy = get_local_id(1);
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;
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
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.
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;
//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, lidx, lidy);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
if(ind < bestTrainIdx_cols)
{
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
}
}
}
//radius_match
__kernel void BruteForceMatch_RadiusMatch(
__global T *query,
__global T *train,
float maxDistance,
//__global float *mask,
__global int *bestTrainIdx,
__global float *bestDistance,
__global int *nMatches,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
......@@ -420,20 +322,34 @@ __kernel void BruteForceMatch_RadiusMatch(
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 = mad24(BLOCK_SIZE, groupidy, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
const int trainIdx = mad24(BLOCK_SIZE, groupidx, lidx);
const int trainOffset = min(mad24(BLOCK_SIZE, groupidx, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
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.
const int loadx = lidx + i * BLOCK_SIZE;
const int loadx = mad24(BLOCK_SIZE, i, lidx);
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[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = 0;
s_train[mad24(BLOCK_SIZE_ODD, lidx, lidy)] = 0;
if (loadx < query_cols)
{
s_query[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = query_vec[loadx];
s_train[mad24(BLOCK_SIZE_ODD, lidx, lidy)] = train_vec[loadx];
}
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -442,28 +358,23 @@ __kernel void BruteForceMatch_RadiusMatch(
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows &&
convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows && convert_float(result) < maxDistance)
{
int ind = atom_inc(nMatches + queryIdx);
if(ind < bestTrainIdx_cols)
{
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
bestTrainIdx[mad24(queryIdx, ostep, ind)] = trainIdx;
bestDistance[mad24(queryIdx, ostep, ind)] = result;
}
}
}
__kernel void BruteForceMatch_knnUnrollMatch(
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
......@@ -475,31 +386,45 @@ __kernel void BruteForceMatch_knnUnrollMatch(
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
const int queryIdx = mad24(BLOCK_SIZE, groupidx, lidy);
const int queryOffset = min(queryIdx, query_rows - 1) * step;
__global TN *query_vec = (__global TN *)(query + queryOffset);
query_cols /= kercn;
__local float sharebuffer[SHARED_MEM_SZ];
__local value_type *s_query = (__local value_type *)sharebuffer;
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
#if 0 < MAX_DESC_LEN
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
// load the query into local memory.
#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 = mad24(BLOCK_SIZE, i, lidx);
s_query[mad24(MAX_DESC_LEN, lidy, loadx)] = loadx < query_cols ? query_vec[loadx] : 0;
}
#else
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE;
#endif
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
//loopUnrolledCached
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++)
{
result_type result = 0;
int trainOffset = min(mad24(BLOCK_SIZE, t, lidy), train_rows - 1) * step;
__global TN *train_vec = (__global TN *)(train + trainOffset);
#if 0 < MAX_DESC_LEN
#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;
const int loadx = mad24(BLOCK_SIZE, i, lidx);
s_train[mad24(BLOCK_SIZE, lidx, lidy)] = loadx < train_cols ? train_vec[loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -508,143 +433,18 @@ __kernel void BruteForceMatch_knnUnrollMatch(
barrier(CLK_LOCAL_MEM_FENCE);
}
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows)
{
if (result < myBestDistance1)
{
myBestDistance2 = myBestDistance1;
myBestTrainIdx2 = myBestTrainIdx1;
myBestDistance1 = result;
myBestTrainIdx1 = trainIdx;
}
else if (result < myBestDistance2)
{
myBestDistance2 = result;
myBestTrainIdx2 = trainIdx;
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
__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;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
float bestDistance1 = MAX_FLOAT;
float bestDistance2 = MAX_FLOAT;
int bestTrainIdx1 = -1;
int bestTrainIdx2 = -1;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
bestTrainIdx2 = bestTrainIdx1;
bestDistance1 = val;
bestTrainIdx1 = s_trainIdx[i];
}
else if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
barrier(CLK_LOCAL_MEM_FENCE);
s_distance[lidx] = myBestDistance2;
s_trainIdx[lidx] = myBestTrainIdx2;
barrier(CLK_LOCAL_MEM_FENCE);
if (lidx == 0)
{
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance2)
{
bestDistance2 = val;
bestTrainIdx2 = s_trainIdx[i];
}
}
}
myBestDistance1 = bestDistance1;
myBestDistance2 = bestDistance2;
myBestTrainIdx1 = bestTrainIdx1;
myBestTrainIdx2 = bestTrainIdx2;
if (queryIdx < query_rows && lidx == 0)
{
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
}
}
__kernel void BruteForceMatch_knnMatch(
__global T *query,
__global T *train,
//__global float *mask,
__global int2 *bestTrainIdx,
__global float2 *bestDistance,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
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;
__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;
int myBestTrainIdx1 = -1;
int myBestTrainIdx2 = -1;
//loop
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
result_type result = 0.0f;
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
#else
for (int i = 0, endq = (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE; i < endq ; i++)
{
const int loadx = lidx + i * BLOCK_SIZE;
const int loadx = mad24(BLOCK_SIZE, i, lidx);
//load query and train into local memory
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
s_query[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = 0;
s_train[mad24(BLOCK_SIZE_ODD, lidx, 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[mad24(BLOCK_SIZE_ODD, lidy, lidx)] = query_vec[loadx];
s_train[mad24(BLOCK_SIZE_ODD, lidx, lidy)] = train_vec[loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
......@@ -653,12 +453,12 @@ __kernel void BruteForceMatch_knnMatch(
barrier(CLK_LOCAL_MEM_FENCE);
}
#endif
result = DIST_RES(result);
const int trainIdx = t * BLOCK_SIZE + lidx;
const int trainIdx = mad24(BLOCK_SIZE, t, lidx);
if (queryIdx < query_rows && trainIdx < train_rows /*&& mask(queryIdx, trainIdx)*/)
if (queryIdx < query_rows && trainIdx < train_rows)
{
if (result < myBestDistance1)
{
......@@ -678,12 +478,11 @@ __kernel void BruteForceMatch_knnMatch(
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
//findBestMatch
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE_ODD * BLOCK_SIZE);
// find BestMatch
s_distance += lidy * BLOCK_SIZE_ODD;
s_trainIdx += lidy * BLOCK_SIZE_ODD;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
......@@ -746,44 +545,4 @@ __kernel void BruteForceMatch_knnMatch(
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
}
}
kernel void BruteForceMatch_calcDistanceUnrolled(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step)
{
/* Todo */
}
kernel void BruteForceMatch_calcDistance(
__global T *query,
__global T *train,
//__global float *mask,
__global float *allDist,
__local float *sharebuffer,
int query_rows,
int query_cols,
int train_rows,
int train_cols,
int step)
{
/* Todo */
}
kernel void BruteForceMatch_findBestMatch(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
int k
)
{
/* Todo */
}
}
\ No newline at end of file
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