generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Enable runtime type definition in kernels

Browse Source
This commit is contained in:
Peng Xiao 2013-04-13 12:50:17 +08:00
parent fd1528795e
commit 1db20099a9
2 changed files with 101 additions and 79 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
modules/ocl/src/brute_force_matcher.cpp
View File

@ -64,7 +64,14 @@ namespace cv
static const int OPT_SIZE = 100;
static const char * T_ARR [] = {"uchar", "char", "ushort", "short", "int", "float", "double"};
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*/,
@ -100,7 +107,7 @@ 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);
}
}
@ -126,7 +133,6 @@ void match(const oclMat &query, const oclMat &train, const oclMat &/*mask*/,
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 ));
@ -143,7 +149,7 @@ 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);
}
}
@ -192,7 +198,7 @@ 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);
}
}
@ -234,7 +240,7 @@ 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);
}
}
@ -330,7 +336,7 @@ 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);
}
}
@ -366,7 +372,7 @@ 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);
}
}
@ -403,7 +409,7 @@ 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);
}
}
@ -438,7 +444,7 @@ 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);
}
}
@ -500,7 +506,7 @@ 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);
}
}

152
modules/ocl/src/opencl/brute_force_match.cl
View File

@ -65,7 +65,7 @@
int bit1Count(int x)
{
int c = 0;
int ix = (int)x;
int ix = x;
for (int i = 0 ; i < 32 ; i++)
{
c += ix & 0x1;
@ -74,42 +74,60 @@ int bit1Count(int x)
return c;
}
#if (DIST_TYPE == 0)
#define DIST(x, y) fabs((x) - (y))
#elif (DIST_TYPE == 1)
// 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
#elif (DIST_TYPE == 1) // L2Dist
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
#elif (DIST_TYPE == 2)
#define DIST(x, y) bit1Count((uint)(x) ^ (uint)(y))
#endif
typedef float value_type;
typedef float result_type;
#elif (DIST_TYPE == 2) // Hamming
#define DIST(x, y) bit1Count(((x) ^ (y))
typedef int value_type;
typedef int result_type;
#endif
float reduce_block(__local float *s_query,
__local float *s_train,
int lidx,
int lidy
)
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 * 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;
}
float reduce_multi_block(__local float *s_query,
__local float *s_train,
int block_index,
int lidx,
int lidy
)
result_type reduce_multi_block(
__local value_type *s_query,
__local value_type *s_train,
int block_index,
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 * MAX_DESC_LEN + block_index * BLOCK_SIZE + j],
s_train[j * BLOCK_SIZE + lidx]);
}
return result;
}
@ -117,9 +135,9 @@ float reduce_multi_block(__local float *s_query,
/* 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,
@ -131,13 +149,12 @@ __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 = sharebuffer;
__local value_type *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
int queryIdx = groupidx * BLOCK_SIZE + lidy;
// load the query into local memory.
@ -155,7 +172,7 @@ __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++)
{
@ -211,9 +228,9 @@ __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,
@ -234,14 +251,13 @@ __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 = sharebuffer;
__local value_type *s_train = 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;
@ -303,9 +319,9 @@ __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,
@ -329,10 +345,10 @@ __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 = sharebuffer;
__local value_type *s_train = 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.
@ -363,9 +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,
@ -389,10 +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 = sharebuffer;
__local value_type *s_train = 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.
@ -423,9 +439,9 @@ __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,
@ -442,8 +458,8 @@ __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 = sharebuffer;
local value_type *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 ++)
@ -461,7 +477,7 @@ __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;
@ -569,9 +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,
@ -588,8 +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 = sharebuffer;
local value_type *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
@ -599,7 +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;
@ -712,9 +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,
@ -727,9 +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,
@ -742,7 +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,