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Capitalize macro namings.

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This commit is contained in:
peng xiao 2013-04-12 16:56:49 +08:00
parent 1bea9ee26c
commit 2338a895f5
2 changed files with 100 additions and 100 deletions
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16
modules/ocl/src/brute_force_matcher.cpp
View File

@ -78,7 +78,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, const oclMat
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D distType=%d -D block_size=%d -D max_desc_len=%d", distType, block_size, m_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -119,7 +119,7 @@ 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 distType=%d -D block_size=%d", distType, block_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
if(globalSize[0] != 0)
{
@ -162,7 +162,7 @@ void matchUnrolledCached(const oclMat &query, const oclMat &train, float maxDist
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D distType=%d -D block_size=%d -D max_desc_len=%d", distType, block_size, m_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -202,7 +202,7 @@ 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 distType=%d -D block_size=%d", distType, block_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
if(globalSize[0] != 0)
{
@ -300,7 +300,7 @@ 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 distType=%d -D block_size=%d -D max_desc_len=%d", distType, block_size, m_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d -D MAX_DESC_LEN=%d", distType, block_size, m_size);
if(globalSize[0] != 0)
{
@ -334,7 +334,7 @@ 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 distType=%d -D block_size=%d", distType, block_size);
sprintf(opt, "-D DIST_TYPE=%d -D BLOCK_SIZE=%d", distType, block_size);
if(globalSize[0] != 0)
{
@ -368,7 +368,7 @@ void calcDistanceUnrolled(const oclMat &query, const oclMat &train, const oclMat
vector< pair<size_t, const void *> > args;
char opt [OPT_SIZE] = "";
sprintf(opt, "-D distType=%d", distType);
sprintf(opt, "-D DIST_TYPE=%d", distType);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));
@ -401,7 +401,7 @@ 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 distType=%d", distType);
sprintf(opt, "-D DIST_TYPE=%d", distType);
if(globalSize[0] != 0)
{
args.push_back( make_pair( sizeof(cl_mem), (void *)&query.data ));

184
modules/ocl/src/opencl/brute_force_match.cl
View File

@ -47,11 +47,11 @@
#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
#define MAX_FLOAT 3.40282e+038f
#ifndef block_size
#define block_size 16
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
#ifndef max_desc_len
#define max_desc_len 64
#ifndef MAX_DESC_LEN
#define MAX_DESC_LEN 64
#endif
int bit1Count(float x)
@ -66,15 +66,15 @@ int bit1Count(float x)
return (float)c;
}
#ifndef distType
#define distType 0
#ifndef DIST_TYPE
#define DIST_TYPE 0
#endif
#if (distType == 0)
#if (DIST_TYPE == 0)
#define DIST(x, y) fabs((x) - (y))
#elif (distType == 1)
#elif (DIST_TYPE == 1)
#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
#elif (distType == 2)
#elif (DIST_TYPE == 2)
#define DIST(x, y) bit1Count((uint)(x) ^ (uint)(y))
#endif
@ -87,9 +87,9 @@ float reduce_block(__local float *s_query,
{
float result = 0;
#pragma unroll
for (int j = 0 ; j < block_size ; j++)
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;
}
@ -103,15 +103,15 @@ float reduce_multi_block(__local float *s_query,
{
float result = 0;
#pragma unroll
for (int j = 0 ; j < block_size ; j++)
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;
}
/* 2dim launch, global size: dim0 is (query rows + block_size - 1) / block_size * block_size, dim1 is block_size
local size: dim0 is block_size, dim1 is block_size.
/* 2dim launch, global size: dim0 is (query rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, dim1 is BLOCK_SIZE
local size: dim0 is BLOCK_SIZE, dim1 is BLOCK_SIZE.
*/
__kernel void BruteForceMatch_UnrollMatch_D5(
__global float *query,
@ -133,15 +133,15 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
const int groupidx = get_group_id(0);
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + block_size * max_desc_len;
__local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
int queryIdx = groupidx * block_size + lidy;
int queryIdx = groupidx * BLOCK_SIZE + lidy;
// load the query into local memory.
#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;
int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
}
float myBestDistance = MAX_FLOAT;
@ -149,15 +149,15 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
// loopUnrolledCached to find the best trainIdx and best distance.
volatile int imgIdx = 0;
for (int t = 0, endt = (train_rows + block_size - 1) / block_size; t < endt; t++)
for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
{
float result = 0;
#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;
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
const int loadx = lidx + i * BLOCK_SIZE;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
@ -167,7 +167,7 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
int trainIdx = t * block_size + lidx;
int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance/* && mask(queryIdx, trainIdx)*/)
{
@ -179,11 +179,11 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float*)(sharebuffer);
__local int* s_trainIdx = (__local int *)(sharebuffer + block_size * block_size);
__local int* s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
//find BestMatch
s_distance += lidy * block_size;
s_trainIdx += lidy * block_size;
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
@ -191,7 +191,7 @@ __kernel void BruteForceMatch_UnrollMatch_D5(
//reduce -- now all reduce implement in each threads.
#pragma unroll
for (int k = 0 ; k < block_size; k++)
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
{
@ -225,30 +225,30 @@ __kernel void BruteForceMatch_Match_D5(
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * block_size + lidy;
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
float myBestDistance = MAX_FLOAT;
int myBestTrainIdx = -1;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + block_size * block_size;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
// loop
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
//Dist dist;
float result = 0;
for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; i++)
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * block_size;
const int loadx = lidx + i * BLOCK_SIZE;
//load query and train into local memory
s_query[lidy * block_size + lidx] = 0;
s_train[lidx * block_size + lidy] = 0;
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
if (loadx < query_cols)
{
s_query[lidy * block_size + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
s_train[lidx * block_size + lidy] = train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
@ -258,7 +258,7 @@ __kernel void BruteForceMatch_Match_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
const int trainIdx = t * block_size + lidx;
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/)
{
@ -271,18 +271,18 @@ __kernel void BruteForceMatch_Match_D5(
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + block_size * block_size);
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
//findBestMatch
s_distance += lidy * block_size;
s_trainIdx += lidy * block_size;
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance;
s_trainIdx[lidx] = myBestTrainIdx;
barrier(CLK_LOCAL_MEM_FENCE);
//reduce -- now all reduce implement in each threads.
for (int k = 0 ; k < block_size; k++)
for (int k = 0 ; k < BLOCK_SIZE; k++)
{
if (myBestDistance > s_distance[k])
{
@ -322,20 +322,20 @@ __kernel void BruteForceMatch_RadiusUnrollMatch_D5(
const int groupidx = get_group_id(0);
const int groupidy = get_group_id(1);
const int queryIdx = groupidy * block_size + lidy;
const int trainIdx = groupidx * block_size + lidx;
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + block_size * block_size;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; ++i)
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i)
{
//load a block_size * block_size block into local train.
const int loadx = lidx + i * block_size;
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * block_size + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_train[lidx * block_size + lidy] = loadx < query_cols ? train[min(groupidx * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
@ -382,20 +382,20 @@ __kernel void BruteForceMatch_RadiusMatch_D5(
const int groupidx = get_group_id(0);
const int groupidy = get_group_id(1);
const int queryIdx = groupidy * block_size + lidy;
const int trainIdx = groupidx * block_size + lidx;
const int queryIdx = groupidy * BLOCK_SIZE + lidy;
const int trainIdx = groupidx * BLOCK_SIZE + lidx;
__local float *s_query = sharebuffer;
__local float *s_train = sharebuffer + block_size * block_size;
__local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float result = 0;
for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; ++i)
for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
{
//load a block_size * block_size block into local train.
const int loadx = lidx + i * block_size;
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
const int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * block_size + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_train[lidx * block_size + lidy] = loadx < query_cols ? train[min(groupidx * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
@ -437,15 +437,15 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * block_size + lidy;
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + block_size * max_desc_len;
local float *s_train = sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
// load the query into local memory.
for (int i = 0 ; i < max_desc_len / block_size; i ++)
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
{
int loadx = lidx + i * block_size;
s_query[lidy * max_desc_len + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
int loadx = lidx + i * BLOCK_SIZE;
s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
}
float myBestDistance1 = MAX_FLOAT;
@ -455,15 +455,15 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
//loopUnrolledCached
volatile int imgIdx = 0;
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; i++)
for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
{
const int loadX = lidx + i * block_size;
//load a block_size * block_size block into local train.
const int loadx = lidx + i * block_size;
s_train[lidx * block_size + lidy] = loadx < train_cols ? train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
const int loadX = lidx + i * BLOCK_SIZE;
//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
const int loadx = lidx + i * BLOCK_SIZE;
s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
@ -473,7 +473,7 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
const int trainIdx = t * block_size + lidx;
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows)
{
@ -495,11 +495,11 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
local float *s_distance = (local float *)sharebuffer;
local int *s_trainIdx = (local int *)(sharebuffer + block_size * block_size);
local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
// find BestMatch
s_distance += lidy * block_size;
s_trainIdx += lidy * block_size;
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
@ -512,7 +512,7 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
if (lidx == 0)
{
for (int i = 0 ; i < block_size ; i++)
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
@ -540,7 +540,7 @@ __kernel void BruteForceMatch_knnUnrollMatch_D5(
if (lidx == 0)
{
for (int i = 0 ; i < block_size ; i++)
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
@ -583,9 +583,9 @@ __kernel void BruteForceMatch_knnMatch_D5(
const int lidy = get_local_id(1);
const int groupidx = get_group_id(0);
const int queryIdx = groupidx * block_size + lidy;
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
local float *s_query = sharebuffer;
local float *s_train = sharebuffer + block_size * block_size;
local float *s_train = sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
float myBestDistance1 = MAX_FLOAT;
float myBestDistance2 = MAX_FLOAT;
@ -593,20 +593,20 @@ __kernel void BruteForceMatch_knnMatch_D5(
int myBestTrainIdx2 = -1;
//loop
for (int t = 0 ; t < (train_rows + block_size - 1) / block_size ; t++)
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
{
float result = 0.0f;
for (int i = 0 ; i < (query_cols + block_size -1) / block_size ; i++)
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
{
const int loadx = lidx + i * block_size;
const int loadx = lidx + i * BLOCK_SIZE;
//load query and train into local memory
s_query[lidy * block_size + lidx] = 0;
s_train[lidx * block_size + lidy] = 0;
s_query[lidy * BLOCK_SIZE + lidx] = 0;
s_train[lidx * BLOCK_SIZE + lidy] = 0;
if (loadx < query_cols)
{
s_query[lidy * block_size + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
s_train[lidx * block_size + lidy] = train[min(t * block_size + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
}
barrier(CLK_LOCAL_MEM_FENCE);
@ -616,7 +616,7 @@ __kernel void BruteForceMatch_knnMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
}
const int trainIdx = t * block_size + lidx;
const int trainIdx = t * BLOCK_SIZE + lidx;
if (queryIdx < query_rows && trainIdx < train_rows /*&& mask(queryIdx, trainIdx)*/)
{
@ -638,11 +638,11 @@ __kernel void BruteForceMatch_knnMatch_D5(
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)sharebuffer;
__local int *s_trainIdx = (__local int *)(sharebuffer + block_size * block_size);
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
//findBestMatch
s_distance += lidy * block_size;
s_trainIdx += lidy * block_size;
s_distance += lidy * BLOCK_SIZE;
s_trainIdx += lidy * BLOCK_SIZE;
s_distance[lidx] = myBestDistance1;
s_trainIdx[lidx] = myBestTrainIdx1;
@ -655,7 +655,7 @@ __kernel void BruteForceMatch_knnMatch_D5(
if (lidx == 0)
{
for (int i = 0 ; i < block_size ; i++)
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
@ -683,7 +683,7 @@ __kernel void BruteForceMatch_knnMatch_D5(
if (lidx == 0)
{
for (int i = 0 ; i < block_size ; i++)
for (int i = 0 ; i < BLOCK_SIZE ; i++)
{
float val = s_distance[i];