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提交 ad6aae45 编写于 作者: Y yao
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more fix of mismatch functions on CPU OCL

上级 2c06e59a
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Showing with 231 addition and 798 deletion
modules/ocl/src/haar.cpp
浏览文件 @ ad6aae45
......@@ -953,8 +953,8 @@ CvSeq *cv::ocl::OclCascadeClassifier::oclHaarDetectObjects( oclMat &gimg, CvMemS
//int flag = 0;
oclMat gimg1(gimg.rows, gimg.cols, CV_8UC1);
oclMat gsum(totalheight, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight, gimg.cols + 1, CV_32FC1);
oclMat gsum(totalheight + 4, gimg.cols + 1, CV_32SC1);
oclMat gsqsum(totalheight + 4, gimg.cols + 1, CV_32FC1);
//cl_mem cascadebuffer;
cl_mem stagebuffer;
......
modules/ocl/src/moments.cpp
浏览文件 @ ad6aae45
......@@ -106,7 +106,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* mom )
bool is_float = CV_SEQ_ELTYPE(contour) == CV_32FC2;
if (!cv::ocl::Context::getContext()->impl->double_support && is_float)
if (!cv::ocl::Context::getContext()->supportsFeature(Context::CL_DOUBLE) && is_float)
{
CV_Error(CV_StsUnsupportedFormat, "Moments - double is not supported by your GPU!");
}
......@@ -146,7 +146,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* mom )
cv::Mat dst(dst_a);
a00 = a10 = a01 = a20 = a11 = a02 = a30 = a21 = a12 = a03 = 0.0;
if (!cv::ocl::Context::getContext()->impl->double_support)
if (!cv::ocl::Context::getContext()->supportsFeature(Context::CL_DOUBLE))
{
for (int i = 0; i < contour->total; ++i)
{
......@@ -161,7 +161,7 @@ static void icvContourMoments( CvSeq* contour, CvMoments* mom )
a12 += dst.at<cl_long>(8, i);
a03 += dst.at<cl_long>(9, i);
}
}
}
else
{
a00 = cv::sum(dst.row(0))[0];
......
modules/ocl/src/opencl/brute_force_match.cl
浏览文件 @ ad6aae45
......@@ -5,19 +5,93 @@ int bit1Count(float x)
{
int c = 0;
int ix = (int)x;
for (int i = 0 ; i < 32 ; i++)
{
c += ix & 0x1;
ix >>= 1;
}
return (float)c;
}
float reduce_block(__local float *s_query,
__local float *s_train,
int block_size,
int lidx,
int lidy,
int distType
)
{
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
float result = 0;
switch(distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^ (uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
return result;
}
float reduce_multi_block(__local float *s_query,
__local float *s_train,
int max_desc_len,
int block_size,
int block_index,
int lidx,
int lidy,
int distType
)
{
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
float result = 0;
switch(distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + block_index * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + block_index * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
}
break;
}
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_UnrollMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -42,7 +116,6 @@ __kernel void BruteForceMatch_UnrollMatch(
__local float *s_train = sharebuffer + block_size * max_desc_len;
int queryIdx = groupidx * block_size + lidy;
// load the query into local memory.
for (int i = 0 ; i < max_desc_len / block_size; i ++)
{
......@@ -55,11 +128,9 @@ __kernel void BruteForceMatch_UnrollMatch(
// loopUnrolledCached to find the best trainIdx and best distance.
volatile int imgIdx = 0;
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++)
{
//load a block_size * block_size block into local train.
......@@ -69,38 +140,7 @@ __kernel void BruteForceMatch_UnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + i * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + i * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -116,8 +156,8 @@ __kernel void BruteForceMatch_UnrollMatch(
}
barrier(CLK_LOCAL_MEM_FENCE);
__local float *s_distance = (__local float *)(sharebuffer);
__local int *s_trainIdx = (__local int *)(sharebuffer + block_size * block_size);
__local float *s_distance = (__local float*)(sharebuffer);
__local int* s_trainIdx = (__local int *)(sharebuffer + block_size * block_size);
//find BestMatch
s_distance += lidy * block_size;
......@@ -144,7 +184,7 @@ __kernel void BruteForceMatch_UnrollMatch(
}
}
__kernel void BruteForceMatch_Match(
__kernel void BruteForceMatch_Match_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -177,7 +217,6 @@ __kernel void BruteForceMatch_Match(
{
//Dist dist;
float result = 0;
for (int i = 0 ; i < (query_cols + block_size - 1) / block_size ; i++)
{
const int loadx = lidx + i * block_size;
......@@ -193,38 +232,7 @@ __kernel void BruteForceMatch_Match(
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -270,7 +278,7 @@ __kernel void BruteForceMatch_Match(
}
//radius_unrollmatch
__kernel void BruteForceMatch_RadiusUnrollMatch(
__kernel void BruteForceMatch_RadiusUnrollMatch_D5(
__global float *query,
__global float *train,
float maxDistance,
......@@ -303,7 +311,6 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
__local float *s_train = sharebuffer + block_size * block_size;
float result = 0;
for (int i = 0 ; i < max_desc_len / block_size ; ++i)
{
//load a block_size * block_size block into local train.
......@@ -315,37 +322,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are three types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; ++j)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; ++j)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; ++j)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -354,7 +331,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
if (ind < bestTrainIdx_cols)
if(ind < bestTrainIdx_cols)
{
//bestImgIdx = imgIdx;
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
......@@ -364,7 +341,7 @@ __kernel void BruteForceMatch_RadiusUnrollMatch(
}
//radius_match
__kernel void BruteForceMatch_RadiusMatch(
__kernel void BruteForceMatch_RadiusMatch_D5(
__global float *query,
__global float *train,
float maxDistance,
......@@ -396,7 +373,6 @@ __kernel void BruteForceMatch_RadiusMatch(
__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)
{
//load a block_size * block_size block into local train.
......@@ -408,46 +384,16 @@ __kernel void BruteForceMatch_RadiusMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are three types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; ++j)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; ++j)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; ++j)
{
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
if (queryIdx < query_rows && trainIdx < train_rows && result < maxDistance/* && mask(queryIdx, trainIdx)*/)
{
unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
unsigned int ind = atom_inc(nMatches + queryIdx);
if (ind < bestTrainIdx_cols)
if(ind < bestTrainIdx_cols)
{
//bestImgIdx = imgIdx;
bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
......@@ -457,7 +403,7 @@ __kernel void BruteForceMatch_RadiusMatch(
}
__kernel void BruteForceMatch_knnUnrollMatch(
__kernel void BruteForceMatch_knnUnrollMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -496,11 +442,9 @@ __kernel void BruteForceMatch_knnUnrollMatch(
//loopUnrolledCached
volatile int imgIdx = 0;
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++)
{
const int loadX = lidx + i * block_size;
......@@ -511,38 +455,7 @@ __kernel void BruteForceMatch_knnUnrollMatch(
//synchronize to make sure each elem for reduceIteration in share memory is written already.
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * max_desc_len + i * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * max_desc_len + i * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * max_desc_len + i * block_size + j] ^(uint)s_train[j * block_size + lidx]);
}
break;
}
result += reduce_multi_block(s_query, s_train, max_desc_len, block_size, i, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -589,7 +502,6 @@ __kernel void BruteForceMatch_knnUnrollMatch(
for (int i = 0 ; i < block_size ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
......@@ -640,7 +552,7 @@ __kernel void BruteForceMatch_knnUnrollMatch(
}
}
__kernel void BruteForceMatch_knnMatch(
__kernel void BruteForceMatch_knnMatch_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -673,8 +585,7 @@ __kernel void BruteForceMatch_knnMatch(
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;
//load query and train into local memory
......@@ -689,38 +600,7 @@ __kernel void BruteForceMatch_knnMatch(
barrier(CLK_LOCAL_MEM_FENCE);
/* there are threee types in the reducer. the first is L1Dist, which to sum the abs(v1, v2), the second is L2Dist, which to
sum the (v1 - v2) * (v1 - v2), the third is humming, which to popc(v1 ^ v2), popc is to count the bits are set to 1*/
switch (distType)
{
case 0:
for (int j = 0 ; j < block_size ; j++)
{
result += fabs(s_query[lidy * block_size + j] - s_train[j * block_size + lidx]);
}
break;
case 1:
for (int j = 0 ; j < block_size ; j++)
{
float qr = s_query[lidy * block_size + j] - s_train[j * block_size + lidx];
result += qr * qr;
}
break;
case 2:
for (int j = 0 ; j < block_size ; j++)
{
//result += popcount((uint)s_query[lidy * block_size + j] ^ (uint)s_train[j * block_size + lidx]);
result += bit1Count((uint)s_query[lidy * block_size + j] ^(uint)s_train[(uint)j * block_size + lidx]);
}
break;
}
result += reduce_block(s_query, s_train, block_size, lidx, lidy, distType);
barrier(CLK_LOCAL_MEM_FENCE);
}
......@@ -767,7 +647,6 @@ __kernel void BruteForceMatch_knnMatch(
for (int i = 0 ; i < block_size ; i++)
{
float val = s_distance[i];
if (val < bestDistance1)
{
bestDistance2 = bestDistance1;
......@@ -818,7 +697,7 @@ __kernel void BruteForceMatch_knnMatch(
}
}
kernel void BruteForceMatch_calcDistanceUnrolled(
kernel void BruteForceMatch_calcDistanceUnrolled_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -836,7 +715,7 @@ kernel void BruteForceMatch_calcDistanceUnrolled(
/* Todo */
}
kernel void BruteForceMatch_calcDistance(
kernel void BruteForceMatch_calcDistance_D5(
__global float *query,
__global float *train,
//__global float *mask,
......@@ -853,7 +732,7 @@ kernel void BruteForceMatch_calcDistance(
/* Todo */
}
kernel void BruteForceMatch_findBestMatch(
kernel void BruteForceMatch_findBestMatch_D5(
__global float *allDist,
__global int *bestTrainIdx,
__global float *bestDistance,
......
modules/ocl/src/opencl/haarobjectdetect.cl
浏览文件 @ ad6aae45
......@@ -211,10 +211,14 @@ __kernel void __attribute__((reqd_work_group_size(8,8,1)))gpuRunHaarClassifierCa
int4 data = *(__global int4*)&sum[glb_off];
int lcl_off = mad24(lcl_y, readwidth, lcl_x<<2);
#if OFF
lcldata[lcl_off] = data.x;
lcldata[lcl_off+1] = data.y;
lcldata[lcl_off+2] = data.z;
lcldata[lcl_off+3] = data.w;
#else
vstore4(data, 0, &lcldata[lcl_off]);
#endif
}
lcloutindex[lcl_id] = 0;
......@@ -559,3 +563,7 @@ if(result)
}
}
*/
modules/ocl/test/test_brute_force_matcher.cpp
浏览文件 @ ad6aae45
......@@ -110,7 +110,7 @@ namespace
}
};
TEST_P(BruteForceMatcher, DISABLED_Match_Single)
TEST_P(BruteForceMatcher, Match_Single)
{
cv::ocl::BruteForceMatcher_OCL_base matcher(distType);
......@@ -130,7 +130,7 @@ namespace
ASSERT_EQ(0, badCount);
}
TEST_P(BruteForceMatcher, DISABLED_KnnMatch_2_Single)
TEST_P(BruteForceMatcher, KnnMatch_2_Single)
{
const int knn = 2;
......
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