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used new stratehy in cv::accumulate**

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This commit is contained in:
Ilya Lavrenov
2014-07-09 20:06:54 +04:00
parent 2c6b7a52e9
commit 7017b1250a
3 changed files with 22 additions and 24 deletions
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5
modules/core/include/opencv2/core/ocl.hpp
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@@ -617,6 +617,11 @@ CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noAr
InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(), InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
OclVectorStrategy strat = OCL_VECTOR_DEFAULT); OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
// with OCL_VECTOR_MAX strategy
CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray());
CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m); CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m);
class CV_EXPORTS Image2D class CV_EXPORTS Image2D

30
modules/core/src/ocl.cpp
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@@ -4462,6 +4462,7 @@ String kernelToStr(InputArray _kernel, int ddepth, const char * name)
offsets.push_back(src.offset()); \ offsets.push_back(src.offset()); \
steps.push_back(src.step()); \ steps.push_back(src.step()); \
dividers.push_back(ckercn * CV_ELEM_SIZE1(ctype)); \ dividers.push_back(ckercn * CV_ELEM_SIZE1(ctype)); \
kercns.push_back(ckercn); \
} \ } \
} \ } \
while ((void)0, 0) while ((void)0, 0)
@@ -4483,13 +4484,13 @@ int predictOptimalVectorWidth(InputArray src1, InputArray src2, InputArray src3,
if (vectorWidths[0] == 1) if (vectorWidths[0] == 1)
{ {
// it's heuristic // it's heuristic
vectorWidths[0] = vectorWidths[1] = 4; vectorWidths[CV_8U] = vectorWidths[CV_8S] = 16;
vectorWidths[2] = vectorWidths[3] = 2; vectorWidths[CV_16U] = vectorWidths[CV_16S] = 8;
vectorWidths[4] = vectorWidths[5] = vectorWidths[6] = 4; vectorWidths[CV_32S] = vectorWidths[CV_32F] = vectorWidths[CV_64F] = 1;
} }
std::vector<size_t> offsets, steps, cols; std::vector<size_t> offsets, steps, cols;
std::vector<int> dividers; std::vector<int> dividers, kercns;
PROCESS_SRC(src1); PROCESS_SRC(src1);
PROCESS_SRC(src2); PROCESS_SRC(src2);
PROCESS_SRC(src3); PROCESS_SRC(src3);
@@ -4503,23 +4504,22 @@ int predictOptimalVectorWidth(InputArray src1, InputArray src2, InputArray src3,
size_t size = offsets.size(); size_t size = offsets.size();
for (size_t i = 0; i < size; ++i) for (size_t i = 0; i < size; ++i)
while (offsets[i] % dividers[i] != 0 || steps[i] % dividers[i] != 0 || cols[i] % dividers[i] != 0) while (offsets[i] % dividers[i] != 0 || steps[i] % dividers[i] != 0 || cols[i] % kercns[i] != 0)
dividers[i] >>= 1; dividers[i] >>= 1, kercns[i] >>= 1;
// default strategy // default strategy
int kercn = *std::min_element(dividers.begin(), dividers.end()); int kercn = *std::min_element(kercns.begin(), kercns.end());
// another strategy
// for (size_t i = 0; i < size; ++i)
// if (dividers[i] != wsz)
// {
// kercn = 1;
// break;
// }
return kercn; return kercn;
} }
int predictOptimalVectorWidthMax(InputArray src1, InputArray src2, InputArray src3,
InputArray src4, InputArray src5, InputArray src6,
InputArray src7, InputArray src8, InputArray src9)
{
return predictOptimalVectorWidth(src1, src2, src3, src4, src5, src6, src7, src8, src9, OCL_VECTOR_MAX);
}
#undef PROCESS_SRC #undef PROCESS_SRC

11
modules/imgproc/src/accum.cpp
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@@ -370,16 +370,9 @@ static bool ocl_accumulate( InputArray _src, InputArray _src2, InputOutputArray
op_type == ACCUMULATE_PRODUCT || op_type == ACCUMULATE_WEIGHTED); op_type == ACCUMULATE_PRODUCT || op_type == ACCUMULATE_WEIGHTED);
const ocl::Device & dev = ocl::Device::getDefault(); const ocl::Device & dev = ocl::Device::getDefault();
int vectorWidths[] = { 4, 4, 2, 2, 1, 1, 1, -1 }; bool haveMask = !_mask.empty(), doubleSupport = dev.doubleFPConfig() > 0;
int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype), ddepth = _dst.depth(); int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype), ddepth = _dst.depth();
int pcn = std::max(vectorWidths[sdepth], vectorWidths[ddepth]), sesz = CV_ELEM_SIZE(sdepth) * pcn, int kercn = haveMask ? cn : ocl::predictOptimalVectorWidthMax(_src, _src2, _dst), rowsPerWI = dev.isIntel() ? 4 : 1;
desz = CV_ELEM_SIZE(ddepth) * pcn, rowsPerWI = dev.isIntel() ? 4 : 1;
bool doubleSupport = dev.doubleFPConfig() > 0, haveMask = !_mask.empty(),
usepcn = _src.offset() % sesz == 0 && _src.step() % sesz == 0 && (_src.cols() * cn) % pcn == 0 &&
_src2.offset() % desz == 0 && _src2.step() % desz == 0 &&
_dst.offset() % pcn == 0 && _dst.step() % desz == 0 && !haveMask;
int kercn = usepcn ? pcn : haveMask ? cn : 1;
if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F)) if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
return false; return false;