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used new stratehy in cv::accumulate**
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@ -617,6 +617,11 @@ CV_EXPORTS int predictOptimalVectorWidth(InputArray src1, InputArray src2 = noAr
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InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray(),
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OclVectorStrategy strat = OCL_VECTOR_DEFAULT);
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// with OCL_VECTOR_MAX strategy
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CV_EXPORTS int predictOptimalVectorWidthMax(InputArray src1, InputArray src2 = noArray(), InputArray src3 = noArray(),
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InputArray src4 = noArray(), InputArray src5 = noArray(), InputArray src6 = noArray(),
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InputArray src7 = noArray(), InputArray src8 = noArray(), InputArray src9 = noArray());
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CV_EXPORTS void buildOptionsAddMatrixDescription(String& buildOptions, const String& name, InputArray _m);
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class CV_EXPORTS Image2D
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@ -4462,6 +4462,7 @@ String kernelToStr(InputArray _kernel, int ddepth, const char * name)
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offsets.push_back(src.offset()); \
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steps.push_back(src.step()); \
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dividers.push_back(ckercn * CV_ELEM_SIZE1(ctype)); \
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kercns.push_back(ckercn); \
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} \
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} \
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while ((void)0, 0)
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@ -4483,13 +4484,13 @@ int predictOptimalVectorWidth(InputArray src1, InputArray src2, InputArray src3,
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if (vectorWidths[0] == 1)
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{
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// it's heuristic
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vectorWidths[0] = vectorWidths[1] = 4;
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vectorWidths[2] = vectorWidths[3] = 2;
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vectorWidths[4] = vectorWidths[5] = vectorWidths[6] = 4;
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vectorWidths[CV_8U] = vectorWidths[CV_8S] = 16;
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vectorWidths[CV_16U] = vectorWidths[CV_16S] = 8;
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vectorWidths[CV_32S] = vectorWidths[CV_32F] = vectorWidths[CV_64F] = 1;
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}
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std::vector<size_t> offsets, steps, cols;
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std::vector<int> dividers;
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std::vector<int> dividers, kercns;
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PROCESS_SRC(src1);
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PROCESS_SRC(src2);
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PROCESS_SRC(src3);
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@ -4503,23 +4504,22 @@ int predictOptimalVectorWidth(InputArray src1, InputArray src2, InputArray src3,
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size_t size = offsets.size();
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for (size_t i = 0; i < size; ++i)
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while (offsets[i] % dividers[i] != 0 || steps[i] % dividers[i] != 0 || cols[i] % dividers[i] != 0)
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dividers[i] >>= 1;
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while (offsets[i] % dividers[i] != 0 || steps[i] % dividers[i] != 0 || cols[i] % kercns[i] != 0)
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dividers[i] >>= 1, kercns[i] >>= 1;
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// default strategy
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int kercn = *std::min_element(dividers.begin(), dividers.end());
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// another strategy
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// for (size_t i = 0; i < size; ++i)
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// if (dividers[i] != wsz)
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// {
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// kercn = 1;
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// break;
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// }
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int kercn = *std::min_element(kercns.begin(), kercns.end());
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return kercn;
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}
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int predictOptimalVectorWidthMax(InputArray src1, InputArray src2, InputArray src3,
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InputArray src4, InputArray src5, InputArray src6,
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InputArray src7, InputArray src8, InputArray src9)
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{
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return predictOptimalVectorWidth(src1, src2, src3, src4, src5, src6, src7, src8, src9, OCL_VECTOR_MAX);
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}
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#undef PROCESS_SRC
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@ -370,16 +370,9 @@ static bool ocl_accumulate( InputArray _src, InputArray _src2, InputOutputArray
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op_type == ACCUMULATE_PRODUCT || op_type == ACCUMULATE_WEIGHTED);
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const ocl::Device & dev = ocl::Device::getDefault();
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int vectorWidths[] = { 4, 4, 2, 2, 1, 1, 1, -1 };
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bool haveMask = !_mask.empty(), doubleSupport = dev.doubleFPConfig() > 0;
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int stype = _src.type(), sdepth = CV_MAT_DEPTH(stype), cn = CV_MAT_CN(stype), ddepth = _dst.depth();
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int pcn = std::max(vectorWidths[sdepth], vectorWidths[ddepth]), sesz = CV_ELEM_SIZE(sdepth) * pcn,
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desz = CV_ELEM_SIZE(ddepth) * pcn, rowsPerWI = dev.isIntel() ? 4 : 1;
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bool doubleSupport = dev.doubleFPConfig() > 0, haveMask = !_mask.empty(),
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usepcn = _src.offset() % sesz == 0 && _src.step() % sesz == 0 && (_src.cols() * cn) % pcn == 0 &&
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_src2.offset() % desz == 0 && _src2.step() % desz == 0 &&
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_dst.offset() % pcn == 0 && _dst.step() % desz == 0 && !haveMask;
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int kercn = usepcn ? pcn : haveMask ? cn : 1;
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int kercn = haveMask ? cn : ocl::predictOptimalVectorWidthMax(_src, _src2, _dst), rowsPerWI = dev.isIntel() ? 4 : 1;
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if (!doubleSupport && (sdepth == CV_64F || ddepth == CV_64F))
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return false;
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