diff --git a/modules/core/CMakeLists.txt b/modules/core/CMakeLists.txt index c4f073713c..bfa2fd1d98 100644 --- a/modules/core/CMakeLists.txt +++ b/modules/core/CMakeLists.txt @@ -5,6 +5,7 @@ ocv_add_dispatched_file(stat SSE4_2 AVX2) ocv_add_dispatched_file(arithm SSE2 SSE4_1 AVX2 VSX3) ocv_add_dispatched_file(convert SSE2 AVX2) ocv_add_dispatched_file(convert_scale SSE2 AVX2) +ocv_add_dispatched_file(sum SSE2 AVX2) # dispatching for accuracy tests ocv_add_dispatched_file_force_all(test_intrin128 TEST SSE2 SSE3 SSSE3 SSE4_1 SSE4_2 AVX FP16 AVX2) diff --git a/modules/core/src/sum.dispatch.cpp b/modules/core/src/sum.dispatch.cpp new file mode 100644 index 0000000000..6ca5f9ded9 --- /dev/null +++ b/modules/core/src/sum.dispatch.cpp @@ -0,0 +1,239 @@ +// This file is part of OpenCV project. +// It is subject to the license terms in the LICENSE file found in the top-level directory +// of this distribution and at http://opencv.org/license.html + + +#include "precomp.hpp" +#include "opencl_kernels_core.hpp" +#include "stat.hpp" + +#include "sum.simd.hpp" +#include "sum.simd_declarations.hpp" // defines CV_CPU_DISPATCH_MODES_ALL=AVX2,...,BASELINE based on CMakeLists.txt content + +namespace cv +{ + +SumFunc getSumFunc(int depth) +{ + CV_INSTRUMENT_REGION(); + CV_CPU_DISPATCH(getSumFunc, (depth), + CV_CPU_DISPATCH_MODES_ALL); +} + +#ifdef HAVE_OPENCL + +bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask, + InputArray _src2, bool calc2, const Scalar & res2 ) +{ + CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR); + + const ocl::Device & dev = ocl::Device::getDefault(); + bool doubleSupport = dev.doubleFPConfig() > 0, + haveMask = _mask.kind() != _InputArray::NONE, + haveSrc2 = _src2.kind() != _InputArray::NONE; + int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), + kercn = cn == 1 && !haveMask ? ocl::predictOptimalVectorWidth(_src, _src2) : 1, + mcn = std::max(cn, kercn); + CV_Assert(!haveSrc2 || _src2.type() == type); + int convert_cn = haveSrc2 ? mcn : cn; + + if ( (!doubleSupport && depth == CV_64F) || cn > 4 ) + return false; + + int ngroups = dev.maxComputeUnits(), dbsize = ngroups * (calc2 ? 2 : 1); + size_t wgs = dev.maxWorkGroupSize(); + + int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth), + dtype = CV_MAKE_TYPE(ddepth, cn); + CV_Assert(!haveMask || _mask.type() == CV_8UC1); + + int wgs2_aligned = 1; + while (wgs2_aligned < (int)wgs) + wgs2_aligned <<= 1; + wgs2_aligned >>= 1; + + static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" }; + char cvt[2][40]; + String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D dstT1=%s -D ddepth=%d -D cn=%d" + " -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s%s%s -D kercn=%d%s%s%s -D convertFromU=%s", + ocl::typeToStr(CV_MAKE_TYPE(depth, mcn)), ocl::typeToStr(depth), + ocl::typeToStr(dtype), ocl::typeToStr(CV_MAKE_TYPE(ddepth, mcn)), + ocl::typeToStr(ddepth), ddepth, cn, + ocl::convertTypeStr(depth, ddepth, mcn, cvt[0]), + opMap[sum_op], (int)wgs, wgs2_aligned, + doubleSupport ? " -D DOUBLE_SUPPORT" : "", + haveMask ? " -D HAVE_MASK" : "", + _src.isContinuous() ? " -D HAVE_SRC_CONT" : "", + haveMask && _mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn, + haveSrc2 ? " -D HAVE_SRC2" : "", calc2 ? " -D OP_CALC2" : "", + haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "", + depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, convert_cn, cvt[1]) : "noconvert"); + + ocl::Kernel k("reduce", ocl::core::reduce_oclsrc, opts); + if (k.empty()) + return false; + + UMat src = _src.getUMat(), src2 = _src2.getUMat(), + db(1, dbsize, dtype), mask = _mask.getUMat(); + + ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src), + dbarg = ocl::KernelArg::PtrWriteOnly(db), + maskarg = ocl::KernelArg::ReadOnlyNoSize(mask), + src2arg = ocl::KernelArg::ReadOnlyNoSize(src2); + + if (haveMask) + { + if (haveSrc2) + k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg, src2arg); + else + k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg); + } + else + { + if (haveSrc2) + k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, src2arg); + else + k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg); + } + + size_t globalsize = ngroups * wgs; + if (k.run(1, &globalsize, &wgs, false)) + { + typedef Scalar (*part_sum)(Mat m); + part_sum funcs[3] = { ocl_part_sum, ocl_part_sum, ocl_part_sum }, + func = funcs[ddepth - CV_32S]; + + Mat mres = db.getMat(ACCESS_READ); + if (calc2) + const_cast(res2) = func(mres.colRange(ngroups, dbsize)); + + res = func(mres.colRange(0, ngroups)); + return true; + } + return false; +} + +#endif + +#ifdef HAVE_IPP +static bool ipp_sum(Mat &src, Scalar &_res) +{ + CV_INSTRUMENT_REGION_IPP(); + +#if IPP_VERSION_X100 >= 700 + int cn = src.channels(); + if (cn > 4) + return false; + size_t total_size = src.total(); + int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0; + if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) ) + { + IppiSize sz = { cols, rows }; + int type = src.type(); + typedef IppStatus (CV_STDCALL* ippiSumFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm); + typedef IppStatus (CV_STDCALL* ippiSumFuncNoHint)(const void*, int, IppiSize, double *); + ippiSumFuncHint ippiSumHint = + type == CV_32FC1 ? (ippiSumFuncHint)ippiSum_32f_C1R : + type == CV_32FC3 ? (ippiSumFuncHint)ippiSum_32f_C3R : + type == CV_32FC4 ? (ippiSumFuncHint)ippiSum_32f_C4R : + 0; + ippiSumFuncNoHint ippiSum = + type == CV_8UC1 ? (ippiSumFuncNoHint)ippiSum_8u_C1R : + type == CV_8UC3 ? (ippiSumFuncNoHint)ippiSum_8u_C3R : + type == CV_8UC4 ? (ippiSumFuncNoHint)ippiSum_8u_C4R : + type == CV_16UC1 ? (ippiSumFuncNoHint)ippiSum_16u_C1R : + type == CV_16UC3 ? (ippiSumFuncNoHint)ippiSum_16u_C3R : + type == CV_16UC4 ? (ippiSumFuncNoHint)ippiSum_16u_C4R : + type == CV_16SC1 ? (ippiSumFuncNoHint)ippiSum_16s_C1R : + type == CV_16SC3 ? (ippiSumFuncNoHint)ippiSum_16s_C3R : + type == CV_16SC4 ? (ippiSumFuncNoHint)ippiSum_16s_C4R : + 0; + CV_Assert(!ippiSumHint || !ippiSum); + if( ippiSumHint || ippiSum ) + { + Ipp64f res[4]; + IppStatus ret = ippiSumHint ? + CV_INSTRUMENT_FUN_IPP(ippiSumHint, src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) : + CV_INSTRUMENT_FUN_IPP(ippiSum, src.ptr(), (int)src.step[0], sz, res); + if( ret >= 0 ) + { + for( int i = 0; i < cn; i++ ) + _res[i] = res[i]; + return true; + } + } + } +#else + CV_UNUSED(src); CV_UNUSED(_res); +#endif + return false; +} +#endif + +Scalar sum(InputArray _src) +{ + CV_INSTRUMENT_REGION(); + +#if defined HAVE_OPENCL || defined HAVE_IPP + Scalar _res; +#endif + +#ifdef HAVE_OPENCL + CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2, + ocl_sum(_src, _res, OCL_OP_SUM), + _res) +#endif + + Mat src = _src.getMat(); + CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_sum(src, _res), _res); + + int k, cn = src.channels(), depth = src.depth(); + SumFunc func = getSumFunc(depth); + CV_Assert( cn <= 4 && func != 0 ); + + const Mat* arrays[] = {&src, 0}; + uchar* ptrs[1] = {}; + NAryMatIterator it(arrays, ptrs); + Scalar s; + int total = (int)it.size, blockSize = total, intSumBlockSize = 0; + int j, count = 0; + AutoBuffer _buf; + int* buf = (int*)&s[0]; + size_t esz = 0; + bool blockSum = depth < CV_32S; + + if( blockSum ) + { + intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15); + blockSize = std::min(blockSize, intSumBlockSize); + _buf.allocate(cn); + buf = _buf.data(); + + for( k = 0; k < cn; k++ ) + buf[k] = 0; + esz = src.elemSize(); + } + + for( size_t i = 0; i < it.nplanes; i++, ++it ) + { + for( j = 0; j < total; j += blockSize ) + { + int bsz = std::min(total - j, blockSize); + func( ptrs[0], 0, (uchar*)buf, bsz, cn ); + count += bsz; + if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) ) + { + for( k = 0; k < cn; k++ ) + { + s[k] += buf[k]; + buf[k] = 0; + } + count = 0; + } + ptrs[0] += bsz*esz; + } + } + return s; +} + +} // namespace diff --git a/modules/core/src/sum.cpp b/modules/core/src/sum.simd.hpp similarity index 59% rename from modules/core/src/sum.cpp rename to modules/core/src/sum.simd.hpp index 30cee85b4c..2232013b24 100644 --- a/modules/core/src/sum.cpp +++ b/modules/core/src/sum.simd.hpp @@ -4,11 +4,14 @@ #include "precomp.hpp" -#include "opencl_kernels_core.hpp" #include "stat.hpp" -namespace cv -{ +namespace cv { +CV_CPU_OPTIMIZATION_NAMESPACE_BEGIN + +SumFunc getSumFunc(int depth); + +#ifndef CV_CPU_OPTIMIZATION_DECLARATIONS_ONLY template struct Sum_SIMD @@ -409,25 +412,25 @@ static int sum_(const T* src0, const uchar* mask, ST* dst, int len, int cn ) static int sum8u( const uchar* src, const uchar* mask, int* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum8s( const schar* src, const uchar* mask, int* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum16u( const ushort* src, const uchar* mask, int* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum16s( const short* src, const uchar* mask, int* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum32s( const int* src, const uchar* mask, double* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum32f( const float* src, const uchar* mask, double* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } static int sum64f( const double* src, const uchar* mask, double* dst, int len, int cn ) -{ return sum_(src, mask, dst, len, cn); } +{ CV_INSTRUMENT_REGION(); return sum_(src, mask, dst, len, cn); } SumFunc getSumFunc(int depth) { @@ -443,220 +446,7 @@ SumFunc getSumFunc(int depth) return sumTab[depth]; } -#ifdef HAVE_OPENCL - -bool ocl_sum( InputArray _src, Scalar & res, int sum_op, InputArray _mask, - InputArray _src2, bool calc2, const Scalar & res2 ) -{ - CV_Assert(sum_op == OCL_OP_SUM || sum_op == OCL_OP_SUM_ABS || sum_op == OCL_OP_SUM_SQR); - - const ocl::Device & dev = ocl::Device::getDefault(); - bool doubleSupport = dev.doubleFPConfig() > 0, - haveMask = _mask.kind() != _InputArray::NONE, - haveSrc2 = _src2.kind() != _InputArray::NONE; - int type = _src.type(), depth = CV_MAT_DEPTH(type), cn = CV_MAT_CN(type), - kercn = cn == 1 && !haveMask ? ocl::predictOptimalVectorWidth(_src, _src2) : 1, - mcn = std::max(cn, kercn); - CV_Assert(!haveSrc2 || _src2.type() == type); - int convert_cn = haveSrc2 ? mcn : cn; - - if ( (!doubleSupport && depth == CV_64F) || cn > 4 ) - return false; - - int ngroups = dev.maxComputeUnits(), dbsize = ngroups * (calc2 ? 2 : 1); - size_t wgs = dev.maxWorkGroupSize(); - - int ddepth = std::max(sum_op == OCL_OP_SUM_SQR ? CV_32F : CV_32S, depth), - dtype = CV_MAKE_TYPE(ddepth, cn); - CV_Assert(!haveMask || _mask.type() == CV_8UC1); - - int wgs2_aligned = 1; - while (wgs2_aligned < (int)wgs) - wgs2_aligned <<= 1; - wgs2_aligned >>= 1; - - static const char * const opMap[3] = { "OP_SUM", "OP_SUM_ABS", "OP_SUM_SQR" }; - char cvt[2][40]; - String opts = format("-D srcT=%s -D srcT1=%s -D dstT=%s -D dstTK=%s -D dstT1=%s -D ddepth=%d -D cn=%d" - " -D convertToDT=%s -D %s -D WGS=%d -D WGS2_ALIGNED=%d%s%s%s%s -D kercn=%d%s%s%s -D convertFromU=%s", - ocl::typeToStr(CV_MAKE_TYPE(depth, mcn)), ocl::typeToStr(depth), - ocl::typeToStr(dtype), ocl::typeToStr(CV_MAKE_TYPE(ddepth, mcn)), - ocl::typeToStr(ddepth), ddepth, cn, - ocl::convertTypeStr(depth, ddepth, mcn, cvt[0]), - opMap[sum_op], (int)wgs, wgs2_aligned, - doubleSupport ? " -D DOUBLE_SUPPORT" : "", - haveMask ? " -D HAVE_MASK" : "", - _src.isContinuous() ? " -D HAVE_SRC_CONT" : "", - haveMask && _mask.isContinuous() ? " -D HAVE_MASK_CONT" : "", kercn, - haveSrc2 ? " -D HAVE_SRC2" : "", calc2 ? " -D OP_CALC2" : "", - haveSrc2 && _src2.isContinuous() ? " -D HAVE_SRC2_CONT" : "", - depth <= CV_32S && ddepth == CV_32S ? ocl::convertTypeStr(CV_8U, ddepth, convert_cn, cvt[1]) : "noconvert"); - - ocl::Kernel k("reduce", ocl::core::reduce_oclsrc, opts); - if (k.empty()) - return false; - - UMat src = _src.getUMat(), src2 = _src2.getUMat(), - db(1, dbsize, dtype), mask = _mask.getUMat(); - - ocl::KernelArg srcarg = ocl::KernelArg::ReadOnlyNoSize(src), - dbarg = ocl::KernelArg::PtrWriteOnly(db), - maskarg = ocl::KernelArg::ReadOnlyNoSize(mask), - src2arg = ocl::KernelArg::ReadOnlyNoSize(src2); - - if (haveMask) - { - if (haveSrc2) - k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg, src2arg); - else - k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, maskarg); - } - else - { - if (haveSrc2) - k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg, src2arg); - else - k.args(srcarg, src.cols, (int)src.total(), ngroups, dbarg); - } - - size_t globalsize = ngroups * wgs; - if (k.run(1, &globalsize, &wgs, false)) - { - typedef Scalar (*part_sum)(Mat m); - part_sum funcs[3] = { ocl_part_sum, ocl_part_sum, ocl_part_sum }, - func = funcs[ddepth - CV_32S]; - - Mat mres = db.getMat(ACCESS_READ); - if (calc2) - const_cast(res2) = func(mres.colRange(ngroups, dbsize)); - - res = func(mres.colRange(0, ngroups)); - return true; - } - return false; -} - -#endif - -#ifdef HAVE_IPP -static bool ipp_sum(Mat &src, Scalar &_res) -{ - CV_INSTRUMENT_REGION_IPP(); - -#if IPP_VERSION_X100 >= 700 - int cn = src.channels(); - if (cn > 4) - return false; - size_t total_size = src.total(); - int rows = src.size[0], cols = rows ? (int)(total_size/rows) : 0; - if( src.dims == 2 || (src.isContinuous() && cols > 0 && (size_t)rows*cols == total_size) ) - { - IppiSize sz = { cols, rows }; - int type = src.type(); - typedef IppStatus (CV_STDCALL* ippiSumFuncHint)(const void*, int, IppiSize, double *, IppHintAlgorithm); - typedef IppStatus (CV_STDCALL* ippiSumFuncNoHint)(const void*, int, IppiSize, double *); - ippiSumFuncHint ippiSumHint = - type == CV_32FC1 ? (ippiSumFuncHint)ippiSum_32f_C1R : - type == CV_32FC3 ? (ippiSumFuncHint)ippiSum_32f_C3R : - type == CV_32FC4 ? (ippiSumFuncHint)ippiSum_32f_C4R : - 0; - ippiSumFuncNoHint ippiSum = - type == CV_8UC1 ? (ippiSumFuncNoHint)ippiSum_8u_C1R : - type == CV_8UC3 ? (ippiSumFuncNoHint)ippiSum_8u_C3R : - type == CV_8UC4 ? (ippiSumFuncNoHint)ippiSum_8u_C4R : - type == CV_16UC1 ? (ippiSumFuncNoHint)ippiSum_16u_C1R : - type == CV_16UC3 ? (ippiSumFuncNoHint)ippiSum_16u_C3R : - type == CV_16UC4 ? (ippiSumFuncNoHint)ippiSum_16u_C4R : - type == CV_16SC1 ? (ippiSumFuncNoHint)ippiSum_16s_C1R : - type == CV_16SC3 ? (ippiSumFuncNoHint)ippiSum_16s_C3R : - type == CV_16SC4 ? (ippiSumFuncNoHint)ippiSum_16s_C4R : - 0; - CV_Assert(!ippiSumHint || !ippiSum); - if( ippiSumHint || ippiSum ) - { - Ipp64f res[4]; - IppStatus ret = ippiSumHint ? - CV_INSTRUMENT_FUN_IPP(ippiSumHint, src.ptr(), (int)src.step[0], sz, res, ippAlgHintAccurate) : - CV_INSTRUMENT_FUN_IPP(ippiSum, src.ptr(), (int)src.step[0], sz, res); - if( ret >= 0 ) - { - for( int i = 0; i < cn; i++ ) - _res[i] = res[i]; - return true; - } - } - } -#else - CV_UNUSED(src); CV_UNUSED(_res); -#endif - return false; -} -#endif - -} // cv:: - -cv::Scalar cv::sum( InputArray _src ) -{ - CV_INSTRUMENT_REGION(); - -#if defined HAVE_OPENCL || defined HAVE_IPP - Scalar _res; -#endif - -#ifdef HAVE_OPENCL - CV_OCL_RUN_(OCL_PERFORMANCE_CHECK(_src.isUMat()) && _src.dims() <= 2, - ocl_sum(_src, _res, OCL_OP_SUM), - _res) #endif - Mat src = _src.getMat(); - CV_IPP_RUN(IPP_VERSION_X100 >= 700, ipp_sum(src, _res), _res); - - int k, cn = src.channels(), depth = src.depth(); - SumFunc func = getSumFunc(depth); - CV_Assert( cn <= 4 && func != 0 ); - - const Mat* arrays[] = {&src, 0}; - uchar* ptrs[1] = {}; - NAryMatIterator it(arrays, ptrs); - Scalar s; - int total = (int)it.size, blockSize = total, intSumBlockSize = 0; - int j, count = 0; - AutoBuffer _buf; - int* buf = (int*)&s[0]; - size_t esz = 0; - bool blockSum = depth < CV_32S; - - if( blockSum ) - { - intSumBlockSize = depth <= CV_8S ? (1 << 23) : (1 << 15); - blockSize = std::min(blockSize, intSumBlockSize); - _buf.allocate(cn); - buf = _buf.data(); - - for( k = 0; k < cn; k++ ) - buf[k] = 0; - esz = src.elemSize(); - } - - for( size_t i = 0; i < it.nplanes; i++, ++it ) - { - for( j = 0; j < total; j += blockSize ) - { - int bsz = std::min(total - j, blockSize); - func( ptrs[0], 0, (uchar*)buf, bsz, cn ); - count += bsz; - if( blockSum && (count + blockSize >= intSumBlockSize || (i+1 >= it.nplanes && j+bsz >= total)) ) - { - for( k = 0; k < cn; k++ ) - { - s[k] += buf[k]; - buf[k] = 0; - } - count = 0; - } - ptrs[0] += bsz*esz; - } - } - return s; -} +CV_CPU_OPTIMIZATION_NAMESPACE_END +} // namespace