mirror of
https://github.com/opencv/opencv.git
synced 2025-06-27 23:11:57 +08:00
Merge pull request #24480 from savuor:backport_patch_nans
Backport to 4.x: patchNaNs() SIMD acceleration #24480 backport from #23098 connected PR in extra: [#1118@extra](https://github.com/opencv/opencv_extra/pull/1118) ### This PR contains: * new SIMD code for `patchNaNs()` * CPU perf test <details> <summary>Performance comparison</summary> Geometric mean (ms) |Name of Test|noopt|sse2|avx2|sse2 vs noopt (x-factor)|avx2 vs noopt (x-factor)| |---|:-:|:-:|:-:|:-:|:-:| |PatchNaNs::OCL_PatchNaNsFixture::(640x480, 32FC1)|0.019|0.017|0.018|1.11|1.07| |PatchNaNs::OCL_PatchNaNsFixture::(640x480, 32FC4)|0.037|0.037|0.033|1.00|1.10| |PatchNaNs::OCL_PatchNaNsFixture::(1280x720, 32FC1)|0.032|0.032|0.033|0.99|0.98| |PatchNaNs::OCL_PatchNaNsFixture::(1280x720, 32FC4)|0.072|0.072|0.070|1.00|1.03| |PatchNaNs::OCL_PatchNaNsFixture::(1920x1080, 32FC1)|0.051|0.051|0.050|1.00|1.01| |PatchNaNs::OCL_PatchNaNsFixture::(1920x1080, 32FC4)|0.137|0.138|0.128|0.99|1.06| |PatchNaNs::OCL_PatchNaNsFixture::(3840x2160, 32FC1)|0.137|0.128|0.129|1.07|1.06| |PatchNaNs::OCL_PatchNaNsFixture::(3840x2160, 32FC4)|0.450|0.450|0.448|1.00|1.01| |PatchNaNs::PatchNaNsFixture::(640x480, 32FC1)|0.149|0.029|0.020|5.13|7.44| |PatchNaNs::PatchNaNsFixture::(640x480, 32FC2)|0.304|0.058|0.040|5.25|7.65| |PatchNaNs::PatchNaNsFixture::(640x480, 32FC3)|0.448|0.086|0.059|5.22|7.55| |PatchNaNs::PatchNaNsFixture::(640x480, 32FC4)|0.601|0.133|0.083|4.51|7.23| |PatchNaNs::PatchNaNsFixture::(1280x720, 32FC1)|0.451|0.093|0.060|4.83|7.52| |PatchNaNs::PatchNaNsFixture::(1280x720, 32FC2)|0.892|0.184|0.126|4.85|7.06| |PatchNaNs::PatchNaNsFixture::(1280x720, 32FC3)|1.345|0.311|0.230|4.32|5.84| |PatchNaNs::PatchNaNsFixture::(1280x720, 32FC4)|1.831|0.546|0.436|3.35|4.20| |PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC1)|1.017|0.250|0.160|4.06|6.35| |PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC2)|2.077|0.646|0.605|3.21|3.43| |PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC3)|3.134|1.053|0.961|2.97|3.26| |PatchNaNs::PatchNaNsFixture::(1920x1080, 32FC4)|4.222|1.436|1.288|2.94|3.28| |PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC1)|4.225|1.401|1.277|3.01|3.31| |PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC2)|8.310|2.953|2.635|2.81|3.15| |PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC3)|12.396|4.455|4.252|2.78|2.92| |PatchNaNs::PatchNaNsFixture::(3840x2160, 32FC4)|17.174|5.831|5.824|2.95|2.95| </details> ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
This commit is contained in:
Rostislav Vasilikhin
GitHub
parent
7c9231ffba
commit
ea47cb3ffe
@ -1697,7 +1697,7 @@ elements.
|
||||
CV_EXPORTS_W bool checkRange(InputArray a, bool quiet = true, CV_OUT Point* pos = 0,
|
||||
double minVal = -DBL_MAX, double maxVal = DBL_MAX);
|
||||
|
||||
/** @brief converts NaNs to the given number
|
||||
/** @brief Replaces NaNs by given number
|
||||
@param a input/output matrix (CV_32F type).
|
||||
@param val value to convert the NaNs
|
||||
*/
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
#include "perf_precomp.hpp"
|
||||
#include <numeric>
|
||||
#include "opencv2/core/softfloat.hpp"
|
||||
|
||||
namespace opencv_test
|
||||
{
|
||||
@ -451,4 +452,69 @@ INSTANTIATE_TEST_CASE_P(/*nothing*/ , BinaryOpTest,
|
||||
)
|
||||
);
|
||||
|
||||
///////////// PatchNaNs ////////////////////////
|
||||
|
||||
template<typename _Tp>
|
||||
_Tp randomNan(RNG& rng);
|
||||
|
||||
template<>
|
||||
float randomNan(RNG& rng)
|
||||
{
|
||||
uint32_t r = rng.next();
|
||||
Cv32suf v;
|
||||
v.u = r;
|
||||
// exp & set a bit to avoid zero mantissa
|
||||
v.u = v.u | 0x7f800001;
|
||||
return v.f;
|
||||
}
|
||||
|
||||
template<>
|
||||
double randomNan(RNG& rng)
|
||||
{
|
||||
uint32_t r0 = rng.next();
|
||||
uint32_t r1 = rng.next();
|
||||
Cv64suf v;
|
||||
v.u = (uint64_t(r0) << 32) | uint64_t(r1);
|
||||
// exp &set a bit to avoid zero mantissa
|
||||
v.u = v.u | 0x7ff0000000000001;
|
||||
return v.f;
|
||||
}
|
||||
|
||||
typedef Size_MatType PatchNaNsFixture;
|
||||
|
||||
PERF_TEST_P_(PatchNaNsFixture, PatchNaNs)
|
||||
{
|
||||
const Size_MatType_t params = GetParam();
|
||||
Size srcSize = get<0>(params);
|
||||
const int type = get<1>(params), cn = CV_MAT_CN(type);
|
||||
|
||||
Mat src(srcSize, type);
|
||||
declare.in(src, WARMUP_RNG).out(src);
|
||||
|
||||
// generating NaNs
|
||||
{
|
||||
srcSize.width *= cn;
|
||||
RNG& rng = theRNG();
|
||||
for (int y = 0; y < srcSize.height; ++y)
|
||||
{
|
||||
float *const ptrf = src.ptr<float>(y);
|
||||
for (int x = 0; x < srcSize.width; ++x)
|
||||
{
|
||||
ptrf[x] = (x + y) % 2 == 0 ? randomNan<float >(rng) : ptrf[x];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
TEST_CYCLE() cv::patchNaNs(src, 17.7);
|
||||
|
||||
SANITY_CHECK(src);
|
||||
}
|
||||
|
||||
INSTANTIATE_TEST_CASE_P(/*nothing*/ , PatchNaNsFixture,
|
||||
testing::Combine(
|
||||
testing::Values(szVGA, sz720p, sz1080p, sz2160p),
|
||||
testing::Values(CV_32FC1, CV_32FC2, CV_32FC3, CV_32FC4)
|
||||
)
|
||||
);
|
||||
|
||||
} // namespace
|
||||
|
||||
@ -1610,30 +1610,37 @@ void patchNaNs( InputOutputArray _a, double _val )
|
||||
const Mat* arrays[] = {&a, 0};
|
||||
int* ptrs[1] = {};
|
||||
NAryMatIterator it(arrays, (uchar**)ptrs);
|
||||
size_t len = it.size*a.channels();
|
||||
int len = (int)(it.size*a.channels());
|
||||
Cv32suf val;
|
||||
val.f = (float)_val;
|
||||
|
||||
#if (CV_SIMD || CV_SIMD_SCALABLE)
|
||||
v_int32 v_mask1 = vx_setall_s32(0x7fffffff), v_mask2 = vx_setall_s32(0x7f800000);
|
||||
v_int32 v_val = vx_setall_s32(val.i);
|
||||
#endif
|
||||
|
||||
for( size_t i = 0; i < it.nplanes; i++, ++it )
|
||||
{
|
||||
int* tptr = ptrs[0];
|
||||
size_t j = 0;
|
||||
int j = 0;
|
||||
|
||||
#if (CV_SIMD || CV_SIMD_SCALABLE)
|
||||
size_t cWidth = (size_t)VTraits<v_int32>::vlanes();
|
||||
for ( ; j + cWidth <= len; j += cWidth)
|
||||
v_int32 v_pos_mask = vx_setall_s32(0x7fffffff), v_exp_mask = vx_setall_s32(0x7f800000);
|
||||
v_int32 v_val = vx_setall_s32(val.i);
|
||||
|
||||
int cWidth = VTraits<v_int32>::vlanes();
|
||||
for (; j < len - cWidth * 2 + 1; j += cWidth * 2)
|
||||
{
|
||||
v_int32 v_src = vx_load(tptr + j);
|
||||
v_int32 v_cmp_mask = v_lt(v_mask2, v_and(v_src, v_mask1));
|
||||
v_int32 v_dst = v_select(v_cmp_mask, v_val, v_src);
|
||||
v_store(tptr + j, v_dst);
|
||||
v_int32 v_src0 = vx_load(tptr + j);
|
||||
v_int32 v_src1 = vx_load(tptr + j + cWidth);
|
||||
|
||||
v_int32 v_cmp_mask0 = v_lt(v_exp_mask, v_and(v_src0, v_pos_mask));
|
||||
v_int32 v_cmp_mask1 = v_lt(v_exp_mask, v_and(v_src1, v_pos_mask));
|
||||
|
||||
if (v_check_any(v_or(v_cmp_mask0, v_cmp_mask1)))
|
||||
{
|
||||
v_int32 v_dst0 = v_select(v_cmp_mask0, v_val, v_src0);
|
||||
v_int32 v_dst1 = v_select(v_cmp_mask1, v_val, v_src1);
|
||||
|
||||
v_store(tptr + j, v_dst0);
|
||||
v_store(tptr + j + cWidth, v_dst1);
|
||||
}
|
||||
}
|
||||
vx_cleanup();
|
||||
#endif
|
||||
|
||||
for( ; j < len; j++ )
|
||||
|
||||
Loading…
Reference in New Issue
Block a user