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Merged
asmorkalov merged 18 commits intoopencv:4.xfromchacha21:REDUCE_SUM2
Apr 28, 2023
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add REDUCE_SUM2#13879

asmorkalov merged 18 commits intoopencv:4.xfromchacha21:REDUCE_SUM2
Apr 28, 2023

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@chacha21
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proposal to add REDUCE_SUM2 to cv::reduce, an operation that sums up the square of elements

proposal to add REDUCE_SUM2 to cv::reduce, an operation that sums up the square of elements
put AutoBuffer outside the main reduceC_ loop
Added SUM_REDUCE2 to tests
fixed test of REDUCE_SUM2
@chacha21
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Can anyone help me understand the build failure related to perf_test ? I do not understand how I am supposed to fix that.

elseif( op == CV_REDUCE_SUM2 )
{
if(sdepth == CV_8U && ddepth == CV_32S)
func =GET_OPTIMIZED(reduceSum2R8u32s);
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RemoveGET_OPTIMIZED().
There is no corresponding function anyway.

Also changes in "ReduceFunc" broke all corresponding calls (changes can be reverted, see corresponding comment).


template<typename T,typename ST,classOp>staticvoid
reduceR_(const Mat& srcmat, Mat& dstmat)
reduceR_(const Mat& srcmat, Mat& dstmat,bool applyOpOnFirstRow =false)
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Implementation changes doesn't look good.
What if you want to calculate product instead of sum? "0" value doesnt' work anymore

IMHO, It is better to define separate "Init" functor:

  • NoOp for old cases
  • new SqValue
-buf[i] = src[i];+buf[i] = initOp(src[i]);

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Good idea, I am on it

#defineCV_REDUCE_AVG 1
#defineCV_REDUCE_MAX 2
#defineCV_REDUCE_MIN 3
#defineCV_REDUCE_SUM2 4
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Please don't touch ".c" files anymore (deprecated).

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Ok, but in that case I will have to change all references to CV_REDUCE_* in cpp code, in favor of cv::REDUCE_*

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Ok, please use enum values:REDUCE_ (cv:: prefix is not needed)

+prefer cv::REDUCE_* instead of CV_REDUCE throughout the code, so that core_c.h remains untouched and doesn't know about CV_REDUCE_SUM2+get rid of GET_OPTIMIZED() in reduceR  and reduceC+add an additional Operator template functor to reduceR and reduceC that is responsible for the very first value (by default just copies the value, and will square it for REDUCE_SUM2)
@chacha21
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chacha21 commentedFeb 22, 2019•
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I am sorry, I have read the doc, but I stil don't know how to understand the perf_test failure :
https://pullrequest.opencv.org/buildbot/builders/precommit_linux64/builds/20244/steps/perf_core/logs/tests%20summary

@alalek
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Failed
No regression data

You can check it locally by passing "--perf_verify_sanity" argument.

https://github.com/opencv/opencv/wiki/HowToUsePerfTests#how-to-update-perf-data

Need to update test data and prepare PR into opencv_extra (with the same branch name as this PR: "REDUCE_SUM2")

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Once the "perf_test" problem will be resolved, I plan to add REDUCE_SUMABS to compute the absolute sum of elements.
After that, I will consider parallelizing the work on each line/column, since it is not already the case.
Then, cv::reduce() will be very helpful to batch-compute the norm of different vectors stored either in a 2D matrix, or sequentially in a 1D buffer that could be reshaped() to match reduce() input.

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I have followed the instructions ofhttps://github.com/opencv/opencv/wiki/HowToUsePerfTests#how-to-update-perf-data, but I get a totally different xml that the original core.xml

Here is the beginning of my output :

<?xml version="1.0" encoding="UTF-8"?> <testsuites tests="3949" failures="12" disabled="0" errors="0" timestamp="2019-02-25T12:19:41" time="118.031" cv_module_name="core" cv_implementation="plain" cv_num_threads="-1" cv_version="4.0.1-dev" cv_version_build="4.0.1-dev" cv_vcs_version="4.0.1-252-g1ed46da" cv_build_type="" cv_build_type_build="Release" cv_compiler="C:/Program Files (x86)/Microsoft Visual Studio/2017/Community/VC/Tools/MSVC/14.16.27023/bin/Hostx86/x64/cl.exe (ver 19.16.27027.1)" cv_parallel_framework="ms-concurrency" cv_cpu_features="SSE SSE2 SSE3 *SSE4.1 *SSE4.2 *FP16 *AVX *AVX2" cv_ipp_version="ippIP AVX2 (l9) 2019.0.0 Gold (-) Jul 26 2018" cv_ocl_platform_0_device_0="(Platform=NVIDIA CUDA)(Type=dGPU)(Name=GeForce GTX 750)(Version=OpenCL 1.2 CUDA)" cv_ocl_current_deviceType="dGPU" cv_ocl_current_deviceName="GeForce GTX 750" cv_ocl_current_deviceVersion="OpenCL 1.2 CUDA" cv_ocl_current_driverVersion="418.91" cv_ocl_current_addressBits="64" cv_ocl_current_maxComputeUnits="4" cv_ocl_current_maxWorkGroupSize="1024" cv_ocl_current_localMemSize="49152" cv_ocl_current_maxMemAllocSize="536870912" cv_ocl_current_haveDoubleSupport="1" cv_ocl_current_hostUnifiedMemory="0" cv_ocl_current_extensions="cl_khr_global_int32_base_atomics cl_khr_global_int32_extended_atomics cl_khr_local_int32_base_atomics cl_khr_local_int32_extended_atomics cl_khr_fp64 cl_khr_byte_addressable_store cl_khr_icd cl_khr_gl_sharing cl_nv_compiler_options cl_nv_device_attribute_query cl_nv_pragma_unroll cl_nv_d3d9_sharing cl_nv_d3d10_sharing cl_khr_d3d10_sharing cl_nv_d3d11_sharing cl_nv_copy_opts cl_nv_create_buffer" cv_ocl_current_AmdBlas="0" cv_ocl_current_AmdFft="0" cv_ocl_current_preferredVectorWidthChar="1" cv_ocl_current_preferredVectorWidthShort="1" cv_ocl_current_preferredVectorWidthInt="1" cv_ocl_current_preferredVectorWidthLong="1" cv_ocl_current_preferredVectorWidthFloat="1" cv_ocl_current_preferredVectorWidthDouble="1" name="AllTests"> <testsuite name="OCL_BufferPoolFixture_BufferPool_UMatCreation100" tests="2" failures="0" disabled="0" errors="0" time="0.003"> <testcase name="BufferPool_UMatCreation100/0" value_param="false" status="run" time="0" classname="OCL_BufferPoolFixture_BufferPool_UMatCreation100"> <properties> <property name="bytesIn" value="0"/> <property name="bytesOut" value="0"/> <property name="term" value="0"/> <property name="samples" value="1"/> <property name="outliers" value="0"/> <property name="frequency" value="3513613"/> <property name="min" value="1239"/> <property name="median" value="1239"/> <property name="gmean" value="1239"/> <property name="gstddev" value="0.000000"/> <property name="mean" value="1239"/> <property name="stddev" value="0"/> </properties> </testcase> <testcase name="BufferPool_UMatCreation100/1" value_param="true" status="run" time="0.001" classname="OCL_BufferPoolFixture_BufferPool_UMatCreation100"> <properties> <property name="bytesIn" value="0"/> <property name="bytesOut" value="0"/> <property name="term" value="0"/> <property name="samples" value="1"/> <property name="outliers" value="0"/> <property name="frequency" value="3513613"/> <property name="min" value="443"/> <property name="median" value="443"/> <property name="gmean" value="443"/> <property name="gstddev" value="0.000000"/> <property name="mean" value="443"/> <property name="stddev" value="0"/>

@alalek
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This is completely different XML (Google Test output).

You need to specifyOPENCV_TEST_DATA_PATH and--perf_write_sanity

@chacha21
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chacha21 commentedFeb 25, 2019•
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I did not forget, neitherOPENCV_TEST_DATA_PATH, nor --perf_write_sanity, and I keep getting the same unexpected XML output.
I wonder if it can come from a wrong package of python, or something related to Cygwin.
I keep trying, though.

@alalek
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unexpected XML output

--perf_write_sanity writes into file underOPENCV_TEST_DATA_PATH (into opencv_extra/testdata).
--gtest_output=xml is completely different output - not needed here.

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I did not use --gtest_output at all, I just ran the python scripts as mentioned in HowToUsePerfTests. The xml I get in the generated core-[date].xml file is the one state above, very different from the expected<opencv_storage>...</opencv_storage>

There must be something I misunderstood. Here is a recap :
-the current PR is blocked because the perf tests are not up-to-date with the added REDUCE_SUM2 feature
-"updating the perf tests" is needed
-I assume that I have to submit a modified core.xml of opencv_extra/testdata
-so far, I wasn't able to generate a new core.xml file with a comparable structure. Whatever the way I run opencv_test_core or opencv_perf_core, using the python scripts mentioned in HowToUsePerfTests, or by direct call, I always get the same very different XML structure (quoted in a previous message)
-so, how is core.xml supposed to be updated ?

@chacha21
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I am sorry, but I am still unable to provide the perf test, I still don't understand how to do that.

@chacha21chacha21 mentioned this pull requestOct 21, 2019
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@chacha21
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I dont understand the buildbot error " No regression data for dst argument"

@alalek
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Performance tests has some accuracy checks:

@asmorkalovasmorkalov added category: core Hackathonhttps://opencv.org/opencv-hackathon-starts-next-week/ and removed pr: Discussion Required labelsJan 29, 2020
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asmorkalov commentedJan 31, 2020•
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Remaining item for the PR:

  • disable sanity checks for new performance test
  • squash commits.

@asenyaev
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jenkins cn please retry a build

@mshabunin
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@asmorkalov , I've updated the PR:

  • removed sanity check in all affected perf tests
  • removed old commented code
  • fixed minor warnings

Below are my performance results for 1, 4 and 8 threads. There is a slowdown when reduce is performed along 0th dimension for smaller image sizes when number of threads is more than 1.

Name of Testlog1log4log8log4 vs log1 (x-factor)log8 vs log1 (x-factor)
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 0, REDUCE_AVG)0.0330.0740.0480.440.69
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 0, REDUCE_SUM2)0.0330.0580.0560.570.59
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 0, REDUCE_SUM)0.0320.0720.0300.451.06
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 1, REDUCE_AVG)0.5920.1580.0883.746.73
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 1, REDUCE_SUM2)0.5950.1600.0903.736.59
Reduce::OCL_ReduceAccFixture::(640x480, (32FC1, 32FC1), 1, REDUCE_SUM)0.5930.1580.0883.746.76
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 0, REDUCE_AVG)0.3140.1770.1291.782.43
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 0, REDUCE_SUM2)0.3190.1820.1231.762.60
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 0, REDUCE_SUM)0.3190.1710.1281.862.49
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 1, REDUCE_AVG)0.8210.1930.1274.266.47
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 1, REDUCE_SUM2)0.7060.1890.1313.745.38
Reduce::OCL_ReduceAccFixture::(640x480, (8UC4, 32SC4), 1, REDUCE_SUM)0.8370.1930.1274.346.58
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 0, REDUCE_AVG)0.0960.1920.0860.501.12
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 0, REDUCE_SUM2)0.1000.1940.1200.520.83
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 0, REDUCE_SUM)0.0950.1930.1050.490.90
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 1, REDUCE_AVG)1.8100.4660.2633.886.89
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 1, REDUCE_SUM2)1.8060.4710.2803.846.45
Reduce::OCL_ReduceAccFixture::(1280x720, (32FC1, 32FC1), 1, REDUCE_SUM)1.8190.4700.2623.876.95
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 0, REDUCE_AVG)0.9450.4000.3402.372.78
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 0, REDUCE_SUM2)0.9590.3990.3402.402.82
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 0, REDUCE_SUM)0.9560.4050.3382.362.83
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 1, REDUCE_AVG)2.4700.5850.3734.226.63
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 1, REDUCE_SUM2)2.1310.5580.3863.825.52
Reduce::OCL_ReduceAccFixture::(1280x720, (8UC4, 32SC4), 1, REDUCE_SUM)2.3170.5810.3723.986.23
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 0, REDUCE_AVG)0.2610.2910.3240.900.81
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 0, REDUCE_SUM2)0.2730.3370.3290.810.83
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 0, REDUCE_SUM)0.2570.2920.4240.880.61
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 1, REDUCE_AVG)4.1631.0750.5773.877.22
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 1, REDUCE_SUM2)4.1541.0730.5953.876.98
Reduce::OCL_ReduceAccFixture::(1920x1080, (32FC1, 32FC1), 1, REDUCE_SUM)4.1491.0770.5803.857.15
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 0, REDUCE_AVG)2.2091.0580.7842.092.82
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 0, REDUCE_SUM2)2.2061.0590.8022.082.75
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 0, REDUCE_SUM)2.2241.0480.7852.122.84
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 1, REDUCE_AVG)5.2251.3530.8423.866.20
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 1, REDUCE_SUM2)4.7781.2650.8623.785.54
Reduce::OCL_ReduceAccFixture::(1920x1080, (8UC4, 32SC4), 1, REDUCE_SUM)5.2551.3400.8403.926.25
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 0, REDUCE_AVG)1.3451.6161.5750.830.85
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 0, REDUCE_SUM2)1.3631.5871.5490.860.88
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 0, REDUCE_SUM)1.3361.5731.5240.850.88
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 1, REDUCE_AVG)16.4324.2932.3163.837.09
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 1, REDUCE_SUM2)16.4644.3192.5133.816.55
Reduce::OCL_ReduceAccFixture::(3840x2160, (32FC1, 32FC1), 1, REDUCE_SUM)16.4134.3102.3153.817.09
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 0, REDUCE_AVG)9.0783.8413.1682.362.87
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 0, REDUCE_SUM2)9.0783.9263.3452.312.71
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 0, REDUCE_SUM)9.0023.8203.1762.362.83
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 1, REDUCE_AVG)22.1265.4673.4084.056.49
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 1, REDUCE_SUM2)19.1005.0303.4573.805.52
Reduce::OCL_ReduceAccFixture::(3840x2160, (8UC4, 32SC4), 1, REDUCE_SUM)21.8505.5263.4203.956.39
Reduce::OCL_ReduceMinMaxFixture::(640x480, (8UC1, 8UC1), 0, CV_REDUCE_MAX)0.1360.1380.1210.991.13
Reduce::OCL_ReduceMinMaxFixture::(640x480, (8UC1, 8UC1), 0, CV_REDUCE_MIN)0.1360.1380.1220.991.12
Reduce::OCL_ReduceMinMaxFixture::(640x480, (8UC1, 8UC1), 1, CV_REDUCE_MAX)0.6070.1580.0863.847.09
Reduce::OCL_ReduceMinMaxFixture::(640x480, (8UC1, 8UC1), 1, CV_REDUCE_MIN)0.5960.1570.0863.816.97
Reduce::OCL_ReduceMinMaxFixture::(640x480, (32FC4, 32FC4), 0, CV_REDUCE_MAX)0.1380.1600.1190.861.16
Reduce::OCL_ReduceMinMaxFixture::(640x480, (32FC4, 32FC4), 0, CV_REDUCE_MIN)0.1430.1470.1220.981.17
Reduce::OCL_ReduceMinMaxFixture::(640x480, (32FC4, 32FC4), 1, CV_REDUCE_MAX)0.6000.1570.0913.846.58
Reduce::OCL_ReduceMinMaxFixture::(640x480, (32FC4, 32FC4), 1, CV_REDUCE_MIN)0.6080.1560.0913.906.66
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (8UC1, 8UC1), 0, CV_REDUCE_MAX)0.4040.3220.3661.261.11
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (8UC1, 8UC1), 0, CV_REDUCE_MIN)0.4070.4030.3511.011.16
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (8UC1, 8UC1), 1, CV_REDUCE_MAX)1.8380.4710.2503.907.36
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (8UC1, 8UC1), 1, CV_REDUCE_MIN)1.8160.4660.2503.907.28
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (32FC4, 32FC4), 0, CV_REDUCE_MAX)0.5730.4800.4371.191.31
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (32FC4, 32FC4), 0, CV_REDUCE_MIN)0.5910.4990.4141.181.43
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (32FC4, 32FC4), 1, CV_REDUCE_MAX)1.8780.4940.2823.806.65
Reduce::OCL_ReduceMinMaxFixture::(1280x720, (32FC4, 32FC4), 1, CV_REDUCE_MIN)1.8730.4930.2823.806.63
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (8UC1, 8UC1), 0, CV_REDUCE_MAX)0.9291.0780.7520.861.24
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (8UC1, 8UC1), 0, CV_REDUCE_MIN)0.9240.8120.5311.141.74
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (8UC1, 8UC1), 1, CV_REDUCE_MAX)4.0971.0610.5573.867.36
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (8UC1, 8UC1), 1, CV_REDUCE_MIN)4.0871.0620.5563.857.35
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (32FC4, 32FC4), 0, CV_REDUCE_MAX)1.4041.2481.0531.121.33
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (32FC4, 32FC4), 0, CV_REDUCE_MIN)1.4231.2661.0841.121.31
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (32FC4, 32FC4), 1, CV_REDUCE_MAX)4.2561.1080.7043.846.05
Reduce::OCL_ReduceMinMaxFixture::(1920x1080, (32FC4, 32FC4), 1, CV_REDUCE_MIN)4.2461.1290.7003.766.06
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (8UC1, 8UC1), 0, CV_REDUCE_MAX)3.7662.6972.8271.401.33
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (8UC1, 8UC1), 0, CV_REDUCE_MIN)3.7532.4862.5551.511.47
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (8UC1, 8UC1), 1, CV_REDUCE_MAX)16.2744.2422.2273.847.31
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (8UC1, 8UC1), 1, CV_REDUCE_MIN)16.2654.2262.2193.857.33
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (32FC4, 32FC4), 0, CV_REDUCE_MAX)5.7785.0644.6521.141.24
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (32FC4, 32FC4), 0, CV_REDUCE_MIN)5.7135.0594.7591.131.20
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (32FC4, 32FC4), 1, CV_REDUCE_MAX)16.8854.5713.1233.695.41
Reduce::OCL_ReduceMinMaxFixture::(3840x2160, (32FC4, 32FC4), 1, CV_REDUCE_MIN)17.0024.5593.1173.735.46

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@asmorkalovasmorkalov merged commit6dd8a9b intoopencv:4.xApr 28, 2023
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@asmorkalov , thank you!
@chacha21 , thank you for contribution!

@asmorkalovasmorkalov mentioned this pull requestMay 31, 2023
thewoz pushed a commit to thewoz/opencv that referenced this pull requestJan 4, 2024
add REDUCE_SUM2opencv#13879 proposal to add REDUCE_SUM2 to cv::reduce, an operation that sums up the square of elements
thewoz pushed a commit to thewoz/opencv that referenced this pull requestMay 29, 2024
add REDUCE_SUM2opencv#13879 proposal to add REDUCE_SUM2 to cv::reduce, an operation that sums up the square of elements
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