features2d: reduce accuracy test avg memory consumption#25424
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asmorkalov merged 4 commits intoopencv:4.xfrom Apr 17, 2024
Merged
features2d: reduce accuracy test avg memory consumption#25424asmorkalov merged 4 commits intoopencv:4.xfrom
asmorkalov merged 4 commits intoopencv:4.xfrom
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Apparently xfeatures2d tests directly use features2d test, so that has to be fixed too: opencv/opencv_contrib#3722 |
asmorkalov
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Apr 17, 2024
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| PARAM_TEST_CASE(Feature2DFixture, Ptr<Feature2D>, std::string) |
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I got the idea, to make detector initialization lazy and move it from GTest cases generator inside the test body. I propose to add comment with the solution description. Without the memory consumption knowledge the test design looks very strange and ambiguous.
asmorkalov
reviewed
Apr 17, 2024
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| #define SHOW_DEBUG_LOG 1 | ||
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| typedef tuple<std::string, Ptr<FeatureDetector>, Ptr<DescriptorExtractor>, float> |
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Please add comment with design decision here too.
asmorkalov
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Apr 17, 2024
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klatism
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May 17, 2024
features2d: reduce accuracy test avg memory consumption opencv#25424 **Merge with contrib**: opencv/opencv_contrib#3722 I've observed high memory consumption in the `opencv_test_features2d` (x86_64, Ubuntu 22.04, Debug):  It's always more than 180 MiB with peak at 535 MiB This was caused by pointers to the algorithm object instances stored in the tests parameters. I've replaced them with factory functions/lambdas with the following result:  Now peak is at 355 MiB and permanent consumption level is ~ 1-2 MiB **Note:** current peak is caused by KAZE features allocating 8x image size utility buffers. Not sure if we can or should do anything about it: https://github.com/opencv/opencv/blob/66fb5021e9253e3decab376d00322eaf2b279c89/modules/features2d/src/kaze/KAZEFeatures.cpp#L61-L68
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Merge with contrib: opencv/opencv_contrib#3722
I've observed high memory consumption in the
opencv_test_features2d(x86_64, Ubuntu 22.04, Debug):It's always more than 180 MiB with peak at 535 MiB
This was caused by pointers to the algorithm object instances stored in the tests parameters. I've replaced them with factory functions/lambdas with the following result:
Now peak is at 355 MiB and permanent consumption level is ~ 1-2 MiB
Note: current peak is caused by KAZE features allocating 8x image size utility buffers. Not sure if we can or should do anything about it:
opencv/modules/features2d/src/kaze/KAZEFeatures.cpp
Lines 61 to 68 in 66fb502