From f7dd7df170add1655de75b5e95f20f41d2a88293 Mon Sep 17 00:00:00 2001 From: Alexander Smorkalov <2536374+asmorkalov@users.noreply.github.com> Date: Fri, 17 Jul 2026 13:32:48 +0300 Subject: [PATCH] Merge pull request #29531 from asmorkalov:as/openvino_ci_fail Disable test test that sporadically fails with OpenVINO on CI #29531 ### 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 - [ ] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [ ] The feature is well documented and sample code can be built with the project CMake --- modules/dnn/test/test_int8_layers.cpp | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/modules/dnn/test/test_int8_layers.cpp b/modules/dnn/test/test_int8_layers.cpp index 236966a0b4..277dcf887b 100644 --- a/modules/dnn/test/test_int8_layers.cpp +++ b/modules/dnn/test/test_int8_layers.cpp @@ -6,6 +6,8 @@ #include "npy_blob.hpp" #include #include +#include + namespace opencv_test { namespace { testing::internal::ParamGenerator< tuple > dnnBackendsAndTargetsInt8() @@ -34,6 +36,7 @@ public: int numInps = 1, int numOuts = 1, bool useCaffeModel = false, bool useCommonInputBlob = true, bool hasText = false, bool perChannel = true) { + std::cout << "Testning layer " << basename << std::endl; CV_Assert_N(numInps >= 1, numInps <= 10, numOuts >= 1, numOuts <= 10); std::vector inps(numInps), inps_int8(numInps); std::vector refs(numOuts), outs_int8(numOuts), outs_dequantized(numOuts); @@ -239,7 +242,10 @@ TEST_P(Test_Int8_layers, MaxPooling) TEST_P(Test_Int8_layers, Reduce) { - testLayer("reduce_mean", "TensorFlow", 0.0005, 0.0014); + // Test fails on some CI hosts + if (backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) + testLayer("reduce_mean", "TensorFlow", 0.0005, 0.0014); + testLayer("reduce_mean", "ONNX", 0.00062, 0.0014); testLayer("reduce_mean_axis1", "ONNX", 0.00032, 0.0007); testLayer("reduce_mean_axis2", "ONNX", 0.00033, 0.001);