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

@@ -6,6 +6,8 @@
 #include "npy_blob.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
 #include <opencv2/dnn/all_layers.hpp>
+#include <iostream>
+
 namespace opencv_test { namespace {
 
 testing::internal::ParamGenerator< tuple<Backend, Target> > 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<Mat> inps(numInps), inps_int8(numInps);
         std::vector<Mat> 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);