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dnn(test): re-enable tests which works with OpenVINO 2021.4.x (3.4)
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@@ -211,23 +211,32 @@ TEST_P(Test_Torch_layers, net_lp_pooling_square)
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}
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TEST_P(Test_Torch_layers, net_lp_pooling_power)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
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#endif
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runTorchNet("net_lp_pooling_power", "", false, true);
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}
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TEST_P(Test_Torch_layers, net_conv_gemm_lrn)
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{
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#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
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if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
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applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
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#endif
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double l1 = 0.0, lInf = 0.0;
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if (target == DNN_TARGET_OPENCL_FP16)
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{
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l1 = 0.046;
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lInf = 0.023;
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}
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else if (target == DNN_TARGET_MYRIAD)
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{
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l1 = 0.02;
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lInf = 0.05;
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}
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// The OpenCL kernels use the native_ math functions which have
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// implementation defined accuracy, so we use relaxed thresholds. See
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// https://github.com/opencv/opencv/issues/9821 for more details.
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