Do not use size_t for nGraph layers
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@@ -440,12 +440,14 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_single)
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const Backend backendId = get<0>(get<1>(GetParam()));
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const Target targetId = get<1>(get<1>(GetParam()));
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == 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 (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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throw SkipTestException("No support for async forward");
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const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
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const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
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const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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@@ -499,12 +501,14 @@ TEST_P(Async, model_optimizer_pipeline_set_and_forward_all)
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const Backend backendId = get<0>(get<1>(GetParam()));
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const Target targetId = get<1>(get<1>(GetParam()));
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == 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 (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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throw SkipTestException("No support for async forward");
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const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
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const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
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const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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@@ -673,9 +677,11 @@ TEST_P(Test_Model_Optimizer, forward_two_nets)
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const Backend backendId = get<0>(GetParam());
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const Target targetId = get<1>(GetParam());
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const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
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const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == 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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const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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@@ -712,12 +718,14 @@ TEST_P(Test_Model_Optimizer, readFromBuffer)
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const Backend backendId = get<0>(GetParam());
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const Target targetId = get<1>(GetParam());
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == 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 (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
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throw SkipTestException("No support for async forward");
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const std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
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const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
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const std::string& modelFile = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
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const std::string& weightsFile = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& modelFile = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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@@ -765,8 +773,11 @@ TEST_P(Test_Model_Optimizer, flexible_inputs)
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const Backend backendId = get<0>(GetParam());
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const Target targetId = get<1>(GetParam());
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const std::string& model = findDataFile("dnn/layers/layer_convolution_fp16.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution_fp16.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == 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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const std::string& model = findDataFile("dnn/layers/layer_convolution.bin");
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const std::string& proto = findDataFile("dnn/layers/layer_convolution.xml");
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if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
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setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
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