From f7569232711ca499556711577c63d78c9c8b54eb Mon Sep 17 00:00:00 2001
From: Alexander Alekhin <alexander.a.alekhin@gmail.com>
Date: Wed, 22 Apr 2020 22:33:12 +0000
Subject: [PATCH] dnn(ie): avoid conversion to legacy CNNNetworkImpl

---
 modules/dnn/src/dnn.cpp             | 58 ++++++++++++++++++++++++++---
 modules/dnn/test/test_ie_models.cpp | 14 +++++--
 2 files changed, 64 insertions(+), 8 deletions(-)

diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index f8a3dcab9b..0f23ed6d01 100644
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -3157,6 +3157,8 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
 {
     CV_TRACE_FUNCTION();
 
+    CV_TRACE_REGION("register_inputs");
+
     std::vector<String> inputsNames;
     std::vector<MatShape> inp_shapes;
     for (auto& it : ieNet.getInputsInfo())
@@ -3175,6 +3177,8 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
         cvNet.setInputShape(inputsNames[inp_id], inp_shapes[inp_id]);
     }
 
+    CV_TRACE_REGION_NEXT("backendNode");
+
     Ptr<BackendNode> backendNode;
 #ifdef HAVE_DNN_NGRAPH
     if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
@@ -3195,8 +3199,26 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
         CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
 #endif
     }
+
+    CV_TRACE_REGION_NEXT("register_outputs");
+
+#ifdef HAVE_DNN_NGRAPH
+    auto ngraphFunction = ieNet.getFunction();
+#if INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_2)
+    std::list< std::shared_ptr<ngraph::Node> > ngraphOperations;
+#else
+    std::vector< std::shared_ptr<ngraph::Node> > ngraphOperations;
+#endif
+    if (ngraphFunction)
+    {
+        ngraphOperations = ngraphFunction->get_ops();
+    }
+#endif
+
     for (auto& it : ieNet.getOutputsInfo())
     {
+        CV_TRACE_REGION("output");
+
         LayerParams lp;
         int lid = cvNet.addLayer(it.first, "", lp);
 
@@ -3205,15 +3227,38 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
 #ifdef HAVE_DNN_NGRAPH
         if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
         {
+            const auto& outputName = it.first;
             Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet));
+            cvLayer->name = outputName;
+            cvLayer->type = "_unknown_";
 
-            InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
-            CV_Assert(ieLayer);
+            if (ngraphFunction)
+            {
+                CV_TRACE_REGION("ngraph_function");
+                bool found = false;
+                for (const auto& op : ngraphOperations)
+                {
+                    CV_Assert(op);
+                    if (op->get_friendly_name() == outputName)
+                    {
+                        const std::string typeName = op->get_type_info().name;
+                        cvLayer->type = typeName;
+                        found = true;
+                        break;
+                    }
+                }
+                if (!found)
+                    CV_LOG_WARNING(NULL, "DNN/IE: Can't determine output layer type: '" << outputName << "'");
+            }
+            else
+            {
+                CV_TRACE_REGION("legacy_cnn_layer");
+                InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
+                CV_Assert(ieLayer);
 
-            cvLayer->name = it.first;
-            cvLayer->type = ieLayer->type;
+                cvLayer->type = ieLayer->type;
+            }
             ld.layerInstance = cvLayer;
-
             ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode;
         }
         else
@@ -3238,6 +3283,9 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
         for (int i = 0; i < inputsNames.size(); ++i)
             cvNet.connect(0, i, lid, i);
     }
+
+    CV_TRACE_REGION_NEXT("finalize");
+
     cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam());
 
     cvNet.impl->skipInfEngineInit = true;
diff --git a/modules/dnn/test/test_ie_models.cpp b/modules/dnn/test/test_ie_models.cpp
index d8096daa7b..4191fa892f 100644
--- a/modules/dnn/test/test_ie_models.cpp
+++ b/modules/dnn/test/test_ie_models.cpp
@@ -273,9 +273,18 @@ TEST_P(DNNTestOpenVINO, models)
 
     const Backend backendId = get<0>(get<0>(GetParam()));
     const Target targetId = get<1>(get<0>(GetParam()));
+    std::string modelName = get<1>(GetParam());
 
-    if (backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-        throw SkipTestException("No support for async forward");
+    ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) <<
+        "Inference Engine backend is required";
+
+#if INF_ENGINE_VER_MAJOR_GE(2020020000)
+    if (targetId == DNN_TARGET_MYRIAD && backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+    {
+        if (modelName == "person-detection-retail-0013")  // IRv10
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+    }
+#endif
 
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@@ -284,7 +293,6 @@ TEST_P(DNNTestOpenVINO, models)
     else
         FAIL() << "Unknown backendId";
 
-    std::string modelName = get<1>(GetParam());
     bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);
 
     const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();