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dnn(ie): avoid conversion to legacy CNNNetworkImpl

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This commit is contained in:
Alexander Alekhin 2020-04-22 22:33:12 +00:00
parent 775d031366
commit f756923271
2 changed files with 64 additions and 8 deletions
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58
modules/dnn/src/dnn.cpp
View File

@ -3157,6 +3157,8 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
{ {
CV_TRACE_FUNCTION(); CV_TRACE_FUNCTION();
CV_TRACE_REGION("register_inputs");
std::vector<String> inputsNames; std::vector<String> inputsNames;
std::vector<MatShape> inp_shapes; std::vector<MatShape> inp_shapes;
for (auto& it : ieNet.getInputsInfo()) 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]); cvNet.setInputShape(inputsNames[inp_id], inp_shapes[inp_id]);
} }
CV_TRACE_REGION_NEXT("backendNode");
Ptr<BackendNode> backendNode; Ptr<BackendNode> backendNode;
#ifdef HAVE_DNN_NGRAPH #ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam()) 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"); CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
#endif #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()) for (auto& it : ieNet.getOutputsInfo())
{ {
CV_TRACE_REGION("output");
LayerParams lp; LayerParams lp;
int lid = cvNet.addLayer(it.first, "", lp); int lid = cvNet.addLayer(it.first, "", lp);
@ -3205,15 +3227,38 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
#ifdef HAVE_DNN_NGRAPH #ifdef HAVE_DNN_NGRAPH
if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam()) if (DNN_BACKEND_INFERENCE_ENGINE_NGRAPH == getInferenceEngineBackendTypeParam())
{ {
const auto& outputName = it.first;
Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet)); Ptr<Layer> cvLayer(new NgraphBackendLayer(ieNet));
cvLayer->name = outputName;
cvLayer->type = "_unknown_";
InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str()); if (ngraphFunction)
CV_Assert(ieLayer); {
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.layerInstance = cvLayer;
ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode; ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NGRAPH] = backendNode;
} }
else else
@ -3238,6 +3283,9 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
for (int i = 0; i < inputsNames.size(); ++i) for (int i = 0; i < inputsNames.size(); ++i)
cvNet.connect(0, i, lid, i); cvNet.connect(0, i, lid, i);
} }
CV_TRACE_REGION_NEXT("finalize");
cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam()); cvNet.setPreferableBackend(getInferenceEngineBackendTypeParam());
cvNet.impl->skipInfEngineInit = true; cvNet.impl->skipInfEngineInit = true;

14
modules/dnn/test/test_ie_models.cpp
View File

@ -273,9 +273,18 @@ TEST_P(DNNTestOpenVINO, models)
const Backend backendId = get<0>(get<0>(GetParam())); const Backend backendId = get<0>(get<0>(GetParam()));
const Target targetId = get<1>(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) ASSERT_FALSE(backendId != DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && backendId != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) <<
throw SkipTestException("No support for async forward"); "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) if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API); setInferenceEngineBackendType(CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API);
@ -284,7 +293,6 @@ TEST_P(DNNTestOpenVINO, models)
else else
FAIL() << "Unknown backendId"; FAIL() << "Unknown backendId";
std::string modelName = get<1>(GetParam());
bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD); bool isFP16 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD);
const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels(); const std::map<std::string, OpenVINOModelTestCaseInfo>& models = getOpenVINOTestModels();