dnn(IE): use HAVE_DNN_IE_NN_BUILDER_2019 for NN Builder API code

- CMake option: OPENCV_DNN_IE_NN_BUILDER_2019

diff --git a/modules/dnn/CMakeLists.txt b/modules/dnn/CMakeLists.txt
index 547410f..194324c 100644 (file)
--- a/modules/dnn/CMakeLists.txt
+++ b/modules/dnn/CMakeLists.txt
@@ -113,9 +113,15 @@ endif()
 
 set(dnn_runtime_libs "")
 if(INF_ENGINE_TARGET)
+  ocv_option(OPENCV_DNN_IE_NN_BUILDER_2019 "Build with Inference Engine NN Builder API support" ON)
+  if(OPENCV_DNN_IE_NN_BUILDER_2019)
+    message(STATUS "DNN: Enabling Inference Engine NN Builder API support")
+    add_definitions(-DHAVE_DNN_IE_NN_BUILDER_2019=1)
+  endif()
   list(APPEND dnn_runtime_libs ${INF_ENGINE_TARGET})
 endif()
 if(HAVE_NGRAPH)
+  message(STATUS "DNN: Enabling Inference Engine nGraph API support")
   add_definitions(-DHAVE_DNN_NGRAPH)
   list(APPEND dnn_runtime_libs ngraph::ngraph)
 endif()

diff --git a/modules/dnn/src/dnn.cpp b/modules/dnn/src/dnn.cpp
index 2dd06c5..96c2e0a 100644 (file)
--- a/modules/dnn/src/dnn.cpp
+++ b/modules/dnn/src/dnn.cpp
@@ -185,30 +185,40 @@ private:
 
 #ifdef HAVE_INF_ENGINE
         if (checkIETarget(DNN_TARGET_CPU)) {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_CPU));
+#endif
 #ifdef HAVE_DNN_NGRAPH
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_CPU));
 #endif
         }
         if (checkIETarget(DNN_TARGET_MYRIAD)) {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_MYRIAD));
+#endif
 #ifdef HAVE_DNN_NGRAPH
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_MYRIAD));
 #endif
         }
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         if (checkIETarget(DNN_TARGET_FPGA))
             backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_FPGA));
+#endif
 #ifdef HAVE_OPENCL
         if (cv::ocl::useOpenCL() && ocl::Device::getDefault().isIntel())
         {
             if (checkIETarget(DNN_TARGET_OPENCL)) {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL));
+#endif
 #ifdef HAVE_DNN_NGRAPH
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL));
 #endif
             }
             if (checkIETarget(DNN_TARGET_OPENCL_FP16)) {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019, DNN_TARGET_OPENCL_FP16));
+#endif
 #ifdef HAVE_DNN_NGRAPH
                 backends.push_back(std::make_pair(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH, DNN_TARGET_OPENCL_FP16));
 #endif
@@ -797,7 +807,7 @@ struct DataLayer : public Layer
         }
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         CV_CheckEQ(inputsData.size(), (size_t)1, "");
@@ -829,7 +839,7 @@ struct DataLayer : public Layer
         addConstantData("biases", biases, ieLayer);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
     std::vector<String> outNames;
     std::vector<MatShape> shapes;
@@ -1087,10 +1097,10 @@ static Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
     }
     else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
     {
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         return Ptr<BackendWrapper>(new InfEngineBackendWrapper(targetId, m));
 #else
-        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine API support");
+        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
 #endif
     }
     else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@@ -1587,10 +1597,10 @@ struct Net::Impl
             initHalideBackend();
         else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         {
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
             initInfEngineBackend(blobsToKeep_);
 #else
-            CV_Assert(false && "This OpenCV version is built without Inference Engine API support");
+            CV_Assert(false && "This OpenCV version is built without Inference Engine NN Builder API support");
 #endif
         }
         else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
@@ -1664,7 +1674,7 @@ struct Net::Impl
         }
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     // Before launching Inference Engine graph we need to specify output blobs.
     // This function requests output blobs based on inputs references of
     // layers from default backend or layers from different graphs.
@@ -1969,7 +1979,7 @@ struct Net::Impl
             }
         }
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH
@@ -3377,8 +3387,12 @@ struct Net::Impl
         CV_Assert(preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
 
         if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019) {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
             Ptr<InfEngineBackendWrapper> wrapper = ld.outputBlobsWrappers[pin.oid].dynamicCast<InfEngineBackendWrapper>();
             return std::move(wrapper->futureMat);
+#else
+            CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
+#endif
         }
         else if (preferableBackend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
@@ -3470,9 +3484,13 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
     else
 #endif
     {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         Ptr<InfEngineBackendNode> backendNodeNN(new InfEngineBackendNode(InferenceEngine::Builder::Layer("")));
         backendNodeNN->net = Ptr<InfEngineBackendNet>(new InfEngineBackendNet(ieNet));
         backendNode = backendNodeNN;
+#else
+        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
+#endif
     }
 
     for (auto& it : ieNet.getOutputsInfo())
@@ -3499,6 +3517,7 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
         else
 #endif
         {
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
             Ptr<Layer> cvLayer(new InfEngineBackendLayer(ieNet));
 
             InferenceEngine::CNNLayerPtr ieLayer = ieNet.getLayerByName(it.first.c_str());
@@ -3509,6 +3528,9 @@ Net Net::Impl::createNetworkFromModelOptimizer(InferenceEngine::CNNNetwork& ieNe
             ld.layerInstance = cvLayer;
 
             ld.backendNodes[DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019] = backendNode;
+#else
+            CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
+#endif
         }
 
         for (int i = 0; i < inputsNames.size(); ++i)

diff --git a/modules/dnn/src/ie_ngraph.cpp b/modules/dnn/src/ie_ngraph.cpp
index e8cfd12..f5467a4 100644 (file)
--- a/modules/dnn/src/ie_ngraph.cpp
+++ b/modules/dnn/src/ie_ngraph.cpp
@@ -25,8 +25,8 @@ namespace cv { namespace dnn {
 // For networks with input layer which has an empty name, IE generates a name id[some_number].
 // OpenCV lets users use an empty input name and to prevent unexpected naming,
 // we can use some predefined name.
-static std::string kDefaultInpLayerName = "empty_inp_layer_name";
-static constexpr const char* kOpenCVLayersType = "OpenCVLayer";
+static std::string kDefaultInpLayerName = "opencv_ngraph_empty_inp_layer_name";
+static constexpr const char* kOpenCVLayersType = "opencv_ngraph_layer";
 
 static std::string shapesToStr(const std::vector<Mat>& mats)
 {
@@ -77,7 +77,6 @@ public:
         return type_info;
     }
 
-    NgraphCustomOp() {};
     NgraphCustomOp(const ngraph::NodeVector& inputs,
                    const std::map<std::string, InferenceEngine::Parameter>& params = {}):
         Op(inputs), params(params)
@@ -85,6 +84,11 @@ public:
         constructor_validate_and_infer_types();
     }
 
+    ~NgraphCustomOp()
+    {
+        // nothing
+    }
+
     void validate_and_infer_types() override
     {
         std::vector<std::vector<size_t> > shapes;
@@ -116,6 +120,136 @@ private:
     std::map<std::string, InferenceEngine::Parameter> params;
 };
 
+
+class InfEngineNgraphCustomLayer : public InferenceEngine::ILayerExecImpl
+{
+public:
+    explicit InfEngineNgraphCustomLayer(const InferenceEngine::CNNLayer& layer) : cnnLayer(layer)
+    {
+        std::istringstream iss(layer.GetParamAsString("impl"));
+        size_t ptr;
+        iss >> ptr;
+        cvLayer = (Layer*)ptr;
+
+        std::vector<std::vector<size_t> > shapes;
+        strToShapes(layer.GetParamAsString("internals"), shapes);
+        internals.resize(shapes.size());
+        for (int i = 0; i < shapes.size(); ++i)
+            internals[i].create(std::vector<int>(shapes[i].begin(), shapes[i].end()), CV_32F);
+    }
+
+    ~InfEngineNgraphCustomLayer()
+    {
+        // nothing
+    }
+
+    virtual InferenceEngine::StatusCode execute(std::vector<InferenceEngine::Blob::Ptr>& inputs,
+                                                std::vector<InferenceEngine::Blob::Ptr>& outputs,
+                                                InferenceEngine::ResponseDesc *resp) noexcept
+    {
+        std::vector<Mat> inpMats, outMats;
+        infEngineBlobsToMats(inputs, inpMats);
+        infEngineBlobsToMats(outputs, outMats);
+
+        try
+        {
+            cvLayer->forward(inpMats, outMats, internals);
+            return InferenceEngine::StatusCode::OK;
+        }
+        catch (...)
+        {
+            return InferenceEngine::StatusCode::GENERAL_ERROR;
+        }
+    }
+
+    virtual InferenceEngine::StatusCode
+    getSupportedConfigurations(std::vector<InferenceEngine::LayerConfig>& conf,
+                               InferenceEngine::ResponseDesc* resp) noexcept
+    {
+        std::vector<InferenceEngine::DataConfig> inDataConfig;
+        std::vector<InferenceEngine::DataConfig> outDataConfig;
+        for (auto& it : cnnLayer.insData)
+        {
+            InferenceEngine::DataConfig conf;
+            conf.desc = it.lock()->getTensorDesc();
+            inDataConfig.push_back(conf);
+        }
+
+        for (auto& it : cnnLayer.outData)
+        {
+            InferenceEngine::DataConfig conf;
+            conf.desc = it->getTensorDesc();
+            outDataConfig.push_back(conf);
+        }
+
+        InferenceEngine::LayerConfig layerConfig;
+        layerConfig.inConfs = inDataConfig;
+        layerConfig.outConfs = outDataConfig;
+
+        conf.push_back(layerConfig);
+        return InferenceEngine::StatusCode::OK;
+    }
+
+    InferenceEngine::StatusCode init(InferenceEngine::LayerConfig& config,
+                                     InferenceEngine::ResponseDesc *resp) noexcept
+    {
+        return InferenceEngine::StatusCode::OK;
+    }
+
+private:
+    InferenceEngine::CNNLayer cnnLayer;
+    dnn::Layer* cvLayer;
+    std::vector<Mat> internals;
+};
+
+
+class InfEngineNgraphCustomLayerFactory : public InferenceEngine::ILayerImplFactory {
+public:
+    explicit InfEngineNgraphCustomLayerFactory(const InferenceEngine::CNNLayer* layer) : cnnLayer(*layer)
+    {
+        // nothing
+    }
+
+    InferenceEngine::StatusCode
+    getImplementations(std::vector<InferenceEngine::ILayerImpl::Ptr>& impls,
+                       InferenceEngine::ResponseDesc* resp) noexcept override
+    {
+        impls.push_back(std::make_shared<InfEngineNgraphCustomLayer>(cnnLayer));
+        return InferenceEngine::StatusCode::OK;
+    }
+
+private:
+    InferenceEngine::CNNLayer cnnLayer;
+};
+
+
+class InfEngineNgraphExtension : public InferenceEngine::IExtension
+{
+public:
+    virtual void SetLogCallback(InferenceEngine::IErrorListener&) noexcept {}
+    virtual void Unload() noexcept {}
+    virtual void Release() noexcept {}
+    virtual void GetVersion(const InferenceEngine::Version*&) const noexcept {}
+
+    virtual InferenceEngine::StatusCode getPrimitiveTypes(char**&, unsigned int&,
+                                                          InferenceEngine::ResponseDesc*) noexcept
+    {
+        return InferenceEngine::StatusCode::OK;
+    }
+
+    InferenceEngine::StatusCode getFactoryFor(InferenceEngine::ILayerImplFactory*& factory,
+                                              const InferenceEngine::CNNLayer* cnnLayer,
+                                              InferenceEngine::ResponseDesc* resp) noexcept
+    {
+        if (cnnLayer->type != kOpenCVLayersType)
+            return InferenceEngine::StatusCode::NOT_IMPLEMENTED;
+        factory = new InfEngineNgraphCustomLayerFactory(cnnLayer);
+        return InferenceEngine::StatusCode::OK;
+    }
+};
+
+
+
 InfEngineNgraphNode::InfEngineNgraphNode(std::shared_ptr<ngraph::Node>&& _node)
     : BackendNode(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH), node(std::move(_node)) {}
 
@@ -423,11 +557,11 @@ void InfEngineNgraphNet::initPlugin(InferenceEngine::CNNNetwork& net)
             // OpenCV fallbacks as extensions.
             try
             {
-                ie.AddExtension(std::make_shared<InfEngineExtension>(), "CPU");
+                ie.AddExtension(std::make_shared<InfEngineNgraphExtension>(), "CPU");
             }
             catch(const std::exception& e)
             {
-                CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers extension: " << e.what());
+                CV_LOG_INFO(NULL, "DNN-IE: Can't register OpenCV custom layers nGraph extension: " << e.what());
             }
 #ifndef _WIN32
             // Limit the number of CPU threads.

diff --git a/modules/dnn/src/layers/batch_norm_layer.cpp b/modules/dnn/src/layers/batch_norm_layer.cpp
index f1619cc..9fa4425 100644 (file)
--- a/modules/dnn/src/layers/batch_norm_layer.cpp
+++ b/modules/dnn/src/layers/batch_norm_layer.cpp
@@ -373,7 +373,7 @@ public:
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
@@ -382,7 +382,7 @@ public:
         addConstantData("biases", wrapToInfEngineBlob(bias_, {numChannels}, InferenceEngine::Layout::C), ieLayer);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

diff --git a/modules/dnn/src/layers/blank_layer.cpp b/modules/dnn/src/layers/blank_layer.cpp
index 0867d8c..229590e 100644 (file)
--- a/modules/dnn/src/layers/blank_layer.cpp
+++ b/modules/dnn/src/layers/blank_layer.cpp
@@ -127,7 +127,7 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -150,7 +150,7 @@ public:
         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/concat_layer.cpp b/modules/dnn/src/layers/concat_layer.cpp
index 577c575..0ed9e3f 100644 (file)
--- a/modules/dnn/src/layers/concat_layer.cpp
+++ b/modules/dnn/src/layers/concat_layer.cpp
@@ -335,7 +335,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -345,7 +345,7 @@ public:
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/const_layer.cpp b/modules/dnn/src/layers/const_layer.cpp
index e72b87e..afca48b 100644 (file)
--- a/modules/dnn/src/layers/const_layer.cpp
+++ b/modules/dnn/src/layers/const_layer.cpp
@@ -75,14 +75,14 @@ public:
         blobs[0].copyTo(outputs[0]);
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::ConstLayer ieLayer(name);
         ieLayer.setData(wrapToInfEngineBlob(blobs[0]));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
@@ -93,7 +93,7 @@ public:
                                                            blobs[0].data);
         return Ptr<BackendNode>(new InfEngineNgraphNode(node));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_CUDA
     Ptr<BackendNode> initCUDA(

diff --git a/modules/dnn/src/layers/convolution_layer.cpp b/modules/dnn/src/layers/convolution_layer.cpp
index 110d5f7..05ef360 100644 (file)
--- a/modules/dnn/src/layers/convolution_layer.cpp
+++ b/modules/dnn/src/layers/convolution_layer.cpp
@@ -622,7 +622,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -683,7 +683,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,
@@ -1558,6 +1558,7 @@ public:
             return group == 1;
         }
 
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         {
             if (kernel_size.size() == 3 && preferableTarget != DNN_TARGET_CPU) {
@@ -1601,10 +1602,12 @@ public:
                 return std::accumulate(dilations.begin(), dilations.end(), 1, std::multiplies<size_t>()) == 1;
             return true;
         }
-        else
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 #endif  // HAVE_INF_ENGINE
+        {
             return backendId == DNN_BACKEND_CUDA ||
             (kernel_size.size() == 2 && (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE));
+        }
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -2244,7 +2247,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &) CV_OVERRIDE
     {
         InferenceEngine::Layout layout = blobs[0].dims == 5? InferenceEngine::Layout::NCDHW :
@@ -2299,7 +2302,7 @@ public:
             addConstantData("biases", wrapToInfEngineBlob(biasesMat, {(size_t)numOutput}, InferenceEngine::Layout::C), l);
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/detection_output_layer.cpp b/modules/dnn/src/layers/detection_output_layer.cpp
index 1730be7..4bc0017 100644 (file)
--- a/modules/dnn/src/layers/detection_output_layer.cpp
+++ b/modules/dnn/src/layers/detection_output_layer.cpp
@@ -924,7 +924,7 @@ public:
         }
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::DetectionOutputLayer ieLayer(name);
@@ -946,7 +946,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/elementwise_layers.cpp b/modules/dnn/src/layers/elementwise_layers.cpp
index 34b8e21..85b39dd 100644 (file)
--- a/modules/dnn/src/layers/elementwise_layers.cpp
+++ b/modules/dnn/src/layers/elementwise_layers.cpp
@@ -163,14 +163,14 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::Layer ieLayer = func.initInfEngineBuilderAPI();
         ieLayer.setName(this->name);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
@@ -299,9 +299,11 @@ struct ReLUFunctor : public BaseFunctor
 
     bool supportBackend(int backendId, int)
     {
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
             return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
+#endif
+#ifdef HAVE_DNN_NGRAPH
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
 #endif
@@ -408,12 +410,12 @@ struct ReLUFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(slope);
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -535,12 +537,12 @@ struct ReLU6Functor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::ClampLayer("").setMinValue(minValue).setMaxValue(maxValue);
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -627,12 +629,12 @@ struct TanHFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::TanHLayer("");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -718,12 +720,12 @@ struct SwishFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         CV_Error(Error::StsNotImplemented, "");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -810,12 +812,12 @@ struct MishFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         CV_Error(Error::StsNotImplemented, "");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -908,12 +910,12 @@ struct SigmoidFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::SigmoidLayer("");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1000,12 +1002,12 @@ struct ELUFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::ELULayer("");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1095,12 +1097,12 @@ struct AbsValFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::ReLULayer("").setNegativeSlope(-0.999999f);
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1191,12 +1193,12 @@ struct BNLLFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         CV_Error(Error::StsNotImplemented, "");
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1328,14 +1330,14 @@ struct PowerFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         return InferenceEngine::Builder::PowerLayer("").setPower(power)
                                                        .setScale(scale)
                                                        .setShift(shift);
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)
@@ -1497,7 +1499,7 @@ struct ChannelsPReLUFunctor : public BaseFunctor
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     InferenceEngine::Builder::Layer initInfEngineBuilderAPI()
     {
         InferenceEngine::Builder::Layer l = InferenceEngine::Builder::PReLULayer("");
@@ -1505,7 +1507,7 @@ struct ChannelsPReLUFunctor : public BaseFunctor
         addConstantData("weights", wrapToInfEngineBlob(scale, {numChannels}, InferenceEngine::Layout::C), l);
         return l;
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     std::shared_ptr<ngraph::Node> initNgraphAPI(const std::shared_ptr<ngraph::Node>& node)

diff --git a/modules/dnn/src/layers/eltwise_layer.cpp b/modules/dnn/src/layers/eltwise_layer.cpp
index 6d2827c..10ce70f 100644 (file)
--- a/modules/dnn/src/layers/eltwise_layer.cpp
+++ b/modules/dnn/src/layers/eltwise_layer.cpp
@@ -715,7 +715,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::Builder::EltwiseLayer ieLayer(name);
@@ -739,7 +739,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/flatten_layer.cpp b/modules/dnn/src/layers/flatten_layer.cpp
index 9ba5a18..fc3f5fc 100644 (file)
--- a/modules/dnn/src/layers/flatten_layer.cpp
+++ b/modules/dnn/src/layers/flatten_layer.cpp
@@ -183,7 +183,7 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::Builder::Layer ieLayer(name);
@@ -195,7 +195,7 @@ public:
         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
 virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
@@ -223,7 +223,6 @@ virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inp
         return Ptr<BackendNode>(new InfEngineNgraphNode(reshape));
     }
 #endif  // HAVE_DNN_NGRAPH
-  // HAVE_INF_ENGINE
 
     int _startAxis;
     int _endAxis;

diff --git a/modules/dnn/src/layers/fully_connected_layer.cpp b/modules/dnn/src/layers/fully_connected_layer.cpp
index e757aef..9fdcd32 100644 (file)
--- a/modules/dnn/src/layers/fully_connected_layer.cpp
+++ b/modules/dnn/src/layers/fully_connected_layer.cpp
@@ -466,7 +466,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::FullyConnectedLayer ieLayer(name);
@@ -481,7 +481,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/lrn_layer.cpp b/modules/dnn/src/layers/lrn_layer.cpp
index 82267c9..71970a3 100644 (file)
--- a/modules/dnn/src/layers/lrn_layer.cpp
+++ b/modules/dnn/src/layers/lrn_layer.cpp
@@ -444,7 +444,7 @@ public:
 #endif  // HAVE_HALIDE
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         float alphaSize = alpha;
@@ -461,7 +461,7 @@ public:
         l.getParameters()["k"] = bias;
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

diff --git a/modules/dnn/src/layers/mvn_layer.cpp b/modules/dnn/src/layers/mvn_layer.cpp
index 9443495..386446e 100644 (file)
--- a/modules/dnn/src/layers/mvn_layer.cpp
+++ b/modules/dnn/src/layers/mvn_layer.cpp
@@ -118,14 +118,17 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
             return !zeroDev && (preferableTarget != DNN_TARGET_MYRIAD || eps <= 1e-7f);
-        else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+#endif
+#ifdef HAVE_DNN_NGRAPH
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
-        else
-#endif  // HAVE_INF_ENGINE
+#endif
+        {
             return backendId == DNN_BACKEND_OPENCV;
+        }
     }
 
 #ifdef HAVE_OPENCL
@@ -375,7 +378,7 @@ public:
         }
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::MVNLayer ieLayer(name);
@@ -384,7 +387,7 @@ public:
         ieLayer.setEpsilon(eps);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

diff --git a/modules/dnn/src/layers/normalize_bbox_layer.cpp b/modules/dnn/src/layers/normalize_bbox_layer.cpp
index f793a51..e669e0d 100644 (file)
--- a/modules/dnn/src/layers/normalize_bbox_layer.cpp
+++ b/modules/dnn/src/layers/normalize_bbox_layer.cpp
@@ -295,7 +295,7 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -344,7 +344,7 @@ public:
             return Ptr<BackendNode>(new InfEngineBackendNode(l));
         }
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

diff --git a/modules/dnn/src/layers/padding_layer.cpp b/modules/dnn/src/layers/padding_layer.cpp
index 9bfa2eb..c83cf02 100644 (file)
--- a/modules/dnn/src/layers/padding_layer.cpp
+++ b/modules/dnn/src/layers/padding_layer.cpp
@@ -212,7 +212,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::Layer ieLayer(name);

diff --git a/modules/dnn/src/layers/permute_layer.cpp b/modules/dnn/src/layers/permute_layer.cpp
index dfcfb73..8624740 100644 (file)
--- a/modules/dnn/src/layers/permute_layer.cpp
+++ b/modules/dnn/src/layers/permute_layer.cpp
@@ -403,14 +403,14 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::PermuteLayer ieLayer(name);
         ieLayer.setOrder(_order);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

diff --git a/modules/dnn/src/layers/pooling_layer.cpp b/modules/dnn/src/layers/pooling_layer.cpp
index 6582432..8ed3a11 100644 (file)
--- a/modules/dnn/src/layers/pooling_layer.cpp
+++ b/modules/dnn/src/layers/pooling_layer.cpp
@@ -188,15 +188,15 @@ public:
         {
             return type == MAX || type == AVE || type == ROI;
         }
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
         else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         {
             if (computeMaxIdx)
                 return false;
-#ifdef HAVE_INF_ENGINE
             if (kernel_size.size() == 3)
                 return preferableTarget == DNN_TARGET_CPU;
             if (preferableTarget == DNN_TARGET_MYRIAD) {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
+#if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
                 if (type == MAX && (pad_l == 1 && pad_t == 1) && stride == Size(2, 2) ) {
                     return !isMyriadX();
                 }
@@ -205,14 +205,13 @@ public:
             }
             else
                 return type != STOCHASTIC;
-#else
-            return false;
-#endif
         }
-        else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
+#endif
+        else if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        {
             return !computeMaxIdx && type != STOCHASTIC;
         }
-        else
+        else if (backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE || backendId == DNN_BACKEND_VKCOM)
         {
             if (kernel_size.size() == 3)
                 return (backendId == DNN_BACKEND_OPENCV && preferableTarget == DNN_TARGET_CPU);
@@ -225,6 +224,7 @@ public:
             else
                 return false;
         }
+        return false;
     }
 
 #ifdef HAVE_OPENCL
@@ -454,7 +454,7 @@ public:
             return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         if (type == MAX || type == AVE)
@@ -500,7 +500,7 @@ public:
             CV_Error(Error::StsNotImplemented, "Unsupported pooling type");
         return Ptr<BackendNode>();
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 

diff --git a/modules/dnn/src/layers/prior_box_layer.cpp b/modules/dnn/src/layers/prior_box_layer.cpp
index aaedd24..053e6da 100644 (file)
--- a/modules/dnn/src/layers/prior_box_layer.cpp
+++ b/modules/dnn/src/layers/prior_box_layer.cpp
@@ -555,7 +555,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         if (_explicitSizes)
@@ -615,7 +615,7 @@ public:
             return Ptr<BackendNode>(new InfEngineBackendNode(l));
         }
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs, const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE

diff --git a/modules/dnn/src/layers/proposal_layer.cpp b/modules/dnn/src/layers/proposal_layer.cpp
index e0930e8..2420dbf 100644 (file)
--- a/modules/dnn/src/layers/proposal_layer.cpp
+++ b/modules/dnn/src/layers/proposal_layer.cpp
@@ -327,7 +327,7 @@ public:
         layerOutputs[0].col(2).copyTo(dst);
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::ProposalLayer ieLayer(name);
@@ -351,7 +351,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/reorg_layer.cpp b/modules/dnn/src/layers/reorg_layer.cpp
index 95f900b..9764088 100644 (file)
--- a/modules/dnn/src/layers/reorg_layer.cpp
+++ b/modules/dnn/src/layers/reorg_layer.cpp
@@ -205,14 +205,14 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::ReorgYoloLayer ieLayer(name);
         ieLayer.setStride(reorgStride);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> > &inputs,

diff --git a/modules/dnn/src/layers/reshape_layer.cpp b/modules/dnn/src/layers/reshape_layer.cpp
index 5532087..62774db 100644 (file)
--- a/modules/dnn/src/layers/reshape_layer.cpp
+++ b/modules/dnn/src/layers/reshape_layer.cpp
@@ -279,7 +279,7 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::Builder::ReshapeLayer ieLayer(name);
@@ -287,7 +287,7 @@ public:
         ieLayer.setDims(outShapes[0]);
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

diff --git a/modules/dnn/src/layers/resize_layer.cpp b/modules/dnn/src/layers/resize_layer.cpp
index 3a06bb0..e29b953 100644 (file)
--- a/modules/dnn/src/layers/resize_layer.cpp
+++ b/modules/dnn/src/layers/resize_layer.cpp
@@ -65,8 +65,7 @@ public:
             return interpolation == "nearest" || interpolation == "bilinear";
 
 #ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
-            backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
             return (interpolation == "nearest" && scaleWidth == scaleHeight) ||
                    (interpolation == "bilinear");
@@ -192,9 +191,9 @@ public:
     }
 #endif
 
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
-#ifdef HAVE_INF_ENGINE
         InferenceEngine::Builder::Layer ieLayer(name);
         ieLayer.setName(name);
         if (interpolation == "nearest")
@@ -220,9 +219,8 @@ public:
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
-#endif  // HAVE_INF_ENGINE
-        return Ptr<BackendNode>();
     }
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/scale_layer.cpp b/modules/dnn/src/layers/scale_layer.cpp
index c60a417..f316d94 100644 (file)
--- a/modules/dnn/src/layers/scale_layer.cpp
+++ b/modules/dnn/src/layers/scale_layer.cpp
@@ -227,7 +227,7 @@ public:
     }
 #endif  // HAVE_HALIDE
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
     {
         InferenceEngine::Builder::Layer l = InferenceEngine::Builder::ScaleShiftLayer(name);
@@ -253,7 +253,7 @@ public:
             addConstantData("biases", wrapToInfEngineBlob(blobs.back(), {numChannels}, InferenceEngine::Layout::C), l);
         return Ptr<BackendNode>(new InfEngineBackendNode(l));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 
 #ifdef HAVE_DNN_NGRAPH

diff --git a/modules/dnn/src/layers/slice_layer.cpp b/modules/dnn/src/layers/slice_layer.cpp
index 2d70cc1..80d3d86 100644 (file)
--- a/modules/dnn/src/layers/slice_layer.cpp
+++ b/modules/dnn/src/layers/slice_layer.cpp
@@ -119,14 +119,17 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_OPENCV ||
-               backendId == DNN_BACKEND_CUDA ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && sliceRanges.size() == 1) ||
-               (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
-#ifdef HAVE_INF_ENGINE
-                INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
+            return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
+                sliceRanges.size() == 1 && sliceRanges[0].size() == 4;
+#endif
+#ifdef HAVE_DNN_NGRAPH
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+            return sliceRanges.size() == 1;
 #endif
-                sliceRanges.size() == 1 && sliceRanges[0].size() == 4);
+        return backendId == DNN_BACKEND_OPENCV ||
+               backendId == DNN_BACKEND_CUDA;
     }
 
     bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -292,7 +295,7 @@ public:
     }
 #endif
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
 #if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {

diff --git a/modules/dnn/src/layers/softmax_layer.cpp b/modules/dnn/src/layers/softmax_layer.cpp
index 8e45fee..6715c86 100644 (file)
--- a/modules/dnn/src/layers/softmax_layer.cpp
+++ b/modules/dnn/src/layers/softmax_layer.cpp
@@ -348,7 +348,7 @@ public:
         return Ptr<BackendNode>();
     }
 
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
@@ -358,7 +358,7 @@ public:
 
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
-#endif  // HAVE_INF_ENGINE
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #ifdef HAVE_DNN_NGRAPH
     virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,

diff --git a/modules/dnn/src/op_inf_engine.cpp b/modules/dnn/src/op_inf_engine.cpp
index 238b684..11cb897 100644 (file)
--- a/modules/dnn/src/op_inf_engine.cpp
+++ b/modules/dnn/src/op_inf_engine.cpp
@@ -42,8 +42,8 @@ Backend& getInferenceEngineBackendTypeParam()
 {
     static Backend param = parseInferenceEngineBackendType(
         utils::getConfigurationParameterString("OPENCV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019_TYPE",
-#ifdef HAVE_NGRAPH
-            CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API  // future: CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
+#ifndef HAVE_DNN_IE_NN_BUILDER_2019
+            CV_DNN_BACKEND_INFERENCE_ENGINE_NGRAPH
 #else
             CV_DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_API
 #endif
@@ -69,6 +69,36 @@ cv::String setInferenceEngineBackendType(const cv::String& newBackendType)
 
 CV__DNN_INLINE_NS_END
 
+
+Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
+{
+    // NOTE: Inference Engine sizes are reversed.
+    std::vector<size_t> dims = blob->getTensorDesc().getDims();
+    std::vector<int> size(dims.begin(), dims.end());
+    auto precision = blob->getTensorDesc().getPrecision();
+
+    int type = -1;
+    switch (precision)
+    {
+        case InferenceEngine::Precision::FP32: type = CV_32F; break;
+        case InferenceEngine::Precision::U8: type = CV_8U; break;
+        default:
+            CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
+    }
+    return Mat(size, type, (void*)blob->buffer());
+}
+
+void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
+                          std::vector<Mat>& mats)
+{
+    mats.resize(blobs.size());
+    for (int i = 0; i < blobs.size(); ++i)
+        mats[i] = infEngineBlobToMat(blobs[i]);
+}
+
+
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
+
 // For networks with input layer which has an empty name, IE generates a name id[some_number].
 // OpenCV lets users use an empty input name and to prevent unexpected naming,
 // we can use some predefined name.
@@ -556,6 +586,7 @@ void InfEngineBackendWrapper::setHostDirty()
 
 }
 
+#endif // HAVE_DNN_IE_NN_BUILDER_2019
 
 #if INF_ENGINE_VER_MAJOR_LE(INF_ENGINE_RELEASE_2019R1)
 static std::map<std::string, InferenceEngine::InferenceEnginePluginPtr>& getSharedPlugins()
@@ -687,6 +718,9 @@ static bool detectMyriadX_()
 }
 #endif  // !defined(OPENCV_DNN_IE_VPU_TYPE_DEFAULT)
 
+
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
+
 void InfEngineBackendNet::initPlugin(InferenceEngine::CNNNetwork& net)
 {
     CV_Assert(!isInitialized());
@@ -985,32 +1019,6 @@ void InfEngineBackendNet::forward(const std::vector<Ptr<BackendWrapper> >& outBl
     }
 }
 
-Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob)
-{
-    // NOTE: Inference Engine sizes are reversed.
-    std::vector<size_t> dims = blob->getTensorDesc().getDims();
-    std::vector<int> size(dims.begin(), dims.end());
-    auto precision = blob->getTensorDesc().getPrecision();
-
-    int type = -1;
-    switch (precision)
-    {
-        case InferenceEngine::Precision::FP32: type = CV_32F; break;
-        case InferenceEngine::Precision::U8: type = CV_8U; break;
-        default:
-            CV_Error(Error::StsNotImplemented, "Unsupported blob precision");
-    }
-    return Mat(size, type, (void*)blob->buffer());
-}
-
-void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
-                          std::vector<Mat>& mats)
-{
-    mats.resize(blobs.size());
-    for (int i = 0; i < blobs.size(); ++i)
-        mats[i] = infEngineBlobToMat(blobs[i]);
-}
-
 bool InfEngineBackendLayer::getMemoryShapes(const std::vector<MatShape> &inputs,
                                             const int requiredOutputs,
                                             std::vector<MatShape> &outputs,
@@ -1077,6 +1085,8 @@ void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
 #endif
 }
 
+#endif // HAVE_DNN_IE_NN_BUILDER_2019
+
 #endif  // HAVE_INF_ENGINE
 
 bool haveInfEngine()
@@ -1092,11 +1102,13 @@ void forwardInfEngine(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
                       Ptr<BackendNode>& node, bool isAsync)
 {
     CV_Assert(haveInfEngine());
-#ifdef HAVE_INF_ENGINE
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
     CV_Assert(!node.empty());
     Ptr<InfEngineBackendNode> ieNode = node.dynamicCast<InfEngineBackendNode>();
     CV_Assert(!ieNode.empty());
     ieNode->net->forward(outBlobsWrappers, isAsync);
+#else
+    CV_Error(Error::StsNotImplemented, "This OpenCV version is built without Inference Engine NN Builder API support");
 #endif  // HAVE_INF_ENGINE
 }
 

diff --git a/modules/dnn/src/op_inf_engine.hpp b/modules/dnn/src/op_inf_engine.hpp
index 5c7f7fa..9468261 100644 (file)
--- a/modules/dnn/src/op_inf_engine.hpp
+++ b/modules/dnn/src/op_inf_engine.hpp
@@ -41,6 +41,7 @@
 #pragma GCC diagnostic ignored "-Wsuggest-override"
 #endif
 
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
 //#define INFERENCE_ENGINE_DEPRECATED  // turn off deprecation warnings from IE
 //there is no way to suppress warnings from IE only at this moment, so we are forced to suppress warnings globally
 #if defined(__GNUC__)
@@ -49,6 +50,7 @@
 #ifdef _MSC_VER
 #pragma warning(disable: 4996)  // was declared deprecated
 #endif
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
 
 #if defined(__GNUC__) && INF_ENGINE_VER_MAJOR_LT(INF_ENGINE_RELEASE_2020_1)
 #pragma GCC visibility push(default)
@@ -74,6 +76,13 @@ namespace cv { namespace dnn {
 
 Backend& getInferenceEngineBackendTypeParam();
 
+Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
+
+void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
+                          std::vector<Mat>& mats);
+
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
+
 class InfEngineBackendNet
 {
 public:
@@ -180,11 +189,6 @@ InferenceEngine::Blob::Ptr wrapToInfEngineBlob(const Mat& m, const std::vector<s
 
 InferenceEngine::DataPtr infEngineDataNode(const Ptr<BackendWrapper>& ptr);
 
-Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
-
-void infEngineBlobsToMats(const std::vector<InferenceEngine::Blob::Ptr>& blobs,
-                          std::vector<Mat>& mats);
-
 // Convert Inference Engine blob with FP32 precision to FP16 precision.
 // Allocates memory for a new blob.
 InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
@@ -233,6 +237,8 @@ public:
                                               InferenceEngine::ResponseDesc* resp) noexcept;
 };
 
+#endif  // HAVE_DNN_IE_NN_BUILDER_2019
+
 
 CV__DNN_INLINE_NS_BEGIN
 

diff --git a/modules/dnn/test/test_common.impl.hpp b/modules/dnn/test/test_common.impl.hpp
index 16114d5..a15edfb 100644 (file)
--- a/modules/dnn/test/test_common.impl.hpp
+++ b/modules/dnn/test/test_common.impl.hpp
@@ -392,7 +392,10 @@ void initDNNTests()
 #ifdef HAVE_DNN_NGRAPH
         CV_TEST_TAG_DNN_SKIP_IE_NGRAPH,
 #endif
-        CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
+        CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER,
+#endif
+        ""
     );
 #endif
     registerGlobalSkipTag(

diff --git a/modules/dnn/test/test_misc.cpp b/modules/dnn/test/test_misc.cpp
index 922a44b..36c5bc4 100644 (file)
--- a/modules/dnn/test/test_misc.cpp
+++ b/modules/dnn/test/test_misc.cpp
@@ -130,14 +130,18 @@ void test_readNet_IE_do_not_call_setInput(Backend backendId)
     EXPECT_TRUE(res.empty()) << res.size;
 }
 
+#ifdef HAVE_DNN_IE_NN_BUILDER_2019
 TEST(readNet, do_not_call_setInput_IE_NN_BUILDER_2019)
 {
     test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019);
 }
+#endif
+#ifdef HAVE_DNN_NGRAPH
 TEST(readNet, do_not_call_setInput_IE_NGRAPH)
 {
     test_readNet_IE_do_not_call_setInput(DNN_BACKEND_INFERENCE_ENGINE_NGRAPH);
 }
+#endif
 #endif  // HAVE_INF_ENGINE
 
 typedef testing::TestWithParam<tuple<Backend, Target> > dump;

diff --git a/modules/ts/src/ts_tags.cpp b/modules/ts/src/ts_tags.cpp
index 906f0d7..8bed1b7 100644 (file)
--- a/modules/ts/src/ts_tags.cpp
+++ b/modules/ts/src/ts_tags.cpp
@@ -62,6 +62,8 @@ static std::vector<std::string>& getTestTagsSkipList()
 
 void registerGlobalSkipTag(const std::string& skipTag)
 {
+    if (skipTag.empty())
+        return;  // do nothing
     std::vector<std::string>& skipTags = getTestTagsSkipList();
     for (size_t i = 0; i < skipTags.size(); ++i)
     {