Merge pull request #15184 from l-bat:IE_R2

Support new IE API (#15184)

* Add support OpenVINO R2 for layers

* Add Core API

* Fix tests

* Fix expectNoFallbacksFromIE for ONNX nets

* Remove deprecated API

* Remove td

* Remove TargetDevice

* Fix Async

* Add test

* Fix detectMyriadX

* Fix test

* Fix warning

modules/dnn/src/dnn.cpp

@@ -713,21 +713,23 @@ struct DataLayer : public Layer
         CV_Assert(numChannels <= 4);
 
         // Scale
-        auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
-                                                                {numChannels});
+        InferenceEngine::TensorDesc td(InferenceEngine::Precision::FP32, {numChannels},
+                                       InferenceEngine::Layout::C);
+        auto weights = InferenceEngine::make_shared_blob<float>(td);
         weights->allocate();
-        weights->set(std::vector<float>(numChannels, scaleFactors[0]));
+
+        float* weight_buf = weights->buffer().as<float*>();
+        std::fill(weight_buf, weight_buf + numChannels, scaleFactors[0]);
 
         // Mean subtraction
-        auto biases = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
-                                                               {numChannels});
+        auto biases = InferenceEngine::make_shared_blob<float>(td);
         biases->allocate();
-        std::vector<float> biasesVec(numChannels);
+        float* bias_buf = biases->buffer().as<float*>();
+
         for (int i = 0; i < numChannels; ++i)
         {
-            biasesVec[i] = -means[0][i] * scaleFactors[0];
+            bias_buf[i] = -means[0][i] * scaleFactors[0];
         }
-        biases->set(biasesVec);
 
         InferenceEngine::Builder::Layer ieLayer = InferenceEngine::Builder::ScaleShiftLayer(name);
         addConstantData("weights", weights, ieLayer);
@@ -1473,7 +1475,11 @@ struct Net::Impl
                 for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
                 {
                     InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
                     dataPtr->name = netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i];
+#else
+                    dataPtr->setName(netInputLayer->outNames.empty() ? ld.name : netInputLayer->outNames[i]);
+#endif
                 }
             }
             else
@@ -1481,7 +1487,11 @@ struct Net::Impl
                 for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
                 {
                     InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
                     dataPtr->name = ld.name;
+#else
+                    dataPtr->setName(ld.name);
+#endif
                 }
             }
         }
@@ -1502,7 +1512,11 @@ struct Net::Impl
                     for (int i = 0; i < ld.inputBlobsWrappers.size(); ++i)
                     {
                         InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.inputBlobsWrappers[i]);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
                         dataPtr->name = netInputLayer->outNames[i];
+#else
+                        dataPtr->setName(netInputLayer->outNames[i]);
+#endif
                     }
                 }
                 else
@@ -1510,7 +1524,11 @@ struct Net::Impl
                     for (int i = 0; i < ld.outputBlobsWrappers.size(); ++i)
                     {
                         InferenceEngine::DataPtr dataPtr = infEngineDataNode(ld.outputBlobsWrappers[i]);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
                         dataPtr->name = ld.name;
+#else
+                        dataPtr->setName(ld.name);
+#endif
                     }
                 }
                 ieNode->net->addBlobs(ld.inputBlobsWrappers);

modules/dnn/src/layers/blank_layer.cpp

@@ -111,7 +111,8 @@ public:
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        CV_Assert(!input->dims.empty());
+        std::vector<size_t> dims = input->getDims();
+        CV_Assert(!dims.empty());
 
         InferenceEngine::Builder::Layer ieLayer(name);
         ieLayer.setName(name);
@@ -122,12 +123,10 @@ public:
         else
         {
             ieLayer.setType("Split");
-            ieLayer.getParameters()["axis"] = input->dims.size() - 1;
-            ieLayer.getParameters()["out_sizes"] = input->dims[0];
+            ieLayer.getParameters()["axis"] = dims.size() - 1;
+            ieLayer.getParameters()["out_sizes"] = dims[0];
         }
-        std::vector<size_t> shape(input->dims);
-        std::reverse(shape.begin(), shape.end());
-        ieLayer.setInputPorts({InferenceEngine::Port(shape)});
+        ieLayer.setInputPorts({InferenceEngine::Port(dims)});
         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }

modules/dnn/src/layers/concat_layer.cpp

@@ -304,7 +304,7 @@ public:
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::ConcatLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axis, input->dims.size()));
+        ieLayer.setAxis(clamp(axis, input->getDims().size()));
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(inputs.size()));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }

modules/dnn/src/layers/convolution_layer.cpp

@@ -465,14 +465,13 @@ public:
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> > &inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        CV_Assert(input->dims.size() == 4 || input->dims.size() == 5);
-
-        const int inpCn = input->dims[input->dims.size() - 2];  // NOTE: input->dims are reversed (WHIO or WHDIO)
+        std::vector<size_t> dims = input->getDims();
+        CV_Assert(dims.size() == 4 || dims.size() == 5);
+        const int inpCn = dims[1];
         const int outCn = blobs[0].size[0];
         const int inpGroupCn = blobs[0].size[1];
         const int group = inpCn / inpGroupCn;
-
-        InferenceEngine::Layout layout = (input->dims.size() == 4) ? InferenceEngine::Layout::OIHW :
+        InferenceEngine::Layout layout = (dims.size() == 4) ? InferenceEngine::Layout::OIHW :
                                                               InferenceEngine::Layout::NCDHW;
 
         auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
@@ -485,9 +484,10 @@ public:
             }
             else
             {
-                ieWeights = InferenceEngine::make_shared_blob<float>(
-                                    InferenceEngine::Precision::FP32, layout,
-                                    ieWeights->dims());
+                ieWeights = InferenceEngine::make_shared_blob<float>({
+                                InferenceEngine::Precision::FP32,
+                                ieWeights->getTensorDesc().getDims(), layout
+                            });
                 ieWeights->allocate();
 
                 Mat newWeights = infEngineBlobToMat(ieWeights).reshape(1, outCn);
@@ -1877,9 +1877,10 @@ public:
         auto ieWeights = wrapToInfEngineBlob(blobs[0], layout);
         if (fusedWeights)
         {
-            ieWeights = InferenceEngine::make_shared_blob<float>(
-                                InferenceEngine::Precision::FP32, layout,
-                                ieWeights->dims());
+            ieWeights = InferenceEngine::make_shared_blob<float>({
+                            InferenceEngine::Precision::FP32,
+                            ieWeights->getTensorDesc().getDims(), layout
+                        });
             ieWeights->allocate();
 
             int inpCn = blobs[0].size[0];

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -261,7 +261,8 @@ public:
     virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >& inputs) CV_OVERRIDE
     {
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
-        if (input->dims.size() == 4)
+        std::vector<size_t> dims = input->getDims();
+        if (dims.size() == 4)
         {
             InferenceEngine::Builder::NormalizeLayer ieLayer(name);
 
@@ -270,13 +271,14 @@ public:
             ieLayer.setEpsilon(epsilon);
 
             InferenceEngine::Builder::Layer l = ieLayer;
-            const int numChannels = input->dims[2];  // NOTE: input->dims are reversed (whcn)
+            const int numChannels = dims[1];
             InferenceEngine::Blob::Ptr weights;
             if (blobs.empty())
             {
-                weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
-                                                                   InferenceEngine::Layout::C,
-                                                                   {(size_t)numChannels});
+                weights = InferenceEngine::make_shared_blob<float>({
+                              InferenceEngine::Precision::FP32,
+                              {(size_t)numChannels}, InferenceEngine::Layout::C
+                          });
                 weights->allocate();
 
                 Mat weightsMat = infEngineBlobToMat(weights).reshape(1, numChannels);

modules/dnn/src/layers/pooling_layer.cpp

@@ -166,9 +166,11 @@ public:
             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 (type == MAX && (pad_l == 1 && pad_t == 1) && stride == Size(2, 2) ) {
                     return !isMyriadX();
                 }
+#endif
                 return type == MAX || type == AVE;
             }
             else

modules/dnn/src/layers/scale_layer.cpp

@@ -207,12 +207,13 @@ public:
         }
         else
         {
-            auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
-                                                                    {numChannels});
+            auto weights = InferenceEngine::make_shared_blob<float>({
+                               InferenceEngine::Precision::FP32, {(size_t)numChannels},
+                               InferenceEngine::Layout::C
+                           });
             weights->allocate();
-
-            std::vector<float> ones(numChannels, 1);
-            weights->set(ones);
+            float* buf = weights->buffer().as<float*>();
+            std::fill(buf, buf + numChannels, 1);
             addConstantData("weights", weights, l);
         }
         if (hasBias)

modules/dnn/src/layers/slice_layer.cpp

@@ -301,14 +301,14 @@ public:
         {
             std::vector<size_t> outShape(numDims);
             for (int i = 0; i < numDims; ++i)
-                outShape[numDims - 1 - i] = sliceRanges[0][i].size();
+                outShape[i] = sliceRanges[0][i].size();
 
             ieLayer.getInputPorts()[1].setParameter("type", "weights");
 
-            // Fake blob which will be moved to inputs (as weights).
-            auto shapeSource = InferenceEngine::make_shared_blob<float>(
-                                   InferenceEngine::Precision::FP32,
-                                   InferenceEngine::Layout::ANY, outShape);
+            auto shapeSource = InferenceEngine::make_shared_blob<float>({
+                                   InferenceEngine::Precision::FP32, outShape,
+                                   InferenceEngine::Layout::ANY
+                               });
             shapeSource->allocate();
             addConstantData("weights", shapeSource, ieLayer);
         }

modules/dnn/src/layers/softmax_layer.cpp

@@ -315,7 +315,8 @@ public:
         InferenceEngine::DataPtr input = infEngineDataNode(inputs[0]);
 
         InferenceEngine::Builder::SoftMaxLayer ieLayer(name);
-        ieLayer.setAxis(clamp(axisRaw, input->dims.size()));
+        ieLayer.setAxis(clamp(axisRaw, input->getDims().size()));
+
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
     }
 #endif  // HAVE_INF_ENGINE

modules/dnn/src/op_inf_engine.hpp

@@ -92,18 +92,22 @@ public:
     void forward(const std::vector<Ptr<BackendWrapper> >& outBlobsWrappers,
                  bool isAsync);
 
-    void initPlugin(InferenceEngine::ICNNNetwork& net);
+    void initPlugin(InferenceEngine::CNNNetwork& net);
 
     void addBlobs(const std::vector<cv::Ptr<BackendWrapper> >& ptrs);
 
 private:
     InferenceEngine::Builder::Network netBuilder;
 
-    InferenceEngine::InferenceEnginePluginPtr enginePtr;
-    InferenceEngine::InferencePlugin plugin;
     InferenceEngine::ExecutableNetwork netExec;
     InferenceEngine::BlobMap allBlobs;
-    InferenceEngine::TargetDevice targetDevice;
+    std::string device_name;
+#if INF_ENGINE_VER_MAJOR_LE(2019010000)
+    InferenceEngine::InferenceEnginePluginPtr enginePtr;
+    InferenceEngine::InferencePlugin plugin;
+#else
+    bool isInit = false;
+#endif
 
     struct InfEngineReqWrapper
     {

modules/dnn/test/test_ie_models.cpp

@@ -136,13 +136,10 @@ static const std::vector<std::string> getOpenVINOTestModelsList()
 
 static inline void genData(const std::vector<size_t>& dims, Mat& m, Blob::Ptr& dataPtr)
 {
-    std::vector<int> reversedDims(dims.begin(), dims.end());
-    std::reverse(reversedDims.begin(), reversedDims.end());
-
-    m.create(reversedDims, CV_32F);
+    m.create(std::vector<int>(dims.begin(), dims.end()), CV_32F);
     randu(m, -1, 1);
 
-    dataPtr = make_shared_blob<float>(Precision::FP32, dims, (float*)m.data);
+    dataPtr = make_shared_blob<float>({Precision::FP32, dims, Layout::ANY}, (float*)m.data);
 }
 
 void runIE(Target target, const std::string& xmlPath, const std::string& binPath,
@@ -154,32 +151,42 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
 
     CNNNetwork net = reader.getNetwork();
 
+    std::string device_name;
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
+    Core ie;
+#else
     InferenceEnginePluginPtr enginePtr;
     InferencePlugin plugin;
+#endif
     ExecutableNetwork netExec;
     InferRequest infRequest;
+
     try
     {
-        auto dispatcher = InferenceEngine::PluginDispatcher({""});
         switch (target)
         {
             case DNN_TARGET_CPU:
-                enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eCPU);
+                device_name = "CPU";
                 break;
             case DNN_TARGET_OPENCL:
             case DNN_TARGET_OPENCL_FP16:
-                enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eGPU);
+                device_name = "GPU";
                 break;
             case DNN_TARGET_MYRIAD:
-                enginePtr = dispatcher.getSuitablePlugin(TargetDevice::eMYRIAD);
+                device_name = "MYRIAD";
                 break;
             case DNN_TARGET_FPGA:
-                enginePtr = dispatcher.getPluginByDevice("HETERO:FPGA,CPU");
+                device_name = "FPGA";
                 break;
             default:
                 CV_Error(Error::StsNotImplemented, "Unknown target");
         };
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2019010000)
+        auto dispatcher = InferenceEngine::PluginDispatcher({""});
+        enginePtr = dispatcher.getPluginByDevice(device_name);
+#endif
         if (target == DNN_TARGET_CPU || target == DNN_TARGET_FPGA)
         {
             std::string suffixes[] = {"_avx2", "_sse4", ""};
@@ -202,16 +209,23 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
                 try
                 {
                     IExtensionPtr extension = make_so_pointer<IExtension>(libName);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
+                    ie.AddExtension(extension, device_name);
+#else
                     enginePtr->AddExtension(extension, 0);
+#endif
                     break;
                 }
                 catch(...) {}
             }
             // Some of networks can work without a library of extra layers.
         }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2019010000)
+        netExec = ie.LoadNetwork(net, device_name);
+#else
         plugin = InferencePlugin(enginePtr);
-
         netExec = plugin.LoadNetwork(net, {});
+#endif
         infRequest = netExec.CreateInferRequest();
     }
     catch (const std::exception& ex)
@@ -224,7 +238,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
     BlobMap inputBlobs;
     for (auto& it : net.getInputsInfo())
     {
-        genData(it.second->getDims(), inputsMap[it.first], inputBlobs[it.first]);
+        genData(it.second->getTensorDesc().getDims(), inputsMap[it.first], inputBlobs[it.first]);
     }
     infRequest.SetInput(inputBlobs);
 
@@ -233,7 +247,7 @@ void runIE(Target target, const std::string& xmlPath, const std::string& binPath
     BlobMap outputBlobs;
     for (auto& it : net.getOutputsInfo())
     {
-        genData(it.second->dims, outputsMap[it.first], outputBlobs[it.first]);
+        genData(it.second->getTensorDesc().getDims(), outputsMap[it.first], outputBlobs[it.first]);
     }
     infRequest.SetOutput(outputBlobs);
 

modules/dnn/test/test_misc.cpp

@@ -467,6 +467,42 @@ INSTANTIATE_TEST_CASE_P(/**/, Async, Combine(
   Values(CV_32F, CV_8U),
   testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
 ));
+
+typedef testing::TestWithParam<Target>  Test_Model_Optimizer;
+TEST_P(Test_Model_Optimizer, forward_two_nets)
+{
+    const int target = GetParam();
+
+    const std::string suffix = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? "_fp16" : "";
+    const std::string& model = findDataFile("dnn/layers/layer_convolution" + suffix + ".bin");
+    const std::string& proto = findDataFile("dnn/layers/layer_convolution" + suffix + ".xml");
+
+    Net net0 = readNet(model, proto);
+    net0.setPreferableTarget(target);
+
+    Net net1 = readNet(model, proto);
+    net1.setPreferableTarget(target);
+
+    // Generate inputs.
+    int blobSize[] = {2, 6, 75, 113};
+    Mat input(4, &blobSize[0], CV_32F);
+    randu(input, 0, 255);
+
+    net0.setInput(input);
+    Mat ref0 = net0.forward().clone();
+
+    net1.setInput(input);
+    Mat ref1 = net1.forward();
+
+    net0.setInput(input);
+    Mat ref2 = net0.forward();
+
+    normAssert(ref0, ref2, 0, 0);
+}
+INSTANTIATE_TEST_CASE_P(/**/, Test_Model_Optimizer,
+  testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
+);
+
 #endif  // HAVE_INF_ENGINE
 
 }} // namespace