Merge pull request #13799 from dkurt:dnn_ie_future_2

modules/dnn/src/dnn.cpp

@@ -1637,6 +1637,27 @@ struct Net::Impl
                  preferableTarget == DNN_TARGET_MYRIAD ||
                  preferableTarget == DNN_TARGET_FPGA) && !fused)
             {
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+                bool hasWeights = false;
+                for (const std::string& name : {"weights", "biases"})
+                {
+                    auto it = ieNode->layer.getParameters().find(name);
+                    if (it != ieNode->layer.getParameters().end())
+                    {
+                        InferenceEngine::Blob::CPtr bp = it->second.as<InferenceEngine::Blob::CPtr>();
+                        it->second = (InferenceEngine::Blob::CPtr)convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(bp));
+                        hasWeights = true;
+                    }
+                }
+                if (!hasWeights)
+                {
+                    InferenceEngine::Blob::Ptr blob = InferenceEngine::make_shared_blob<int16_t>(
+                                                          InferenceEngine::Precision::FP16,
+                                                          InferenceEngine::Layout::C, {1});
+                    blob->allocate();
+                    ieNode->layer.getParameters()["weights"] = (InferenceEngine::Blob::CPtr)blob;
+                }
+#else
                 auto& blobs = ieNode->layer.getConstantData();
                 if (blobs.empty())
                 {
@@ -1653,6 +1674,7 @@ struct Net::Impl
                     for (auto& it : blobs)
                         it.second = convertFp16(std::const_pointer_cast<InferenceEngine::Blob>(it.second));
                 }
+#endif
             }
 
             if (!fused)
@@ -1724,7 +1746,7 @@ struct Net::Impl
 
             if (!ieNode->net->isInitialized())
             {
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R3)
+#if INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2018R4)
                 // For networks which is built in runtime we need to specify a
                 // version of it's hyperparameters.
                 std::string versionTrigger = "<net name=\"TestInput\" version=\"3\" batch=\"1\">"

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -276,23 +276,29 @@ public:
 
             InferenceEngine::Builder::Layer l = ieLayer;
             const int numChannels = input->dims[2];  // NOTE: input->dims are reversed (whcn)
+            InferenceEngine::Blob::Ptr weights;
             if (blobs.empty())
             {
-                auto weights = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
+                auto onesBlob = InferenceEngine::make_shared_blob<float>(InferenceEngine::Precision::FP32,
                                                                          InferenceEngine::Layout::C,
                                                                          {(size_t)numChannels});
-                weights->allocate();
+                onesBlob->allocate();
                 std::vector<float> ones(numChannels, 1);
-                weights->set(ones);
+                onesBlob->set(ones);
-                l.addConstantData("weights", weights);
+                weights = onesBlob;
                 l.getParameters()["channel_shared"] = false;
             }
             else
             {
                 CV_Assert(numChannels == blobs[0].total());
-                l.addConstantData("weights", wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C));
+                weights = wrapToInfEngineBlob(blobs[0], {(size_t)numChannels}, InferenceEngine::Layout::C);
                 l.getParameters()["channel_shared"] = blobs[0].total() == 1;
             }
+#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+            l.getParameters()["weights"] = (InferenceEngine::Blob::CPtr)weights;
+#else
+            l.addConstantData("weights", weights);
+#endif
             l.getParameters()["across_spatial"] = acrossSpatial;
             return Ptr<BackendNode>(new InfEngineBackendNode(l));
         }

modules/dnn/src/layers/resize_layer.cpp

@@ -173,7 +173,7 @@ public:
             ieLayer.getParameters()["antialias"] = false;
             if (scaleWidth != scaleHeight)
                 CV_Error(Error::StsNotImplemented, "resample with sw != sh");
-            ieLayer.getParameters()["factor"] = 1.0 / scaleWidth;
+            ieLayer.getParameters()["factor"] = 1.0f / scaleWidth;
         }
         else if (interpolation == "bilinear")
         {

modules/dnn/src/op_inf_engine.cpp

@@ -766,7 +766,7 @@ void InfEngineBackendLayer::forward(InputArrayOfArrays inputs, OutputArrayOfArra
     CV_Error(Error::StsInternal, "Choose Inference Engine as a preferable backend.");
 }
 
-InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
+InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
 {
     auto halfs = InferenceEngine::make_shared_blob<int16_t>(InferenceEngine::Precision::FP16, blob->layout(), blob->dims());
     halfs->allocate();

modules/dnn/src/op_inf_engine.hpp

@@ -36,6 +36,7 @@
 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))
 #define INF_ENGINE_VER_MAJOR_GE(ver) (((INF_ENGINE_RELEASE) / 10000) >= ((ver) / 10000))
 #define INF_ENGINE_VER_MAJOR_LT(ver) (((INF_ENGINE_RELEASE) / 10000) < ((ver) / 10000))
+#define INF_ENGINE_VER_MAJOR_EQ(ver) (((INF_ENGINE_RELEASE) / 10000) == ((ver) / 10000))
 
 #if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
 #include <ie_builders.hpp>
@@ -252,7 +253,7 @@ Mat infEngineBlobToMat(const InferenceEngine::Blob::Ptr& blob);
 
 // Convert Inference Engine blob with FP32 precision to FP16 precision.
 // Allocates memory for a new blob.
-InferenceEngine::TBlob<int16_t>::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
+InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob);
 
 // This is a fake class to run networks from Model Optimizer. Objects of that
 // class simulate responses of layers are imported by OpenCV and supported by

modules/dnn/test/test_halide_layers.cpp

@@ -694,6 +694,11 @@ TEST_P(Eltwise, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE > 2018050000
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_OPENCL)
+        throw SkipTestException("");
+#endif
+
     Net net;
 
     std::vector<int> convLayerIds(numConv);