dnn: use OpenVINO 2019R1 defines

cmake/OpenCVDetectInferenceEngine.cmake

@@ -87,9 +87,9 @@ endif()
 
 if(INF_ENGINE_TARGET)
   if(NOT INF_ENGINE_RELEASE)
-    message(WARNING "InferenceEngine version have not been set, 2018R5 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
+    message(WARNING "InferenceEngine version have not been set, 2019R1 will be used by default. Set INF_ENGINE_RELEASE variable if you experience build errors.")
   endif()
-  set(INF_ENGINE_RELEASE "2018050000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
+  set(INF_ENGINE_RELEASE "2019010000" CACHE STRING "Force IE version, should be in form YYYYAABBCC (e.g. 2018R2.0.2 -> 2018020002)")
   set_target_properties(${INF_ENGINE_TARGET} PROPERTIES
     INTERFACE_COMPILE_DEFINITIONS "HAVE_INF_ENGINE=1;INF_ENGINE_RELEASE=${INF_ENGINE_RELEASE}"
   )

modules/dnn/src/dnn.cpp

@@ -1637,7 +1637,7 @@ struct Net::Impl
                  preferableTarget == DNN_TARGET_MYRIAD ||
                  preferableTarget == DNN_TARGET_FPGA) && !fused)
             {
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
                 for (const std::string& name : {"weights", "biases"})
                 {
                     auto it = ieNode->layer.getParameters().find(name);

modules/dnn/src/layers/normalize_bbox_layer.cpp

@@ -290,7 +290,7 @@ public:
                 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)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
             l.getParameters()["weights"] = weights;
 #else
             l.addConstantData("weights", weights);

modules/dnn/src/op_inf_engine.cpp

@@ -130,7 +130,7 @@ void InfEngineBackendNet::init(int targetId)
         for (int id : unconnectedLayersIds)
         {
             InferenceEngine::Builder::OutputLayer outLayer("myconv1");
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
             // Inference Engine determines network precision by ports.
             InferenceEngine::Precision p = (targetId == DNN_TARGET_MYRIAD ||
                                             targetId == DNN_TARGET_OPENCL_FP16) ?
@@ -188,7 +188,7 @@ void InfEngineBackendNet::init(int targetId)
 
 void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer)
 {
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
     // Add weights to network and connect them after input blobs.
     std::map<std::string, InferenceEngine::Parameter>& params = layer.getParameters();
     std::vector<int> blobsIds;
@@ -229,7 +229,7 @@ void InfEngineBackendNet::addLayer(InferenceEngine::Builder::Layer& layer)
     CV_Assert(layers.insert({layerName, id}).second);
     unconnectedLayersIds.insert(id);
 
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
     // By default, all the weights are connected to last ports ids.
     for (int i = 0; i < blobsIds.size(); ++i)
     {
@@ -903,7 +903,7 @@ InferenceEngine::Blob::Ptr convertFp16(const InferenceEngine::Blob::Ptr& blob)
 void addConstantData(const std::string& name, InferenceEngine::Blob::Ptr data,
                      InferenceEngine::Builder::Layer& l)
 {
-#if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2018R5)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1)
     l.getParameters()[name] = data;
 #else
     l.addConstantData(name, data);

modules/dnn/src/op_inf_engine.hpp

@@ -27,10 +27,11 @@
 #define INF_ENGINE_RELEASE_2018R3 2018030000
 #define INF_ENGINE_RELEASE_2018R4 2018040000
 #define INF_ENGINE_RELEASE_2018R5 2018050000
+#define INF_ENGINE_RELEASE_2019R1 2019010000
 
 #ifndef INF_ENGINE_RELEASE
-#warning("IE version have not been provided via command-line. Using 2018R5 by default")
-#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2018R5
+#warning("IE version have not been provided via command-line. Using 2019R1 by default")
+#define INF_ENGINE_RELEASE INF_ENGINE_RELEASE_2019R1
 #endif
 
 #define INF_ENGINE_VER_MAJOR_GT(ver) (((INF_ENGINE_RELEASE) / 10000) > ((ver) / 10000))

modules/dnn/test/test_backends.cpp

@@ -289,7 +289,7 @@ TEST_P(DNNTestNetwork, OpenFace)
 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("Test is disabled for Myriad targets");
-#elif INF_ENGINE_VER_MAJOR_GT(2018050000)
+#elif INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
     )

modules/dnn/test/test_darknet_importer.cpp

@@ -267,7 +267,7 @@ public:
 
 TEST_P(Test_Darknet_nets, YoloVoc)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is disabled");
 #endif

modules/dnn/test/test_halide_layers.cpp

@@ -169,7 +169,7 @@ TEST_P(Deconvolution, Accuracy)
         throw SkipTestException("Test is disabled for OpenVINO 2018R4");
 #endif
 
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
             && inChannels == 6 && outChannels == 4 && group == 1
@@ -351,7 +351,7 @@ TEST_P(MaxPooling, Accuracy)
         throw SkipTestException("Problems with output dimension in OpenVINO 2018R5");
 #endif
 
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
             && (stride == Size(1, 1) || stride == Size(2, 2))
@@ -561,7 +561,7 @@ TEST_P(ReLU, Accuracy)
     float negativeSlope = get<0>(GetParam());
     Backend backendId = get<0>(get<1>(GetParam()));
     Target targetId = get<1>(get<1>(GetParam()));
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE
             && negativeSlope < 0
     )
@@ -589,7 +589,7 @@ TEST_P(NoParamActivation, Accuracy)
     LayerParams lp;
     lp.type = get<0>(GetParam());
     lp.name = "testLayer";
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE
             && lp.type == "AbsVal"
     )
@@ -688,7 +688,7 @@ TEST_P(Concat, Accuracy)
         throw SkipTestException("Test is disabled for Myriad target");  // crash
 #endif
 
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU
             && inSize == Vec3i(1, 4, 5) && numChannels == Vec3i(1, 6, 2)
     )
@@ -769,7 +769,7 @@ TEST_P(Eltwise, Accuracy)
         throw SkipTestException("Test is disabled for Myriad target");
 #endif
 
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && numConv > 1)
         throw SkipTestException("Test is disabled for DLIE backend");
 #endif

modules/dnn/test/test_layers.cpp

@@ -236,7 +236,7 @@ TEST_P(Test_Caffe_layers, Dropout)
 
 TEST_P(Test_Caffe_layers, Concat)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("Test is disabled for Myriad targets");
 #endif
@@ -247,7 +247,7 @@ TEST_P(Test_Caffe_layers, Concat)
 
 TEST_P(Test_Caffe_layers, Fused_Concat)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE due negative_slope parameter");
 #endif

modules/dnn/test/test_onnx_importer.cpp

@@ -319,7 +319,7 @@ TEST_P(Test_ONNX_nets, ResNet50v1)
 
 TEST_P(Test_ONNX_nets, ResNet101_DUC_HDC)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE targets");
 #endif

modules/dnn/test/test_tf_importer.cpp

@@ -140,7 +140,7 @@ TEST_P(Test_TensorFlow_layers, padding)
 
 TEST_P(Test_TensorFlow_layers, padding_same)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE");
 #endif
@@ -197,7 +197,7 @@ TEST_P(Test_TensorFlow_layers, pooling)
 TEST_P(Test_TensorFlow_layers, ave_pool_same)
 {
     // Reference output values are in range [-0.519531, 0.112976]
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
     )
@@ -241,7 +241,7 @@ TEST_P(Test_TensorFlow_layers, reshape)
 
 TEST_P(Test_TensorFlow_layers, flatten)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE");
 #endif
@@ -257,7 +257,7 @@ TEST_P(Test_TensorFlow_layers, flatten)
 
 TEST_P(Test_TensorFlow_layers, unfused_flatten)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE");
 #endif
@@ -279,7 +279,7 @@ TEST_P(Test_TensorFlow_layers, leaky_relu)
 
 TEST_P(Test_TensorFlow_layers, l2_normalize)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
     )
@@ -587,7 +587,7 @@ TEST_P(Test_TensorFlow_layers, fp16_weights)
 
 TEST_P(Test_TensorFlow_layers, fp16_padding_same)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GT(2018050000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE)
         throw SkipTestException("Test is disabled for DLIE");
 #endif