Merge pull request #13493 from dkurt:dnn_ie_r5

modules/dnn/src/layers/mvn_layer.cpp

@@ -116,9 +116,15 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+#if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
+            return !zeroDev && eps <= 1e-7f;
+#else
             return !zeroDev && (preferableTarget == DNN_TARGET_CPU || eps <= 1e-7f);
+#endif
         else
+#endif  // HAVE_INF_ENGINE
             return backendId == DNN_BACKEND_OPENCV;
     }
 

modules/dnn/src/onnx/onnx_importer.cpp

@@ -420,31 +420,30 @@ void ONNXImporter::populateNet(Net dstNet)
         }
         else if (layer_type == "Sub")
         {
-            Mat blob = (-1.0f) * getBlob(node_proto, constBlobs, 1);
-            blob = blob.reshape(1, 1);
+            Mat blob = getBlob(node_proto, constBlobs, 1);
             if (blob.total() == 1) {
                 layerParams.type = "Power";
-                layerParams.set("shift", blob.at<float>(0));
+                layerParams.set("shift", -blob.at<float>(0));
             }
             else {
                 layerParams.type = "Scale";
                 layerParams.set("has_bias", true);
-                layerParams.blobs.push_back(blob);
+                layerParams.blobs.push_back(-1.0f * blob.reshape(1, 1));
             }
         }
         else if (layer_type == "Div")
         {
             Mat blob = getBlob(node_proto, constBlobs, 1);
             CV_Assert_N(blob.type() == CV_32F, blob.total());
-            divide(1.0, blob, blob);
             if (blob.total() == 1)
             {
-                layerParams.set("scale", blob.at<float>(0));
+                layerParams.set("scale", 1.0f / blob.at<float>(0));
                 layerParams.type = "Power";
             }
             else
             {
                 layerParams.type = "Scale";
+                divide(1.0, blob, blob);
                 layerParams.blobs.push_back(blob);
                 layerParams.set("bias_term", false);
             }

modules/dnn/test/test_backends.cpp

@@ -226,9 +226,9 @@ TEST_P(DNNTestNetwork, OpenPose_pose_mpi_faster_4_stages)
 TEST_P(DNNTestNetwork, OpenFace)
 {
 #if defined(INF_ENGINE_RELEASE)
-#if INF_ENGINE_RELEASE < 2018030000
+#if (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+        throw SkipTestException("");
 #elif INF_ENGINE_RELEASE < 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");

modules/dnn/test/test_ie_models.cpp

@@ -190,6 +190,14 @@ TEST_P(DNNTestOpenVINO, models)
                                          modelName == "landmarks-regression-retail-0009" ||
                                           modelName == "semantic-segmentation-adas-0001")))
         throw SkipTestException("");
+#elif INF_ENGINE_RELEASE == 2018050000
+    if (modelName == "single-image-super-resolution-0063" ||
+        modelName == "single-image-super-resolution-1011" ||
+        modelName == "single-image-super-resolution-1021" ||
+        (target == DNN_TARGET_OPENCL_FP16 && modelName == "face-reidentification-retail-0095") ||
+        (target == DNN_TARGET_MYRIAD && (modelName == "license-plate-recognition-barrier-0001" ||
+                                         modelName == "semantic-segmentation-adas-0001")))
+        throw SkipTestException("");
 #endif
 #endif
 

modules/dnn/test/test_layers.cpp

@@ -295,6 +295,10 @@ TEST_P(Test_Caffe_layers, Eltwise)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("Test is disabled for OpenVINO 2018R5");
+#endif
     testLayerUsingCaffeModels("layer_eltwise");
 }
 

modules/dnn/test/test_onnx_importer.cpp

@@ -164,6 +164,8 @@ TEST_P(Test_ONNX_layers, MultyInputs)
 
 TEST_P(Test_ONNX_layers, DynamicReshape)
 {
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("");
     testONNXModels("dynamic_reshape");
 }
 
@@ -249,6 +251,10 @@ TEST_P(Test_ONNX_nets, VGG16)
     else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL) {
         lInf = 1.2e-4;
     }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.131;
+#endif
     testONNXModels("vgg16", pb, l1, lInf);
 }
 
@@ -327,7 +333,7 @@ TEST_P(Test_ONNX_nets, CNN_MNIST)
 TEST_P(Test_ONNX_nets, MobileNet_v2)
 {
     // output range: [-166; 317]
-    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.38 : 7e-5;
+    const double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.4 : 7e-5;
     const double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 2.87 : 5e-4;
     testONNXModels("mobilenetv2", pb, l1, lInf);
 }
@@ -350,7 +356,17 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
 
 TEST_P(Test_ONNX_nets, Emotion_ferplus)
 {
-    testONNXModels("emotion_ferplus", pb);
+    double l1 = default_l1;
+    double lInf = default_lInf;
+    // Output values are in range [-2.01109, 2.11111]
+    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
+        l1 = 0.007;
+    else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
+    {
+        l1 = 0.021;
+        lInf = 0.034;
+    }
+    testONNXModels("emotion_ferplus", pb, l1, lInf);
 }
 
 TEST_P(Test_ONNX_nets, Inception_v2)
@@ -371,6 +387,10 @@ TEST_P(Test_ONNX_nets, DenseNet121)
 
 TEST_P(Test_ONNX_nets, Inception_v1)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     testONNXModels("inception_v1", pb);
 }
 

modules/dnn/test/test_tf_importer.cpp

@@ -241,6 +241,10 @@ TEST_P(Test_TensorFlow_layers, unfused_flatten)
 
 TEST_P(Test_TensorFlow_layers, leaky_relu)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("leaky_relu_order1");
     runTensorFlowNet("leaky_relu_order2");
     runTensorFlowNet("leaky_relu_order3");
@@ -383,6 +387,10 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
 
 TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD_PPN)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        throw SkipTestException("Unstable test case");
+#endif
     checkBackend();
     std::string proto = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pbtxt", false);
     std::string model = findDataFile("dnn/ssd_mobilenet_v1_ppn_coco.pb", false);
@@ -560,6 +568,10 @@ TEST_P(Test_TensorFlow_layers, slice)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE &&
         (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     runTensorFlowNet("slice_4d");
 }
 

modules/dnn/test/test_torch_importer.cpp

@@ -266,9 +266,9 @@ class Test_Torch_nets : public DNNTestLayer {};
 
 TEST_P(Test_Torch_nets, OpenFace_accuracy)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018030000
+#if defined(INF_ENGINE_RELEASE) && (INF_ENGINE_RELEASE < 2018030000 || INF_ENGINE_RELEASE == 2018050000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
-        throw SkipTestException("Test is enabled starts from OpenVINO 2018R3");
+        throw SkipTestException("");
 #endif
     checkBackend();
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
@@ -389,6 +389,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
 //   -model models/instance_norm/feathers.t7
 TEST_P(Test_Torch_nets, FastNeuralStyle_accuracy)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018050000
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     checkBackend();
     std::string models[] = {"dnn/fast_neural_style_eccv16_starry_night.t7",
                             "dnn/fast_neural_style_instance_norm_feathers.t7"};