Merge pull request #13387 from dkurt:dnn_minor_ie_fixes

modules/dnn/src/op_inf_engine.cpp

@@ -152,6 +152,7 @@ InfEngineBackendNet::InfEngineBackendNet()
 {
     targetDevice = InferenceEngine::TargetDevice::eCPU;
     precision = InferenceEngine::Precision::FP32;
+    hasNetOwner = false;
 }
 
 InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
@@ -162,6 +163,7 @@ InfEngineBackendNet::InfEngineBackendNet(InferenceEngine::CNNNetwork& net)
     outputs = net.getOutputsInfo();
     layers.resize(net.layerCount());  // A hack to execute InfEngineBackendNet::layerCount correctly.
     netOwner = net;
+    hasNetOwner = true;
 }
 
 void InfEngineBackendNet::Release() noexcept
@@ -178,12 +180,12 @@ void InfEngineBackendNet::setPrecision(InferenceEngine::Precision p) noexcept
 
 InferenceEngine::Precision InfEngineBackendNet::getPrecision() noexcept
 {
-    return precision;
+    return hasNetOwner ? netOwner.getPrecision() : precision;
 }
 
 InferenceEngine::Precision InfEngineBackendNet::getPrecision() const noexcept
 {
-    return precision;
+    return hasNetOwner ? netOwner.getPrecision() : precision;
 }
 
 // Assume that outputs of network is unconnected blobs.

modules/dnn/src/op_inf_engine.hpp

@@ -134,6 +134,9 @@ private:
     InferenceEngine::InferRequest infRequest;
     // In case of models from Model Optimizer we need to manage their lifetime.
     InferenceEngine::CNNNetwork netOwner;
+    // There is no way to check if netOwner is initialized or not so we use
+    // a separate flag to determine if the model has been loaded from IR.
+    bool hasNetOwner;
 
     std::string name;
 

modules/dnn/test/test_caffe_importer.cpp

@@ -471,6 +471,7 @@ TEST(Test_Caffe, shared_weights)
 
   net.setInput(blob_1, "input_1");
   net.setInput(blob_2, "input_2");
+  net.setPreferableBackend(DNN_BACKEND_OPENCV);
 
   Mat sum = net.forward();
 

modules/dnn/test/test_darknet_importer.cpp

@@ -306,7 +306,7 @@ TEST_P(Test_Darknet_nets, TinyYoloVoc)
     // batch size 1
     testDarknetModel(config_file, weights_file, ref.rowRange(0, 2), scoreDiff, iouDiff);
 
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_MYRIAD)
 #endif
     // batch size 2

modules/dnn/test/test_halide_layers.cpp

@@ -166,7 +166,7 @@ TEST_P(Deconvolution, Accuracy)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
         dilation.width == 2 && dilation.height == 2)
         throw SkipTestException("");
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE && targetId == DNN_TARGET_CPU &&
         hasBias && group != 1)
         throw SkipTestException("Test is disabled for OpenVINO 2018R4");

modules/dnn/test/test_layers.cpp

@@ -137,7 +137,7 @@ TEST_P(Test_Caffe_layers, Convolution)
 
 TEST_P(Test_Caffe_layers, DeConvolution)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU)
         throw SkipTestException("Test is disabled for OpenVINO 2018R4");
 #endif
@@ -918,8 +918,11 @@ INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_DWconv_Prelu, Combine(Values(3, 6), Val
 // Using Intel's Model Optimizer generate .xml and .bin files:
 // ./ModelOptimizer -w /path/to/caffemodel -d /path/to/prototxt \
 //                  -p FP32 -i -b ${batch_size} -o /path/to/output/folder
-TEST(Layer_Test_Convolution_DLDT, Accuracy)
+typedef testing::TestWithParam<Target> Layer_Test_Convolution_DLDT;
+TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
 {
+    Target targetId = GetParam();
+
     Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
     Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
 
@@ -930,6 +933,10 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
     Mat outDefault = netDefault.forward();
 
     net.setInput(inp);
+    net.setPreferableTarget(targetId);
+
+    if (targetId != DNN_TARGET_MYRIAD)
+    {
         Mat out = net.forward();
 
         normAssert(outDefault, out);
@@ -937,10 +944,18 @@ TEST(Layer_Test_Convolution_DLDT, Accuracy)
         std::vector<int> outLayers = net.getUnconnectedOutLayers();
         ASSERT_EQ(net.getLayer(outLayers[0])->name, "output_merge");
         ASSERT_EQ(net.getLayer(outLayers[0])->type, "Concat");
+    }
+    else
+    {
+        // An assertion is expected because the model is in FP32 format but
+        // Myriad plugin supports only FP16 models.
+        ASSERT_ANY_THROW(net.forward());
+    }
 }
 
-TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
+TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
 {
+    Target targetId = GetParam();
     Mat inp = blobFromNPY(_tf("blob.npy"));
 
     Mat inputs[] = {Mat(inp.dims, inp.size, CV_8U), Mat()};
@@ -951,12 +966,25 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
     for (int i = 0; i < 2; ++i)
     {
         Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
+        net.setPreferableTarget(targetId);
         net.setInput(inputs[i]);
+        if (targetId != DNN_TARGET_MYRIAD)
+        {
             outs[i] = net.forward();
             ASSERT_EQ(outs[i].type(), CV_32F);
         }
+        else
+        {
+            // An assertion is expected because the model is in FP32 format but
+            // Myriad plugin supports only FP16 models.
+            ASSERT_ANY_THROW(net.forward());
+        }
+    }
+    if (targetId != DNN_TARGET_MYRIAD)
         normAssert(outs[0], outs[1]);
 }
+INSTANTIATE_TEST_CASE_P(/**/, Layer_Test_Convolution_DLDT,
+    testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE)));
 
 // 1. Create a .prototxt file with the following network:
 // layer {
@@ -980,14 +1008,17 @@ TEST(Layer_Test_Convolution_DLDT, setInput_uint8)
 // net.save('/path/to/caffemodel')
 //
 // 3. Convert using ModelOptimizer.
-typedef testing::TestWithParam<tuple<int, int> > Test_DLDT_two_inputs;
+typedef testing::TestWithParam<tuple<int, int, Target> > Test_DLDT_two_inputs;
 TEST_P(Test_DLDT_two_inputs, as_IR)
 {
     int firstInpType = get<0>(GetParam());
     int secondInpType = get<1>(GetParam());
-    // TODO: It looks like a bug in Inference Engine.
+    Target targetId = get<2>(GetParam());
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE < 2018040000
     if (secondInpType == CV_8U)
-        throw SkipTestException("");
+        throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
+#endif
 
     Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
     int inpSize[] = {1, 2, 3};
@@ -998,11 +1029,21 @@ TEST_P(Test_DLDT_two_inputs, as_IR)
 
     net.setInput(firstInp, "data");
     net.setInput(secondInp, "second_input");
+    net.setPreferableTarget(targetId);
+    if (targetId != DNN_TARGET_MYRIAD)
+    {
         Mat out = net.forward();
 
         Mat ref;
         cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
         normAssert(out, ref);
+    }
+    else
+    {
+        // An assertion is expected because the model is in FP32 format but
+        // Myriad plugin supports only FP16 models.
+        ASSERT_ANY_THROW(net.forward());
+    }
 }
 
 TEST_P(Test_DLDT_two_inputs, as_backend)
@@ -1010,6 +1051,8 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
     static const float kScale = 0.5f;
     static const float kScaleInv = 1.0f / kScale;
 
+    Target targetId = get<2>(GetParam());
+
     Net net;
     LayerParams lp;
     lp.type = "Eltwise";
@@ -1018,9 +1061,9 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
     int eltwiseId = net.addLayerToPrev(lp.name, lp.type, lp);  // connect to a first input
     net.connect(0, 1, eltwiseId, 1);  // connect to a second input
 
-    int inpSize[] = {1, 2, 3};
-    Mat firstInp(3, &inpSize[0], get<0>(GetParam()));
-    Mat secondInp(3, &inpSize[0], get<1>(GetParam()));
+    int inpSize[] = {1, 2, 3, 4};
+    Mat firstInp(4, &inpSize[0], get<0>(GetParam()));
+    Mat secondInp(4, &inpSize[0], get<1>(GetParam()));
     randu(firstInp, 0, 255);
     randu(secondInp, 0, 255);
 
@@ -1028,15 +1071,20 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
     net.setInput(firstInp, "data", kScale);
     net.setInput(secondInp, "second_input", kScaleInv);
     net.setPreferableBackend(DNN_BACKEND_INFERENCE_ENGINE);
+    net.setPreferableTarget(targetId);
     Mat out = net.forward();
 
     Mat ref;
     addWeighted(firstInp, kScale, secondInp, kScaleInv, 0, ref, CV_32F);
-    normAssert(out, ref);
+    // Output values are in range [0, 637.5].
+    double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.06 : 1e-6;
+    double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 0.3 : 1e-5;
+    normAssert(out, ref, "", l1, lInf);
 }
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(
-  Values(CV_8U, CV_32F), Values(CV_8U, CV_32F)
+  Values(CV_8U, CV_32F), Values(CV_8U, CV_32F),
+  testing::ValuesIn(getAvailableTargets(DNN_BACKEND_INFERENCE_ENGINE))
 ));
 
 class UnsupportedLayer : public Layer

modules/dnn/test/test_torch_importer.cpp

@@ -136,7 +136,7 @@ TEST_P(Test_Torch_layers, run_reshape_change_batch_size)
 
 TEST_P(Test_Torch_layers, run_reshape)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("Test is disabled for OpenVINO 2018R4");
 #endif
@@ -172,7 +172,7 @@ TEST_P(Test_Torch_layers, run_depth_concat)
 
 TEST_P(Test_Torch_layers, run_deconv)
 {
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE == 2018040000
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2018040000
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD)
         throw SkipTestException("Test is disabled for OpenVINO 2018R4");
 #endif