Merge pull request #13692 from dkurt:dnn_do_not_crash_myriad_in_tests

modules/dnn/src/dnn.cpp

@@ -142,7 +142,13 @@ private:
 #else
         cv::dnn::Net net;
         cv::dnn::LayerParams lp;
-        net.addLayerToPrev("testLayer", "Identity", lp);
+        lp.set("kernel_size", 1);
+        lp.set("num_output", 1);
+        lp.set("bias_term", false);
+        lp.type = "Convolution";
+        lp.name = "testLayer";
+        lp.blobs.push_back(Mat({1, 2, 1, 1}, CV_32F, Scalar(1)));
+        net.addLayerToPrev(lp.name, lp.type, lp);
         net.setPreferableBackend(cv::dnn::DNN_BACKEND_INFERENCE_ENGINE);
         net.setPreferableTarget(target);
         static int inpDims[] = {1, 2, 3, 4};

modules/dnn/src/layers/convolution_layer.cpp

@@ -481,13 +481,13 @@ public:
 #if INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2018R5)
         InferenceEngine::Builder::ConvolutionLayer ieLayer(name);
 
-        ieLayer.setKernel({kernel.height, kernel.width});
-        ieLayer.setStrides({stride.height, stride.width});
-        ieLayer.setDilation({dilation.height, dilation.width});
-        ieLayer.setPaddingsBegin({pad.height, pad.width});
-        ieLayer.setPaddingsEnd({pad.height, pad.width});
-        ieLayer.setGroup(group);
-        ieLayer.setOutDepth(outCn);
+        ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
+        ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
+        ieLayer.setDilation({(size_t)dilation.height, (size_t)dilation.width});
+        ieLayer.setPaddingsBegin({(size_t)pad.height, (size_t)pad.width});
+        ieLayer.setPaddingsEnd({(size_t)pad.height, (size_t)pad.width});
+        ieLayer.setGroup((size_t)group);
+        ieLayer.setOutDepth((size_t)outCn);
 
         ieLayer.setWeights(ieWeights);
         if (ieBiases)
@@ -1713,13 +1713,13 @@ public:
 
         InferenceEngine::Builder::DeconvolutionLayer ieLayer(name);
 
-        ieLayer.setKernel({kernel.height, kernel.width});
-        ieLayer.setStrides({stride.height, stride.width});
-        ieLayer.setDilation({dilation.height, dilation.width});
-        ieLayer.setPaddingsBegin({pad.height, pad.width});
-        ieLayer.setPaddingsEnd({pad.height, pad.width});
-        ieLayer.setGroup(group);
-        ieLayer.setOutDepth(numOutput);
+        ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
+        ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
+        ieLayer.setDilation({(size_t)dilation.height, (size_t)dilation.width});
+        ieLayer.setPaddingsBegin({(size_t)pad.height, (size_t)pad.width});
+        ieLayer.setPaddingsEnd({(size_t)pad.height, (size_t)pad.width});
+        ieLayer.setGroup((size_t)group);
+        ieLayer.setOutDepth((size_t)numOutput);
 
         ieLayer.setWeights(wrapToInfEngineBlob(blobs[0], InferenceEngine::Layout::OIHW));
         if (hasBias())

modules/dnn/src/layers/pooling_layer.cpp

@@ -261,10 +261,10 @@ public:
         if (type == MAX || type == AVE)
         {
             InferenceEngine::Builder::PoolingLayer ieLayer(name);
-            ieLayer.setKernel({kernel.height, kernel.width});
-            ieLayer.setStrides({stride.height, stride.width});
-            ieLayer.setPaddingsBegin({pad_t, pad_l});
-            ieLayer.setPaddingsEnd({pad_b, pad_r});
+            ieLayer.setKernel({(size_t)kernel.height, (size_t)kernel.width});
+            ieLayer.setStrides({(size_t)stride.height, (size_t)stride.width});
+            ieLayer.setPaddingsBegin({(size_t)pad_t, (size_t)pad_l});
+            ieLayer.setPaddingsEnd({(size_t)pad_b, (size_t)pad_r});
             ieLayer.setPoolingType(type == MAX ?
                                    InferenceEngine::Builder::PoolingLayer::PoolingType::MAX :
                                    InferenceEngine::Builder::PoolingLayer::PoolingType::AVG);

modules/dnn/src/op_inf_engine.cpp

@@ -82,7 +82,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
     CV_Assert(it != layers.end());
 
     const int layerId = it->second;
-    for (int i = 0; i < inpWrappers.size(); ++i)
+    for (size_t i = 0; i < inpWrappers.size(); ++i)
     {
         const auto& inp = inpWrappers[i];
         const std::string& inpName = inp->dataPtr->name;
@@ -103,7 +103,7 @@ void InfEngineBackendNet::connect(const std::vector<Ptr<BackendWrapper> >& input
         else
             inpId = it->second;
 
-        netBuilder.connect(inpId, {layerId, i});
+        netBuilder.connect((size_t)inpId, {(size_t)layerId, i});
         unconnectedLayersIds.erase(inpId);
     }
     CV_Assert(!outputs.empty());
@@ -119,7 +119,7 @@ void InfEngineBackendNet::init(int targetId)
         for (int id : unconnectedLayersIds)
         {
             InferenceEngine::Builder::OutputLayer outLayer("myconv1");
-            netBuilder.addLayer({id}, outLayer);
+            netBuilder.addLayer({InferenceEngine::PortInfo(id)}, outLayer);
         }
         cnn = InferenceEngine::CNNNetwork(InferenceEngine::Builder::convertToICNNNetwork(netBuilder.build()));
     }

modules/dnn/test/test_layers.cpp

@@ -923,8 +923,9 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
 {
     Target targetId = GetParam();
 
+    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
     Net netDefault = readNet(_tf("layer_convolution.caffemodel"), _tf("layer_convolution.prototxt"));
-    Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
+    Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".bin"));
 
     Mat inp = blobFromNPY(_tf("blob.npy"));
 
@@ -935,22 +936,15 @@ TEST_P(Layer_Test_Convolution_DLDT, Accuracy)
     net.setInput(inp);
     net.setPreferableTarget(targetId);
 
-    if (targetId != DNN_TARGET_MYRIAD)
-    {
-        Mat out = net.forward();
+    Mat out = net.forward();
 
-        normAssert(outDefault, out);
+    double l1 = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.4e-3 : 1e-5;
+    double lInf = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? 1.8e-2 : 1e-4;
+    normAssert(outDefault, out, "", l1, lInf);
 
-        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());
-    }
+    std::vector<int> outLayers = net.getUnconnectedOutLayers();
+    ASSERT_EQ(net.getLayer(outLayers[0])->name, "output");
+    ASSERT_EQ(net.getLayer(outLayers[0])->type, "Convolution");
 }
 
 TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
@@ -962,23 +956,16 @@ TEST_P(Layer_Test_Convolution_DLDT, setInput_uint8)
     randu(inputs[0], 0, 255);
     inputs[0].convertTo(inputs[1], CV_32F);
 
+    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
+
     Mat outs[2];
     for (int i = 0; i < 2; ++i)
     {
-        Net net = readNet(_tf("layer_convolution.xml"), _tf("layer_convolution.bin"));
+        Net net = readNet(_tf("layer_convolution" + suffix + ".xml"), _tf("layer_convolution" + suffix + ".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());
-        }
+        outs[i] = net.forward();
+        ASSERT_EQ(outs[i].type(), CV_32F);
     }
     if (targetId != DNN_TARGET_MYRIAD)
         normAssert(outs[0], outs[1]);
@@ -1020,7 +1007,8 @@ TEST_P(Test_DLDT_two_inputs_3dim, as_IR)
         throw SkipTestException("Test is enabled starts from OpenVINO 2018R4");
 #endif
 
-    Net net = readNet(_tf("net_two_inputs.xml"), _tf("net_two_inputs.bin"));
+    std::string suffix = (targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) ? "_fp16" : "";
+    Net net = readNet(_tf("net_two_inputs" + suffix + ".xml"), _tf("net_two_inputs.bin"));
     std::vector<int> inpSize = get<3>(GetParam());
     Mat firstInp(3, inpSize.data(), firstInpType);
     Mat secondInp(3, inpSize.data(), secondInpType);
@@ -1030,20 +1018,17 @@ TEST_P(Test_DLDT_two_inputs_3dim, 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());
-    }
+    double l1 = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
+                 (firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.06 : 0.0;
+    double lInf = ((targetId == DNN_TARGET_OPENCL_FP16 || targetId == DNN_TARGET_MYRIAD) &&
+                   (firstInpType == CV_32F || secondInpType == CV_32F)) ? 0.23 : 0.0;
+
+    Mat out = net.forward();
+
+    Mat ref;
+    cv::add(firstInp, secondInp, ref, Mat(), CV_32F);
+    normAssert(out, ref, "", l1, lInf);
 }
 
 std::vector< std::vector<int> > list_sizes{ {1, 2, 3}, {3, 2, 1}, {5, 5, 5}, {13, 7, 11} };