Enable some tests for Inference Engine 2019R1

modules/dnn/src/dnn.cpp

@@ -1160,12 +1160,6 @@ struct Net::Impl
                 continue;
 
             currLayer->unsetAttached();
-
-            Ptr<PoolingLayer> poolingLayer = currLayer.dynamicCast<PoolingLayer>();
-            if( !poolingLayer.empty() )
-            {
-                poolingLayer->computeMaxIdx = true;
-            }
         }
 
         layersTimings.clear();
@@ -2082,30 +2076,11 @@ struct Net::Impl
                     }
                 }
             }
-            // the optimization #2. if there is no layer that takes max pooling layer's computed
-            // max indices (and only some semantical segmentation networks might need this;
-            // many others only take the maximum values), then we switch the max pooling
-            // layer to the faster operating mode.
-            Ptr<PoolingLayer> poolingLayer = ld.layerInstance.dynamicCast<PoolingLayer>();
-            if( !poolingLayer.empty() && !ld.consumers.empty() )
-            {
-                size_t i = 0, nconsumers = ld.consumers.size();
-                for( ; i < nconsumers; i++ )
-                    if( ld.consumers[i].oid > 0 )
-                        break;
-                // if there is no layer that takes the second output pin of the pooling layer
-                // on input then we don't need to compute the indices
-                if( i >= nconsumers )
-                {
-                    poolingLayer->computeMaxIdx = false;
-                    printf_(("\tsimplified pooling layer %s\n", poolingLayer->name.c_str()));
-                }
-            }
 
             if (preferableBackend != DNN_BACKEND_OPENCV)
                 continue;  // Go to the next layer.
 
-            // the optimization #3. if there is concat layer that concatenates channels
+            // the optimization #2. if there is concat layer that concatenates channels
             // from the inputs together (i.e. axis == 1) then we make the inputs of
             // the concat layer to write to the concatenation output buffer
             // (and so we eliminate the concatenation layer, because the channels

modules/dnn/src/layers/elementwise_layers.cpp

@@ -256,8 +256,11 @@ struct ReLUFunctor
 
     bool supportBackend(int backendId, int)
     {
-        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE;
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+            return slope >= 0 || !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
+#endif
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
     }
 
     void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const
@@ -741,8 +744,11 @@ struct AbsValFunctor
 
     bool supportBackend(int backendId, int)
     {
-        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE ||
-               backendId == DNN_BACKEND_INFERENCE_ENGINE;
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+            return !INF_ENGINE_VER_MAJOR_EQ(INF_ENGINE_RELEASE_2019R1);
+#endif
+        return backendId == DNN_BACKEND_OPENCV || backendId == DNN_BACKEND_HALIDE;
     }
 
     void apply(const float* srcptr, float* dstptr, int len, size_t planeSize, int cn0, int cn1) const

modules/dnn/src/layers/flatten_layer.cpp

@@ -159,8 +159,8 @@ public:
         InferenceEngine::Builder::Layer ieLayer(name);
         ieLayer.setName(name);
         ieLayer.setType("Flatten");
-        ieLayer.getParameters()["axis"] = _startAxis;
-        ieLayer.getParameters()["end_axis"] = _endAxis;
+        ieLayer.getParameters()["axis"] = (size_t)_startAxis;
+        ieLayer.getParameters()["end_axis"] = _endAxis;  // Do not cast to size_t because it might be negative.
         ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
         ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
         return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));

modules/dnn/src/layers/padding_layer.cpp

@@ -12,6 +12,7 @@ Implementation of padding layer, which adds paddings to input blob.
 #include "../precomp.hpp"
 #include "layers_common.hpp"
 #include "../op_halide.hpp"
+#include "../op_inf_engine.hpp"
 #include <vector>
 
 namespace cv
@@ -68,28 +69,36 @@ public:
 
         // Compute dstRanges.
         const MatSize& inpShape = inputs[0].size;
-        dstRanges.resize(paddings.size());
 
-        int offset = 0;
         if (inputDims != -1 && inputs[0].dims != inputDims)
         {
-            dstRanges.insert(dstRanges.begin(), Range::all());
-            offset = 1;
+            paddings.insert(paddings.begin(), std::make_pair(0, 0));
         }
 
+        dstRanges.resize(paddings.size());
         for (int i = 0; i < paddings.size(); ++i)
         {
-            dstRanges[offset + i].start = paddings[i].first;
-            dstRanges[offset + i].end = paddings[i].first + inpShape[offset + i];
+            dstRanges[i].start = paddings[i].first;
+            dstRanges[i].end = paddings[i].first + inpShape[i];
         }
 
         // Add the rest of dimensions.
         for (int i = dstRanges.size(); i < inputs[0].dims; ++i)
+        {
             dstRanges.push_back(Range::all());
+            paddings.push_back(std::make_pair(0, 0));
+        }
+        inputDims = -1;  // Next time paddings are filled for all the dimensions.
     }
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_INF_ENGINE
+        if (backendId == DNN_BACKEND_INFERENCE_ENGINE)
+            return INF_ENGINE_VER_MAJOR_GE(INF_ENGINE_RELEASE_2019R1) &&
+                   (preferableTarget != DNN_TARGET_MYRIAD ||
+                    (dstRanges.size() == 4 && paddings[0].first == 0 && paddings[0].second == 0));
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && dstRanges.size() == 4);
     }
@@ -109,7 +118,7 @@ public:
             {
                 std::vector<float> paddingValue_fp32(1, paddingValue);
                 std::vector<int16_t> paddingValue_fp16(1);
-                convertFp16(paddingValue_fp32, paddingValue_fp16);
+                cv::convertFp16(paddingValue_fp32, paddingValue_fp16);
                 outputs[0].setTo(paddingValue_fp16[0]);
             }
             else
@@ -173,6 +182,32 @@ public:
         return Ptr<BackendNode>();
     }
 
+    virtual Ptr<BackendNode> initInfEngine(const std::vector<Ptr<BackendWrapper> >&) CV_OVERRIDE
+    {
+#ifdef HAVE_INF_ENGINE
+        InferenceEngine::Builder::Layer ieLayer(name);
+        ieLayer.setName(name);
+        ieLayer.setType("Pad");
+
+        std::vector<int> begins(paddings.size(), 0), ends(paddings.size(), 0);
+        for (int i = 0; i < paddings.size(); ++i)
+        {
+            begins[i] = paddings[i].first;
+            ends[i] = paddings[i].second;
+        }
+        ieLayer.getParameters()["pads_begin"] = begins;
+        ieLayer.getParameters()["pads_end"] = ends;
+        ieLayer.getParameters()["pad_mode"] = paddingType;
+        if (paddingType == "constant")
+            ieLayer.getParameters()["pad_value"] = paddingValue;
+
+        ieLayer.setInputPorts(std::vector<InferenceEngine::Port>(1));
+        ieLayer.setOutputPorts(std::vector<InferenceEngine::Port>(1));
+        return Ptr<BackendNode>(new InfEngineBackendNode(ieLayer));
+#endif
+        return Ptr<BackendNode>();
+    }
+
 private:
     std::vector<std::pair<int, int> > paddings;  // Pairs pad before, pad after.
     std::vector<Range> dstRanges;

modules/dnn/src/layers/pooling_layer.cpp

@@ -140,7 +140,7 @@ public:
 #ifdef HAVE_OPENCL
         poolOp.release();
 #endif
-        computeMaxIdx = type == MAX;
+        computeMaxIdx = type == MAX && outputs.size() == 2;
     }
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE

modules/dnn/test/test_backends.cpp

@@ -289,12 +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_GE(2019010000)
-    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
-            && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
-    )
-        throw SkipTestException("Test is disabled for MyriadX target");
-#else
+#elif INF_ENGINE_VER_MAJOR_EQ(2018030000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test has been fixed in OpenVINO 2018R4");
 #endif

modules/dnn/test/test_halide_layers.cpp

@@ -561,12 +561,6 @@ 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_GE(2019010000)
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE
-            && negativeSlope < 0
-    )
-        throw SkipTestException("Test is disabled");
-#endif
 
     LayerParams lp;
     lp.set("negative_slope", negativeSlope);
@@ -589,13 +583,6 @@ TEST_P(NoParamActivation, Accuracy)
     LayerParams lp;
     lp.type = get<0>(GetParam());
     lp.name = "testLayer";
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE
-            && lp.type == "AbsVal"
-    )
-        throw SkipTestException("Test is disabled");
-#endif
-
     testInPlaceActivation(lp, backendId, targetId);
 }
 INSTANTIATE_TEST_CASE_P(Layer_Test_Halide, NoParamActivation, Combine(

modules/dnn/test/test_onnx_importer.cpp

@@ -379,7 +379,7 @@ TEST_P(Test_ONNX_nets, LResNet100E_IR)
         lInf = 0.035;
     }
     else if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_CPU) {
-        l1 = 4.5e-5;
+        l1 = 4.6e-5;
         lInf = 1.9e-4;
     }
     testONNXModels("LResNet100E_IR", pb, l1, lInf);

modules/dnn/test/test_tf_importer.cpp

@@ -140,10 +140,6 @@ TEST_P(Test_TensorFlow_layers, padding)
 
 TEST_P(Test_TensorFlow_layers, padding_same)
 {
-#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
 #if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
@@ -251,10 +247,6 @@ TEST_P(Test_TensorFlow_layers, reshape)
 
 TEST_P(Test_TensorFlow_layers, flatten)
 {
-#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
 #if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_2
@@ -267,11 +259,6 @@ TEST_P(Test_TensorFlow_layers, flatten)
 
 TEST_P(Test_TensorFlow_layers, unfused_flatten)
 {
-#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
-
     runTensorFlowNet("unfused_flatten");
     runTensorFlowNet("unfused_flatten_unknown_batch");
 }
@@ -320,11 +307,14 @@ class Test_TensorFlow_nets : public DNNTestLayer {};
 
 TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
 {
-    checkBackend();
-    if ((backend == DNN_BACKEND_INFERENCE_ENGINE && target != DNN_TARGET_CPU) ||
-        (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
-        throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
+            && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X
+    )
+        throw SkipTestException("Test is disabled for MyriadX");
+#endif
 
+    checkBackend();
     std::string netPath = findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false);
     std::string netConfig = findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false);
     std::string imgPath = findDataFile("dnn/street.png", false);
@@ -333,30 +323,18 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
     resize(imread(imgPath), inp, Size(300, 300));
     inp = blobFromImage(inp, 1.0f / 127.5, Size(), Scalar(127.5, 127.5, 127.5), true);
 
-    std::vector<String> outNames(3);
-    outNames[0] = "concat";
-    outNames[1] = "concat_1";
-    outNames[2] = "detection_out";
-
-    std::vector<Mat> refs(outNames.size());
-    for (int i = 0; i < outNames.size(); ++i)
-    {
-        std::string path = findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco." + outNames[i] + ".npy", false);
-        refs[i] = blobFromNPY(path);
-    }
+    Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/ssd_mobilenet_v1_coco.detection_out.npy", false));
 
     Net net = readNetFromTensorflow(netPath, netConfig);
     net.setPreferableBackend(backend);
     net.setPreferableTarget(target);
 
     net.setInput(inp);
+    Mat out = net.forward();
 
-    std::vector<Mat> output;
-    net.forward(output, outNames);
-
-    normAssert(refs[0].reshape(1, 1), output[0].reshape(1, 1), "", 1e-5, 1.5e-4);
-    normAssert(refs[1].reshape(1, 1), output[1].reshape(1, 1), "", 1e-5, 3e-4);
-    normAssertDetections(refs[2], output[2], "", 0.2);
+    double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0043 : default_l1;
+    double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.037 : default_lInf;
+    normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
 }
 
 TEST_P(Test_TensorFlow_nets, Inception_v2_SSD)
@@ -597,10 +575,6 @@ TEST_P(Test_TensorFlow_layers, fp16_weights)
 
 TEST_P(Test_TensorFlow_layers, fp16_padding_same)
 {
-#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
 #if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE && target == DNN_TARGET_MYRIAD
             && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X