Merge remote-tracking branch 'upstream/3.4' into merge-3.4

modules/dnn/perf/perf_net.cpp

@@ -111,6 +111,10 @@ PERF_TEST_P_(DNNTestNetwork, ENet)
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||
         (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16))
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        throw SkipTestException("");
+#endif
     processNet("dnn/Enet-model-best.net", "", "enet.yml",
             Mat(cv::Size(512, 256), CV_32FC3));
 }
@@ -202,6 +206,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv3)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
         throw SkipTestException("Test is disabled in OpenVINO 2020.4");
 #endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
 
     Mat sample = imread(findDataFile("dnn/dog416.png"));
     cvtColor(sample, sample, COLOR_BGR2RGB);
@@ -214,7 +222,7 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
-    if (target == DNN_TARGET_MYRIAD)
+    if (target == DNN_TARGET_MYRIAD)  // not enough resources
         throw SkipTestException("");
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)  // nGraph compilation failure
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
@@ -233,6 +241,10 @@ PERF_TEST_P_(DNNTestNetwork, YOLOv4_tiny)
 {
     if (backend == DNN_BACKEND_HALIDE)
         throw SkipTestException("");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("");
+#endif
     Mat sample = imread(findDataFile("dnn/dog416.png"));
     cvtColor(sample, sample, COLOR_BGR2RGB);
     Mat inp;
@@ -263,6 +275,10 @@ PERF_TEST_P_(DNNTestNetwork, Inception_v2_Faster_RCNN)
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2019020000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         throw SkipTestException("Test is disabled in OpenVINO 2019R2");
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
+        throw SkipTestException("Test is disabled in OpenVINO 2021.1 / MYRIAD");
 #endif
     if (backend == DNN_BACKEND_HALIDE ||
         (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && target != DNN_TARGET_CPU) ||

modules/dnn/src/dnn.cpp

@@ -2654,12 +2654,15 @@ struct Net::Impl : public detail::NetImplBase
 
                 // OpenCL: fuse convolution layer followed by eltwise + relu
                 // CUDA: fuse convolution layer followed by eltwise (and optional activation)
-                if ((IS_DNN_OPENCL_TARGET(preferableTarget) || IS_DNN_CUDA_TARGET(preferableTarget)) &&
-                    ld.layerInstance->type == "Convolution" )
+                while (nextData &&
+                    (IS_DNN_OPENCL_TARGET(preferableTarget) || IS_DNN_CUDA_TARGET(preferableTarget)) &&
+                    ld.layerInstance->type == "Convolution"
+                )  // semantic of 'if'
                 {
-                    Ptr<EltwiseLayer> nextEltwiseLayer;
-                    if( nextData )
-                        nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
+                    Ptr<EltwiseLayer> nextEltwiseLayer = nextData->layerInstance.dynamicCast<EltwiseLayer>();
+                    if (nextEltwiseLayer.empty())
+                        break;
+
 #ifdef HAVE_CUDA
                     // CUDA backend supports fusion with eltwise sum (without variable channels)
                     // `nextEltwiseLayer` is reset if eltwise layer doesn't have a compatible configuration for fusion
@@ -2675,7 +2678,37 @@ struct Net::Impl : public detail::NetImplBase
                             nextEltwiseLayer = Ptr<EltwiseLayer>();
                     }
 #endif
-                    if (!nextEltwiseLayer.empty() && nextData && nextData->inputBlobsId.size() == 2)
+
+                    if (pinsToKeep.count(lpNext) != 0)
+                        break;
+                    if (nextData->inputBlobsId.size() != 2)
+                        break;
+
+                    if (!nextData->params.has("operation") || toLowerCase(nextData->params.get<String>("operation")) == "sum")
+                    {
+                        if (nextData->params.has("coeff"))
+                        {
+                            DictValue paramCoeff = nextData->params.get("coeff");
+                            int n = paramCoeff.size();
+                            bool isCoeffOneOne = (n == 2);
+                            for (int i = 0; isCoeffOneOne && i < n; i++)
+                            {
+                                float c = paramCoeff.get<float>(i);
+                                isCoeffOneOne &= (c == 1.0f);
+                            }
+                            if (!isCoeffOneOne)
+                            {
+                                CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion of 'Sum' without coeffs (or {1.0, 1.0}) is supported only");
+                                break;
+                            }
+                        }
+                    }
+                    else
+                    {
+                        CV_LOG_DEBUG(NULL, "DNN/OpenCL: fusion with eltwise operation is not supported: " << nextData->params.get<String>("operation"));
+                        break;
+                    }
+
                     {
                         LayerData *eltwiseData = nextData;
 
@@ -2732,11 +2765,13 @@ struct Net::Impl : public detail::NetImplBase
                                 // we need to check them separately; hence, the fuse variables
                                 bool fuse_eltwise = false, fuse_activation = false;
 
+                                Ptr<PowerLayer> activ_power;
                                 if (IS_DNN_OPENCL_TARGET(preferableTarget) && !nextFusabeleActivLayer.empty() &&
                                     nextData &&
                                     (!nextData->type.compare("ReLU") ||
                                      !nextData->type.compare("ChannelsPReLU") ||
-                                     !nextData->type.compare("Power")) &&
+                                     (!nextData->type.compare("Power") && (activ_power = nextFusabeleActivLayer.dynamicCast<PowerLayer>()) && activ_power->scale == 1.0f)
+                                    ) &&
                                     currLayer->setActivation(nextFusabeleActivLayer))
                                 {
                                     fuse_eltwise = true;
@@ -2868,6 +2903,8 @@ struct Net::Impl : public detail::NetImplBase
                             }
                         }
                     }
+
+                    break;
                 }
             }
 
@@ -3107,11 +3144,11 @@ struct Net::Impl : public detail::NetImplBase
 
         Ptr<Layer> layer = ld.layerInstance;
 
-        TickMeter tm;
-        tm.start();
-
         if( !ld.skip )
         {
+            TickMeter tm;
+            tm.start();
+
             std::map<int, Ptr<BackendNode> >::iterator it = ld.backendNodes.find(preferableBackend);
             if (preferableBackend == DNN_BACKEND_OPENCV || it == ld.backendNodes.end() || it->second.empty())
             {
@@ -3320,12 +3357,15 @@ struct Net::Impl : public detail::NetImplBase
                     CV_Error(Error::StsNotImplemented, "Unknown backend identifier");
                 }
             }
+
+            tm.stop();
+            int64 t = tm.getTimeTicks();
+            layersTimings[ld.id] = (t > 0) ? t : t + 1;  // zero for skipped layers only
         }
         else
-            tm.reset();
-
-        tm.stop();
-        layersTimings[ld.id] = tm.getTimeTicks();
+        {
+            layersTimings[ld.id] = 0;
+        }
 
         ld.flag = 1;
     }

modules/dnn/src/layers/convolution_layer.cpp

@@ -48,6 +48,8 @@
 #include "../ie_ngraph.hpp"
 #include "../op_vkcom.hpp"
 
+#include <opencv2/core/utils/logger.hpp>
+
 #include "opencv2/core/hal/hal.hpp"
 #include "opencv2/core/hal/intrin.hpp"
 #include <iostream>
@@ -436,6 +438,14 @@ public:
             Ptr<PowerLayer> activ_power = activ.dynamicCast<PowerLayer>();
             if (!activ_power.empty())
             {
+                if (activ_power->scale != 1.0f)  // not supported well by implementation, #17964
+                {
+                    // FIXIT no way to check number of blobs (like, eltwise input)
+                    CV_LOG_DEBUG(NULL, "DNN/OpenCL: can't configure Power activation (scale != 1.0f)");
+                    activ.release();
+                    newActiv = false;
+                    return false;
+                }
                 if (activ_power->scale != 1.f || activ_power->shift != 0.f)
                 {
                     const int outCh = blobs[0].size[0];

modules/dnn/test/test_common.impl.hpp

@@ -67,10 +67,10 @@ void normAssert(
         double l1 /*= 0.00001*/, double lInf /*= 0.0001*/)
 {
     double normL1 = cvtest::norm(ref, test, cv::NORM_L1) / ref.getMat().total();
-    EXPECT_LE(normL1, l1) << comment;
+    EXPECT_LE(normL1, l1) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 
     double normInf = cvtest::norm(ref, test, cv::NORM_INF);
-    EXPECT_LE(normInf, lInf) << comment;
+    EXPECT_LE(normInf, lInf) << comment << "  |ref| = " << cvtest::norm(ref, cv::NORM_INF);
 }
 
 std::vector<cv::Rect2d> matToBoxes(const cv::Mat& m)

modules/dnn/test/test_darknet_importer.cpp

@@ -656,6 +656,11 @@ TEST_P(Test_Darknet_nets, YOLOv4_tiny)
         target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_512MB : CV_TEST_TAG_MEMORY_1GB
     );
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021010000)  // nGraph compilation failure
+    if (target == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+
     const double confThreshold = 0.5;
     // batchId, classId, confidence, left, top, right, bottom
     const int N0 = 2;

modules/dnn/test/test_layers.cpp

@@ -2264,10 +2264,6 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
     Backend backendId = get<0>(get<2>(GetParam()));
     Target targetId = get<1>(get<2>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayerToPrev(activationParams.name, activationParams.type, activationParams);
@@ -2280,7 +2276,7 @@ TEST_P(ConvolutionActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // all activations are fused
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId);
         }
     }
@@ -2390,21 +2386,6 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17945
-    if ((eltwiseOp != "sum" || weightedEltwise) && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
-    // bug: https://github.com/opencv/opencv/issues/17953
-    if (eltwiseOp == "sum" && actType == "ChannelsPReLU" && bias_term == false &&
-        backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-    {
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-    }
-
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int eltwiseId = net.addLayer(eltwiseParams.name, eltwiseParams.type, eltwiseParams);
@@ -2421,7 +2402,9 @@ TEST_P(ConvolutionEltwiseActivationFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation is fused with eltwise layer
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "Power")
+            if (eltwiseOp == "sum" && !weightedEltwise &&
+                (actType == "ReLU" || actType == "ChannelsPReLU" /*|| actType == "Power"*/)
+            )
             {
                 expectedFusedLayers.push_back(eltwiseId);
                 expectedFusedLayers.push_back(activId);
@@ -2483,17 +2466,6 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
-    // bug: https://github.com/opencv/opencv/issues/17964
-    if (actType == "Power" && backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-
-    // bug: https://github.com/opencv/opencv/issues/17953
-    if (actType == "ChannelsPReLU" && bias_term == false &&
-        backendId == DNN_BACKEND_OPENCV && (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16))
-    {
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
-    }
-
     Net net;
     int convId = net.addLayer(convParams.name, convParams.type, convParams);
     int activId = net.addLayer(activationParams.name, activationParams.type, activationParams);
@@ -2510,7 +2482,7 @@ TEST_P(ConvolutionActivationEltwiseFusion, Accuracy)
             expectedFusedLayers.push_back(activId); // activation fused with convolution
         else if (targetId == DNN_TARGET_OPENCL || targetId == DNN_TARGET_OPENCL_FP16)
         {
-            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" || actType == "Power")
+            if (actType == "ReLU" || actType == "ChannelsPReLU" || actType == "ReLU6" || actType == "TanH" /*|| actType == "Power"*/)
                 expectedFusedLayers.push_back(activId); // activation fused with convolution
         }
     }

modules/dnn/test/test_torch_importer.cpp

@@ -409,6 +409,10 @@ TEST_P(Test_Torch_nets, ENet_accuracy)
         if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
         throw SkipTestException("");
     }
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2021010000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 #endif
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
     {