dnn(test): update InferenceEngine tests

diff --git a/modules/dnn/test/test_backends.cpp b/modules/dnn/test/test_backends.cpp
index 5426a11..19e0729 100644 (file)
--- a/modules/dnn/test/test_backends.cpp
+++ b/modules/dnn/test/test_backends.cpp
@@ -209,8 +209,16 @@ TEST_P(DNNTestNetwork, MobileNet_SSD_Caffe_Different_Width_Height)
 #if defined(INF_ENGINE_RELEASE)
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) &&
         target == DNN_TARGET_MYRIAD && getInferenceEngineVPUType() == CV_DNN_INFERENCE_ENGINE_VPU_TYPE_MYRIAD_X)
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X);
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD_X, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE exception: Ngraph operation Transpose with name conv15_2_mbox_conf_perm has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
 #endif
+
     Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f / 127.5, Size(300, 560), Scalar(127.5, 127.5, 127.5), false);
     float diffScores  = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.029 : 0.0;
@@ -280,12 +288,23 @@ TEST_P(DNNTestNetwork, SSD_VGG16)
                  CV_TEST_TAG_DEBUG_VERYLONG);
     if (backend == DNN_BACKEND_HALIDE && target == DNN_TARGET_CPU)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_HALIDE);  // TODO HALIDE_CPU
-    double scoreThreshold = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.0325 : 0.0;
-    const float lInf = (target == DNN_TARGET_MYRIAD) ? 0.032 : 0.0;
+
     Mat sample = imread(findDataFile("dnn/street.png"));
     Mat inp = blobFromImage(sample, 1.0f, Size(300, 300), Scalar(), false);
+
+    float scoreDiff = 0.0, iouDiff = 0.0;
+    if (target == DNN_TARGET_OPENCL_FP16)
+    {
+        scoreDiff = 0.04;
+    }
+    else if (target == DNN_TARGET_MYRIAD)
+    {
+        scoreDiff = 0.0325;
+        iouDiff = 0.032;
+    }
+
     processNet("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel",
-               "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreThreshold, lInf);
+               "dnn/ssd_vgg16.prototxt", inp, "detection_out", "", scoreDiff, iouDiff);
     expectNoFallbacksFromIE(net);
 }
 

diff --git a/modules/dnn/test/test_caffe_importer.cpp b/modules/dnn/test/test_caffe_importer.cpp
index 1f809f5..18b8d5a 100644 (file)
--- a/modules/dnn/test/test_caffe_importer.cpp
+++ b/modules/dnn/test/test_caffe_importer.cpp
@@ -489,10 +489,12 @@ TEST_P(Test_Caffe_nets, Colorization)
     {
         l1 = 0.5; lInf = 11;
     }
+#if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
     {
-        l1 = 0.26; lInf = 6.5;
+        l1 = 0.3; lInf = 10;
     }
+#endif
 
     normAssert(out, ref, "", l1, lInf);
     expectNoFallbacksFromIE(net);
@@ -682,6 +684,13 @@ TEST_P(Test_Caffe_nets, FasterRCNN_zf)
 #endif
         CV_TEST_TAG_DEBUG_LONG
     );
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE exception: Ngraph operation Reshape with name rpn_cls_score_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
          backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);
@@ -701,6 +710,11 @@ TEST_P(Test_Caffe_nets, RFCN)
         CV_TEST_TAG_LONG,
         CV_TEST_TAG_DEBUG_VERYLONG
     );
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
          backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_OPENCL_FP16)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16);

diff --git a/modules/dnn/test/test_common.hpp b/modules/dnn/test/test_common.hpp
index 5fc7447..02a676d 100644 (file)
--- a/modules/dnn/test/test_common.hpp
+++ b/modules/dnn/test/test_common.hpp
@@ -155,16 +155,19 @@ public:
 
     static void checkBackend(int backend, int target, Mat* inp = 0, Mat* ref = 0)
     {
+        CV_UNUSED(backend); CV_UNUSED(target); CV_UNUSED(inp); CV_UNUSED(ref);
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021000000)
         if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             && target == DNN_TARGET_MYRIAD)
         {
             if (inp && ref && inp->dims == 4 && ref->dims == 4 &&
                 inp->size[0] != 1 && inp->size[0] != ref->size[0])
             {
+                std::cout << "Inconsistent batch size of input and output blobs for Myriad plugin" << std::endl;
                 applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD);
-                throw SkipTestException("Inconsistent batch size of input and output blobs for Myriad plugin");
             }
         }
+#endif
     }
 
     void expectNoFallbacks(Net& net, bool raiseError = true)

diff --git a/modules/dnn/test/test_darknet_importer.cpp b/modules/dnn/test/test_darknet_importer.cpp
index ea70057..885bd6e 100644 (file)
--- a/modules/dnn/test/test_darknet_importer.cpp
+++ b/modules/dnn/test/test_darknet_importer.cpp
@@ -245,13 +245,13 @@ public:
                 nms_boxes.push_back(box);
                 nms_confidences.push_back(conf);
                 nms_classIds.push_back(class_id);
-#if 0  // use to update test reference data
-                std::cout << b << ", " << class_id << ", " << conf << "f, "
-                          << box.x << "f, " << box.y << "f, "
-                          << box.x + box.width << "f, " << box.y + box.height << "f,"
-                          << std::endl;
-#endif
-
+                if (cvtest::debugLevel > 0)
+                {
+                    std::cout << b << ", " << class_id << ", " << conf << "f, "
+                              << box.x << "f, " << box.y << "f, "
+                              << box.x + box.width << "f, " << box.y + box.height << "f,"
+                              << std::endl;
+                }
             }
 
             if (cvIsNaN(iouDiff))
@@ -347,10 +347,22 @@ TEST_P(Test_Darknet_nets, YoloVoc)
                                     1, 6,  0.667770f, 0.446555f, 0.453578f, 0.499986f, 0.519167f,  // a car
                                     1, 6,  0.844947f, 0.637058f, 0.460398f, 0.828508f, 0.66427f);  // a car
 
-    double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 1e-2 : 8e-5;
-    double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : 3e-4;
     double nmsThreshold = (target == DNN_TARGET_MYRIAD) ? 0.397 : 0.4;
 
+    double scoreDiff = 8e-5, iouDiff = 3e-4;
+    if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+    {
+        scoreDiff = 1e-2;
+        iouDiff = 0.018;
+    }
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+    {
+        iouDiff = std::numeric_limits<double>::quiet_NaN();
+    }
+#endif
+
     std::string config_file = "yolo-voc.cfg";
     std::string weights_file = "yolo-voc.weights";
 
@@ -363,6 +375,12 @@ TEST_P(Test_Darknet_nets, YoloVoc)
     SCOPED_TRACE("batch size 2");
     testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff, 0.24, nmsThreshold);
     }
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
 }
 
 TEST_P(Test_Darknet_nets, TinyYoloVoc)
@@ -584,6 +602,14 @@ TEST_P(Test_Darknet_nets, YOLOv4)
     std::string config_file = "yolov4.cfg";
     std::string weights_file = "yolov4.weights";
 
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy (batch 1)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+    {
+        iouDiff = std::numeric_limits<double>::quiet_NaN();
+    }
+#endif
 #if defined(INF_ENGINE_RELEASE)
     if ((backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ||
          backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) && target == DNN_TARGET_MYRIAD &&
@@ -602,6 +628,13 @@ TEST_P(Test_Darknet_nets, YOLOv4)
     {
         SCOPED_TRACE("batch size 2");
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy (batch 1)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+    {
+        iouDiff = 0.45f;
+    }
+#endif
 #if defined(INF_ENGINE_RELEASE)
         if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         {
@@ -617,6 +650,12 @@ TEST_P(Test_Darknet_nets, YOLOv4)
 
         testDarknetModel(config_file, weights_file, ref, scoreDiff, iouDiff);
     }
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
 }
 
 TEST_P(Test_Darknet_nets, YOLOv4_tiny)
@@ -685,6 +724,13 @@ TEST_P(Test_Darknet_nets, YOLOv4x_mish)
 {
     applyTestTag(CV_TEST_TAG_LONG, (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB));
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE exception: Ngraph operation Transpose with name permute_168 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
 #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)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_VERSION);

diff --git a/modules/dnn/test/test_halide_layers.cpp b/modules/dnn/test/test_halide_layers.cpp
index c9c0bc1..b24968b 100644 (file)
--- a/modules/dnn/test/test_halide_layers.cpp
+++ b/modules/dnn/test/test_halide_layers.cpp
@@ -39,12 +39,13 @@ static void test(Mat& input, Net& net, Backend backendId, Target targetId, bool
         l1 = default_l1;
     if (lInf == 0.0)
         lInf = default_lInf;
-#if 0
-    std::cout << "l1=" << l1 << "  lInf=" << lInf << std::endl;
-    std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl;
-    std::cout << outputHalide.reshape(1, outputDefault.total()).t() << std::endl;
-#endif
     normAssert(outputDefault, outputHalide, "", l1, lInf);
+    if (cvtest::debugLevel > 0 || testing::Test::HasFailure())
+    {
+        std::cout << "l1=" << l1 << "  lInf=" << lInf << std::endl;
+        std::cout << outputDefault.reshape(1, outputDefault.total()).t() << std::endl;
+        std::cout << outputHalide.reshape(1, outputDefault.total()).t() << std::endl;
+    }
 }
 
 static void test(LayerParams& params, Mat& input, Backend backendId, Target targetId, bool skipCheck = false, double l1 = 0.0, double lInf = 0.0)
@@ -795,6 +796,16 @@ TEST_P(Eltwise, Accuracy)
     Backend backendId = get<0>(get<4>(GetParam()));
     Target targetId = get<1>(get<4>(GetParam()));
 
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL &&
+        inSize == Vec3i(1, 4, 5) && op == "sum" && numConv == 1 && !weighted)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL &&
+        inSize == Vec3i(2, 8, 6) && op == "sum" && numConv == 1 && !weighted)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2018050000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD &&
         inSize == Vec3i(1, 4, 5))

diff --git a/modules/dnn/test/test_layers.cpp b/modules/dnn/test/test_layers.cpp
index fbe9605..836b0aa 100644 (file)
--- a/modules/dnn/test/test_layers.cpp
+++ b/modules/dnn/test/test_layers.cpp
@@ -373,7 +373,7 @@ TEST_P(Test_Caffe_layers, layer_prelu_fc)
     // Reference output values are in range [-0.0001, 10.3906]
     double l1 = (target == DNN_TARGET_MYRIAD) ? 0.005 : 0.0;
     double lInf = (target == DNN_TARGET_MYRIAD) ? 0.021 : 0.0;
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
     {
         l1 = 0.006f; lInf = 0.05f;
@@ -1416,6 +1416,14 @@ TEST_P(Test_DLDT_two_inputs, as_backend)
     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);
+    if (cvtest::debugLevel > 0 || HasFailure())
+    {
+        std::cout << "input1 scale=" << kScale << " input2 scale=" << kScaleInv << std::endl;
+        std::cout << "input1: " << firstInp.size << " " << firstInp.reshape(1, 1) << std::endl;
+        std::cout << "input2: " << secondInp.size << " " << secondInp.reshape(1, 1) << std::endl;
+        std::cout << "ref: " << ref.reshape(1, 1) << std::endl;
+        std::cout << "out: " << out.reshape(1, 1) << std::endl;
+    }
 }
 
 INSTANTIATE_TEST_CASE_P(/*nothing*/, Test_DLDT_two_inputs, Combine(

diff --git a/modules/dnn/test/test_onnx_importer.cpp b/modules/dnn/test/test_onnx_importer.cpp
index daba29e..0fd5d08 100644 (file)
--- a/modules/dnn/test/test_onnx_importer.cpp
+++ b/modules/dnn/test/test_onnx_importer.cpp
@@ -686,6 +686,14 @@ TEST_P(Test_ONNX_layers, Split_EltwiseMax)
 
 TEST_P(Test_ONNX_layers, LSTM_Activations)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE Exception: Ngraph operation Reshape with name Block1237_Output_0_before_reshape has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
+
     testONNXModels("lstm_cntk_tanh", pb, 0, 0, false, false);
 }
 
@@ -810,6 +818,13 @@ TEST_P(Test_ONNX_layers, Conv1d_variable_weight_bias)
 
 TEST_P(Test_ONNX_layers, GatherMultiOutput)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE Exception: Ngraph operation Reshape with name 6 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);  // exception
@@ -817,7 +832,7 @@ TEST_P(Test_ONNX_layers, GatherMultiOutput)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);  // exception
 #endif
 
-#if defined(INF_ENGINE_RELEASE)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LE(2021030000)
     if (target == DNN_TARGET_MYRIAD)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE);
 #endif
@@ -827,14 +842,25 @@ TEST_P(Test_ONNX_layers, GatherMultiOutput)
 
 TEST_P(Test_ONNX_layers, DynamicAxes)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // accuracy
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
+#if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
     {
         if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
     }
+#if INF_ENGINE_VER_MAJOR_LT(2021000000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
     {
         if (target == DNN_TARGET_MYRIAD) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
     }
+#endif
+#endif
     testONNXModels("squeeze_and_conv_dynamic_axes");
     testONNXModels("unsqueeze_and_conv_dynamic_axes");
     testONNXModels("gather_dynamic_axes");
@@ -914,6 +940,13 @@ TEST_P(Test_ONNX_layers, PoolConv1d)
 
 TEST_P(Test_ONNX_layers, ConvResizePool1d)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE Exception: Ngraph operation Reshape with name 15 has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
 #if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
     {
@@ -1116,8 +1149,12 @@ TEST_P(Test_ONNX_nets, TinyYolov2)
 #endif
 
     // output range: [-11; 8]
-    double l1 = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.017 : default_l1;
-    double lInf = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.14 : default_lInf;
+    double l1 =  default_l1, lInf = default_lInf;
+    if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+    {
+        l1 = 0.02;
+        lInf = 0.2;
+    }
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
     {
@@ -1202,10 +1239,10 @@ TEST_P(Test_ONNX_nets, Emotion_ferplus)
         l1 = 2.4e-4;
         lInf = 6e-4;
     }
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2020040000)
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
     {
-        l1 = 0.012f; lInf = 0.035f;
+        l1 = 0.013f; lInf = 0.035f;
     }
 #endif
 

diff --git a/modules/dnn/test/test_tf_importer.cpp b/modules/dnn/test/test_tf_importer.cpp
index b688c31..d02b713 100644 (file)
--- a/modules/dnn/test/test_tf_importer.cpp
+++ b/modules/dnn/test/test_tf_importer.cpp
@@ -83,6 +83,10 @@ public:
     void runTensorFlowNet(const std::string& prefix, bool hasText = false,
                           double l1 = 0.0, double lInf = 0.0, bool memoryLoad = false, const std::string& groupPrefix = "")
     {
+        if (cvtest::debugLevel > 0)
+        {
+            std::cout << prefix << groupPrefix << std::endl;
+        }
         std::string netPath = path(prefix + groupPrefix + "_net.pb");
         std::string netConfig = (hasText ? path(prefix + groupPrefix + "_net.pbtxt") : "");
         std::string inpPath = path(prefix + "_in.npy");
@@ -118,6 +122,16 @@ public:
         net.setInput(input);
         cv::Mat output = net.forward();
         normAssert(ref, output, "", l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
+
+        if (cvtest::debugLevel > 0 || HasFailure())
+        {
+            std::cout << "input: " << input.size << std::endl;
+            std::cout << input.reshape(1, 1) << std::endl;
+            std::cout << "ref " << ref.size << std::endl;
+            std::cout << ref.reshape(1, 1) << std::endl;
+            std::cout << "output: " << output.size << std::endl;
+            std::cout << output.reshape(1, 1) << std::endl;
+        }
     }
 };
 
@@ -132,7 +146,7 @@ TEST_P(Test_TensorFlow_layers, reduce_max)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER);
-    runTensorFlowNet("max_pool_by_axis");
+    runTensorFlowNet("max_pool_by_axis", false, 0.0f, 0.0f);
 }
 
 TEST_P(Test_TensorFlow_layers, reduce_sum)
@@ -144,7 +158,11 @@ TEST_P(Test_TensorFlow_layers, reduce_sum)
 
 TEST_P(Test_TensorFlow_layers, reduce_max_channel)
 {
-    runTensorFlowNet("reduce_max_channel");
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)  // incorrect result
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+    runTensorFlowNet("reduce_max_channel", false, 0.0f, 0.0f);
 }
 
 TEST_P(Test_TensorFlow_layers, reduce_sum_channel)
@@ -154,6 +172,10 @@ TEST_P(Test_TensorFlow_layers, reduce_sum_channel)
 
 TEST_P(Test_TensorFlow_layers, reduce_max_channel_keep_dims)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020040000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)  // incorrect result
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
     runTensorFlowNet("reduce_max_channel", false, 0.0, 0.0, false, "_keep_dims");
 }
 
@@ -220,13 +242,49 @@ TEST_P(Test_TensorFlow_layers, padding)
     runTensorFlowNet("keras_pad_concat");
 }
 
-TEST_P(Test_TensorFlow_layers, padding_asymmetric)
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_1)
 {
     runTensorFlowNet("conv2d_asymmetric_pads_nchw");
+}
+
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_2)
+{
     runTensorFlowNet("conv2d_asymmetric_pads_nhwc");
+}
+
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_3)
+{
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)  // Exception: Unsupported pad value
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)  // Exception: Unsupported pad value
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
     runTensorFlowNet("max_pool2d_asymmetric_pads_nchw");
+}
+
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_4)
+{
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_CPU)  // Exception: Unsupported pad value
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_CPU, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2020020000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)  // Exception: Unsupported pad value
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
     runTensorFlowNet("max_pool2d_asymmetric_pads_nhwc");
+}
+
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_5)
+{
     runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nchw");
+}
+
+TEST_P(Test_TensorFlow_layers, padding_asymmetric_6)
+{
     runTensorFlowNet("conv2d_backprop_input_asymmetric_pads_nhwc");
 }
 
@@ -267,6 +325,13 @@ TEST_P(Test_TensorFlow_layers, pad_and_concat)
 
 TEST_P(Test_TensorFlow_layers, concat_axis_1)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE Exception: Ngraph operation Transpose with name Flatten_1/flatten/Reshape/nhwc has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021030000)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);  // exception
@@ -413,19 +478,77 @@ TEST_P(Test_TensorFlow_layers, pooling_reduce_sum)
     runTensorFlowNet("reduce_sum");  // a SUM pooling over all spatial dimensions.
 }
 
-TEST_P(Test_TensorFlow_layers, pooling_reduce_sum2)
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_0_false)
 {
-    int axises[] = {0, 1, 2, 3};
-    for (int keepdims = 0; keepdims <= 1; ++keepdims)
+    runTensorFlowNet("reduce_sum_0_False");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_false)
+{
+    runTensorFlowNet("reduce_sum_1_False");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_2_false)
+{
+    runTensorFlowNet("reduce_sum_2_False");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_3_false)
+{
+    runTensorFlowNet("reduce_sum_3_False");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_2_false)
+{
+#if defined(INF_ENGINE_RELEASE)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
     {
-        for (int i = 0; i < sizeof(axises)/sizeof(axises[0]); ++i)
-        {
-            runTensorFlowNet(cv::format("reduce_sum_%d_%s", axises[i], (keepdims ? "True" : "False")));
-        }
-        runTensorFlowNet(cv::format("reduce_sum_1_2_%s", keepdims ? "True" : "False"));
+        default_l1 = 0.01f;
+    }
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+    {
+        default_l1 = 0.01f;
+    }
+#endif
+    runTensorFlowNet("reduce_sum_1_2_False");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_0_true)
+{
+    runTensorFlowNet("reduce_sum_0_True");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_true)
+{
+    runTensorFlowNet("reduce_sum_1_True");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_2_true)
+{
+    runTensorFlowNet("reduce_sum_2_True");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_3_true)
+{
+    runTensorFlowNet("reduce_sum_3_True");
+}
+
+TEST_P(Test_TensorFlow_layers, pooling_reduce_sum_1_2_true)
+{
+#if defined(INF_ENGINE_RELEASE)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
+    {
+        default_l1 = 0.01f;
+    }
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+    {
+        default_l1 = 0.01f;
     }
+#endif
+    runTensorFlowNet("reduce_sum_1_2_True");
 }
 
+
 TEST_P(Test_TensorFlow_layers, max_pool_grad)
 {
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019)
@@ -642,8 +765,13 @@ TEST_P(Test_TensorFlow_nets, MobileNet_SSD)
     net.setInput(inp);
     Mat out = net.forward();
 
-    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;
+    double scoreDiff = default_l1, iouDiff = default_lInf;
+    if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+    {
+        scoreDiff = 0.01;
+        iouDiff = 0.1;
+    }
+
     normAssertDetections(ref, out, "", 0.2, scoreDiff, iouDiff);
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_RELEASE >= 2019010000
     expectNoFallbacksFromIE(net);
@@ -720,16 +848,13 @@ TEST_P(Test_TensorFlow_nets, MobileNet_v1_SSD)
     expectNoFallbacksFromIE(net);
 }
 
-TEST_P(Test_TensorFlow_nets, Faster_RCNN)
+TEST_P(Test_TensorFlow_nets, Faster_RCNN_inception_v2_coco_2018_01_28)
 {
-    // FIXIT split test
     applyTestTag(
         (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
         CV_TEST_TAG_LONG,
         CV_TEST_TAG_DEBUG_VERYLONG
     );
-    static std::string names[] = {"faster_rcnn_inception_v2_coco_2018_01_28",
-                                  "faster_rcnn_resnet50_coco_2018_01_28"};
 
 #ifdef INF_ENGINE_RELEASE
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
@@ -740,21 +865,89 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
         backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 #endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
     // segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
     // Assertion `prior_height > 0' failed.
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+#endif
 
-    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
+    checkBackend();
+
+    double scoresDiff = 1e-5;
+    double iouDiff = 1e-4;
+
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 || backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
+    {
+        scoresDiff = 0.02;
+        iouDiff = 0.1;
+    }
+
+    std::string name = "faster_rcnn_inception_v2_coco_2018_01_28";
+    {
+        std::string proto = findDataFile("dnn/" + name + ".pbtxt");
+        std::string model = findDataFile("dnn/" + name + ".pb", false);
+
+        Net net = readNetFromTensorflow(model, proto);
+        net.setPreferableBackend(backend);
+        net.setPreferableTarget(target);
+        Mat img = imread(findDataFile("dnn/dog416.png"));
+        Mat blob = blobFromImage(img, 1.0f, Size(800, 600), Scalar(), true, false);
+
+        net.setInput(blob);
+        Mat out = net.forward();
+
+        Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + name + ".detection_out.npy"));
+
+        // accuracy (both OpenCV & IE)
+        if (target == DNN_TARGET_OPENCL_FP16)
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
+
+        normAssertDetections(ref, out, name.c_str(), 0.3, scoresDiff, iouDiff);
+    }
+}
+
+TEST_P(Test_TensorFlow_nets, Faster_RCNN_resnet50_coco_2018_01_28)
+{
+    applyTestTag(
+        (target == DNN_TARGET_CPU ? CV_TEST_TAG_MEMORY_1GB : CV_TEST_TAG_MEMORY_2GB),
+        CV_TEST_TAG_LONG,
+        CV_TEST_TAG_DEBUG_VERYLONG
+    );
+
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    // IE exception: Ngraph operation Transpose with name FirstStageBoxPredictor/ClassPredictor/reshape_1/nhwc has dynamic output shape on 0 port, but CPU plug-in supports only static shape
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+        applyTestTag(target == DNN_TARGET_OPENCL ? CV_TEST_TAG_DNN_SKIP_IE_OPENCL : CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16,
+            CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION
+        );
+#endif
+
+#ifdef INF_ENGINE_RELEASE
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 &&
+        (INF_ENGINE_VER_MAJOR_LT(2019020000) || target != DNN_TARGET_CPU))
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NN_BUILDER, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+
+    if (INF_ENGINE_VER_MAJOR_GT(2019030000) &&
+        backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_MYRIAD)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+#endif
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_LT(2021040000)
+    // segfault: inference-engine/thirdparty/clDNN/src/gpu/detection_output_cpu.cpp:111:
+    // Assertion `prior_height > 0' failed.
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+#endif
 
     checkBackend();
 
     double scoresDiff = backend == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 ? 2.9e-5 : 1e-5;
-    for (int i = 0; i < 2; ++i)
+    double iouDiff = 1e-4;
+
+    std::string name = "faster_rcnn_resnet50_coco_2018_01_28";
     {
-        std::string proto = findDataFile("dnn/" + names[i] + ".pbtxt");
-        std::string model = findDataFile("dnn/" + names[i] + ".pb", false);
+        std::string proto = findDataFile("dnn/" + name + ".pbtxt");
+        std::string model = findDataFile("dnn/" + name + ".pb", false);
 
         Net net = readNetFromTensorflow(model, proto);
         net.setPreferableBackend(backend);
@@ -765,8 +958,13 @@ TEST_P(Test_TensorFlow_nets, Faster_RCNN)
         net.setInput(blob);
         Mat out = net.forward();
 
-        Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + names[i] + ".detection_out.npy"));
-        normAssertDetections(ref, out, names[i].c_str(), 0.3, scoresDiff);
+        Mat ref = blobFromNPY(findDataFile("dnn/tensorflow/" + name + ".detection_out.npy"));
+
+        // accuracy
+        if (target == DNN_TARGET_OPENCL_FP16)
+            applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
+
+        normAssertDetections(ref, out, name.c_str(), 0.3, scoresDiff, iouDiff);
     }
 }
 
@@ -1152,6 +1350,10 @@ TEST_P(Test_TensorFlow_layers, resize_bilinear_down)
 
 TEST_P(Test_TensorFlow_layers, resize_concat_optimization)
 {
+#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2021040000)
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU)  // Exception: Function contains several inputs and outputs with one friendly name! (HETERO bug?)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
+#endif
     runTensorFlowNet("resize_concat_optimization");
 }
 
@@ -1271,7 +1473,7 @@ TEST_P(Test_TensorFlow_nets, Mask_RCNN)
     Mat outDetections = outs[0];
     Mat outMasks = outs[1];
 
-    double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.019 : 2e-5;
+    double scoreDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.2 : 2e-5;
     double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.018 : default_lInf;
     normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5, scoreDiff, iouDiff);
 
@@ -1305,7 +1507,7 @@ TEST_P(Test_TensorFlow_nets, Mask_RCNN)
 
     double inter = cv::countNonZero(masks & refMasks);
     double area = cv::countNonZero(masks | refMasks);
-    EXPECT_GE(inter / area, 0.99);
+    EXPECT_GE(inter / area, (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD) ? 0.98 : 0.99);
 
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         expectNoFallbacks(net);

diff --git a/modules/dnn/test/test_torch_importer.cpp b/modules/dnn/test/test_torch_importer.cpp
index 7316a06..96e8ac8 100644 (file)
--- a/modules/dnn/test/test_torch_importer.cpp
+++ b/modules/dnn/test/test_torch_importer.cpp
@@ -164,8 +164,12 @@ TEST_P(Test_Torch_layers, run_concat)
 
 TEST_P(Test_Torch_layers, run_depth_concat)
 {
-    runTorchNet("net_depth_concat", "", false, true, true, 0.0,
-                target == DNN_TARGET_OPENCL_FP16 ? 0.032 : 0.0);
+    double lInf = 0.0;
+    if (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD)
+    {
+        lInf = 0.032;
+    }
+    runTorchNet("net_depth_concat", "", false, true, true, 0.0, lInf);
 }
 
 TEST_P(Test_Torch_layers, run_deconv)