Merge pull request #24039 from dkurt:tflite_test_backends

TFLite models on different backends (tests and improvements) #24039

### Pull Request Readiness Checklist

* MaxUnpooling with OpenVINO
* Fully connected with transposed inputs/weights with OpenVINO
* Enable backends tests for TFLite (related to https://github.com/opencv/opencv/issues/23992#issuecomment-1640691722)
* Increase existing tests thresholds

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [x] The PR is proposed to the proper branch
- [x] There is a reference to the original bug report and related work
- [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [x] The feature is well documented and sample code can be built with the project CMake

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -180,15 +180,12 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         bool tranAorB = transA || transB;
-#ifdef HAVE_INF_ENGINE
-        if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
-            return axis == 1 && !tranAorB;
-#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && axis == 1 && !tranAorB) ||
                (backendId == DNN_BACKEND_WEBNN && axis == 1 && !tranAorB) ||
                backendId == DNN_BACKEND_CANN ||
+               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
                (backendId == DNN_BACKEND_VKCOM && haveVulkan() && !tranAorB);
     }
 
@@ -802,17 +799,26 @@ public:
         if (nodes.size() == 2)
         {
             auto& inp2 = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
-            matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, false, false);
+            matmul = std::make_shared<ngraph::op::MatMul>(ieInpNode, inp2, transA, transB);
         }
         else
         {
-            std::vector<int64_t> data = {(int64_t)ieInpNode->get_shape()[0], (int64_t)blobs[0].size[1]};
-            auto new_shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i64, ngraph::Shape{2}, data.data());
-            auto inp = std::make_shared<ngraph::op::v1::Reshape>(ieInpNode, new_shape, true);
+            std::vector<int> shape(1 + normalize_axis(axis, ieInpNode->get_shape().size()), 0);
+            shape[shape.size() - 1] = -1;
+            auto inp = std::make_shared<ngraph::op::v1::Reshape>(
+                ieInpNode,
+                std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{shape.size()}, shape.data()),
+                true
+            );
 
-            std::vector<size_t> weight_shape{(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
+            std::vector<size_t> weight_shape;
+            if (isMatMul) {
+                weight_shape = getShape<size_t>(oriMat);
+            } else {
+                weight_shape = {(size_t)blobs[0].size[0], (size_t)blobs[0].size[1]};
+            }
             auto ieWeights = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, weight_shape, blobs[0].data);
-            matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, false, true);
+            matmul = std::make_shared<ngraph::op::MatMul>(inp, ieWeights, transA, transB);
         }
 
         if (bias) {

modules/dnn/src/layers/max_unpooling_layer.cpp

@@ -13,6 +13,7 @@ Implementation of Batch Normalization layer.
 #include "layers_common.hpp"
 #include "../op_cuda.hpp"
 #include "../op_halide.hpp"
+#include "../ie_ngraph.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
 #include <opencv2/core/utils/logger.hpp>
 
@@ -41,6 +42,7 @@ public:
     {
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
+               backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH ||
                (backendId == DNN_BACKEND_HALIDE && haveHalide() && !poolPad.width && !poolPad.height);
     }
 
@@ -181,6 +183,50 @@ public:
 #endif  // HAVE_HALIDE
         return Ptr<BackendNode>();
     }
+
+#ifdef HAVE_DNN_NGRAPH
+    virtual Ptr<BackendNode> initNgraph(const std::vector<Ptr<BackendWrapper> >& inputs,
+                                        const std::vector<Ptr<BackendNode> >& nodes) CV_OVERRIDE
+    {
+        auto features = nodes[0].dynamicCast<InfEngineNgraphNode>()->node;
+        auto indices = nodes[1].dynamicCast<InfEngineNgraphNode>()->node;
+
+        std::vector<MatShape> inpShapes(nodes.size());
+        std::vector<MatShape> outShapes, internals;
+        for (int i = 0; i < nodes.size(); ++i) {
+            std::vector<size_t> shape = nodes[i].dynamicCast<InfEngineNgraphNode>()->node->get_shape();
+            inpShapes[i] = std::vector<int>(shape.begin(), shape.end());
+        }
+        getMemoryShapes(inpShapes, 1, outShapes, internals);
+
+        Mat zeros = Mat::zeros(1, total(outShapes[0]), CV_32F);
+        auto zeroInp = std::make_shared<ngraph::op::Constant>(ngraph::element::f32, ngraph::Shape{zeros.total()}, zeros.data);
+
+        int newShape = -1;
+        features = std::make_shared<ngraph::op::v1::Reshape>(
+            features,
+            std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
+            true
+        );
+        indices = std::make_shared<ngraph::op::v1::Reshape>(
+            indices,
+            std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &newShape),
+            true
+        );
+        if (indices->get_element_type() != ngraph::element::i32 && indices->get_element_type() != ngraph::element::i64) {
+            indices = std::make_shared<ngraph::op::Convert>(indices, ngraph::element::i64);
+        }
+
+        int axis = 0;
+        std::shared_ptr<ngraph::Node> unpool = std::make_shared<ngraph::op::ScatterElementsUpdate>(zeroInp, indices, features,
+            std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{1}, &axis));
+
+        auto shape = std::make_shared<ngraph::op::Constant>(ngraph::element::i32, ngraph::Shape{outShapes[0].size()}, outShapes[0].data());
+        unpool = std::make_shared<ngraph::op::v1::Reshape>(unpool, shape, true);
+
+        return Ptr<BackendNode>(new InfEngineNgraphNode(unpool));
+    }
+#endif  // HAVE_DNN_NGRAPH
 };
 
 Ptr<MaxUnpoolLayer> MaxUnpoolLayer::create(const LayerParams& params)

modules/dnn/src/layers/pooling_layer.cpp

@@ -209,7 +209,7 @@ public:
 #ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
         {
-            return !computeMaxIdx && type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
+            return type != STOCHASTIC && kernel_size.size() > 1 && (kernel_size.size() != 3 || !isArmComputePlugin());
         }
 #endif
         if (backendId == DNN_BACKEND_OPENCV)
@@ -613,9 +613,17 @@ public:
             return Ptr<BackendNode>(new InfEngineNgraphNode(reduce_sum));
         }
         else if (type == MAX) {
-            auto max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
+            std::shared_ptr<ngraph::Node> max_pool;
+            if (computeMaxIdx) {
+                std::vector<size_t> dilations(kernel_size.size(), 1);
+                max_pool = std::make_shared<ngraph::op::v8::MaxPool>(ieInpNode, ngraph::Strides(strides), ngraph::Strides(dilations),
                                 ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
                                 rounding_type, pad_type);
+            } else {
+                max_pool = std::make_shared<ngraph::op::v1::MaxPool>(ieInpNode, ngraph::Strides(strides),
+                                ngraph::Shape(pads_begin), ngraph::Shape(pads_end), ngraph::Shape(kernel_size),
+                                rounding_type, pad_type);
+            }
             return Ptr<BackendNode>(new InfEngineNgraphNode(max_pool));
         }
         else if (type == ROI) {

modules/dnn/src/layers/resize_layer.cpp

@@ -410,7 +410,10 @@ public:
         }
         attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES;
 
-        if (alignCorners) {
+        CV_Assert(!halfPixelCenters || !alignCorners);
+        if (halfPixelCenters) {
+            attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+        } else if (alignCorners) {
             attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::ALIGN_CORNERS;
         }
 
@@ -427,7 +430,10 @@ public:
         }
         attrs.shape_calculation_mode = ngraph::op::v4::Interpolate::ShapeCalcMode::sizes;
 
-        if (alignCorners) {
+        CV_Assert(!halfPixelCenters || !alignCorners);
+        if (halfPixelCenters) {
+            attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::half_pixel;
+        } else if (alignCorners) {
             attrs.coordinate_transformation_mode = ngraph::op::v4::Interpolate::CoordinateTransformMode::align_corners;
         }
 

modules/dnn/src/net_openvino.cpp

@@ -476,13 +476,14 @@ void NetImplOpenVINO::initBackend(const std::vector<LayerPin>& blobsToKeep_)
             {
                 int lid = ld.inputBlobsId[i].lid;
                 int oid = ld.inputBlobsId[i].oid;
-                if (oid == 0 || lid == 0)
-                    continue;
 
                 auto ieInpNode = inputNodes[i].dynamicCast<InfEngineNgraphNode>();
                 const auto& ngraph_input_node = ieInpNode->node;
                 CV_LOG_DEBUG(NULL, "DNN/IE: bind output port " << lid << ":" << oid << " (" << ngraph_input_node->get_friendly_name() << ":" << ngraph_input_node->get_type_info().name << ")");
 
+                if ((oid == 0 && ngraph_input_node->get_output_size() == 1) || lid == 0)
+                    continue;
+
                 // Handle parameters from other subnets. Output port is not used in this case
 #if INF_ENGINE_VER_MAJOR_GT(INF_ENGINE_RELEASE_2020_4)
                 if ((ngraph::op::is_parameter(ngraph_input_node) || ngraph::op::is_constant(ngraph_input_node)) &&

modules/dnn/test/test_caffe_importer.cpp

@@ -732,16 +732,9 @@ TEST_P(Test_Caffe_nets, FasterRCNN_vgg16)
 #endif
 
     double scoreDiff = 0.0, iouDiff = 0.0;
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2022010000)
-    // Check 'backward_compatible_check || in_out_elements_equal' failed at core/src/op/reshape.cpp:427:
-    // While validating node 'v1::Reshape bbox_pred_reshape (bbox_pred[0]:f32{1,84}, Constant_265242[0]:i64{4}) -> (f32{?,?,?,?})' with friendly_name 'bbox_pred_reshape':
-    // Requested output shape {1,6300,4,1} is incompatible with input shape {1, 84}
+#if defined(INF_ENGINE_RELEASE)
     if (target == DNN_TARGET_MYRIAD)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
-    if (target == DNN_TARGET_OPENCL_FP16)
-        scoreDiff = 0.02;
-#endif
-#if defined(INF_ENGINE_RELEASE)
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
         iouDiff = 0.02;
         if (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16) {

modules/dnn/test/test_darknet_importer.cpp

@@ -102,11 +102,14 @@ TEST(Test_Darknet, read_yolo_voc_stream)
 class Test_Darknet_layers : public DNNTestLayer
 {
 public:
-    void testDarknetLayer(const std::string& name, bool hasWeights = false, bool testBatchProcessing = true)
+    void testDarknetLayer(const std::string& name, bool hasWeights = false, bool testBatchProcessing = true,
+                          double l1 = 0.0, double lInf = 0.0)
     {
         SCOPED_TRACE(name);
         Mat inp = blobFromNPY(findDataFile("dnn/darknet/" + name + "_in.npy"));
         Mat ref = blobFromNPY(findDataFile("dnn/darknet/" + name + "_out.npy"));
+        l1 = l1 ? l1 : default_l1;
+        lInf = lInf ? lInf : default_lInf;
 
         std::string cfg = findDataFile("dnn/darknet/" + name + ".cfg");
         std::string model = "";
@@ -120,7 +123,7 @@ public:
         net.setPreferableTarget(target);
         net.setInput(inp);
         Mat out = net.forward();
-        normAssert(out, ref, "", default_l1, default_lInf);
+        normAssert(out, ref, "", l1, lInf);
 
         if (inp.size[0] == 1 && testBatchProcessing)  // test handling of batch size
         {
@@ -166,8 +169,8 @@ public:
             }*/
             ASSERT_EQ(out2.dims, ref2.dims) << ref.dims;
 
-            normAssert(out2(ranges0), ref2, "", default_l1, default_lInf);
-            normAssert(out2(ranges1), ref2, "", default_l1, default_lInf);
+            normAssert(out2(ranges0), ref2, "", l1, lInf);
+            normAssert(out2(ranges1), ref2, "", l1, lInf);
         }
     }
 };
@@ -1116,7 +1119,12 @@ TEST_P(Test_Darknet_layers, connected)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
-    testDarknetLayer("connected", true);
+    double l1 = 0.0;
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
+    {
+        l1 = 3e-5;
+    }
+    testDarknetLayer("connected", true, true, l1);
 }
 
 TEST_P(Test_Darknet_layers, relu)

modules/dnn/test/test_halide_layers.cpp

@@ -361,22 +361,9 @@ TEST_P(MaxPooling, Accuracy)
     Backend backendId = get<0>(get<5>(GetParam()));
     Target targetId = get<1>(get<5>(GetParam()));
 
-#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 == Size(7, 6) && kernel == Size(3, 2)
-            && (stride == Size(1, 1) || stride == Size(2, 2))
-            && (pad == Size(0, 1) || pad == Size(1, 1))
-    )
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
-#endif
-
-#if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_EQ(2018050000)
-    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
-            && (kernel == Size(2, 2) || kernel == Size(3, 2))
-            && stride == Size(1, 1) && (pad == Size(0, 0) || pad == Size(0, 1))
-    )
-        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_VERSION);
-#endif
+    // https://github.com/openvinotoolkit/openvino/issues/18731
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && stride != Size(1, 1))
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
 
 #if defined(INF_ENGINE_RELEASE) && INF_ENGINE_VER_MAJOR_GE(2019010000)
     if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019 && targetId == DNN_TARGET_MYRIAD
@@ -467,6 +454,11 @@ TEST_P(FullyConnected, Accuracy)
     {
         l1 = 0.01;
     }
+    if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && targetId == DNN_TARGET_OPENCL)
+    {
+        l1 = 5e-3;
+        lInf = 7e-3;
+    }
 #endif
     if (targetId == DNN_TARGET_CUDA_FP16)
         l1 = 0.015;

modules/dnn/test/test_layers.cpp

@@ -215,7 +215,13 @@ TEST_P(Test_Caffe_layers, InnerProduct)
     if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_CPU_FP16)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_CPU_FP16);
 
-    testLayerUsingCaffeModels("layer_inner_product", true);
+    double l1 = 0.0, lInf = 0.0;
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && (target == DNN_TARGET_OPENCL || target == DNN_TARGET_OPENCL_FP16))
+    {
+        l1 = 5e-3;
+        lInf = 2e-2;
+    }
+    testLayerUsingCaffeModels("layer_inner_product", true, true, l1, lInf);
 }
 
 TEST_P(Test_Caffe_layers, Pooling_max)

modules/dnn/test/test_onnx_importer.cpp

@@ -52,7 +52,7 @@ public:
     }
 
     void testONNXModels(const String& basename, const Extension ext = npy,
-                        const double l1 = 0, const float lInf = 0, const bool useSoftmax = false,
+                        double l1 = 0, double lInf = 0, const bool useSoftmax = false,
                         bool checkNoFallbacks = true, int numInps = 1)
     {
         String onnxmodel = _tf("models/" + basename + ".onnx", required);
@@ -102,6 +102,11 @@ public:
             netSoftmax.setInput(ref);
             ref = netSoftmax.forward();
         }
+        if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
+        {
+            l1 = std::max(l1, 1.4e-3);
+            lInf = std::max(lInf, 8e-3);
+        }
         normAssert(ref, out, basename.c_str(), l1 ? l1 : default_l1, lInf ? lInf : default_lInf);
         if (checkNoFallbacks)
             expectNoFallbacksFromIE(net);

modules/dnn/test/test_tf_importer.cpp

@@ -1818,8 +1818,8 @@ TEST_P(Test_TensorFlow_nets, Mask_RCNN)
     double iouDiff = (target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD || target == DNN_TARGET_CPU_FP16) ? 0.018 : default_lInf;
     if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
     {
-        scoreDiff = std::max(scoreDiff, 0.02);
-        iouDiff = std::max(iouDiff, 0.009);
+        scoreDiff = std::max(scoreDiff, 0.06);
+        iouDiff = std::max(iouDiff, 0.01);
     }
     normAssertDetections(refDetections, outDetections, "", /*threshold for zero confidence*/1e-5, scoreDiff, iouDiff);
 

modules/dnn/test/test_tflite_importer.cpp

@@ -20,6 +20,14 @@ namespace opencv_test { namespace {
 using namespace cv;
 using namespace cv::dnn;
 
+class Test_TFLite : public DNNTestLayer {
+public:
+    void testModel(Net& net, const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0);
+    void testModel(const std::string& modelName, const Mat& input, double l1 = 0, double lInf = 0);
+    void testModel(const std::string& modelName, const Size& inpSize, double l1 = 0, double lInf = 0);
+    void testLayer(const std::string& modelName, double l1 = 0, double lInf = 0);
+};
+
 void testInputShapes(const Net& net, const std::vector<Mat>& inps) {
     std::vector<MatShape> inLayerShapes;
     std::vector<MatShape> outLayerShapes;
@@ -31,8 +39,14 @@ void testInputShapes(const Net& net, const std::vector<Mat>& inps) {
     }
 }
 
-void testModel(Net& net, const std::string& modelName, const Mat& input, double l1 = 1e-5, double lInf = 1e-4)
+void Test_TFLite::testModel(Net& net, const std::string& modelName, const Mat& input, double l1, double lInf)
 {
+    l1 = l1 ? l1 : default_l1;
+    lInf = lInf ? lInf : default_lInf;
+
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
+
     testInputShapes(net, {input});
     net.setInput(input);
 
@@ -48,20 +62,20 @@ void testModel(Net& net, const std::string& modelName, const Mat& input, double
     }
 }
 
-void testModel(const std::string& modelName, const Mat& input, double l1 = 1e-5, double lInf = 1e-4)
+void Test_TFLite::testModel(const std::string& modelName, const Mat& input, double l1, double lInf)
 {
     Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite", false));
     testModel(net, modelName, input, l1, lInf);
 }
 
-void testModel(const std::string& modelName, const Size& inpSize, double l1 = 1e-5, double lInf = 1e-4)
+void Test_TFLite::testModel(const std::string& modelName, const Size& inpSize, double l1, double lInf)
 {
     Mat input = imread(findDataFile("cv/shared/lena.png"));
     input = blobFromImage(input, 1.0 / 255, inpSize, 0, true);
     testModel(modelName, input, l1, lInf);
 }
 
-void testLayer(const std::string& modelName, double l1 = 1e-5, double lInf = 1e-4)
+void Test_TFLite::testLayer(const std::string& modelName, double l1, double lInf)
 {
     Mat inp = blobFromNPY(findDataFile("dnn/tflite/" + modelName + "_inp.npy"));
     Net net = readNet(findDataFile("dnn/tflite/" + modelName + ".tflite"));
@@ -69,29 +83,66 @@ void testLayer(const std::string& modelName, double l1 = 1e-5, double lInf = 1e-
 }
 
 // https://google.github.io/mediapipe/solutions/face_mesh
-TEST(Test_TFLite, face_landmark)
+TEST_P(Test_TFLite, face_landmark)
 {
-    testModel("face_landmark", Size(192, 192), 2e-5, 2e-4);
+    if (backend == DNN_BACKEND_CUDA && target == DNN_TARGET_CUDA_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA_FP16);
+    double l1 = 2e-5, lInf = 2e-4;
+    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
+    {
+        l1 = 0.15;
+        lInf = 0.82;
+    }
+    testModel("face_landmark", Size(192, 192), l1, lInf);
 }
 
 // https://google.github.io/mediapipe/solutions/face_detection
-TEST(Test_TFLite, face_detection_short_range)
+TEST_P(Test_TFLite, face_detection_short_range)
+{
+    double l1 = 0, lInf = 2e-4;
+    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
     {
-    testModel("face_detection_short_range", Size(128, 128));
+        l1 = 0.04;
+        lInf = 0.8;
+    }
+    testModel("face_detection_short_range", Size(128, 128), l1, lInf);
 }
 
 // https://google.github.io/mediapipe/solutions/selfie_segmentation
-TEST(Test_TFLite, selfie_segmentation)
+TEST_P(Test_TFLite, selfie_segmentation)
+{
+    double l1 = 0, lInf = 0;
+    if (target == DNN_TARGET_CPU_FP16 || target == DNN_TARGET_CUDA_FP16 || target == DNN_TARGET_OPENCL_FP16 || target == DNN_TARGET_MYRIAD ||
+        (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL))
     {
-    testModel("selfie_segmentation", Size(256, 256));
+        l1 = 0.01;
+        lInf = 0.48;
+    }
+    testModel("selfie_segmentation", Size(256, 256), l1, lInf);
 }
 
-TEST(Test_TFLite, max_unpooling)
+TEST_P(Test_TFLite, max_unpooling)
 {
+    if (backend == DNN_BACKEND_CUDA)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_CUDA);
+
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target != DNN_TARGET_CPU) {
+        if (target == DNN_TARGET_OPENCL_FP16) applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL_FP16, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+        if (target == DNN_TARGET_OPENCL)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_OPENCL, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+        if (target == DNN_TARGET_MYRIAD)      applyTestTag(CV_TEST_TAG_DNN_SKIP_IE_MYRIAD, CV_TEST_TAG_DNN_SKIP_IE_NGRAPH);
+    }
+
+    if (backend == DNN_BACKEND_OPENCV && target == DNN_TARGET_OPENCL_FP16)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
+
     // Due Max Unpoling is a numerically unstable operation and small difference between frameworks
     // might lead to positional difference of maximal elements in the tensor, this test checks
     // behavior of Max Unpooling layer only.
     Net net = readNet(findDataFile("dnn/tflite/hair_segmentation.tflite", false));
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
 
     Mat input = imread(findDataFile("cv/shared/lena.png"));
     cvtColor(input, input, COLOR_BGR2RGBA);
@@ -101,7 +152,15 @@ TEST(Test_TFLite, max_unpooling)
     net.setInput(input);
 
     std::vector<std::vector<Mat> > outs;
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
+        // TODO: seems like a bug with a retrieving intermediate tensors
+        net.forward(outs, {"conv2d_transpose_4", "p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});
+        outs.erase(outs.begin());
+    }
+    else {
         net.forward(outs, {"p_re_lu_1", "max_pooling_with_argmax2d", "conv2d_86", "max_unpooling2d_2"});
+    }
+
     ASSERT_EQ(outs.size(), 4);
     ASSERT_EQ(outs[0].size(), 1);
     ASSERT_EQ(outs[1].size(), 2);
@@ -117,6 +176,8 @@ TEST(Test_TFLite, max_unpooling)
     ASSERT_EQ(poolOut.size, poolIds.size);
     ASSERT_EQ(poolOut.size, unpoolInp.size);
 
+    ASSERT_EQ(countNonZero(poolInp), poolInp.total());
+
     for (int c = 0; c < 32; ++c) {
         float *poolInpData = poolInp.ptr<float>(0, c);
         float *poolOutData = poolOut.ptr<float>(0, c);
@@ -135,15 +196,19 @@ TEST(Test_TFLite, max_unpooling)
                     }
                 }
                 EXPECT_EQ(poolInpData[maxIdx], poolOutData[y * 64 + x]) << errMsg;
+                if (backend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH) {
                     EXPECT_EQ(poolIdsData[y * 64 + x], (float)maxIdx) << errMsg;
+                }
                 EXPECT_EQ(unpoolOutData[maxIdx], unpoolInpData[y * 64 + x]) << errMsg;
             }
         }
     }
 }
 
-TEST(Test_TFLite, EfficientDet_int8) {
+TEST_P(Test_TFLite, EfficientDet_int8) {
     Net net = readNet(findDataFile("dnn/tflite/coco_efficientdet_lite0_v1_1.0_quant_2021_09_06.tflite", false));
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(target);
 
     Mat img = imread(findDataFile("dnn/dog416.png"));
     Mat blob = blobFromImage(img, 1.0, Size(320, 320));
@@ -158,9 +223,17 @@ TEST(Test_TFLite, EfficientDet_int8) {
     normAssertDetections(ref, out, "", 0.5, 0.05, 0.1);
 }
 
-TEST(Test_TFLite, replicate_by_pack) {
-    testLayer("replicate_by_pack");
+TEST_P(Test_TFLite, replicate_by_pack) {
+    double l1 = 0, lInf = 0;
+    if (backend == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH && target == DNN_TARGET_OPENCL)
+    {
+        l1 = 4e-4;
+        lInf = 2e-3;
     }
+    testLayer("replicate_by_pack", l1, lInf);
+}
+
+INSTANTIATE_TEST_CASE_P(/**/, Test_TFLite, dnnBackendsAndTargets());
 
 }}  // namespace