Merge remote-tracking branch 'github/master' into auto-batch-master

.ci/azure/linux.yml

@@ -88,7 +88,7 @@ jobs:
       rm -rf $(BUILD_SAMPLES_DIR) ; mkdir $(BUILD_SAMPLES_DIR)
       sudo rm -rf $(TMP_DIR) ; sudo mkdir $(TMP_DIR) ; sudo chmod 777 -R $(TMP_DIR)
       sudo mkdir -p $(SHARE_DIR)
-      sudo apt --assume-yes install nfs-common
+      sudo apt --assume-yes update && sudo apt --assume-yes install nfs-common
       sudo mount -vvv -t nfs cinfsshare.file.core.windows.net:/cinfsshare/onnxtestdata $(SHARE_DIR) -o vers=4,minorversion=1,sec=sys
       mkdir -p $(CCACHE_DIR)
     displayName: 'Make dir'

docs/nGraph_DG/nGraphTransformation.md

@@ -306,7 +306,7 @@ In more advanced cases, when replaced operation has several outputs and we add a
 
 ###4. Runtime Info
 
-Runtime info is a map `std::map<std::string, std::shared_ptr<Variant>>` located inside `ngraph::Node` class. It represents additional attributes in `ngraph::Node`.
+Runtime info is a map `std::map<std::string, ov::Any>` located inside `ngraph::Node` class. It represents additional attributes in `ngraph::Node`.
 These attributes can be set by users or by plugins and when executing transformation that changes `ngraph::Function` we need to preserve these attributes as they will not be automatically propagated.
 In most cases, transformations have the following types: 1:1 (replace node with another node), 1:N (replace node with a sub-graph), N:1 (fuse sub-graph into a single node), N:M (any other transformation).
 Currently, there is no mechanism that automatically detects transformation types, so we need to propagate this runtime information manually. See the examples below.

docs/snippets/InferenceEngine_Caching3.cpp

@@ -14,7 +14,7 @@ using namespace InferenceEngine;
     auto it = std::find(keys.begin(), keys.end(), METRIC_KEY(IMPORT_EXPORT_SUPPORT));
 
     // If metric 'IMPORT_EXPORT_SUPPORT' exists, check it's value
-    bool cachingSupported = (it != keys.end()) && ie.GetMetric(deviceName, METRIC_KEY(IMPORT_EXPORT_SUPPORT)).as<bool>();
+    auto cachingSupported = (it != keys.end()) && ie.GetMetric(deviceName, METRIC_KEY(IMPORT_EXPORT_SUPPORT)).as<bool>();
 //! [part3]
     return 0;
 }

docs/template_plugin/tests/functional/op_reference/detection_output.cpp

@@ -45,21 +45,21 @@ struct DetectionOutputParams {
           refData(CreateTensor(iType, oValues)),
           testcaseName(test_name) {
               attrs.num_classes = num_classes;
-              attrs.background_label_id = background_label_id;
-              attrs.top_k = top_k;
-              attrs.variance_encoded_in_target = variance_encoded_in_target;
-              attrs.keep_top_k = keep_top_k;
-              attrs.code_type = code_type;
-              attrs.share_location = share_location;
-              attrs.nms_threshold = nms_threshold;
-              attrs.confidence_threshold = confidence_threshold;
-              attrs.clip_after_nms = clip_after_nms;
-              attrs.clip_before_nms = clip_before_nms;
-              attrs.decrease_label_id = decrease_label_id;
-              attrs.normalized = normalized;
-              attrs.input_height = input_height;
-              attrs.input_width = input_width;
-              attrs.objectness_score = objectness_score;
+              attrs_v8.background_label_id = attrs.background_label_id = background_label_id;
+              attrs_v8.top_k = attrs.top_k = top_k;
+              attrs_v8.variance_encoded_in_target = attrs.variance_encoded_in_target = variance_encoded_in_target;
+              attrs_v8.keep_top_k = attrs.keep_top_k = keep_top_k;
+              attrs_v8.code_type = attrs.code_type = code_type;
+              attrs_v8.share_location = attrs.share_location = share_location;
+              attrs_v8.nms_threshold = attrs.nms_threshold = nms_threshold;
+              attrs_v8.confidence_threshold = attrs.confidence_threshold = confidence_threshold;
+              attrs_v8.clip_after_nms = attrs.clip_after_nms = clip_after_nms;
+              attrs_v8.clip_before_nms = attrs.clip_before_nms = clip_before_nms;
+              attrs_v8.decrease_label_id = attrs.decrease_label_id = decrease_label_id;
+              attrs_v8.normalized = attrs.normalized = normalized;
+              attrs_v8.input_height = attrs.input_height = input_height;
+              attrs_v8.input_width = attrs.input_width = input_width;
+              attrs_v8.objectness_score = attrs.objectness_score = objectness_score;
 
               size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
               size_t prior_box_size = attrs.normalized ? 4 : 5;
@@ -107,21 +107,21 @@ template <class IT>
           auxConfData(CreateTensor(iType, auxConfValues)),
           testcaseName(test_name) {
               attrs.num_classes = num_classes;
-              attrs.background_label_id = background_label_id;
-              attrs.top_k = top_k;
-              attrs.variance_encoded_in_target = variance_encoded_in_target;
-              attrs.keep_top_k = keep_top_k;
-              attrs.code_type = code_type;
-              attrs.share_location = share_location;
-              attrs.nms_threshold = nms_threshold;
-              attrs.confidence_threshold = confidence_threshold;
-              attrs.clip_after_nms = clip_after_nms;
-              attrs.clip_before_nms = clip_before_nms;
-              attrs.decrease_label_id = decrease_label_id;
-              attrs.normalized = normalized;
-              attrs.input_height = input_height;
-              attrs.input_width = input_width;
-              attrs.objectness_score = objectness_score;
+              attrs_v8.background_label_id = attrs.background_label_id = background_label_id;
+              attrs_v8.top_k = attrs.top_k = top_k;
+              attrs_v8.variance_encoded_in_target = attrs.variance_encoded_in_target = variance_encoded_in_target;
+              attrs_v8.keep_top_k = attrs.keep_top_k = keep_top_k;
+              attrs_v8.code_type = attrs.code_type = code_type;
+              attrs_v8.share_location = attrs.share_location = share_location;
+              attrs_v8.nms_threshold = attrs.nms_threshold = nms_threshold;
+              attrs_v8.confidence_threshold = attrs.confidence_threshold = confidence_threshold;
+              attrs_v8.clip_after_nms = attrs.clip_after_nms = clip_after_nms;
+              attrs_v8.clip_before_nms = attrs.clip_before_nms = clip_before_nms;
+              attrs_v8.decrease_label_id = attrs.decrease_label_id = decrease_label_id;
+              attrs_v8.normalized = attrs.normalized = normalized;
+              attrs_v8.input_height = attrs.input_height = input_height;
+              attrs_v8.input_width = attrs.input_width = input_width;
+              attrs_v8.objectness_score = attrs.objectness_score = objectness_score;
 
               size_t num_loc_classes = attrs.share_location ? 1 : attrs.num_classes;
               size_t prior_box_size = attrs.normalized ? 4 : 5;
@@ -135,6 +135,7 @@ template <class IT>
           }
 
     ov::op::v0::DetectionOutput::Attributes attrs;
+    ov::op::v8::DetectionOutput::Attributes attrs_v8;
     ov::PartialShape locShape;
     ov::PartialShape confShape;
     ov::PartialShape priorBoxesShape;
@@ -194,10 +195,61 @@ private:
     }
 };
 
+class ReferenceDetectionOutputV8LayerTest : public testing::TestWithParam<DetectionOutputParams>,
+                                          public CommonReferenceTest {
+public:
+    void SetUp() override {
+        auto params = GetParam();
+        function = CreateFunction(params);
+        if ((params.auxLocShape.size() != 0) && (params.auxConfShape.size() != 0))
+            inputData = {params.locData, params.confData, params.priorBoxesData, params.auxConfData, params.auxLocData};
+        else
+            inputData = {params.locData, params.confData, params.priorBoxesData};
+        refOutData = {params.refData};
+    }
+    static std::string getTestCaseName(const testing::TestParamInfo<DetectionOutputParams>& obj) {
+        auto param = obj.param;
+        std::ostringstream result;
+        result << "locShape=" << param.locShape << "_";
+        result << "confShape=" << param.confShape << "_";
+        result << "priorBoxesShape=" << param.priorBoxesShape << "_";
+        if ((param.auxLocShape.size() != 0) && (param.auxConfShape.size() != 0)) {
+            result << "auxLocShape=" << param.locShape << "_";
+            result << "auxConfShape=" << param.confShape << "_";
+        }
+        result << "iType=" << param.inType;
+        if (param.testcaseName != "")
+            result << "_" << param.testcaseName;
+        return result.str();
+    }
+
+private:
+    static std::shared_ptr<Function> CreateFunction(const DetectionOutputParams& params) {
+        const auto loc = std::make_shared<op::v0::Parameter>(params.inType, params.locShape);
+        const auto conf = std::make_shared<op::v0::Parameter>(params.inType, params.confShape);
+        const auto priorBoxes = std::make_shared<op::v0::Parameter>(params.inType, params.priorBoxesShape);
+        if ((params.auxLocShape.size() != 0) && (params.auxConfShape.size() != 0)) {
+            const auto auxConf = std::make_shared<op::v0::Parameter>(params.inType, params.auxConfShape);
+            const auto auxLoc = std::make_shared<op::v0::Parameter>(params.inType, params.auxLocShape);
+            const auto DetectionOutput =
+                std::make_shared<op::v8::DetectionOutput>(loc, conf, priorBoxes, auxConf, auxLoc, params.attrs_v8);
+            return std::make_shared<ov::Function>(NodeVector{DetectionOutput},
+                                                  ParameterVector{loc, conf, priorBoxes, auxConf, auxLoc});
+        } else {
+            const auto DetectionOutput = std::make_shared<op::v8::DetectionOutput>(loc, conf, priorBoxes, params.attrs_v8);
+            return std::make_shared<ov::Function>(NodeVector{DetectionOutput}, ParameterVector{loc, conf, priorBoxes});
+        }
+    }
+};
+
 TEST_P(ReferenceDetectionOutputLayerTest, CompareWithRefs) {
     Exec();
 }
 
+TEST_P(ReferenceDetectionOutputV8LayerTest, CompareWithRefs) {
+    Exec();
+}
+
 template <element::Type_t IN_ET>
 std::vector<DetectionOutputParams> generateDetectionOutputFloatParams() {
     using T = typename element_type_traits<IN_ET>::value_type;
@@ -517,4 +569,9 @@ std::vector<DetectionOutputParams> generateDetectionOutputCombinedParams() {
 INSTANTIATE_TEST_SUITE_P(smoke_DetectionOutput_With_Hardcoded_Refs, ReferenceDetectionOutputLayerTest,
     testing::ValuesIn(generateDetectionOutputCombinedParams()), ReferenceDetectionOutputLayerTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_DetectionOutput_With_Hardcoded_Refs,
+                         ReferenceDetectionOutputV8LayerTest,
+                         testing::ValuesIn(generateDetectionOutputCombinedParams()),
+                         ReferenceDetectionOutputV8LayerTest::getTestCaseName);
+
 } // namespace

docs/template_plugin/tests/functional/shared_tests_instances/behavior/plugin/core_integration.cpp

@@ -130,10 +130,10 @@ TEST_F(IEClassGetConfigTestTEMPLATE, smoke_GetConfigNoThrow) {
             std::string defaultDeviceID = ie.GetConfig(deviceName, CONFIG_KEY(DEVICE_ID));
             std::cout << CONFIG_KEY(DEVICE_ID) << " : " << defaultDeviceID << std::endl;
         } else if (CONFIG_KEY(PERF_COUNT) == confKey) {
-            bool defaultPerfCount = ie.GetConfig(deviceName, CONFIG_KEY(PERF_COUNT)).as<bool>();
+            auto defaultPerfCount = ie.GetConfig(deviceName, CONFIG_KEY(PERF_COUNT)).as<bool>();
             std::cout << CONFIG_KEY(PERF_COUNT) << " : " << defaultPerfCount << std::endl;
         } else if (CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) == confKey) {
-            bool defaultExclusive = ie.GetConfig(deviceName, CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)).as<bool>();
+            auto defaultExclusive = ie.GetConfig(deviceName, CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS)).as<bool>();
             std::cout << CONFIG_KEY(EXCLUSIVE_ASYNC_REQUESTS) << " : " << defaultExclusive << std::endl;
         }
     }

inference-engine/src/mkldnn_plugin/mkldnn_descriptor.cpp

@@ -94,12 +94,12 @@ MKLDNNDescriptor::operator std::shared_ptr<mkldnn::lrn_forward::desc>() {
     return typeDesc->getPtr();
 }
 
-MKLDNNDescriptor::MKLDNNDescriptor(std::shared_ptr<mkldnn::pooling_forward::desc> desc) {
-    this->desc.reset(new DescFwdImpl<mkldnn::pooling_forward::desc>(desc));
+MKLDNNDescriptor::MKLDNNDescriptor(std::shared_ptr<mkldnn::pooling_v2_forward::desc> desc) {
+    this->desc.reset(new DescFwdImpl<mkldnn::pooling_v2_forward::desc>(desc));
 }
 
-MKLDNNDescriptor::operator std::shared_ptr<mkldnn::pooling_forward::desc>() {
-    auto typeDesc = std::dynamic_pointer_cast<DescFwdImpl<mkldnn::pooling_forward::desc>>(desc);
+MKLDNNDescriptor::operator std::shared_ptr<mkldnn::pooling_v2_forward::desc>() {
+    auto typeDesc = std::dynamic_pointer_cast<DescFwdImpl<mkldnn::pooling_v2_forward::desc>>(desc);
     if (typeDesc == nullptr) {
         IE_THROW() << "Cannot cast descriptor!";
     }

inference-engine/src/mkldnn_plugin/mkldnn_descriptor.h

@@ -28,8 +28,8 @@ public:
     explicit MKLDNNDescriptor(std::shared_ptr<mkldnn::lrn_forward::desc> desc);
     operator std::shared_ptr<mkldnn::lrn_forward::desc>();
 
-    explicit MKLDNNDescriptor(std::shared_ptr<mkldnn::pooling_forward::desc> desc);
-    operator std::shared_ptr<mkldnn::pooling_forward::desc>();
+    explicit MKLDNNDescriptor(std::shared_ptr<mkldnn::pooling_v2_forward::desc> desc);
+    operator std::shared_ptr<mkldnn::pooling_v2_forward::desc>();
 
     explicit MKLDNNDescriptor(std::shared_ptr<mkldnn::softmax_forward::desc> desc);
     operator std::shared_ptr<mkldnn::softmax_forward::desc>();

inference-engine/src/mkldnn_plugin/mkldnn_graph_dumper.cpp

@@ -186,7 +186,7 @@ std::shared_ptr<ngraph::Function> dump_graph_as_ie_ngraph_net(const MKLDNNGraph
         }
 
         for (auto && kvp : meta_data)
-            return_node->get_rt_info()[kvp.first] = std::make_shared<::ngraph::VariantWrapper<std::string>>(kvp.second);
+            return_node->get_rt_info()[kvp.first] = std::make_shared<::ov::RuntimeAttributeWrapper<std::string>>(kvp.second);
         return_node->set_friendly_name(node->getName());
 
         return return_node;

inference-engine/src/mkldnn_plugin/mkldnn_infer_request.cpp

@@ -526,10 +526,13 @@ void MKLDNNPlugin::MKLDNNInferRequest::changeDefaultPtr() {
                     break;
                 }
 
-                if (child->getType() == Concatenation && dynamic_cast<MKLDNNConcatNode*>(child.get())->isOptimized()) {
+                if (child->getType() == Concatenation) {
+                    auto concat = dynamic_cast<MKLDNNConcatNode*>(child.get());
+                    if (concat && concat->isOptimized()) {
                         canBeInPlace = false;
                         break;
                     }
+                }
 
                 // Cannot be in-place before split because split is using different ptrs without offsets
                 if (child->getType() == Split) {

inference-engine/src/mkldnn_plugin/mkldnn_node.cpp

@@ -137,7 +137,7 @@ MKLDNNNode::MKLDNNNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::en
     }
 
     if (op != nullptr) {
-        std::string inputMemoryFormats = ngraph::getMLKDNNInputMemoryFormats(op);
+        std::string inputMemoryFormats = ngraph::getMKLDNNInputMemoryFormats(op);
         if (!inputMemoryFormats.empty()) {
             std::istringstream stream(inputMemoryFormats);
             std::string str;
@@ -148,7 +148,7 @@ MKLDNNNode::MKLDNNNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::en
             }
         }
 
-        std::string outputMemoryFormats = ngraph::getMLKDNNOutputMemoryFormats(op);
+        std::string outputMemoryFormats = ngraph::getMKLDNNOutputMemoryFormats(op);
         if (!outputMemoryFormats.empty()) {
             std::istringstream stream(outputMemoryFormats);
             std::string str;
@@ -162,7 +162,7 @@ MKLDNNNode::MKLDNNNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::en
 
     const auto it = rtInfo.find("enforceBF16evenForGraphTail");
     if (it != rtInfo.end()) {
-        if (const auto value = std::dynamic_pointer_cast<ngraph::VariantImpl<int64_t>>(it->second))
+        if (const auto value = std::dynamic_pointer_cast<ov::RuntimeAttributeImpl<int64_t>>(it->second))
             enforceBF16evenForGraphTail = value->get();
     }
 }

inference-engine/src/mkldnn_plugin/ngraph_transformations/move_eltwise_up_data_movement.cpp

@@ -85,7 +85,7 @@ MKLDNNPlugin::MoveEltwiseUpThroughDataMov::MoveEltwiseUpThroughDataMov() {
         }
 
         // eltwise constant shape should match new input shape
-        if (is_binary_op && current->get_output_shape(0).size() != eltwise->get_input_shape(1).size()) {
+        if (is_binary_op && current->get_output_partial_shape(0).rank().get_length() != eltwise->get_input_partial_shape(1).rank().get_length()) {
             auto old_eltwise_const = std::dynamic_pointer_cast<ngraph::opset8::Constant>(eltwise->get_input_node_shared_ptr(1));
             auto new_constant = std::make_shared<ngraph::opset8::Constant>(*old_eltwise_const.get(), ngraph::Shape{});
             ngraph::replace_node(old_eltwise_const, new_constant);

inference-engine/src/mkldnn_plugin/ngraph_transformations/rnn_sequences_optimization.cpp

@@ -67,7 +67,7 @@ namespace {
             ngraph::replace_node(transposeAfter, {reshape2->output(0)});
         }
 
-        sequenceOp->get_rt_info()["seqAxis"] = std::make_shared<ngraph::VariantWrapper<int64_t>>(seqAxis);
+        sequenceOp->get_rt_info()["seqAxis"] = std::make_shared<ov::RuntimeAttributeWrapper<int64_t>>(seqAxis);
 
         return true;
     }

inference-engine/src/mkldnn_plugin/nodes/mkldnn_embedding_bag_offset_sum_node.h

@@ -39,8 +39,8 @@ private:
     const int* offsetsData_ = nullptr;
     const int* defaultIndices_ = nullptr;
 
-    size_t _indicesLen;
-    size_t _offsetsLen;
+    size_t _indicesLen = 0;
+    size_t _offsetsLen = 0;
 };
 
 }  // namespace MKLDNNPlugin

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.cpp

@@ -215,12 +215,16 @@ static void nms_cf(const float* conf_data,
     detections = (post_nms_topn == -1 ? detections : (std::min)(post_nms_topn, detections));
 }
 
-bool MKLDNNExperimentalDetectronDetectionOutputNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
-    try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
+bool MKLDNNExperimentalDetectronDetectionOutputNode::needShapeInfer() const {
     return false;
 }
+
+bool MKLDNNExperimentalDetectronDetectionOutputNode::needPrepareParams() const {
+    return false;
+}
+
+bool MKLDNNExperimentalDetectronDetectionOutputNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
+    try {
         const auto doOp = ngraph::as_type_ptr<const ngraph::op::v6::ExperimentalDetectronDetectionOutput>(op);
         if (!doOp) {
             errorMessage = "Node is not an instance of the ExperimentalDetectronDetectionOutput from the operations set v6.";
@@ -268,6 +272,12 @@ void MKLDNNExperimentalDetectronDetectionOutputNode::initSupportedPrimitiveDescr
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNExperimentalDetectronDetectionOutputNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
 void MKLDNNExperimentalDetectronDetectionOutputNode::execute(mkldnn::stream strm) {
     const int rois_num = getParentEdgeAt(INPUT_ROIS)->getMemory().getStaticDims()[0];
     assert(classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_SCORES)->getMemory().getStaticDims()[1]));

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_detection_output_node.h

@@ -15,10 +15,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needShapeInfer() const override;
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.cpp

@@ -275,10 +275,6 @@ void fill_output_blobs(const float* proposals, const int* roi_indices,
 bool MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::isSupportedOperation
             (const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto proposalOp = ngraph::as_type_ptr<const ngraph::op::v6::ExperimentalDetectronGenerateProposalsSingleImage>(op);
         if (!proposalOp) {
             errorMessage = "Node is not an instance of the Proposal from the operations set v0.";
@@ -324,6 +320,12 @@ void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::initSupportedP
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
 void MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::execute(mkldnn::stream strm) {
     try {
         if (inputShapes.size() != 4 || outputShapes.size() != 2) {
@@ -431,4 +433,12 @@ bool MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::created() cons
     return getType() == ExperimentalDetectronGenerateProposalsSingleImage;
 }
 
+bool MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::needShapeInfer() const {
+    return false;
+}
+
+bool MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode::needPrepareParams() const {
+    return false;
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNExperimentalDetectronGenerateProposalsSingleImageNode, ExperimentalDetectronGenerateProposalsSingleImage)

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_generate_proposals_single_image_node.h

@@ -16,10 +16,13 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needShapeInfer() const override;
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.cpp

@@ -14,10 +14,6 @@ using namespace InferenceEngine;
 bool MKLDNNExperimentalDetectronPriorGridGeneratorNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op,
                                                                              std::string& errorMessage) noexcept {
     try {
-        if (isDynamicNgraphNode(op)) {
-            errorMessage = "Doesn't support op with dynamic shapes";
-            return false;
-        }
         const auto priorGridGen = std::dynamic_pointer_cast<const ngraph::opset6::ExperimentalDetectronPriorGridGenerator>(op);
         if (!priorGridGen) {
             errorMessage = "Only opset6 ExperimentalDetectronPriorGridGenerator operation is supported";
@@ -42,11 +38,6 @@ MKLDNNExperimentalDetectronPriorGridGeneratorNode::MKLDNNExperimentalDetectronPr
     if (getOriginalInputsNumber() != 3 || getOriginalOutputsNumber() != 1)
         IE_THROW() << errorPrefix << " has incorrect number of input/output edges!";
 
-    if (op->get_input_shape(INPUT_PRIORS).size() != 2 ||
-        op->get_input_shape(INPUT_FEATUREMAP).size() != 4 ||
-        op->get_input_shape(INPUT_IMAGE).size() != 4)
-        IE_THROW() << errorPrefix << " has unsupported input shape";
-
     const auto &attr = priorGridGen->get_attrs();
     grid_w_ = attr.w;
     grid_h_ = attr.h;
@@ -65,6 +56,12 @@ void MKLDNNExperimentalDetectronPriorGridGeneratorNode::initSupportedPrimitiveDe
                          impl_desc_type::ref_any);
 }
 
+void MKLDNNExperimentalDetectronPriorGridGeneratorNode::createPrimitive() {
+    if (inputShapesDefined()) {
+        updateLastInputDims();
+    }
+}
+
 void MKLDNNExperimentalDetectronPriorGridGeneratorNode::execute(mkldnn::stream strm) {
     const int num_priors_ = getParentEdgeAt(INPUT_PRIORS)->getMemory().getStaticDims()[0];
     assert(getParentEdgeAt(INPUT_PRIORS)->getMemory().getStaticDims()[1] == 4);
@@ -95,4 +92,8 @@ bool MKLDNNExperimentalDetectronPriorGridGeneratorNode::created() const {
     return getType() == ExperimentalDetectronPriorGridGenerator;
 }
 
+bool MKLDNNExperimentalDetectronPriorGridGeneratorNode::needPrepareParams() const {
+    return false;
+}
+
 REG_MKLDNN_PRIM_FOR(MKLDNNExperimentalDetectronPriorGridGeneratorNode, ExperimentalDetectronPriorGridGenerator)

inference-engine/src/mkldnn_plugin/nodes/mkldnn_experimental_detectron_priorgridgenerator_node.h

@@ -15,10 +15,12 @@ public:
 
     void getSupportedDescriptors() override {};
     void initSupportedPrimitiveDescriptors() override;
-    void createPrimitive() override {};
+    void createPrimitive() override;
     void execute(mkldnn::stream strm) override;
     bool created() const override;
 
+    bool needPrepareParams() const override;
+    void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
     static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
 
 private:

inference-engine/src/mkldnn_plugin/nodes/mkldnn_generic_node.cpp

@@ -163,26 +163,11 @@ void MKLDNNGenericNode::execLayer() {
 
     // TODO: use ngraph-based extension mechnism if needed to recompute shape
     isDynBatch = false;
-    // TODO: uncomment after using ngraph-based extension mechnism
-    // if (isDynBatch) {
-    //     for (size_t i = 0; i < inputs.size(); i++) {
-    //         auto td = inputs[i]->getTensorDesc();
-    //         td.setDims(inputDescs[i].getDims());
-    //         inputs[i] = make_blob_with_precision(td, getParentEdgeAt(i)->getMemory().GetData());
-    //     }
-    // }
 
     std::vector<InferenceEngine::Blob::Ptr> outputs;
     for (size_t i = 0; i < outputShapes.size(); i++) {
-        if (isDynBatch) {
-            auto out_edge = getChildEdgesAtPort(i)[0];
-            auto td = MemoryDescUtils::convertToTensorDesc(out_edge->getMemory().getDesc());
-            td.setDims(execOutputShapes[i]);
-            outputs.push_back(make_blob_with_precision(td, out_edge->getMemory().GetData()));
-        } else {
         outputs.push_back(MemoryDescUtils::interpretAsBlob(getChildEdgesAtPort(i)[0]->getMemory()));
     }
-    }
     InferenceEngine::ResponseDesc resp;
     InferenceEngine::StatusCode rc = impls[0]->execute(inputs, outputs, &resp);
     if (rc != InferenceEngine::OK) {

inference-engine/src/mkldnn_plugin/nodes/mkldnn_matrix_nms_node.h

@@ -48,10 +48,10 @@ private:
     static const size_t NMS_SELECTED_INDICES = 1;
     static const size_t NMS_VALID_OUTPUTS = 2;
 
-    size_t m_numBatches;
-    size_t m_numBoxes;
-    size_t m_numClasses;
-    size_t m_maxBoxesPerBatch;
+    size_t m_numBatches = 0;
+    size_t m_numBoxes = 0;
+    size_t m_numClasses = 0;
+    size_t m_maxBoxesPerBatch = 0;
 
     MatrixNmsSortResultType m_sortResultType;
     bool m_sortResultAcrossBatch;

inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.cpp

@@ -20,10 +20,15 @@ using namespace mkldnn;
 using namespace MKLDNNPlugin;
 using namespace InferenceEngine;
 
-bool MKLDNNPoolingNode::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
+bool MKLDNNPoolingNode::isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept {
     try {
-        if (!ngraph::as_type_ptr<const ngraph::op::v1::MaxPool>(op) && !ngraph::as_type_ptr<const ngraph::op::v1::AvgPool>(op)) {
-            errorMessage = "Only opset1 MaxPool and AvgPool operations are supported";
+        if (ov::is_type<const ov::op::v8::MaxPool>(op)) {
+            if (!op->get_output_target_inputs(1).empty()) {
+                errorMessage = "MaxPool from opset8 is supported only with one output";
+                return false;
+            }
+        } else if (!ov::is_type<const ov::op::v1::MaxPool>(op) && !ov::is_type<const ov::op::v1::AvgPool>(op)) {
+            errorMessage = "MaxPool and AvgPool from opset1 and MaxPool from opset8 are supported";
             return false;
         }
     } catch (...) {
@@ -32,48 +37,52 @@ bool MKLDNNPoolingNode::isSupportedOperation(const std::shared_ptr<const ngraph:
     return true;
 }
 
-MKLDNNPoolingNode::MKLDNNPoolingNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
+MKLDNNPoolingNode::MKLDNNPoolingNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache)
         : MKLDNNNode(op, eng, cache) {
     std::string errorMessage;
     if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
     }
 
-    auto maxPoolOp = ngraph::as_type_ptr<ngraph::op::v1::MaxPool>(op);
-    auto avgPoolOp = ngraph::as_type_ptr<ngraph::op::v1::AvgPool>(op);
-    if (maxPoolOp) {
+    auto get_attributes = [](std::vector<ptrdiff_t>& internal_attribute, const std::vector<size_t> external_attribute) {
+        for (size_t i = 0; i < external_attribute.size(); i++) {
+            internal_attribute.push_back(static_cast<ptrdiff_t>(external_attribute[i]));
+        }
+    };
+
+    if (auto maxPoolOp_v8 = ov::as_type_ptr<const ov::op::v8::MaxPool>(op)) {
+        isMaxPool8 = true;
         algorithm = PoolingMax;
         exclude_pad = false;
 
-        for (int i = 0; i < maxPoolOp->get_strides().size(); i++) {
-            stride.push_back(static_cast<ptrdiff_t>(maxPoolOp->get_strides()[i]));
-        }
-        for (int i = 0; i < maxPoolOp->get_kernel().size(); i++) {
-            kernel.push_back(static_cast<ptrdiff_t>(maxPoolOp->get_kernel()[i]));
-        }
-        for (int i = 0; i < maxPoolOp->get_pads_begin().size(); i++) {
-            data_pad_begin.push_back(static_cast<ptrdiff_t>(maxPoolOp->get_pads_begin()[i]));
-        }
-        for (int i = 0; i < maxPoolOp->get_pads_end().size(); i++) {
-            data_pad_end.push_back(static_cast<ptrdiff_t>(maxPoolOp->get_pads_end()[i]));
-        }
-        auto_pad = (maxPoolOp->get_auto_pad() == ov::op::PadType::SAME_LOWER || maxPoolOp->get_auto_pad() == ov::op::PadType::SAME_UPPER);
-    } else if (avgPoolOp) {
+        get_attributes(dilation, maxPoolOp_v8->get_dilations());
+        get_attributes(stride, maxPoolOp_v8->get_strides());
+        get_attributes(kernel, maxPoolOp_v8->get_kernel());
+        get_attributes(data_pad_begin, maxPoolOp_v8->get_pads_begin());
+        get_attributes(data_pad_end, maxPoolOp_v8->get_pads_end());
+
+        auto_pad = (maxPoolOp_v8->get_auto_pad() == ov::op::PadType::SAME_LOWER || maxPoolOp_v8->get_auto_pad() == ov::op::PadType::SAME_UPPER);
+    } else if (auto maxPoolOp_v1 = ov::as_type_ptr<const ov::op::v1::MaxPool>(op)) {
+        algorithm = PoolingMax;
+        exclude_pad = false;
+
+        get_attributes(stride, maxPoolOp_v1->get_strides());
+        get_attributes(kernel, maxPoolOp_v1->get_kernel());
+        get_attributes(data_pad_begin, maxPoolOp_v1->get_pads_begin());
+        get_attributes(data_pad_end, maxPoolOp_v1->get_pads_end());
+        dilation.resize(kernel.size(), 1);
+
+        auto_pad = (maxPoolOp_v1->get_auto_pad() == ov::op::PadType::SAME_LOWER || maxPoolOp_v1->get_auto_pad() == ov::op::PadType::SAME_UPPER);
+    } else if (auto avgPoolOp = ov::as_type_ptr<const ov::op::v1::AvgPool>(op)) {
         algorithm = PoolingAvg;
         exclude_pad = avgPoolOp->get_exclude_pad();
 
-        for (int i = 0; i < avgPoolOp->get_strides().size(); i++) {
-            stride.push_back(static_cast<ptrdiff_t>(avgPoolOp->get_strides()[i]));
-        }
-        for (int i = 0; i < avgPoolOp->get_kernel().size(); i++) {
-            kernel.push_back(static_cast<ptrdiff_t>(avgPoolOp->get_kernel()[i]));
-        }
-        for (int i = 0; i < avgPoolOp->get_pads_begin().size(); i++) {
-            data_pad_begin.push_back(static_cast<ptrdiff_t>(avgPoolOp->get_pads_begin()[i]));
-        }
-        for (int i = 0; i < avgPoolOp->get_pads_end().size(); i++) {
-            data_pad_end.push_back(static_cast<ptrdiff_t>(avgPoolOp->get_pads_end()[i]));
-        }
+        get_attributes(stride, avgPoolOp->get_strides());
+        get_attributes(kernel, avgPoolOp->get_kernel());
+        get_attributes(data_pad_begin, avgPoolOp->get_pads_begin());
+        get_attributes(data_pad_end, avgPoolOp->get_pads_end());
+        dilation.resize(kernel.size(), 1);
+
         auto_pad = (avgPoolOp->get_auto_pad() == ov::op::PadType::SAME_LOWER || avgPoolOp->get_auto_pad() == ov::op::PadType::SAME_UPPER);
     }
 }
@@ -94,20 +103,23 @@ std::vector<memory::format_tag> MKLDNNPoolingNode::getAvailableFormatsForDims(co
     return {memory::format_tag::any};
 }
 
-void MKLDNNPoolingNode::initEffectivePad(const Shape &inShape, const Shape &outShape) {
+void MKLDNNPoolingNode::initEffectiveAttributes(const Shape &inShape, const Shape &outShape) {
     effective_pad_begin = data_pad_begin;
     effective_pad_end.resize(data_pad_end.size());
+    effective_dilation.resize(dilation.size(), 0);
 
     const auto &inDims = inShape.getStaticDims();
     const auto &outDims = outShape.getStaticDims();
 
     for (int i = 0; i < effective_pad_end.size(); i++) {
         int krn = kernel[i];
+        int dil = dilation[i];
         int src = inDims[2 + i];
         int dst = outDims[2 + i];
 
-        int calc_dst = (src - krn + data_pad_begin[i]) / stride[i] + 1;
+        int calc_dst = (src - (1 + (krn  - 1) * dil) + data_pad_begin[i]) / stride[i] + 1;
         effective_pad_end[i] = (dst - calc_dst) * stride[i];
+        effective_dilation[i] = dil - 1;
     }
 }
 
@@ -120,8 +132,8 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
     if (getChildEdges().empty())
         IE_THROW() << "Incorrect number of output edges for layer " << getName();
 
-    inputPrecision = getOriginalInputPrecisionAtPort(0);
-    outputPrecision = getOriginalOutputPrecisionAtPort(0);
+    InferenceEngine::Precision inputPrecision = getOriginalInputPrecisionAtPort(0);
+    InferenceEngine::Precision outputPrecision = getOriginalOutputPrecisionAtPort(0);
 
     // WA: LPT transformation has WA which allows average pooling has I8/U8 output precision instead of FP32,
     // so we explicitly set output precision as FP32
@@ -151,7 +163,7 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
     if ((inputRank < 3) || (inputRank > 5))
         IE_THROW() << "Pooling layer. Unsupported mode. Only 3D, 4D and 5D blobs are supported as input.";
 
-    initEffectivePad(MemoryDescUtils::makeDummyShape(parentShape),
+    initEffectiveAttributes(MemoryDescUtils::makeDummyShape(parentShape),
                             MemoryDescUtils::makeDummyShape(childShape));
 
     if (inputPrecision == Precision::I8 || inputPrecision == Precision::U8) {
@@ -185,7 +197,7 @@ void MKLDNNPoolingNode::getSupportedDescriptors() {
     }
 }
 
-std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> MKLDNNPoolingNode::getPaddingFromNode(std::shared_ptr<ngraph::Node> node) const {
+std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> MKLDNNPoolingNode::getPaddingFromNode(std::shared_ptr<ov::Node> node) const {
     const auto convertPadding = [](const VectorDims &newPads) {
         std::vector<ptrdiff_t> pads(newPads.size());
         for (int i = 0; i < newPads.size(); i++) {
@@ -195,12 +207,16 @@ std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> MKLDNNPoolingNode::get
     };
 
     VectorDims padsBegin, padsEnd;
-    if (getAlgorithm() == PoolingMax) {
-        const auto pool = ngraph::as_type_ptr<const ngraph::op::v1::MaxPool>(opToShapeInfer);
+    if (isMaxPool8) {
+        const auto pool = ov::as_type_ptr<const ov::op::v8::MaxPool>(opToShapeInfer);
+        padsBegin = pool->get_pads_begin();
+        padsEnd = pool->get_pads_end();
+    } else if (getAlgorithm() == PoolingMax) {
+        const auto pool = ov::as_type_ptr<const ov::op::v1::MaxPool>(opToShapeInfer);
         padsBegin = pool->get_pads_begin();
         padsEnd = pool->get_pads_end();
     } else if (getAlgorithm() == PoolingAvg) {
-        const auto pool = ngraph::as_type_ptr<const ngraph::op::v1::AvgPool>(opToShapeInfer);
+        const auto pool = ov::as_type_ptr<const ov::op::v1::AvgPool>(opToShapeInfer);
         padsBegin = pool->get_pads_begin();
         padsEnd = pool->get_pads_end();
     }
@@ -231,12 +247,12 @@ void MKLDNNPoolingNode::prepareParams() {
         if (auto_pad) {
             std::tie(data_pad_begin, data_pad_end) = getPaddingFromNode(opToShapeInfer);
         }
-        initEffectivePad(inDesc->getShape(), outDesc->getShape());
+        initEffectiveAttributes(inDesc->getShape(), outDesc->getShape());
     }
 
     mkldnn::algorithm alg = getPoolingAlgorithm();
     MKLDNNDescriptor desc{createDescriptorInternal(in_candidate, out_candidate, alg)};
-    pooling_forward::primitive_desc prim_desc;
+    pooling_v2_forward::primitive_desc prim_desc;
     primitive_desc_iterator itpd = desc.createPrimitiveDescriptorIterator(getEngine(), *attr);
 
     while (static_cast<bool>(itpd)) {
@@ -250,7 +266,7 @@ void MKLDNNPoolingNode::prepareParams() {
             IE_THROW() << "Primitive descriptor was not found for node " << getName() << ".";
     }
 
-    prim.reset(new pooling_forward(prim_desc));
+    prim.reset(new pooling_v2_forward(prim_desc));
 
     auto src = getParentEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
     auto dst = getChildEdgesAtPort(0)[0]->getMemoryPtr()->GetPrimitive();
@@ -296,7 +312,7 @@ mkldnn::algorithm MKLDNNPoolingNode::getPoolingAlgorithm() const {
     }
 }
 
-std::shared_ptr<pooling_forward::desc> MKLDNNPoolingNode::createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
+std::shared_ptr<pooling_v2_forward::desc> MKLDNNPoolingNode::createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
                                                                                       const mkldnn::memory::desc& out_candidate,
                                                                                       const mkldnn::algorithm alg) const {
     if (alg == mkldnn::algorithm::undef) {
@@ -306,11 +322,12 @@ std::shared_ptr<pooling_forward::desc> MKLDNNPoolingNode::createDescriptorIntern
     auto convert = [] (std::vector<ptrdiff_t> orig_dims) {
         return memory::dims(orig_dims.begin(), orig_dims.end());
     };
-    std::shared_ptr<pooling_forward::desc> desc_ptr(
-            new pooling_forward::desc(prop_kind::forward_scoring, alg,
+    std::shared_ptr<pooling_v2_forward::desc> desc_ptr(
+            new pooling_v2_forward::desc(prop_kind::forward_scoring, alg,
                                          in_candidate, out_candidate,
                                          convert(stride),
                                          convert(kernel),
+                                         convert(effective_dilation),
                                          convert(effective_pad_begin),
                                          convert(effective_pad_end)));
 
@@ -343,14 +360,12 @@ void MKLDNNPoolingNode::createDescriptor(const std::vector<MemoryDescPtr> &input
         if (auto_pad) {
             std::tie(data_pad_begin, data_pad_end) = getPaddingFromNode(opToShapeInfer);
         }
-        initEffectivePad(inDesc->getShape(), outDesc->getShape());
+        initEffectiveAttributes(inDesc->getShape(), outDesc->getShape());
     }
     auto dnnlOutDesc = MemoryDescUtils::convertToDnnlBlockedMemoryDesc(*outDesc);
     auto out_candidate = dnnlOutDesc.getDnnlDesc();
 
-    mkldnn::algorithm alg = getPoolingAlgorithm();
-    auto desc_ptr = createDescriptorInternal(in_candidate, out_candidate, alg);
-
+    auto desc_ptr = createDescriptorInternal(in_candidate, out_candidate, getPoolingAlgorithm());
     descs.emplace_back(desc_ptr);
 }
 
@@ -383,6 +398,18 @@ void MKLDNNPoolingNode::initSupportedPrimitiveDescriptors() {
 
                 config.outConfs.push_back(dataConfig);
             }
+
+            // CPU plugin doesn't support second output of MaxPool-8, but anyway we should have out config for second port as stub
+            if (isMaxPool8) {
+                auto& creatorsMap = BlockedDescCreator::getCommonCreators();
+                PortConfig dataConfig;
+                dataConfig.inPlace = -1;
+                dataConfig.constant = false;
+                dataConfig.desc = creatorsMap.at(LayoutType::ncsp)->createSharedDesc(config.outConfs.front().desc->getPrecision(), getOutputShapeAtPort(1));
+
+                config.outConfs.push_back(dataConfig);
+            }
+
             impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
 
             supportedPrimitiveDescriptors.emplace_back(config, impl_type);
@@ -434,6 +461,18 @@ void MKLDNNPoolingNode::initDescriptor(const NodeConfig& config) {
                 dataConfig.desc = getDstMemDesc(itpd, i);
                 cfg.outConfs.push_back(dataConfig);
             }
+
+            // CPU plugin doesn't support second output of MaxPool-8, but anyway we should have out config for second port as stub
+            if (isMaxPool8) {
+                auto& creatorsMap = BlockedDescCreator::getCommonCreators();
+                PortConfig dataConfig;
+                dataConfig.inPlace = -1;
+                dataConfig.constant = false;
+                dataConfig.desc = creatorsMap.at(LayoutType::ncsp)->createSharedDesc(cfg.outConfs.front().desc->getPrecision(), getOutputShapeAtPort(1));
+
+                cfg.outConfs.push_back(dataConfig);
+            }
+
             impl_desc_type impl_type = parse_impl_name(itpd.impl_info_str());
             if (selected_count == selectedPrimitiveDescriptorIndex) {
                 if (impl_type != selectedPD->getImplementationType()) {

inference-engine/src/mkldnn_plugin/nodes/mkldnn_pooling_node.h

@@ -14,7 +14,7 @@ namespace MKLDNNPlugin {
 
 class MKLDNNPoolingNode : public MKLDNNNode {
 public:
-    MKLDNNPoolingNode(const std::shared_ptr<ngraph::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
+    MKLDNNPoolingNode(const std::shared_ptr<ov::Node>& op, const mkldnn::engine& eng, MKLDNNWeightsSharing::Ptr &cache);
 
     void createDescriptor(const std::vector<MemoryDescPtr>& inputDesc,
                           const std::vector<MemoryDescPtr>& outputDesc) override;
@@ -31,7 +31,7 @@ public:
     void prepareParams() override;
     void executeDynamicImpl(mkldnn::stream strm) override { execute(strm); }
 
-    static bool isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept;
+    static bool isSupportedOperation(const std::shared_ptr<const ov::Node>& op, std::string& errorMessage) noexcept;
 
 protected:
     AttrPtr initPrimitiveAttr() const override;
@@ -39,17 +39,19 @@ protected:
 private:
     void setPostOps(mkldnn::primitive_attr &attr, bool initWeights = false) const;
 
-    std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> getPaddingFromNode(std::shared_ptr<ngraph::Node> node) const;
-    void initEffectivePad(const Shape &inDims, const Shape &outDims);
+    std::pair<std::vector<ptrdiff_t>, std::vector<ptrdiff_t>> getPaddingFromNode(std::shared_ptr<ov::Node> node) const;
+    void initEffectiveAttributes(const Shape &inDims, const Shape &outDims);
     mkldnn::algorithm getPoolingAlgorithm() const;
-    std::shared_ptr<mkldnn::pooling_forward::desc> createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
+    std::shared_ptr<mkldnn::pooling_v2_forward::desc> createDescriptorInternal(const mkldnn::memory::desc& in_candidate,
                                                                                const mkldnn::memory::desc& out_candidate,
                                                                                const mkldnn::algorithm alg) const;
 
     AttrPtr pAttr;
 
+    bool isMaxPool8 = false;
     bool auto_pad = false;
     bool exclude_pad = false;
+    std::vector<ptrdiff_t> dilation;
     std::vector<ptrdiff_t> stride;
     std::vector<ptrdiff_t> kernel;
 
@@ -59,15 +61,16 @@ private:
     std::vector<ptrdiff_t> effective_pad_begin;
     std::vector<ptrdiff_t> effective_pad_end;
 
+    /// Effective dilation. Used to define correct dilation for OneDNN.
+    /// For OneDNN default dilation is vector of zero
+    std::vector<ptrdiff_t> effective_dilation;
+
     /// Effective pad value. Describe how much zero element added to input
     /// data tensor. May be less than "Effective padding" values.
     /// If pooling window is out of this padding, the region of averaging
     /// is decreased.
     std::vector<ptrdiff_t> data_pad_begin;
     std::vector<ptrdiff_t> data_pad_end;
-
-    InferenceEngine::Precision inputPrecision = InferenceEngine::Precision::FP32;
-    InferenceEngine::Precision outputPrecision = InferenceEngine::Precision::FP32;
 };
 
 }  // namespace MKLDNNPlugin

inference-engine/src/mkldnn_plugin/nodes/mkldnn_reduce_node.cpp

@@ -138,7 +138,7 @@ private:
     using Vmm = typename conditional3<isa == cpu::x64::sse41, Xbyak::Xmm, isa == cpu::x64::avx2,
             Xbyak::Ymm, Xbyak::Zmm>::type;
     size_t vlen = cpu_isa_traits<isa>::vlen;
-    bool planar_layout;
+    bool planar_layout = false;
 
     Xbyak::Address table_val(int index) { return ptr[reg_table + index * vlen]; }
 
@@ -1136,7 +1136,7 @@ private:
     using Vmm = typename conditional3<isa == cpu::x64::sse41, Xbyak::Xmm, isa == cpu::x64::avx2,
             Xbyak::Ymm, Xbyak::Zmm>::type;
     size_t vlen = cpu_isa_traits<isa>::vlen;
-    bool planar_layout;
+    bool planar_layout = false;
 
     Xbyak::Reg64 reg_dst = r8;
     Xbyak::Reg64 reg_work_amount = r9;

inference-engine/src/mkldnn_plugin/nodes/mkldnn_rnn.cpp

@@ -367,7 +367,7 @@ void MKLDNNRNN::initSeq(const std::shared_ptr<ngraph::Node>& op) {
     const auto rtInfo = op->get_rt_info();
 
     if (rtInfo.count("seqAxis")) {
-        nativeOrder = std::dynamic_pointer_cast<ngraph::VariantWrapper<int64_t>>(rtInfo.at("seqAxis"))->get() == 0;
+        nativeOrder = std::dynamic_pointer_cast<ov::RuntimeAttributeWrapper<int64_t>>(rtInfo.at("seqAxis"))->get() == 0;
     }
     out_data_dims.erase(out_data_dims.begin() + 1);
 

inference-engine/src/mkldnn_plugin/nodes/mkldnn_scatter_update_node.cpp

@@ -114,13 +114,16 @@ void MKLDNNScatterUpdateNode::initSupportedPrimitiveDescriptors() {
                         << "which should be smaller than or equal to input tensor rank";
                 }
 
-                SizeVector expectUpdateShape = {};
                 size_t tupleRank = indicesRank - 1;
+                SizeVector expectUpdateShape(tupleRank + srcRank - k, 0);
+                int updateAxisIter = 0;
                 for (size_t ri = 0; ri < tupleRank; ri++) {
-                    expectUpdateShape.push_back(indicesDim[ri]);
+                    expectUpdateShape[updateAxisIter] = indicesDim[ri];
+                    updateAxisIter++;
                 }
                 for (size_t rd = k; rd < srcRank; rd++) {
-                    expectUpdateShape.push_back(srcDataDim[rd]);
+                    expectUpdateShape[updateAxisIter] = srcDataDim[rd];
+                    updateAxisIter++;
                 }
                 if (expectUpdateShape.size() != updateRank) {
                     IE_THROW() << errorPrefix << " do not have matched tensor rank relationship for input, indices and update";
@@ -315,13 +318,16 @@ void MKLDNNScatterUpdateNode::execute(mkldnn::stream strm) {
             SizeVector updateDim = getParentEdgeAt(UPDATE_ID)->getMemory().getStaticDims();
             size_t indicesRank = indicesDim.size();
             size_t updateRank = updateDim.size();
-            SizeVector expectUpdateShape = {};
+            SizeVector expectUpdateShape(srcRank + indicesRank - 1, 0);
+            int axisIter = 0;
             for (size_t rs = 0; rs < srcRank; rs++) {
                 if (rs != axis) {
-                    expectUpdateShape.push_back(srcDataDim[rs]);
+                    expectUpdateShape[axisIter] = srcDataDim[rs];
+                    axisIter++;
                 } else {
                     for (size_t ri = 0; ri < indicesRank; ri++) {
-                        expectUpdateShape.push_back(indicesDim[ri]);
+                        expectUpdateShape[axisIter] = indicesDim[ri];
+                        axisIter++;
                     }
                 }
             }

inference-engine/src/mkldnn_plugin/nodes/mkldnn_topk_node.h

@@ -92,7 +92,8 @@ private:
     bool sort_value = false;
     bool mode_max = true;
 
-    int dim, before_num;
+    int dim = 0;
+    int before_num = 0;
 
     std::string errorPrefix;
 

inference-engine/src/mkldnn_plugin/utils/ngraph_utils.hpp

@@ -13,7 +13,7 @@ namespace MKLDNNPlugin {
 inline std::string getRTInfoValue(const std::map<std::string, ov::Any>& rtInfo, std::string paramName) {
     auto it = rtInfo.find(paramName);
     if (it != rtInfo.end()) {
-        auto value = std::dynamic_pointer_cast<ngraph::VariantImpl<std::string>>(it->second);
+        auto value = std::dynamic_pointer_cast<ov::RuntimeAttributeImpl<std::string>>(it->second);
         return value->get();
     } else {
         return "";
@@ -23,10 +23,13 @@ inline std::string getRTInfoValue(const std::map<std::string, ov::Any>& rtInfo,
 inline std::string getPrimitivesPriorityValue(const std::shared_ptr<ngraph::Node> &node) {
     const auto &rtInfo = node->get_rt_info();
 
-    if (!rtInfo.count(ov::PrimitivesPriority::get_type_info_static())) return "";
+    auto it_info = rtInfo.find(ov::PrimitivesPriority::get_type_info_static());
 
-    const auto &attr = rtInfo.at(ov::PrimitivesPriority::get_type_info_static());
-    return ngraph::as_type_ptr<ov::PrimitivesPriority>(attr)->get();
+    if (it_info == rtInfo.end()) {
+        return {};
+    }
+
+    return it_info->second.as<ov::PrimitivesPriority>().value;
 }
 
 template <typename T>

inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.cpp

@@ -11,25 +11,25 @@
 using namespace ngraph;
 using namespace ov;
 
-MLKDNNInputMemoryFormats::~MLKDNNInputMemoryFormats() = default;
+MKLDNNInputMemoryFormats::~MKLDNNInputMemoryFormats() = default;
 
-std::string ngraph::getMLKDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
-    auto it_info = node->get_rt_info().find(MLKDNNInputMemoryFormatsAttr);
+std::string ngraph::getMKLDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
+    auto it_info = node->get_rt_info().find(MKLDNNInputMemoryFormats::get_type_info_static());
     if (it_info != node->get_rt_info().end()) {
-        if (auto ptr = it_info->second.as<std::shared_ptr<MLKDNNInputMemoryFormats>>()) {
-            return ptr->getMemoryFormats();
+        if (it_info->second.is<MKLDNNInputMemoryFormats>()) {
+            return it_info->second.as<MKLDNNInputMemoryFormats>().getMemoryFormats();
         }
     }
     return {};
 }
 
-MLKDNNOutputMemoryFormats::~MLKDNNOutputMemoryFormats() = default;
+MKLDNNOutputMemoryFormats::~MKLDNNOutputMemoryFormats() = default;
 
-std::string ngraph::getMLKDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
-    auto it_info = node->get_rt_info().find(MLKDNNOutputMemoryFormatsAttr);
+std::string ngraph::getMKLDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node) {
+    auto it_info = node->get_rt_info().find(MKLDNNOutputMemoryFormats::get_type_info_static());
     if (it_info != node->get_rt_info().end()) {
-        if (auto ptr = it_info->second.as<std::shared_ptr<MLKDNNOutputMemoryFormats>>()) {
-            return ptr->getMemoryFormats();
+        if (it_info->second.is<MKLDNNOutputMemoryFormats>()) {
+            return it_info->second.as<MKLDNNOutputMemoryFormats>().getMemoryFormats();
         }
     }
     return {};

inference-engine/src/mkldnn_plugin/utils/rt_info/memory_formats_attribute.hpp

@@ -12,33 +12,31 @@
 
 namespace ngraph {
 
-constexpr const char *MLKDNNInputMemoryFormatsAttr = "MLKDNNInputMemoryFormats";
-constexpr const char *MLKDNNOutputMemoryFormatsAttr = "MLKDNNOutputMemoryFormats";
+constexpr const char *MKLDNNInputMemoryFormatsAttr = "MKLDNNInputMemoryFormats";
+constexpr const char *MKLDNNOutputMemoryFormatsAttr = "MKLDNNOutputMemoryFormats";
 
 template<typename MemoryFormat>
-class MLKDNNMemoryFormats : public Variant {
+class MKLDNNMemoryFormats : public ov::RuntimeAttribute {
 protected:
     std::string memory_format;
 
 public:
-    MLKDNNMemoryFormats() = default;
-    explicit MLKDNNMemoryFormats(const std::string &_memory_format) : memory_format(_memory_format) {}
+    MKLDNNMemoryFormats() = default;
+    explicit MKLDNNMemoryFormats(const std::string &_memory_format) : memory_format(_memory_format) {}
     std::string getMemoryFormats() const { return memory_format; }
 
-    ov::Any merge(const ngraph::NodeVector & nodes) override {
+    ov::Any merge(const ngraph::NodeVector & nodes) const override {
         std::set<std::string> unique_mem_format;
 
         for (auto &node : nodes) {
-            auto it_info = node->get_rt_info().find(MemoryFormat::get_type_info_static().name);
+            auto it_info = node->get_rt_info().find(MemoryFormat::get_type_info_static());
             if (it_info != node->get_rt_info().end()) {
-                if (auto ptr = it_info->second.template as<std::shared_ptr<MemoryFormat>>()) {
-                    std::string mem_format = ptr->getMemoryFormats();
+                std::string mem_format = it_info->second.template as<MemoryFormat>().getMemoryFormats();
                 if (!mem_format.empty()) {
                     unique_mem_format.insert(mem_format);
                 }
             }
         }
-        }
 
         if (unique_mem_format.size() > 1) {
             throw ngraph::ngraph_error(
@@ -50,28 +48,28 @@ public:
         if (unique_mem_format.size() == 1) {
             final_mem_format = *unique_mem_format.begin();
         }
-        return std::make_shared<MemoryFormat>(final_mem_format);
+        return MemoryFormat{final_mem_format};
     }
 };
 
 
-class MLKDNNInputMemoryFormats : public MLKDNNMemoryFormats<MLKDNNInputMemoryFormats> {
+class MKLDNNInputMemoryFormats : public MKLDNNMemoryFormats<MKLDNNInputMemoryFormats> {
 public:
-    OPENVINO_RTTI(MLKDNNInputMemoryFormatsAttr);
-    MLKDNNInputMemoryFormats() = default;
-    explicit MLKDNNInputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
-    ~MLKDNNInputMemoryFormats() override;
+    OPENVINO_RTTI(MKLDNNInputMemoryFormatsAttr);
+    MKLDNNInputMemoryFormats() = default;
+    explicit MKLDNNInputMemoryFormats(const std::string &_memory_format) : MKLDNNMemoryFormats(_memory_format) {}
+    ~MKLDNNInputMemoryFormats() override;
 };
 
-std::string getMLKDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
+std::string getMKLDNNInputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
 
-class MLKDNNOutputMemoryFormats : public MLKDNNMemoryFormats<MLKDNNOutputMemoryFormats> {
+class MKLDNNOutputMemoryFormats : public MKLDNNMemoryFormats<MKLDNNOutputMemoryFormats> {
 public:
-    OPENVINO_RTTI(MLKDNNOutputMemoryFormatsAttr);
-    MLKDNNOutputMemoryFormats() = default;
-    explicit MLKDNNOutputMemoryFormats(const std::string &_memory_format) : MLKDNNMemoryFormats(_memory_format) {}
-    ~MLKDNNOutputMemoryFormats() override;
+    OPENVINO_RTTI(MKLDNNOutputMemoryFormatsAttr);
+    MKLDNNOutputMemoryFormats() = default;
+    explicit MKLDNNOutputMemoryFormats(const std::string &_memory_format) : MKLDNNMemoryFormats(_memory_format) {}
+    ~MKLDNNOutputMemoryFormats() override;
 };
-std::string getMLKDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
+std::string getMKLDNNOutputMemoryFormats(const std::shared_ptr<ngraph::Node>& node);
 
 }  // namespace ngraph

inference-engine/src/offline_transformations/include/mask_attribute.hpp

@@ -16,7 +16,6 @@
 #include <set>
 
 #include <ngraph/node.hpp>
-#include <ngraph/variant.hpp>
 
 namespace ngraph {
 
@@ -28,6 +27,11 @@ namespace ngraph {
 class Mask : public std::vector<std::set<uint64_t>>,
              public std::enable_shared_from_this<Mask> {
 public:
+    static const ::ov::DiscreteTypeInfo& get_type_info_static() {
+        static const ::ov::DiscreteTypeInfo type_info{"Mask", 0, "0"};
+        return type_info;
+    }
+
     using Ptr = std::shared_ptr<Mask>;
 
     Mask() = default;
@@ -180,6 +184,7 @@ public:
             item.clear();
         }
     }
+
 private:
     bool m_is_shape_like{false};
 
@@ -199,22 +204,3 @@ Mask::Ptr getMask(const Output<Node> & output);
 void setMask(Output<Node> output, const Mask::Ptr & mask);
 
 }  // namespace ngraph
-
-namespace ov {
-
-extern template class VariantImpl<ngraph::Mask::Ptr>;
-
-template<>
-class VariantWrapper<ngraph::Mask::Ptr> : public VariantImpl<ngraph::Mask::Ptr> {
-public:
-    OPENVINO_RTTI("VariantWrapper<Mask::Ptr>");
-    BWDCMP_RTTI_DECLARATION;
-
-    static std::shared_ptr<VariantWrapper<ngraph::Mask::Ptr>> create(const value_type & value) {
-        return std::make_shared<VariantWrapper<ngraph::Mask::Ptr>>(value);
-    }
-
-    explicit VariantWrapper(const value_type &value) : VariantImpl<value_type>(value) {}
-};
-
-}  // namespace ov

inference-engine/src/offline_transformations/src/pruning/mask_attribute.cpp

@@ -14,28 +14,22 @@ namespace ngraph {
 
 Mask::Ptr getMask(const Output<const Node> & output) {
     auto &rtInfo = output.get_rt_info();
-    using MaskWrapper = VariantWrapper<Mask::Ptr>;
+    if (!rtInfo.count(Mask::get_type_info_static())) return nullptr;
 
-    if (!rtInfo.count(MaskWrapper::get_type_info_static().name)) return nullptr;
-
-    const auto &attr = rtInfo.at(MaskWrapper::get_type_info_static().name);
-    return ov::as_type_ptr<MaskWrapper>(attr)->get();
+    const auto &attr = rtInfo.at(Mask::get_type_info_static());
+    return attr.as<Mask::Ptr>();
 }
 
 Mask::Ptr getMask(const Output<Node> & output) {
     auto &rtInfo = output.get_rt_info();
-    using MaskWrapper = VariantWrapper<Mask::Ptr>;
-
-    if (!rtInfo.count(MaskWrapper::get_type_info_static().name)) return nullptr;
-
-    const auto &attr = rtInfo.at(MaskWrapper::get_type_info_static().name);
-    return ov::as_type_ptr<MaskWrapper>(attr)->get();
+    if (!rtInfo.count(Mask::get_type_info_static())) return nullptr;
+    const auto &attr = rtInfo.at(Mask::get_type_info_static());
+    return attr.as<Mask::Ptr>();
 }
 
 void setMask(Output<Node> output, const Mask::Ptr & mask) {
     auto &rtInfo = output.get_rt_info();
-    using MaskWrapper = VariantWrapper<Mask::Ptr>;
-    rtInfo[MaskWrapper::get_type_info_static().name] = MaskWrapper::create(mask);
+    rtInfo[Mask::get_type_info_static()] = mask;
 }
 
 std::ostream & operator<< (std::ostream & out, const Mask & mask) {
@@ -54,11 +48,3 @@ std::ostream & operator<< (std::ostream & out, const Mask & mask) {
 }
 
 }  // namespace ngraph
-
-namespace ov {
-
-template class ngraph::VariantImpl<ngraph::Mask::Ptr>;
-
-BWDCMP_RTTI_DEFINITION(VariantWrapper<ngraph::Mask::Ptr>);
-
-}  // namespace ov

inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_deserialization.cpp

@@ -40,7 +40,7 @@ protected:
         ov::frontend::FrontEnd::Ptr FE;
         ov::frontend::InputModel::Ptr inputModel;
 
-        ov::VariantVector params{ov::make_variant(&modelStream)};
+        ov::RuntimeAttributeVector params{&modelStream};
 
         FE = manager.load_by_model(params);
         if (FE)
@@ -119,7 +119,7 @@ TEST_F(RTInfoDeserialization, NodeV10) {
     ASSERT_NE(nullptr, f);
 
     auto check_rt_info = [](const RTMap& info) {
-        const std::string& key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string& key = ngraph::FusedNames::get_type_info_static();
         EXPECT_FALSE(info.count(key));
 
         const std::string& key_old_api_order = ov::OldApiMapOrder::get_type_info_static();
@@ -278,7 +278,7 @@ TEST_F(RTInfoDeserialization, InputAndOutputV10) {
     ASSERT_NE(nullptr, f);
 
     auto check_rt_info = [](const RTMap& info) {
-        const std::string& key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string& key = ngraph::FusedNames::get_type_info_static();
         ASSERT_FALSE(info.count(key));
     };
 
@@ -421,27 +421,22 @@ TEST_F(RTInfoDeserialization, NodeV11) {
     ASSERT_NE(nullptr, f);
 
     auto check_fused_names = [](const RTMap& info, const std::string& names) {
-        const std::string& key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string& key = ngraph::FusedNames::get_type_info_static();
         ASSERT_TRUE(info.count(key));
-        auto fused_names_attr = std::dynamic_pointer_cast<VariantWrapper<ngraph::FusedNames>>(info.at(key));
-        ASSERT_TRUE(fused_names_attr);
-        EXPECT_EQ(fused_names_attr->get().getNames(), names);
+        auto fused_names_attr = info.at(key).as<ngraph::FusedNames>();
+        EXPECT_EQ(fused_names_attr.getNames(), names);
     };
 
     auto check_old_api_map_order = [](const RTMap & info, const std::vector<uint64_t> & order) {
         const std::string & old_api_map_key = ov::OldApiMapOrder::get_type_info_static();
         ASSERT_TRUE(info.count(old_api_map_key));
-        auto old_api_map_attr = std::dynamic_pointer_cast<ov::OldApiMapOrder>(info.at(old_api_map_key));
-        ASSERT_TRUE(old_api_map_attr);
-        auto old_api_map_attr_val = old_api_map_attr->get();
+        auto old_api_map_attr_val = info.at(old_api_map_key).as<ov::OldApiMapOrder>().value;
         EXPECT_EQ(old_api_map_attr_val, order);
     };
     auto check_old_api_map_type = [](const RTMap & info, const ngraph::element::Type& type) {
         const std::string & old_api_map_key = ov::OldApiMapElementType::get_type_info_static();
         ASSERT_TRUE(info.count(old_api_map_key));
-        auto old_api_map_attr = std::dynamic_pointer_cast<ov::OldApiMapElementType>(info.at(old_api_map_key));
-        ASSERT_TRUE(old_api_map_attr);
-        auto old_api_map_attr_val = old_api_map_attr->get();
+        auto old_api_map_attr_val = info.at(old_api_map_key).as<ov::OldApiMapElementType>().value;
         EXPECT_EQ(old_api_map_attr_val, type);
     };
 
@@ -501,8 +496,7 @@ TEST_F(RTInfoDeserialization, NodeV11) {
         auto round = std::make_shared<opset8::Round>(convert_param,
             ngraph::opset8::Round::RoundMode::HALF_TO_EVEN);
         // TODO: runtime information should migrate as well?
-        round->get_rt_info()[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-            std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames("Round1,Round2"));
+        round->get_rt_info()[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames("Round1,Round2");
 
         // TODO: No guarantee that exactly 'convert, then transpose' will be added by implicit post-processing
         auto constant_result = std::make_shared<opset8::Constant>(ngraph::element::u64,
@@ -722,20 +716,20 @@ TEST_F(RTInfoDeserialization, InputAndOutputV11) {
     check_version(f, 11);
 
     auto check_fused_names = [](const RTMap& info, const std::string& names) {
-        const std::string& key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string& key = ngraph::FusedNames::get_type_info_static();
         ASSERT_TRUE(info.count(key));
-        auto fused_names_attr = std::dynamic_pointer_cast<VariantWrapper<ngraph::FusedNames>>(info.at(key));
-        ASSERT_TRUE(fused_names_attr);
-        ASSERT_EQ(fused_names_attr->get().getNames(), names);
+        auto fused_names_attr = info.at(key).as<ngraph::FusedNames>();
+        ASSERT_EQ(fused_names_attr.getNames(), names);
     };
 
 
     auto param = f->get_parameters()[0];
     check_fused_names(param->output(0).get_rt_info(), "test1,test2");
     EXPECT_EQ(param->get_layout(), "NCHW");
-    auto var0 = std::dynamic_pointer_cast<ov::preprocess::TensorInfoMemoryType>(
-            f->input(0).get_rt_info()[ov::preprocess::TensorInfoMemoryType::get_type_info_static()]);
-    EXPECT_EQ(var0->get(), "test_memory_type");
+    auto var0 = f->input(0).get_rt_info()
+        .at(ov::preprocess::TensorInfoMemoryType::get_type_info_static())
+        .as<ov::preprocess::TensorInfoMemoryType>().value;
+    EXPECT_EQ(var0, "test_memory_type");
 
     auto result = f->get_result();
     check_fused_names(result->input(0).get_rt_info(), "test5,test6");

inference-engine/tests/functional/inference_engine/ir_serialization/rt_info_serialization.cpp

@@ -34,7 +34,7 @@ protected:
         ov::frontend::FrontEnd::Ptr FE;
         ov::frontend::InputModel::Ptr inputModel;
 
-        ov::VariantVector params{ov::make_variant(model_path), ov::make_variant(weights_path)};
+        ov::RuntimeAttributeVector params{model_path, weights_path};
 
         FE = manager.load_by_model(params);
         if (FE)
@@ -52,15 +52,11 @@ private:
 
 TEST_F(RTInfoSerializationTest, all_attributes_latest) {
     auto init_info = [](RTMap & info) {
-        info[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-                std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames("add"));
-        info[ov::PrimitivesPriority::get_type_info_static()] =
-                std::make_shared<ov::PrimitivesPriority>("priority");
-        info[ov::OldApiMapOrder::get_type_info_static()] =
-                std::make_shared<ov::OldApiMapOrder>(std::vector<uint64_t>{0, 2, 3, 1});
-        info[ov::OldApiMapElementType::get_type_info_static()] = std::make_shared<ov::OldApiMapElementType>(
-                ngraph::element::Type_t::f32);
-        info[ov::Decompression::get_type_info_static()] = std::make_shared<ov::Decompression>();
+        info[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames("add");
+        info[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("priority");
+        info[ov::OldApiMapOrder::get_type_info_static()] = ov::OldApiMapOrder(std::vector<uint64_t>{0, 2, 3, 1});
+        info[ov::OldApiMapElementType::get_type_info_static()] = ov::OldApiMapElementType(ngraph::element::Type_t::f32);
+        info[ov::Decompression::get_type_info_static()] = ov::Decompression{};
     };
 
     std::shared_ptr<ngraph::Function> function;
@@ -85,36 +81,29 @@ TEST_F(RTInfoSerializationTest, all_attributes_latest) {
     ASSERT_NE(nullptr, f);
 
     auto check_info = [](const RTMap & info) {
-        const std::string & key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string & key = ngraph::FusedNames::get_type_info_static();
         ASSERT_TRUE(info.count(key));
-        auto fused_names_attr = std::dynamic_pointer_cast<VariantWrapper<ngraph::FusedNames>>(info.at(key));
-        ASSERT_TRUE(fused_names_attr);
-        ASSERT_EQ(fused_names_attr->get().getNames(), "add");
+        auto fused_names_attr = info.at(key).as<ngraph::FusedNames>();
+        ASSERT_EQ(fused_names_attr.getNames(), "add");
 
         const std::string & pkey = ov::PrimitivesPriority::get_type_info_static();
         ASSERT_TRUE(info.count(pkey));
-        auto primitives_priority_attr = std::dynamic_pointer_cast<ov::PrimitivesPriority>(info.at(pkey));
-        ASSERT_TRUE(primitives_priority_attr);
-        ASSERT_EQ(primitives_priority_attr->get(), "priority");
+        auto primitives_priority_attr = info.at(pkey).as<ov::PrimitivesPriority>().value;
+        ASSERT_EQ(primitives_priority_attr, "priority");
 
         const std::string & old_api_map_key_order = ov::OldApiMapOrder::get_type_info_static();
         ASSERT_TRUE(info.count(old_api_map_key_order));
-        auto old_api_map_attr = std::dynamic_pointer_cast<ov::OldApiMapOrder>(info.at(old_api_map_key_order));
-        ASSERT_TRUE(old_api_map_attr);
-        auto old_api_map_attr_val = old_api_map_attr->get();
+        auto old_api_map_attr_val = info.at(old_api_map_key_order).as<ov::OldApiMapOrder>().value;
         ASSERT_EQ(old_api_map_attr_val, std::vector<uint64_t>({0, 2, 3, 1}));
 
         const std::string & old_api_map_key = ov::OldApiMapElementType::get_type_info_static();
         ASSERT_TRUE(info.count(old_api_map_key));
-        auto old_api_map_type = std::dynamic_pointer_cast<ov::OldApiMapElementType>(info.at(old_api_map_key));
-        ASSERT_TRUE(old_api_map_type);
-        auto old_api_map_type_val = old_api_map_type->get();
+        auto old_api_map_type_val = info.at(old_api_map_key).as<ov::OldApiMapElementType>().value;
         ASSERT_EQ(old_api_map_type_val, ngraph::element::Type_t::f32);
 
         const std::string& dkey = ov::Decompression::get_type_info_static();
         ASSERT_TRUE(info.count(dkey));
-        auto decompression_attr = std::dynamic_pointer_cast<ov::Decompression>(info.at(dkey));
-        ASSERT_TRUE(decompression_attr);
+        ASSERT_NO_THROW(info.at(dkey).as<ov::Decompression>());
     };
 
     auto add = f->get_results()[0]->get_input_node_ptr(0);
@@ -128,10 +117,8 @@ TEST_F(RTInfoSerializationTest, all_attributes_latest) {
 
 TEST_F(RTInfoSerializationTest, all_attributes_v10) {
     auto init_info = [](RTMap & info) {
-        info[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-                std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames("add"));
-        info[ov::PrimitivesPriority::get_type_info_static()] =
-                std::make_shared<ov::PrimitivesPriority>("priority");
+        info[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames("add");
+        info["PrimitivesPriority"] = ov::PrimitivesPriority("priority");
     };
 
     std::shared_ptr<ngraph::Function> function;
@@ -154,7 +141,7 @@ TEST_F(RTInfoSerializationTest, all_attributes_v10) {
     ASSERT_NE(nullptr, f);
 
     auto check_info = [](const RTMap & info) {
-        const std::string & key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string & key = ngraph::FusedNames::get_type_info_static();
         ASSERT_FALSE(info.count(key));
     };
 
@@ -168,10 +155,8 @@ TEST_F(RTInfoSerializationTest, all_attributes_v10) {
 
 TEST_F(RTInfoSerializationTest, all_attributes_v11) {
     auto init_info = [](RTMap & info) {
-        info[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-                std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames("add"));
-        info[ov::PrimitivesPriority::get_type_info_static()] =
-                std::make_shared<ov::PrimitivesPriority>("priority");
+        info[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames("add");
+        info[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("priority");
     };
 
     std::shared_ptr<ngraph::Function> function;
@@ -199,24 +184,23 @@ TEST_F(RTInfoSerializationTest, all_attributes_v11) {
     ASSERT_NE(nullptr, f);
 
     auto check_info = [](const RTMap & info) {
-        const std::string & key = VariantWrapper<ngraph::FusedNames>::get_type_info_static();
+        const std::string & key = ngraph::FusedNames::get_type_info_static();
         ASSERT_TRUE(info.count(key));
-        auto fused_names_attr = std::dynamic_pointer_cast<VariantWrapper<ngraph::FusedNames>>(info.at(key));
-        ASSERT_TRUE(fused_names_attr);
-        ASSERT_EQ(fused_names_attr->get().getNames(), "add");
+        auto fused_names_attr = info.at(key).as<ngraph::FusedNames>();
+        ASSERT_EQ(fused_names_attr.getNames(), "add");
 
         const std::string & pkey = ov::PrimitivesPriority::get_type_info_static();
         ASSERT_TRUE(info.count(pkey));
-        auto primitives_priority_attr = std::dynamic_pointer_cast<ov::PrimitivesPriority>(info.at(pkey));
-        ASSERT_TRUE(primitives_priority_attr);
-        ASSERT_EQ(primitives_priority_attr->get(), "priority");
+        auto primitives_priority_attr = info.at(pkey).as<ov::PrimitivesPriority>().value;
+        ASSERT_EQ(primitives_priority_attr, "priority");
     };
 
     auto add = f->get_results()[0]->get_input_node_ptr(0);
     EXPECT_EQ(f->get_parameters()[0]->get_layout(), "NCHW");
-    auto var0 = std::dynamic_pointer_cast<ov::preprocess::TensorInfoMemoryType>(
-            f->input(0).get_rt_info()[ov::preprocess::TensorInfoMemoryType::get_type_info_static()]);
-    EXPECT_EQ(var0->get(), "test_memory_type");
+    auto var0 = f->input(0).get_rt_info()
+        .at(ov::preprocess::TensorInfoMemoryType::get_type_info_static())
+        .as<ov::preprocess::TensorInfoMemoryType>().value;
+    EXPECT_EQ(var0, "test_memory_type");
     EXPECT_EQ(f->get_results()[0]->get_layout(), "????");
     check_info(add->get_rt_info());
     check_info(add->input(0).get_rt_info());

inference-engine/tests/functional/inference_engine/lp_transformations/concat_transformation.cpp

@@ -199,9 +199,9 @@ public:
             testValues.result.convert2,
             testValues.result.dequantization2,
             {
-                make_shared_attribute_ptr<PrecisionPreservedAttribute>(true),
-                make_shared_attribute_ptr<IntervalsAlignmentAttribute>(interval, 256),
-                make_shared_attribute_ptr<QuantizationAlignmentAttribute>(false)
+                PrecisionPreservedAttribute(true),
+                IntervalsAlignmentAttribute(interval, 256),
+                QuantizationAlignmentAttribute(false)
             },
             testValues.result.precisionAfterOperation,
             testValues.result.dequantizationAfter,
@@ -235,13 +235,13 @@ TEST_P(ConcatTransformation, CompareFunctions) {
     ConcatTransformationTestValues testValues = std::get<2>(GetParam());
     const auto actualFakeQuantizes = LayerTransformation::get<opset1::FakeQuantize>(actualFunction);
     if (testValues.axis == 1) {
-        ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<std::shared_ptr<PrecisionsAttribute>>(actualFakeQuantizes)) <<
+        ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) <<
             "PrecisionsAttribute are not the same";
 
         if (testValues.checkIntervalsAlignmentAttributes) {
             auto operations = LayerTransformation::get<opset1::Concat>(actualFunction);
             operations.insert(operations.end(), actualFakeQuantizes.begin(), actualFakeQuantizes.end());
-            ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<std::shared_ptr<IntervalsAlignmentAttribute>>(operations)) <<
+            ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<IntervalsAlignmentAttribute>(operations)) <<
                 "IntervalsAlignmentAttribute are not the same";
         }
     }
@@ -275,13 +275,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2550.f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {0.1f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -304,7 +304,7 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {2.55f}, ngraph::element::f32,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -331,7 +331,7 @@ const std::vector<ConcatTransformationTestValues> testValues = {
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {2.55f}, ngraph::element::f32,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -352,13 +352,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -390,13 +390,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -428,13 +428,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -462,13 +462,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -496,13 +496,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -526,13 +526,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {{1}, {1}, {}, {}}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {{1}, {1}, {}, {}}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -556,13 +556,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {{1, 1, 1, 1}, {1, 1, 1, 1}, {}, {}}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {{1, 1, 1, 1}, {1, 1, 1, 1}, {}, {}}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -586,13 +586,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {1.275f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -616,13 +616,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {{1}, {1}, {}, {}}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {{1}, {1}, {}, {}}, {0.f}, {1.275f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -657,7 +657,7 @@ const std::vector<ConcatTransformationTestValues> testValues = {
                 {{1, 3, 1, 1}, {1, 3, 1, 1}, {}, {}},
                 {0.f, 0.f, 0.f}, {2.55f, 2.55f, 2.55f}, {0.f}, {255.f},
                 ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -666,7 +666,7 @@ const std::vector<ConcatTransformationTestValues> testValues = {
                 {{1, 3, 1, 1}, {1, 3, 1, 1}, {}, {}},
                 {0.f, 0.f, 0.f}, {1.275f, 1.275f, 1.275f}, {0.f}, {255.f},
                 ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -692,13 +692,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {-1.28f}, {1.27f}, {-128.f}, {127.f}, ngraph::element::i8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {-1.28f}, {1.27f}, {-128.f}, {127.f}, ngraph::element::i8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -722,13 +722,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {-1.28f}, {1.27f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -752,13 +752,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {-1.28f}, {1.27f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -896,13 +896,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -940,13 +940,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {-1.28f}, {1.27f}, {-1.28f}, {1.27f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -974,13 +974,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {1.275f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
@@ -1012,13 +1012,13 @@ const std::vector<ConcatTransformationTestValues> testValues = {
         {
             {
                 256ul, {}, {1.275f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul) }
             },
             {},
             {},
             {
                 256ul, {}, {0.f}, {2.55f}, {0.f}, {255.f}, ngraph::element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul)
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{0.f, 2.55f}, 256ul)
             }
             },
             {},

inference-engine/tests/functional/inference_engine/lp_transformations/concat_with_neighbors_transformation_with_convolution.cpp

@@ -160,16 +160,16 @@ TEST_P(ConcatWithNeighborsWithConvolutionTransformation, CompareFunctions) {
     auto actualFakeQuantizes = LayerTransformation::get<opset1::FakeQuantize>(actualFunction);
     ASSERT_EQ(3ul, actualFakeQuantizes.size()) << "unexpected FakeQuantize operations count " << actualFakeQuantizes.size();
 
-    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<std::shared_ptr<PrecisionsAttribute>>(actualFakeQuantizes)) <<
+    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) <<
         "PrecisionsAttribute shared values are not the same";
 
     auto actualConcatOperations = LayerTransformation::get<opset1::Concat>(actualFunction);
     ASSERT_EQ(2ul, actualConcatOperations.size()) << "unexpected concat operations";
-    ASSERT_NE(nullptr, ngraph::pass::low_precision::getAttribute<std::shared_ptr<QuantizationAlignmentAttribute>>(actualConcatOperations[0]));
-    ASSERT_NE(nullptr, ngraph::pass::low_precision::getAttribute<std::shared_ptr<QuantizationAlignmentAttribute>>(actualConcatOperations[1]));
+    ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[0]).empty());
+    ASSERT_FALSE(ngraph::pass::low_precision::getAttribute<QuantizationAlignmentAttribute>(actualConcatOperations[1]).empty());
 
     actualConcatOperations.insert(actualConcatOperations.end(), actualFakeQuantizes.begin(), actualFakeQuantizes.end());
-    ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<std::shared_ptr<IntervalsAlignmentAttribute>>(actualConcatOperations)) <<
+    ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<IntervalsAlignmentAttribute>(actualConcatOperations)) <<
         "IntervalsAlignmentAttribute shared values are not the same";
 
     auto convolutions = LayerTransformation::get<opset1::Convolution>(actualFunction);
@@ -177,8 +177,8 @@ TEST_P(ConcatWithNeighborsWithConvolutionTransformation, CompareFunctions) {
     ASSERT_EQ(2ul, convolutions[0]->input(0).get_rt_info().size()) <<
         "unexpected input 0 attributes count: LowPrecision::PerTensorQuantization & LowPrecision::Precisions";
     ASSERT_EQ(1ul, convolutions[0]->input(1).get_rt_info().size()) << "unexpected input 1 attributes count";
-    auto a1 = std::dynamic_pointer_cast<ngraph::VariantWrapper<std::shared_ptr<PrecisionsAttribute>>>(convolutions[0]->input(1).get_rt_info().begin()->second);
-    ASSERT_EQ(element::i8, *a1->get().get()->sharedValue->precisions.begin());
+    auto& a1 = convolutions[0]->input(1).get_rt_info().begin()->second.as<PrecisionsAttribute>();
+    ASSERT_EQ(element::i8, a1.value().front());
 }
 
 const std::vector<ngraph::element::Type> precisions = {
@@ -204,15 +204,15 @@ const std::vector<ConcatWithNeighborsWithConvolutionTestValues> testValues = {
         {
             {
                 256ul, ngraph::Shape({}), {-1.28f / 3.f}, {1.27f / 3.f}, {0.f}, {255.f}, element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
             },
             {
                 256ul, ngraph::Shape({}), {-1.28f / 2.f}, {1.27f / 2.f}, {64.f}, {192.f}, element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
             },
             {
                 256ul, ngraph::Shape({}), {-1.28f}, {1.27f}, {0.f}, {255.f}, element::u8,
-                { make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
+                { IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 1.27f}, 256ul) }
             },
             ngraph::element::u8,
             {{}, {}, {}},

inference-engine/tests/functional/inference_engine/lp_transformations/concat_with_not_quantized_parent_transformation.cpp

@@ -218,9 +218,9 @@ public:
             testValues.result.dequantization2,
             true,
             {
-                make_shared_attribute_ptr<PrecisionPreservedAttribute>(true),
-                make_shared_attribute_ptr<IntervalsAlignmentAttribute>(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul),
-                make_shared_attribute_ptr<QuantizationAlignmentAttribute>(false)
+                PrecisionPreservedAttribute(true),
+                IntervalsAlignmentAttribute(IntervalsAlignmentSharedValue::Interval{-1.28f, 2.55f}, 256ul),
+                QuantizationAlignmentAttribute(false)
             },
             testValues.result.precisionAfterOperation,
             testValues.result.dequantizationAfter,
@@ -258,14 +258,14 @@ TEST_P(ConcatWithNotQuantizedParentTransformation, CompareFunctions) {
             break;
         }
     }
-    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<std::shared_ptr<PrecisionsAttribute>>(actualFakeQuantizes)) <<
+    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) <<
         "PrecisionsAttribute are not the same";
 
     ConcatWithNotQuantizedParentTransformationTestValues testValues = std::get<2>(GetParam());
     if (testValues.checkIntervalsAlignmentAttributes) {
         auto operations = LayerTransformation::get<opset1::Concat>(actualFunction);
         operations.insert(operations.end(), actualFakeQuantizes.begin(), actualFakeQuantizes.end());
-        ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<std::shared_ptr<IntervalsAlignmentAttribute>>(operations)) <<
+        ASSERT_TRUE(checkIfAttributesSharedValuesAreTheSame<IntervalsAlignmentAttribute>(operations)) <<
             "IntervalsAlignmentAttribute are not the same";
     }
 }

inference-engine/tests/functional/inference_engine/lp_transformations/convert_subtract_constant_transformation.cpp

@@ -169,7 +169,8 @@ const std::vector<ConvertSubtractConstantTransformationTestValues> testValues =
             },
             {
                 { ngraph::element::f32, false },
-                { {127.f}, element::f32, {}, false, 1ul, element::i8, true, {}, { "disabled_constant_folding_0" } },
+                { {127.f}, element::f32, {}, false, 1ul, element::i8, true, {},
+                  { ov::pass::DisableConstantFolding::get_type_info_static() } },
                 { {0.03f}, element::f32, {}, false }
             },
             { std::vector<float>{ 2.f }, ngraph::element::i8},

inference-engine/tests/functional/inference_engine/lp_transformations/convolution_backprop_data_transformation.cpp

@@ -284,7 +284,8 @@ const std::vector<ConvolutionBackpropDataTransformationTestValues> testValues =
         {
             ngraph::element::u8,
             {{}, { { 128.f }, ngraph::element::f32, {}, false }, {}},
-            {{}, { { 2.f }, ngraph::element::f32, {1, 2, 1, 1}, true, 1ul, element::i8, false, { "disabled_constant_folding_0" }  }, {}},
+            {{}, { { 2.f }, ngraph::element::f32, {1, 2, 1, 1}, true, 1ul, element::i8, false,
+                   { ov::pass::DisableConstantFolding::get_type_info_static() }  }, {}},
             {{}, {}, {{ 0.0002f }, ngraph::element::f32, {}}},
             op::Constant::create(ngraph::element::i8, ngraph::Shape{}, std::vector<float>{ 2.f }),
             true

inference-engine/tests/functional/inference_engine/lp_transformations/convolution_qdq_transformation.cpp

@@ -174,7 +174,8 @@ const std::vector<ConvolutionQDqTransformationTestValues> testValues = {
             },
             {
                 {},
-                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false, { "disabled_constant_folding_0" } },
+                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false,
+                  { ov::pass::DisableConstantFolding::get_type_info_static() } },
                 {}
             },
             { std::vector<float>{ 100.f }, ngraph::element::i8},
@@ -349,7 +350,8 @@ const std::vector<ConvolutionQDqTransformationTestValues> testValues = {
             },
             {
                 {},
-                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false, { "disabled_constant_folding_0" } },
+                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false,
+                  { ov::pass::DisableConstantFolding::get_type_info_static() } },
                 {}
             },
             { std::vector<float>{ 2.f }, ngraph::element::i8},
@@ -417,7 +419,8 @@ const std::vector<ConvolutionQDqTransformationTestValues> testValues = {
             },
             {
                 {},
-                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false, { "disabled_constant_folding_0" } },
+                { { 127.f }, ngraph::element::f32, { 6, 1, 1, 1 }, false, 1ul, element::i8, false,
+                  { ov::pass::DisableConstantFolding::get_type_info_static() } },
                 {}
             },
             { std::vector<float>{ 2.f }, ngraph::element::i8},

inference-engine/tests/functional/inference_engine/lp_transformations/disable_convert_on_const_path_transformation.cpp

@@ -145,12 +145,14 @@ const std::vector<DisableConvertOnConstPathTransformationValues> testValues = {
             ngraph::element::u8,
             {
                 {ngraph::element::f32},
-                { {128.f}, element::f32, {}, false, 1ul, element::u8, true, {}, { "disabled_constant_folding_0" } },
+                { {128.f}, element::f32, {}, false, 1ul, element::u8, true, {},
+                  {ov::pass::DisableConstantFolding::get_type_info_static() } },
                 { {0.02f}, element::f32, {}, false }
             },
             {
                 { ngraph::element::f32, false },
-                { {128.f}, element::f32, {}, false, 1ul, element::i8, true, {}, { "disabled_constant_folding_0" } },
+                { {128.f}, element::f32, {}, false, 1ul, element::i8, true, {},
+                  {ov::pass::DisableConstantFolding::get_type_info_static() } },
                 { {0.03f}, element::f32, {}, false }
             },
             { std::vector<float>{ 1.f }, ngraph::element::f32},

inference-engine/tests/functional/inference_engine/lp_transformations/fake_quantize_with_dq_not_optimal_transformation.cpp

@@ -198,7 +198,9 @@ const std::vector<FakeQuantizeWithNotOptimalTransformationTestValues> fakeQuanti
             {},
             {
                 {},
-                { std::vector<float>(64, 127.f), ngraph::element::f32, {64, 1, 1, 1}, false, 1ul, ngraph::element::i8, false, {"disabled_constant_folding_0"}},
+                { std::vector<float>(64, 127.f), ngraph::element::f32,
+                 {64, 1, 1, 1}, false, 1ul, ngraph::element::i8, false,
+                 {ov::pass::DisableConstantFolding::get_type_info_static()}},
                 {}
             },
             {

inference-engine/tests/functional/inference_engine/lp_transformations/group_convolution_transformation.cpp

@@ -473,7 +473,7 @@ const std::vector<GroupConvolutionTestValues> testValuesGroupConv = {
                     1,
                     ngraph::element::i8,
                     false,
-                    {"disabled_constant_folding_0"}
+                    {ov::pass::DisableConstantFolding::get_type_info_static()}
                 },
                 {}
             },

inference-engine/tests/functional/inference_engine/lp_transformations/layer_transformation.hpp

@@ -83,10 +83,9 @@ public:
         for (size_t nodeIndex = 0ul; nodeIndex < nodes.size(); nodeIndex++) {
             auto& rt = nodes[nodeIndex]->get_rt_info();
             for (auto& it : rt) {
-                auto reference = std::dynamic_pointer_cast<VariantWrapper<std::shared_ptr<IntervalsAlignmentAttribute>>>(it.second);
-                assert(reference != nullptr);
-                if ((reference->get()->sharedValue->combinedInterval.low != intervalLow) &&
-                    (reference->get()->sharedValue->combinedInterval.high != intervalHigh)) {
+                auto& reference = it.second.as<IntervalsAlignmentAttribute>();
+                if ((reference.value().combinedInterval.low != intervalLow) &&
+                    (reference.value().combinedInterval.high != intervalHigh)) {
                     return false;
                 }
             }
@@ -96,10 +95,10 @@ public:
     }
 
     static bool compare(
-        const std::shared_ptr<IntervalsAlignmentAttribute>& value1,
-        const std::shared_ptr<IntervalsAlignmentAttribute>& value2) {
-        if ((value1->sharedValue->combinedInterval.low != value2->sharedValue->combinedInterval.low) ||
-            (value1->sharedValue->combinedInterval.high != value2->sharedValue->combinedInterval.high)) {
+        const IntervalsAlignmentAttribute& value1,
+        const IntervalsAlignmentAttribute& value2) {
+        if ((value1.value().combinedInterval.low != value2.value().combinedInterval.low) ||
+            (value1.value().combinedInterval.high != value2.value().combinedInterval.high)) {
             return false;
         }
         return true;
@@ -124,10 +123,8 @@ public:
                     return false;
                 }
 
-                auto reference = std::dynamic_pointer_cast<VariantWrapper<Operation>>(referenceIt->second);
-                auto actual = std::dynamic_pointer_cast<VariantWrapper<Operation>>(actualIt.second);
-                if ((actual != nullptr) && (reference != nullptr)) {
-                    if (!compare(reference->get(), actual->get())) {
+                if (!referenceIt->second.empty() && !actualIt.second.empty()) {
+                    if (!compare(referenceIt->second.template as<Operation>(), actualIt.second.template as<Operation>())) {
                         return false;
                     }
                 }
@@ -139,24 +136,22 @@ public:
 
     template <class Attribute>
     static bool checkIfOutputAttributesAreTheSame(const NodeVector& nodes) {
-        void* first = nullptr;
+        ov::Any first;
         for (auto node : nodes) {
             for (auto output : node->outputs()) {
                 auto& rt = output.get_rt_info();
-                const std::string& name = VariantWrapper<Attribute>::type_info.name;
+                const std::string& name = Attribute::get_type_info_static();
                 auto it = rt.find(name);
                 if (it == rt.end()) {
                     return false;
                 }
 
                 auto value = it->second;
-                OPENVINO_SUPPRESS_DEPRECATED_START
-                if (first == nullptr) {
-                    first = value.get();
-                } else if (value.get() != first) {
+                if (first.empty()) {
+                    first = value;
+                } else if (value.template as<Attribute>().attribute != first.template as<Attribute>().attribute) {
                     return false;
                 }
-                OPENVINO_SUPPRESS_DEPRECATED_END
             }
         }
         return true;
@@ -164,15 +159,15 @@ public:
 
     template <class Attribute>
     static bool checkIfOutputAttributesSharedValuesAreTheSame(const NodeVector& nodes) {
-        std::shared_ptr<Variant> first = nullptr;
+        ov::Any first;
         for (auto node : nodes) {
             for (auto output : node->outputs()) {
                 auto value = ngraph::pass::low_precision::getAttributeFromOutput<Attribute>(output);
-                if (first == nullptr) {
+                if (first.empty()) {
                     first = value;
                 } else {
-                    const auto sharedValue1 = std::dynamic_pointer_cast<ngraph::VariantWrapper<Attribute>>(value)->get()->sharedValue;
-                    const auto sharedValue2 = std::dynamic_pointer_cast<ngraph::VariantWrapper<Attribute>>(first)->get()->sharedValue;
+                    const auto sharedValue1 = value.template as<Attribute>().attribute->sharedValue;
+                    const auto sharedValue2 = first.template as<Attribute>().attribute->sharedValue;
                     if (sharedValue1 != sharedValue2) {
                         return false;
                     }
@@ -184,18 +179,18 @@ public:
 
     template <class Attribute>
     static bool checkIfAttributesSharedValuesAreTheSame(const NodeVector& nodes) {
-        std::shared_ptr<Variant> first = nullptr;
+        ov::Any first;
         for (auto node : nodes) {
             auto value = ngraph::pass::low_precision::getAttribute<Attribute>(node);
-            if (value == nullptr) {
+            if (value.empty()) {
                 return false;
             }
 
-            if (first == nullptr) {
+            if (first.empty()) {
                 first = value;
             } else {
-                const auto sharedValue1 = std::dynamic_pointer_cast<ngraph::VariantWrapper<Attribute>>(value)->get()->sharedValue;
-                const auto sharedValue2 = std::dynamic_pointer_cast<ngraph::VariantWrapper<Attribute>>(first)->get()->sharedValue;
+                const auto sharedValue1 = value.template as<Attribute>().attribute->sharedValue;
+                const auto sharedValue2 = first.template as<Attribute>().attribute->sharedValue;
                 if (sharedValue1 != sharedValue2) {
                     return false;
                 }
@@ -206,16 +201,16 @@ public:
 
     template <class Attribute>
     static bool checkIfAttributesAreTheSame(const NodeVector& nodes) {
-        Variant* first = nullptr;
+        ov::Any first;
         for (auto node : nodes) {
             auto value = ngraph::pass::low_precision::getAttribute<Attribute>(node);
-            if (value == nullptr) {
+            if (value.empty()) {
                 return false;
             }
 
-            if (first == nullptr) {
-                first = value.get();
-            } else if (value.get() != first) {
+            if (first.empty()) {
+                first = value;
+            } else if (value.template as<Attribute>().attribute != first.template as<Attribute>().attribute) {
                 return false;
             }
         }

inference-engine/tests/functional/inference_engine/lp_transformations/lpt_avoid_shapeof_propagation_test.cpp

@@ -227,9 +227,7 @@ TEST(LPT, AvoidDequantizationToShapeOfPropagationFakeQuantizeDecompositionTransf
     auto shapeOf = std::make_shared<opset1::ShapeOf>(fakeQuantize);
 
     auto& outInfo = fakeQuantize->output(0).get_rt_info();
-    auto attribute = ngraph::pass::low_precision::make_shared_attribute<PrecisionsAttribute>(element::TypeVector{ element::u8, element::i8 });
-    auto attributeWrapper = std::make_shared<ngraph::VariantWrapper<std::shared_ptr<PrecisionsAttribute>>>(attribute);
-    outInfo.emplace(ngraph::VariantWrapper<std::shared_ptr<PrecisionsAttribute>>::type_info.name, attributeWrapper);
+    outInfo.emplace(PrecisionsAttribute::get_type_info_static(), PrecisionsAttribute(element::TypeVector{ element::u8, element::i8 }));
 
     auto result1 = std::make_shared<opset1::Result>(fakeQuantize);
     auto result2 = std::make_shared<opset1::Result>(shapeOf);

inference-engine/tests/functional/inference_engine/lp_transformations/markup_avg_pool_precisions_transformation.cpp

@@ -140,14 +140,14 @@ TEST_P(MarkupAvgPoolPrecisionsTransformation, CompareFunctions) {
     ASSERT_EQ(1ul, avgPoolOperations.size()) << "unexpected avgPoolOperations size: " << avgPoolOperations.size();
 
     {
-        auto avgPoolPrecisioinPreservedAttribute = ngraph::pass::low_precision::getAttribute<AvgPoolPrecisionPreservedAttributePtr>(
+        auto avgPoolPrecisioinPreservedAttribute = ngraph::pass::low_precision::getAttribute<AvgPoolPrecisionPreservedAttribute>(
                 *avgPoolOperations.begin());
-        ASSERT_NE(nullptr, avgPoolPrecisioinPreservedAttribute);
-        ASSERT_EQ(true, avgPoolPrecisioinPreservedAttribute->get()->sharedValue->value);
+        ASSERT_FALSE(avgPoolPrecisioinPreservedAttribute.empty());
+        ASSERT_EQ(true, avgPoolPrecisioinPreservedAttribute.as<AvgPoolPrecisionPreservedAttribute>().value());
     }
 
     const auto precisionPreserved = LayerTransformation::get<opset1::MaxPool>(actualFunction);
-    ASSERT_TRUE(checkIfAttributesAreTheSame<std::shared_ptr<AvgPoolPrecisionPreservedAttribute>>(precisionPreserved)) <<
+    ASSERT_TRUE(checkIfAttributesAreTheSame<AvgPoolPrecisionPreservedAttribute>(precisionPreserved)) <<
         "AvgPoolPrecisionPreservedAttribute are not the same";
 
     //auto res = compare_functions(referenceFunction, actualFunction, true, true);

inference-engine/tests/functional/inference_engine/lp_transformations/move_fake_quantize_transformation.cpp

@@ -162,9 +162,9 @@ public:
             testValues.actual.convertAfter,
             testValues.actual.dequantizationAfter,
             {
-                ngraph::builder::subgraph::make_shared_attribute_ptr<PrecisionPreservedAttribute>(true),
-                ngraph::builder::subgraph::make_shared_attribute_ptr<IntervalsAlignmentAttribute>(interval, 256),
-                ngraph::builder::subgraph::make_shared_attribute_ptr<QuantizationAlignmentAttribute>(false)
+                PrecisionPreservedAttribute(true),
+                IntervalsAlignmentAttribute(interval, 256),
+                QuantizationAlignmentAttribute(false)
             },
             ngraph::element::undefined,
             {},
@@ -209,9 +209,9 @@ public:
             testValues.result.convertAfter,
             testValues.result.dequantizationAfter,
             {
-                ngraph::builder::subgraph::make_shared_attribute_ptr<PrecisionPreservedAttribute>(true),
-                ngraph::builder::subgraph::make_shared_attribute_ptr<IntervalsAlignmentAttribute>(interval, 256),
-                ngraph::builder::subgraph::make_shared_attribute_ptr<QuantizationAlignmentAttribute>(false)
+                PrecisionPreservedAttribute(true),
+                IntervalsAlignmentAttribute(interval, 256),
+                QuantizationAlignmentAttribute(false)
             },
             testValues.result.precisionAfterOperation,
             {},
@@ -242,7 +242,7 @@ TEST_P(MoveFakeQuantizeTransformation, CompareFunctions) {
     ASSERT_TRUE(LayerTransformation::allNamesAreUnique(actualFunction)) << "Not all names are unique";
 
     const auto actualFakeQuantizes = LayerTransformation::get<opset1::FakeQuantize>(actualFunction);
-    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<std::shared_ptr<PrecisionsAttribute>>(actualFakeQuantizes)) <<
+    ASSERT_TRUE(checkIfOutputAttributesSharedValuesAreTheSame<PrecisionsAttribute>(actualFakeQuantizes)) <<
         "PrecisionsAttribute are not the same";
 }
 

inference-engine/tests/functional/inference_engine/snippets/registers.cpp

@@ -9,7 +9,6 @@
 #include <ngraph/variant.hpp>
 
 #include <snippets/snippets_isa.hpp>
-#include <snippets/register_info.hpp>
 #include <snippets/pass/assign_registers.hpp>
 
 #include <transformations/init_node_info.hpp>
@@ -53,7 +52,7 @@ TEST(TransformationTests, AssignRegisters) {
 
             auto it_rinfo = rt.find("reginfo");
             if (it_rinfo != rt.end()) {
-                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(it_rinfo->second)->get();
+                auto reginfo = it_rinfo->second.as<std::vector<size_t>>();
                 auto reg = reginfo[0];
                 ASSERT_TRUE(ref_registers[op->get_friendly_name()] == reg);
                 total_ops++;
@@ -127,7 +126,7 @@ TEST(TransformationTests, AssignRegisters2) {
             auto& rt = op->get_rt_info();
             auto it_rinfo = rt.find("reginfo");
             if (it_rinfo != rt.end()) {
-                auto reginfo = ov::as_type_ptr<VariantWrapper<std::vector<size_t>>>(it_rinfo->second)->get();
+                auto reginfo = it_rinfo->second.as<std::vector<size_t>>();
                 auto reg = reginfo[0];
                 ASSERT_TRUE(ref_registers[op->get_friendly_name()] == reg);
                 total_ops++;

inference-engine/tests/functional/inference_engine/transformations/detection_output_downgrade_test.cpp

@@ -0,0 +1,189 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/op_conversions/detection_output_downgrade.hpp"
+
+#include <gtest/gtest.h>
+
+#include <memory>
+#include <ngraph/function.hpp>
+#include <ngraph/op/util/detection_output_base.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset7.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <string>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+#include "transformations/init_node_info.hpp"
+
+using namespace ngraph;
+using namespace testing;
+
+namespace {
+void create_attributes_vectors(std::vector<opset1::DetectionOutput::Attributes>& attrs_v1_vector,
+                               std::vector<opset8::DetectionOutput::Attributes>& attrs_v8_vector) {
+    // initialize attributes affecting shape inference
+    // others remain by default
+    for (int keep_top_k : {10, -1}) {
+        for (int top_k : {5, -1}) {
+            for (bool variance_encoded_in_target : {true, false}) {
+                for (bool share_location : {true, false}) {
+                    for (bool normalized : {true, false}) {
+                        opset1::DetectionOutput::Attributes attrs_v1;
+                        opset8::DetectionOutput::Attributes attrs_v8;
+                        attrs_v1.top_k = attrs_v8.top_k = top_k;
+                        attrs_v1.keep_top_k = attrs_v8.keep_top_k = {keep_top_k};
+                        attrs_v1.variance_encoded_in_target = attrs_v8.variance_encoded_in_target =
+                            variance_encoded_in_target;
+                        attrs_v1.share_location = attrs_v8.share_location = share_location;
+                        attrs_v1.normalized = attrs_v8.normalized = normalized;
+                        attrs_v1_vector.push_back(attrs_v1);
+                        attrs_v8_vector.push_back(attrs_v8);
+                    }
+                }
+            }
+        }
+    }
+}
+}  // namespace
+
+TEST(TransformationTests, DetectionOutput8ToDetectionOutput1) {
+    std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
+    std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;
+    Dimension N = 5;
+    Dimension num_prior_boxes = 100;
+    Dimension priors_batch_size = N;
+    Dimension num_classes = 23;
+
+    create_attributes_vectors(attrs_v1_vector, attrs_v8_vector);
+    ASSERT_TRUE(attrs_v1_vector.size() == attrs_v8_vector.size()) << "Sizes of attribute test vectors must be equal";
+    for (size_t ind = 0; ind < attrs_v1_vector.size(); ++ind) {
+        std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+        // this case covers deducing a number of classes value
+        // since this value is not saved in attributes
+        opset8::DetectionOutput::Attributes attributes_v8 = attrs_v8_vector[ind];
+        opset1::DetectionOutput::Attributes attributes_v1 = attrs_v1_vector[ind];
+        if (num_classes.is_static()) {
+            attributes_v1.num_classes = num_classes.get_length();
+        }
+
+        Dimension num_loc_classes = attributes_v8.share_location ? 1 : num_classes;
+        Dimension prior_box_size = attributes_v8.normalized ? 4 : 5;
+
+        PartialShape box_logits_shape = {N, num_prior_boxes * num_loc_classes * 4};
+        PartialShape class_preds_shape = {N, num_prior_boxes * num_classes};
+        PartialShape proposals_shape = {priors_batch_size,
+                                        attributes_v8.variance_encoded_in_target ? 1 : 2,
+                                        num_prior_boxes * prior_box_size};
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+
+            auto detection_output_v8 =
+                std::make_shared<ngraph::opset8::DetectionOutput>(box_logits, class_preds, proposals, attributes_v8);
+
+            f = std::make_shared<ngraph::Function>(ngraph::NodeVector{detection_output_v8},
+                                                   ngraph::ParameterVector{box_logits, class_preds, proposals});
+
+            ngraph::pass::Manager manager;
+            manager.register_pass<ngraph::pass::ConvertDetectionOutput8ToDetectionOutput1>();
+            manager.run_passes(f);
+        }
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+
+            auto detection_output_v1 =
+                std::make_shared<ngraph::opset1::DetectionOutput>(box_logits, class_preds, proposals, attributes_v1);
+
+            f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{detection_output_v1},
+                                                       ngraph::ParameterVector{box_logits, class_preds, proposals});
+        }
+        auto res = compare_functions(f, f_ref);
+        ASSERT_TRUE(res.first) << res.second;
+    }
+}
+
+TEST(TransformationTests, DetectionOutput8ToDetectionOutput1FiveArguments) {
+    // In this case num_classes attribute value is deduced using inputs shapes
+    std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
+    std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;
+    Dimension N = 5;
+    Dimension num_prior_boxes = 15;
+    Dimension priors_batch_size = N;
+    Dimension num_classes = 23;
+
+    create_attributes_vectors(attrs_v1_vector, attrs_v8_vector);
+    ASSERT_TRUE(attrs_v1_vector.size() == attrs_v8_vector.size()) << "Sizes of attribute test vectors must be equal";
+    for (size_t ind = 0; ind < attrs_v1_vector.size(); ++ind) {
+        std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+        opset8::DetectionOutput::Attributes attributes_v8 = attrs_v8_vector[ind];
+        opset1::DetectionOutput::Attributes attributes_v1 = attrs_v1_vector[ind];
+        if (num_classes.is_static()) {
+            attributes_v1.num_classes = num_classes.get_length();
+        }
+
+        Dimension num_loc_classes = attributes_v8.share_location ? 1 : num_classes;
+        Dimension prior_box_size = attributes_v8.normalized ? 4 : 5;
+
+        PartialShape box_logits_shape = {N, num_prior_boxes * num_loc_classes * 4};
+        PartialShape class_preds_shape = {N, num_prior_boxes * num_classes};
+        PartialShape proposals_shape = {priors_batch_size,
+                                        attributes_v8.variance_encoded_in_target ? 1 : 2,
+                                        num_prior_boxes * prior_box_size};
+        PartialShape ad_class_preds_shape = {N, num_prior_boxes * 2};
+        PartialShape ad_box_preds_shape = {N, num_prior_boxes * num_loc_classes * 4};
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+            auto ad_class_preds =
+                std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_class_preds_shape);
+            auto ad_box_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_box_preds_shape);
+
+            auto detection_output_v8 = std::make_shared<ngraph::opset8::DetectionOutput>(box_logits,
+                                                                                         class_preds,
+                                                                                         proposals,
+                                                                                         ad_class_preds,
+                                                                                         ad_box_preds,
+                                                                                         attributes_v8);
+
+            f = std::make_shared<ngraph::Function>(
+                ngraph::NodeVector{detection_output_v8},
+                ngraph::ParameterVector{box_logits, class_preds, proposals, ad_class_preds, ad_box_preds});
+
+            ngraph::pass::Manager manager;
+            manager.register_pass<ngraph::pass::ConvertDetectionOutput8ToDetectionOutput1>();
+            manager.run_passes(f);
+        }
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+            auto ad_class_preds =
+                std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_class_preds_shape);
+            auto ad_box_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_box_preds_shape);
+
+            auto detection_output_v1 = std::make_shared<ngraph::opset1::DetectionOutput>(box_logits,
+                                                                                         class_preds,
+                                                                                         proposals,
+                                                                                         ad_class_preds,
+                                                                                         ad_box_preds,
+                                                                                         attributes_v1);
+
+            f_ref = std::make_shared<ngraph::Function>(
+                ngraph::NodeVector{detection_output_v1},
+                ngraph::ParameterVector{box_logits, class_preds, proposals, ad_class_preds, ad_box_preds});
+        }
+        auto res = compare_functions(f, f_ref);
+        ASSERT_TRUE(res.first) << res.second;
+    }
+}

inference-engine/tests/functional/inference_engine/transformations/detection_output_upgrade_test.cpp

@@ -0,0 +1,189 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "transformations/op_conversions/detection_output_upgrade.hpp"
+
+#include <gtest/gtest.h>
+
+#include <memory>
+#include <ngraph/function.hpp>
+#include <ngraph/op/util/detection_output_base.hpp>
+#include <ngraph/opsets/opset1.hpp>
+#include <ngraph/opsets/opset7.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <string>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+#include "transformations/init_node_info.hpp"
+
+using namespace ngraph;
+using namespace testing;
+
+namespace {
+void create_attributes_vectors(std::vector<opset1::DetectionOutput::Attributes>& attrs_v1_vector,
+                               std::vector<opset8::DetectionOutput::Attributes>& attrs_v8_vector) {
+    // initialize attributes affecting shape inference
+    // others remain by default
+    for (int keep_top_k : {10, -1}) {
+        for (int top_k : {5, -1}) {
+            for (bool variance_encoded_in_target : {true, false}) {
+                for (bool share_location : {true, false}) {
+                    for (bool normalized : {true, false}) {
+                        opset1::DetectionOutput::Attributes attrs_v1;
+                        opset8::DetectionOutput::Attributes attrs_v8;
+                        attrs_v1.top_k = attrs_v8.top_k = top_k;
+                        attrs_v1.keep_top_k = attrs_v8.keep_top_k = {keep_top_k};
+                        attrs_v1.variance_encoded_in_target = attrs_v8.variance_encoded_in_target =
+                            variance_encoded_in_target;
+                        attrs_v1.share_location = attrs_v8.share_location = share_location;
+                        attrs_v1.normalized = attrs_v8.normalized = normalized;
+                        attrs_v1_vector.push_back(attrs_v1);
+                        attrs_v8_vector.push_back(attrs_v8);
+                    }
+                }
+            }
+        }
+    }
+}
+}  // namespace
+
+TEST(TransformationTests, DetectionOutput1ToDetectionOutput8) {
+    std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
+    std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;
+    Dimension N = 5;
+    Dimension num_prior_boxes = 100;
+    Dimension priors_batch_size = N;
+    Dimension num_classes = 23;
+
+    create_attributes_vectors(attrs_v1_vector, attrs_v8_vector);
+    ASSERT_TRUE(attrs_v1_vector.size() == attrs_v8_vector.size()) << "Sizes of attribute test vectors must be equal";
+    for (size_t ind = 0; ind < attrs_v1_vector.size(); ++ind) {
+        std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+        // this case covers deducing a number of classes value
+        // since this value is not saved in attributes
+        opset8::DetectionOutput::Attributes attributes_v8 = attrs_v8_vector[ind];
+        opset1::DetectionOutput::Attributes attributes_v1 = attrs_v1_vector[ind];
+        if (num_classes.is_static()) {
+            attributes_v1.num_classes = num_classes.get_length();
+        }
+
+        Dimension num_loc_classes = attributes_v8.share_location ? 1 : num_classes;
+        Dimension prior_box_size = attributes_v8.normalized ? 4 : 5;
+
+        PartialShape box_logits_shape = {N, num_prior_boxes * num_loc_classes * 4};
+        PartialShape class_preds_shape = {N, num_prior_boxes * num_classes};
+        PartialShape proposals_shape = {priors_batch_size,
+                                        attributes_v8.variance_encoded_in_target ? 1 : 2,
+                                        num_prior_boxes * prior_box_size};
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+
+            auto detection_output_v1 =
+                std::make_shared<ngraph::opset1::DetectionOutput>(box_logits, class_preds, proposals, attributes_v1);
+
+            f = std::make_shared<ngraph::Function>(ngraph::NodeVector{detection_output_v1},
+                                                   ngraph::ParameterVector{box_logits, class_preds, proposals});
+
+            ngraph::pass::Manager manager;
+            manager.register_pass<ngraph::pass::ConvertDetectionOutput1ToDetectionOutput8>();
+            manager.run_passes(f);
+        }
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+
+            auto detection_output_v8 =
+                std::make_shared<ngraph::opset8::DetectionOutput>(box_logits, class_preds, proposals, attributes_v8);
+
+            f_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{detection_output_v8},
+                                                       ngraph::ParameterVector{box_logits, class_preds, proposals});
+        }
+        auto res = compare_functions(f, f_ref);
+        ASSERT_TRUE(res.first) << res.second;
+    }
+}
+
+TEST(TransformationTests, DetectionOutput1ToDetectionOutput8FiveArguments) {
+    // In this case num_classes attribute value is deduced using inputs shapes
+    std::vector<opset1::DetectionOutput::Attributes> attrs_v1_vector;
+    std::vector<opset8::DetectionOutput::Attributes> attrs_v8_vector;
+    Dimension N = 5;
+    Dimension num_prior_boxes = 15;
+    Dimension priors_batch_size = N;
+    Dimension num_classes = 23;
+
+    create_attributes_vectors(attrs_v1_vector, attrs_v8_vector);
+    ASSERT_TRUE(attrs_v1_vector.size() == attrs_v8_vector.size()) << "Sizes of attribute test vectors must be equal";
+    for (size_t ind = 0; ind < attrs_v1_vector.size(); ++ind) {
+        std::shared_ptr<ngraph::Function> f(nullptr), f_ref(nullptr);
+        opset8::DetectionOutput::Attributes attributes_v8 = attrs_v8_vector[ind];
+        opset1::DetectionOutput::Attributes attributes_v1 = attrs_v1_vector[ind];
+        if (num_classes.is_static()) {
+            attributes_v1.num_classes = num_classes.get_length();
+        }
+
+        Dimension num_loc_classes = attributes_v8.share_location ? 1 : num_classes;
+        Dimension prior_box_size = attributes_v8.normalized ? 4 : 5;
+
+        PartialShape box_logits_shape = {N, num_prior_boxes * num_loc_classes * 4};
+        PartialShape class_preds_shape = {N, num_prior_boxes * num_classes};
+        PartialShape proposals_shape = {priors_batch_size,
+                                        attributes_v8.variance_encoded_in_target ? 1 : 2,
+                                        num_prior_boxes * prior_box_size};
+        PartialShape ad_class_preds_shape = {N, num_prior_boxes * 2};
+        PartialShape ad_box_preds_shape = {N, num_prior_boxes * num_loc_classes * 4};
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+            auto ad_class_preds =
+                std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_class_preds_shape);
+            auto ad_box_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_box_preds_shape);
+
+            auto detection_output_v1 = std::make_shared<ngraph::opset1::DetectionOutput>(box_logits,
+                                                                                         class_preds,
+                                                                                         proposals,
+                                                                                         ad_class_preds,
+                                                                                         ad_box_preds,
+                                                                                         attributes_v1);
+
+            f = std::make_shared<ngraph::Function>(
+                ngraph::NodeVector{detection_output_v1},
+                ngraph::ParameterVector{box_logits, class_preds, proposals, ad_class_preds, ad_box_preds});
+
+            ngraph::pass::Manager manager;
+            manager.register_pass<ngraph::pass::ConvertDetectionOutput1ToDetectionOutput8>();
+            manager.run_passes(f);
+        }
+
+        {
+            auto box_logits = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, box_logits_shape);
+            auto class_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, class_preds_shape);
+            auto proposals = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, proposals_shape);
+            auto ad_class_preds =
+                std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_class_preds_shape);
+            auto ad_box_preds = std::make_shared<ngraph::opset1::Parameter>(ngraph::element::f32, ad_box_preds_shape);
+
+            auto detection_output_v8 = std::make_shared<ngraph::opset8::DetectionOutput>(box_logits,
+                                                                                         class_preds,
+                                                                                         proposals,
+                                                                                         ad_class_preds,
+                                                                                         ad_box_preds,
+                                                                                         attributes_v8);
+
+            f_ref = std::make_shared<ngraph::Function>(
+                ngraph::NodeVector{detection_output_v8},
+                ngraph::ParameterVector{box_logits, class_preds, proposals, ad_class_preds, ad_box_preds});
+        }
+        auto res = compare_functions(f, f_ref);
+        ASSERT_TRUE(res.first) << res.second;
+    }
+}

inference-engine/tests/functional/inference_engine/transformations/nearest_neighbor_upsampling_fusion_test.cpp

@@ -0,0 +1,158 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <gtest/gtest.h>
+
+#include <string>
+#include <memory>
+
+#include <ngraph/function.hpp>
+#include <ngraph/opsets/opset8.hpp>
+#include <ngraph/pass/manager.hpp>
+#include <ngraph/pass/visualize_tree.hpp>
+#include <transformations/common_optimizations/nearest_neighbor_upsampling_fusion.hpp>
+#include <transformations/init_node_info.hpp>
+#include <transformations/utils/utils.hpp>
+
+#include "common_test_utils/ngraph_test_utils.hpp"
+
+using namespace testing;
+
+TEST_F(TransformationTestsF, NearestNeighborUpsamplingFusionSpatial2D1) {
+    ngraph::Shape input_shape { 1, 120, 150, 32 };
+    size_t input_rank = input_shape.size();
+    std::vector<int64_t> new_spatial_shape { 240, 450 };
+    std::vector<float> scales_as_floats { 2.0f, 3.0f };
+    std::vector<int64_t> constants_for_concat_1 { 1, 120, 1, 150, 1, 32 };
+    std::vector<int64_t> constants_for_concat_2 { 1, 240, 450, 32 };
+    ngraph::Shape mul_const_shape {1, 1, 2, 1, 3, 1};
+    std::vector<float> mul_const_value {1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f};
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto shape_node = std::make_shared<ngraph::opset8::ShapeOf>(input);
+
+        auto sslice_begin = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto sslice_end = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{1});
+        std::vector<int64_t> begin_mask = {0};
+        std::vector<int64_t> end_mask = {0};
+        auto strided_slice_node = std::make_shared<ngraph::opset8::StridedSlice>(shape_node, sslice_begin, sslice_end, begin_mask, end_mask);
+
+        ngraph::OutputVector concat_1_inputs_vec(2 + 2 * (input_rank - 2));
+        concat_1_inputs_vec[0] = strided_slice_node;
+        for (size_t i = 1; i < 2 + 2 * (input_rank - 2); ++i) {
+            const auto unsqueezed_const = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{constants_for_concat_1[i]});
+            const auto unsqueeze_axis = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{0});
+            const auto current_unsqueeze = std::make_shared<ngraph::opset8::Unsqueeze>(unsqueezed_const, unsqueeze_axis);
+            concat_1_inputs_vec[i] = current_unsqueeze;
+        }
+        auto concat_1 = std::make_shared<ngraph::opset8::Concat>(concat_1_inputs_vec, 0);
+
+        auto reshape_1 = std::make_shared<ngraph::opset8::Reshape>(input, concat_1, true);
+
+        ngraph::OutputVector concat_2_inputs_vec(input_rank);
+        concat_2_inputs_vec[0] = strided_slice_node;
+        for (size_t i = 1; i < input_rank; ++i) {
+            const auto unsqueezed_const = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{constants_for_concat_2[i]});
+            const auto unsqueeze_axis = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{0});
+            const auto current_unsqueeze = std::make_shared<ngraph::opset8::Unsqueeze>(unsqueezed_const, unsqueeze_axis);
+            concat_2_inputs_vec[i] = current_unsqueeze;
+        }
+        auto concat_2 = std::make_shared<ngraph::opset8::Concat>(concat_2_inputs_vec, 0);
+
+        const auto mul_const = ngraph::opset8::Constant::create(ngraph::element::f32, mul_const_shape, mul_const_value);
+        const auto mul = std::make_shared<ngraph::opset8::Multiply>(reshape_1, mul_const);
+
+        auto reshape_2 = std::make_shared<ngraph::opset8::Reshape>(mul, concat_2, true);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ reshape_2 }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::NearestNeighborUpsamplingFusion>();
+    }
+    {
+        ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+        attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+        attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+        attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+        attrs.pads_begin = std::vector<size_t>{0};
+        attrs.pads_end = std::vector<size_t>{0};
+        attrs.antialias = false;
+        attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+        attrs.cube_coeff = -0.75f;
+
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, {new_spatial_shape.size()}, new_spatial_shape);
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, {scales_as_floats.size()}, scales_as_floats);
+        auto axes_node = ngraph::opset8::Constant::create(ngraph::element::i64, {2}, std::vector<int64_t>{1, 2});
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axes_node, attrs);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}
+
+TEST_F(TransformationTestsF, NearestNeighborUpsamplingFusionSpatial3D1) {
+    ngraph::Shape input_shape { 1, 130, 120, 85, 3 };
+    size_t input_rank = input_shape.size();
+    std::vector<int64_t> new_spatial_shape { 260, 360, 340 };
+    std::vector<float> scales_as_floats { 2.0f, 3.0, 4.0f };
+    std::vector<int64_t> constants_for_concat_1 { 1, 130, 1, 120, 1, 85, 1, 3 };
+    std::vector<int64_t> constants_for_concat_2 { 1, 260, 360, 340, 3 };
+    ngraph::Shape mul_const_shape {1, 1, 2, 1, 3, 1, 4, 1};
+    std::vector<float> mul_const_value(24, 1.0f);
+    {
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto shape_node = std::make_shared<ngraph::opset8::ShapeOf>(input);
+
+        auto sslice_begin = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{0});
+        auto sslice_end = ngraph::opset8::Constant::create(ngraph::element::i64, {1}, std::vector<int64_t>{1});
+        std::vector<int64_t> begin_mask = {0};
+        std::vector<int64_t> end_mask = {0};
+        auto strided_slice_node = std::make_shared<ngraph::opset8::StridedSlice>(shape_node, sslice_begin, sslice_end, begin_mask, end_mask);
+
+        ngraph::OutputVector concat_1_inputs_vec(2 + 2 * (input_rank - 2));
+        concat_1_inputs_vec[0] = strided_slice_node;
+        for (size_t i = 1; i < 2 + 2 * (input_rank - 2); ++i) {
+            const auto unsqueezed_const = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{constants_for_concat_1[i]});
+            const auto unsqueeze_axis = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{0});
+            const auto current_unsqueeze = std::make_shared<ngraph::opset8::Unsqueeze>(unsqueezed_const, unsqueeze_axis);
+            concat_1_inputs_vec[i] = current_unsqueeze;
+        }
+        auto concat_1 = std::make_shared<ngraph::opset8::Concat>(concat_1_inputs_vec, 0);
+
+        auto reshape_1 = std::make_shared<ngraph::opset8::Reshape>(input, concat_1, true);
+
+        ngraph::OutputVector concat_2_inputs_vec(input_rank);
+        concat_2_inputs_vec[0] = strided_slice_node;
+        for (size_t i = 1; i < input_rank; ++i) {
+            const auto unsqueezed_const = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{constants_for_concat_2[i]});
+            const auto unsqueeze_axis = ngraph::opset8::Constant::create(ngraph::element::i64, {}, std::vector<int64_t>{0});
+            const auto current_unsqueeze = std::make_shared<ngraph::opset8::Unsqueeze>(unsqueezed_const, unsqueeze_axis);
+            concat_2_inputs_vec[i] = current_unsqueeze;
+        }
+        auto concat_2 = std::make_shared<ngraph::opset8::Concat>(concat_2_inputs_vec, 0);
+
+        const auto mul_const = ngraph::opset8::Constant::create(ngraph::element::f32, mul_const_shape, mul_const_value);
+        const auto mul = std::make_shared<ngraph::opset8::Multiply>(reshape_1, mul_const);
+
+        auto reshape_2 = std::make_shared<ngraph::opset8::Reshape>(mul, concat_2, true);
+        function = std::make_shared<ngraph::Function>(ngraph::NodeVector{ reshape_2 }, ngraph::ParameterVector{ input });
+        manager.register_pass<ngraph::pass::NearestNeighborUpsamplingFusion>();
+    }
+    {
+        ngraph::opset8::Interpolate::InterpolateAttrs attrs;
+
+        attrs.mode = ngraph::opset8::Interpolate::InterpolateMode::NEAREST;
+        attrs.shape_calculation_mode = ngraph::opset8::Interpolate::ShapeCalcMode::SCALES;
+        attrs.nearest_mode = ngraph::opset8::Interpolate::NearestMode::ROUND_PREFER_FLOOR;
+        attrs.pads_begin = std::vector<size_t>{0};
+        attrs.pads_end = std::vector<size_t>{0};
+        attrs.antialias = false;
+        attrs.coordinate_transformation_mode = ngraph::opset8::Interpolate::CoordinateTransformMode::HALF_PIXEL;
+        attrs.cube_coeff = -0.75f;
+
+        auto input = std::make_shared<ngraph::opset8::Parameter>(ngraph::element::f32, input_shape);
+        auto sizes_node = ngraph::opset8::Constant::create(ngraph::element::i64, {new_spatial_shape.size()}, new_spatial_shape);
+        auto scales_node = ngraph::opset8::Constant::create(ngraph::element::f32, {scales_as_floats.size()}, scales_as_floats);
+        auto axes_node = ngraph::opset8::Constant::create(ngraph::element::i64, {3}, std::vector<int64_t>{1, 2, 3});
+        auto interpolate = std::make_shared<ngraph::opset8::Interpolate>(input, sizes_node, scales_node, axes_node, attrs);
+        function_ref = std::make_shared<ngraph::Function>(ngraph::NodeVector{ interpolate }, ngraph::ParameterVector{ input });
+    }
+}

inference-engine/tests/functional/inference_engine/transformations/primitives_priority_test.cpp

@@ -50,7 +50,7 @@ TEST(TransformationTests, ConvBiasFusion) {
     for (auto & op : nGraph->get_ops()) {
         if (auto conv = std::dynamic_pointer_cast<ngraph::opset1::Convolution>(op)) {
             auto & rtInfo = conv->get_rt_info();
-            rtInfo["PrimitivesPriority"] = std::make_shared<ngraph::VariantWrapper<std::string>>("test");
+            rtInfo[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("test");
             pp[op->get_friendly_name()] = "test";
         }
     }

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp

@@ -0,0 +1,69 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include "single_layer_tests/experimental_detectron_detection_output.hpp"
+
+using namespace ov::test;
+using namespace ov::test::subgraph;
+
+namespace {
+
+const std::vector<float> score_threshold = { 0.01000000074505806f };
+
+const std::vector<float> nms_threshold = { 0.2f };
+
+//// specifies maximal delta of logarithms for width and height
+const std::vector<float> max_delta_log_wh = { 2.0f };
+
+// specifies number of detected classes
+const std::vector<int64_t> num_classes = { 2 };
+
+// specifies maximal number of detections per class
+const std::vector<int64_t> post_nms_count = { 500 };
+
+// specifies maximual number of detections per image
+const std::vector<size_t> max_detections_per_image = { 5 };
+
+// a flag specifies whether to delete background classes or not
+// `true`  means background classes should be deleted,
+// `false` means background classes shouldn't be deleted.
+const std::vector<bool> class_agnostic_box_regression = { true };
+
+// specifies deltas of weights
+const std::vector<std::vector<float>> deltas_weights = { {10.0f, 10.0f, 5.0f, 5.0f} };
+
+const std::vector<std::vector<InputShape>> inputShapes = {
+        // inputRois / inputDeltas / inputScores / inputImInfos
+        static_shapes_to_test_representation({{16, 4}, {16, 8}, {16, 2}, {1, 3}}),
+        {
+            {{-1, -1}, {{16, 4}, {16, 4}}},
+            {{-1, -1}, {{16, 8}, {16, 8}}},
+            {{-1, -1}, {{16, 2}, {16, 2}}},
+            {{-1, -1}, {{1, 3}, {1, 3}}}
+        },
+        {
+            {{{16, 32}, {4, 8}}, {{16, 4}, {16, 4}}},
+            {{{16, 32}, {8, 16}}, {{16, 8}, {16, 8}}},
+            {{{16, 32}, {2, 4}}, {{16, 2}, {16, 2}}},
+            {{{1, 2}, {3, 6}}, {{1, 3}, {1, 3}}}
+        }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput, ExperimentalDetectronDetectionOutputLayerTest,
+         ::testing::Combine(
+                 ::testing::ValuesIn(inputShapes),
+                 ::testing::ValuesIn(score_threshold),
+                 ::testing::ValuesIn(nms_threshold),
+                 ::testing::ValuesIn(max_delta_log_wh),
+                 ::testing::ValuesIn(num_classes),
+                 ::testing::ValuesIn(post_nms_count),
+                 ::testing::ValuesIn(max_detections_per_image),
+                 ::testing::ValuesIn(class_agnostic_box_regression),
+                 ::testing::ValuesIn(deltas_weights),
+                 ::testing::Values(ov::element::Type_t::f32),
+                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+         ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName);
+
+} // namespace

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_generate_proposals_single_image.cpp

@@ -0,0 +1,128 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include "single_layer_tests/experimental_detectron_generate_proposals_single_image.hpp"
+#include "common_test_utils/data_utils.hpp"
+
+using namespace ov::test;
+using namespace ov::test::subgraph;
+
+namespace {
+
+const std::vector<float> min_size = { 0 };
+const std::vector<float> nms_threshold = { 0.699999988079071 };
+const std::vector<int64_t> post_nms_count = { 6 };
+const std::vector<int64_t> pre_nms_count = { 1000 };
+
+const std::vector<std::pair<std::string, std::vector<ov::runtime::Tensor>>> inputTensors = {
+    {
+        "empty",
+        {
+            // 3
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{3}, {1.0f, 1.0f, 1.0f}),
+            // 36 x 4 = 144
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{36, 4}, {
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
+            // 12 x 2 x 6 = 144
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{12, 2, 6}, {
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f}),
+            // {3 x 2 x 6} = 36
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{3, 2, 6}, {
+                5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f,
+                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f})
+        }
+    },
+    {
+        "filled",
+        {
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{3}, {150.0, 150.0, 1.0}),
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{36, 4}, {
+                12.0, 68.0, 102.0, 123.0, 46.0, 80.0,  79.0,  128.0, 33.0, 71.0, 127.0, 86.0,  33.0, 56.0, 150.0, 73.0,
+                5.0,  41.0, 93.0,  150.0, 74.0, 66.0,  106.0, 115.0, 17.0, 37.0, 87.0,  150.0, 31.0, 27.0, 150.0, 39.0,
+                29.0, 23.0, 112.0, 123.0, 41.0, 37.0,  103.0, 150.0, 8.0,  46.0, 98.0,  111.0, 7.0,  69.0, 114.0, 150.0,
+                70.0, 21.0, 150.0, 125.0, 54.0, 19.0,  132.0, 68.0,  62.0, 8.0,  150.0, 101.0, 57.0, 81.0, 150.0, 97.0,
+                79.0, 29.0, 109.0, 130.0, 12.0, 63.0,  100.0, 150.0, 17.0, 33.0, 113.0, 150.0, 90.0, 78.0, 150.0, 111.0,
+                47.0, 68.0, 150.0, 71.0,  66.0, 103.0, 111.0, 150.0, 4.0,  17.0, 112.0, 94.0,  12.0, 8.0,  119.0, 98.0,
+                54.0, 56.0, 120.0, 150.0, 56.0, 29.0,  150.0, 31.0,  42.0, 3.0,  139.0, 92.0,  41.0, 65.0, 150.0, 130.0,
+                49.0, 13.0, 143.0, 30.0,  40.0, 60.0,  150.0, 150.0, 23.0, 73.0, 24.0,  115.0, 56.0, 84.0, 107.0, 108.0,
+                63.0, 8.0,  142.0, 125.0, 78.0, 37.0,  93.0,  144.0, 40.0, 34.0, 150.0, 46.0,  30.0, 21.0, 150.0, 120.0}),
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{12, 2, 6}, {
+                9.062256,   10.883133, 9.8441105,   12.694285,   0.41781136, 8.749107,      14.990341,  6.587644,  1.4206103,
+                13.299262,  12.432549, 2.736371,    0.22732796,  6.3361835,  12.268727,     2.1009045,  4.771589,  2.5131326,
+                5.610736,   9.3604145, 4.27379,     8.317948,    0.60510135, 6.7446275,     1.0207708,  1.1352817, 1.5785321,
+                1.718335,   1.8093798, 0.99247587,  1.3233583,   1.7432803,  1.8534478,     1.2593061,  1.7394226, 1.7686696,
+                1.647999,   1.7611449, 1.3119122,   0.03007332,  1.1106564,  0.55669737,    0.2546148,  1.9181818, 0.7134989,
+                2.0407224,  1.7211134, 1.8565536,   14.562747,   2.8786168,  0.5927796,     0.2064463,  7.6794515, 8.672126,
+                10.139171,  8.002429,  7.002932,    12.6314945,  10.550842,  0.15784842,    0.3194304,  10.752157, 3.709805,
+                11.628928,  0.7136225, 14.619964,   15.177284,   2.2824087,  15.381494,     0.16618137, 7.507227,  11.173228,
+                0.4923559,  1.8227729, 1.4749299,   1.7833921,   1.2363617,  -0.23659119,   1.5737582,  1.779316,  1.9828427,
+                1.0482665,  1.4900246, 1.3563544,   1.5341306,   0.7634312,  4.6216766e-05, 1.6161222,  1.7512476, 1.9363779,
+                0.9195784,  1.4906164, -0.03244795, 0.681073,    0.6192401,  1.8033613,     14.146055,  3.4043705, 15.292292,
+                3.5295358,  11.138999, 9.952057,    5.633434,    12.114562,  9.427372,      12.384038,  9.583308,  8.427233,
+                15.293704,  3.288159,  11.64898,    9.350885,    2.0037227,  13.523184,     4.4176426,  6.1057625, 14.400079,
+                8.248259,   11.815807, 15.713364,   1.0023532,   1.3203261,  1.7100681,     0.7407832,  1.09448,   1.7188418,
+                1.4412547,  1.4862992, 0.74790007,  0.31571656,  0.6398838,  2.0236106,     1.1869069,  1.7265586, 1.2624544,
+                0.09934269, 1.3508598, 0.85212964,  -0.38968498, 1.7059708,  1.6533034,     1.7400402,  1.8123854, -0.43063712}),
+            CommonTestUtils::create_tensor<float>(ov::element::f32, ov::Shape{3, 2, 6}, {
+                0.7719922,  0.35906568, 0.29054508, 0.18124384, 0.5604661,  0.84750974, 0.98948747, 0.009793862, 0.7184191,
+                0.5560748,  0.6952493,  0.6732593,  0.3306898,  0.6790913,  0.41128764, 0.34593266, 0.94296855,  0.7348507,
+                0.24478768, 0.94024557, 0.05405676, 0.06466125, 0.36244348, 0.07942984, 0.10619422, 0.09412837,  0.9053611,
+                0.22870538, 0.9237487,  0.20986171, 0.5067282,  0.29709867, 0.53138554, 0.189101,   0.4786443,   0.88421875}),
+        }
+    }
+};
+
+const std::vector<std::vector<InputShape>> dynamicInputShape = {
+    // im_info / anchors / deltas / scores
+    static_shapes_to_test_representation({{3}, {36, 4}, {12, 2, 6}, {3, 2, 6}}),
+    {
+        {{-1}, {{3}}},
+        {{-1, -1}, {{36, 4}}},
+        {{-1, -1, -1}, {{12, 2, 6}}},
+        {{-1, -1, -1}, {{3, 2, 6}}}
+    },
+    {
+        {{{3, 6}}, {{3}}},
+        {{{36, 72}, {4, 8}}, {{36, 4}}},
+        {{{12, 24}, {2, 4}, {6, 12}}, {{12, 2, 6}}},
+        {{{3, 6}, {2, 4}, {6, 12}}, {{3, 2, 6}}}
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(
+    smoke_ExperimentalDetectronGenerateProposalsSingleImageLayerTest,
+    ExperimentalDetectronGenerateProposalsSingleImageLayerTest,
+    ::testing::Combine(
+        ::testing::ValuesIn(dynamicInputShape),
+        ::testing::ValuesIn(min_size),
+        ::testing::ValuesIn(nms_threshold),
+        ::testing::ValuesIn(post_nms_count),
+        ::testing::ValuesIn(pre_nms_count),
+        ::testing::ValuesIn(inputTensors),
+        ::testing::Values(ov::element::Type_t::f32),
+        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+    ExperimentalDetectronGenerateProposalsSingleImageLayerTest::getTestCaseName);
+} // namespace

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/experimental_detectron_prior_grid_generator.cpp

@@ -0,0 +1,106 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include "single_layer_tests/experimental_detectron_prior_grid_generator.hpp"
+#include "common_test_utils/data_utils.hpp"
+
+using namespace ov::test;
+using namespace ov::test::subgraph;
+
+namespace {
+
+const std::vector<ov::test::subgraph::ExperimentalDetectronPriorGridGeneratorTestParam> params = {
+    // flatten = true (output tensor is 2D)
+    {
+        {true, 0, 0, 4.0f, 4.0f},
+        ov::test::static_shapes_to_test_representation({{3, 4}, {1, 16, 4, 5}, {1, 3, 100, 200}})
+    },
+    // task #72587
+    //{
+    //    {true, 3, 6, 64.0f, 64.0f},
+    //    ov::test::static_shapes_to_test_representation({{3, 4}, {1, 16, 100, 100}, {1, 3, 100, 200}})
+    //},
+    {
+        {true, 0, 0, 4.0f, 4.0f},
+        {
+            // priors
+            {{-1, -1}, {{3, 4}, {3, 4}}},
+            // feature_map
+            {{-1, -1, -1, -1}, {{1, 16, 4, 5}, {1, 16, 100, 100}}},
+            // im_data
+            {{-1, -1, -1, -1}, {{1, 3, 100, 200}, {1, 3, 100, 200}}}
+        }
+    },
+    // flatten = false (output tensor is 4D)
+    {
+        {false, 0, 0, 8.0f, 8.0f},
+        ov::test::static_shapes_to_test_representation({{3, 4}, {1, 16, 3, 7}, {1, 3, 100, 200}})
+    },
+    // task #72587
+    //{
+    //    {false, 5, 3, 32.0f, 32.0f},
+    //    ov::test::static_shapes_to_test_representation({{3, 4}, {1, 16, 100, 100}, {1, 3, 100, 200}})
+    //},
+    {
+        {false, 0, 0, 8.0f, 8.0f},
+        {
+            // priors
+            {{-1, -1}, {{3, 4}, {3, 4}}},
+            // feature_map
+            {{-1, -1, -1, -1}, {{1, 16, 3, 7}, {1, 16, 100, 100}}},
+            // im_data
+            {{-1, -1, -1, -1}, {{1, 3, 100, 200}, {1, 3, 100, 200}}}
+        }
+    }
+};
+
+std::vector<std::pair<std::string, std::vector<ov::runtime::Tensor>>> inputTensors = {
+    {
+        "test#1",
+        {
+            CommonTestUtils::create_tensor<float>(
+                    ov::element::f32,
+                    ov::Shape{3, 4},
+                    {-24.5, -12.5, 24.5, 12.5, -16.5, -16.5, 16.5, 16.5, -12.5, -24.5, 12.5, 24.5})
+        }
+    },
+    {
+        "test#2",
+        {
+            CommonTestUtils::create_tensor<float>(
+                    ov::element::f32,
+                    ov::Shape{3, 4},
+                    {-44.5, -24.5, 44.5, 24.5, -32.5, -32.5, 32.5, 32.5, -24.5, -44.5, 24.5, 44.5})
+        }
+    },
+    {
+        "test#3",
+        {
+            CommonTestUtils::create_tensor<float>(
+                    ov::element::f32,
+                    ov::Shape{3, 4},
+                    {-364.5, -184.5, 364.5, 184.5, -256.5, -256.5, 256.5, 256.5, -180.5, -360.5, 180.5, 360.5})
+        }
+    },
+    {
+        "test#4",
+        {
+            CommonTestUtils::create_tensor<float>(
+                    ov::element::f32,
+                    ov::Shape{3, 4},
+                    {-180.5, -88.5, 180.5, 88.5, -128.5, -128.5, 128.5, 128.5, -92.5, -184.5, 92.5, 184.5})
+        }
+    }
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronPriorGridGenerator, ExperimentalDetectronPriorGridGeneratorLayerTest,
+     ::testing::Combine(
+        ::testing::ValuesIn(params),
+        ::testing::ValuesIn(inputTensors),
+        ::testing::Values(ov::element::Type_t::f32),
+        ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+     ExperimentalDetectronPriorGridGeneratorLayerTest::getTestCaseName);
+
+} // namespace

inference-engine/tests/functional/plugin/cpu/shared_tests_instances/single_layer_tests/pooling.cpp

@@ -351,6 +351,172 @@ INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_SameLowerPad_CeilRounding_5Dinput, Poolin
                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                         PoolingLayerTest::getTestCaseName);
 
+////* ========== Max Pooling V8 ========== */
+
+const std::vector<std::vector<size_t>> dilation = {{1, 1}, {2, 2}};
+const std::vector<std::vector<size_t >> dilation3D = {{1, 1, 1}, {2, 2, 2}};
+
+/* ========== Explicit Pad Floor Rounding ========== */
+const auto maxPoolv8_ExplicitPad_FloorRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilation),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_ExplicitPad_FloorRounding, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_ExplicitPad_FloorRounding_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========== Same Upper Pad Floor Rounding ========== */
+const auto maxPoolv8_SameUpperPad_FloorRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilation),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::SAME_UPPER)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameUpperPad_FloorRounding, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_SameUpperPad_FloorRounding_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========== Same Lower Pad Floor Rounding ========== */
+const auto maxPoolv8_SameLowerPad_FloorRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilation),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::SAME_LOWER)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameLowerPad_FloorRounding, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_SameLowerPad_FloorRounding_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========= Explicit Pad Floor Rounding 5D input========== */
+const auto maxPoolv8_ExplicitPad_FloorRounding_5Dinput_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernel3D),
+        ::testing::ValuesIn(strides3D),
+        ::testing::Values(dilation3D[0]),
+        ::testing::ValuesIn(padBegins3D),
+        ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_ExplicitPad_FloorRounding_5Dinput, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_ExplicitPad_FloorRounding_5Dinput_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========= Same Upper Pad Floor Rounding 5D input========== */
+const auto maxPoolv8_SameUpperPad_FloorRounding_5Dinput_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernel3D),
+        ::testing::ValuesIn(strides3D),
+        ::testing::ValuesIn(dilation3D),
+        ::testing::ValuesIn(padBegins3D),
+        ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),
+        ::testing::Values(ngraph::op::PadType::SAME_UPPER)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameUpperPad_FloorRounding_5Dinput, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_SameUpperPad_FloorRounding_5Dinput_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========= Same Lower Pad Ceil Rounding 5D input========== */
+const auto maxPoolv8_SameLowerPad_CeilRounding_5Dinput_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernel3D),
+        ::testing::ValuesIn(strides3D),
+        ::testing::ValuesIn(dilation3D),
+        ::testing::ValuesIn(padBegins3D),
+        ::testing::ValuesIn(padEnds3D),
+        ::testing::Values(ngraph::op::RoundingType::CEIL),
+        ::testing::Values(ngraph::op::PadType::SAME_LOWER)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_SameLowerPad_CeilRounding_5Dinput, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_SameLowerPad_CeilRounding_5Dinput_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({32, 32, 2, 2, 2})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
+/* ========= Explicit Pad Ceil Rounding ========== */
+const auto maxPoolv8_ExplicitPad_CeilRounding_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilation),
+        ::testing::ValuesIn(padBegins),
+        ::testing::ValuesIn(padEnds),
+        ::testing::Values(ngraph::op::RoundingType::CEIL),
+        ::testing::Values(ngraph::op::PadType::EXPLICIT)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolv8_ExplicitPad_CeilRounding, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_ExplicitPad_CeilRounding_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
 ////* ========== Avg and Max Polling Cases ========== */
 /* ========== Valid Pad Rounding Not Applicable ========== */
 const auto allPools_ValidPad_Params = ::testing::Combine(
@@ -376,4 +542,27 @@ INSTANTIATE_TEST_SUITE_P(smoke_MAX_and_AVGPool_ValidPad, PoolingLayerTest,
                                  ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
                                  ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                          PoolingLayerTest::getTestCaseName);
+
+const auto maxPoolv8_ValidPad_Params = ::testing::Combine(
+        ::testing::ValuesIn(kernels),
+        ::testing::ValuesIn(strides),
+        ::testing::ValuesIn(dilation),
+        ::testing::Values(std::vector<size_t>({0, 0})),
+        ::testing::Values(std::vector<size_t>({0, 0})),
+        ::testing::Values(ngraph::op::RoundingType::FLOOR),  // placeholder value - Rounding Type not applicable for Valid pad type
+        ::testing::Values(ngraph::op::PadType::VALID)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MAXPoolv8_ValidPad, MaxPoolingV8LayerTest,
+                         ::testing::Combine(
+                                 maxPoolv8_ValidPad_Params,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Precision::UNSPECIFIED),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(InferenceEngine::Layout::ANY),
+                                 ::testing::Values(std::vector<size_t >({1, 3, 30, 30})),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                         MaxPoolingV8LayerTest::getTestCaseName);
+
 }  // namespace

inference-engine/tests/functional/plugin/cpu/single_layer_tests/deformable_convolution.cpp

@@ -226,6 +226,20 @@ const std::vector<InferenceEngine::Precision> netPrecisions = {
     InferenceEngine::Precision::FP32
 };
 
+const auto defConvSpecificParams_Smoke = ::testing::Combine(
+    ::testing::ValuesIn(std::vector<bool> {
+        true,
+        false
+    }),  // with_bilinear_interpolation_pad
+    ::testing::ValuesIn(std::vector<bool> {
+        true,
+        false
+    }),  // with_modulation
+    ::testing::ValuesIn(std::vector<OffsetType> {
+        OffsetType::REAL_MISC,
+    })  // offset type
+);
+
 const auto defConvSpecificParams = ::testing::Combine(
     ::testing::ValuesIn(std::vector<bool> {
         true,
@@ -251,8 +265,8 @@ std::vector<ngraph::op::PadType> padTypes = {
 
 const auto spParams1 = ::testing::Combine(
     ::testing::Values(1),  // batch
-    ::testing::Values(std::vector<size_t>({68, 68})),  // in. spat. shape
-    ::testing::Values(std::vector<size_t>({66, 66})),  // off. spat. shape
+    ::testing::Values(std::vector<size_t>({34, 34})),  // in. spat. shape
+    ::testing::Values(std::vector<size_t>({32, 32})),  // off. spat. shape
     ::testing::Values(std::vector<size_t>({3, 3})),  // ker. spat. shape
     ::testing::ValuesIn(padTypes),  // pad. type
     ::testing::Values(std::vector<ptrdiff_t>({0, 0})),  // pad. begin
@@ -308,6 +322,52 @@ const auto chParamsMulGr = ::testing::Combine(
         ::testing::ValuesIn(std::vector<size_t> {3, 7}),  // in. ch. per gr.
         ::testing::ValuesIn(std::vector<size_t> {3, 7}));  // out. ch. per gr.
 
+const auto params1_Smoke = ::testing::Combine(
+                             ::testing::Combine(
+                                 spParams1,
+                                 chParamsSingleGr,
+                                 defConvSpecificParams_Smoke,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                             ::testing::ValuesIn(filterCPUInfoForDevice()));
+const auto params2_Smoke = ::testing::Combine(
+                             ::testing::Combine(
+                                 spParams2,
+                                 chParamsSingleGr,
+                                 defConvSpecificParams_Smoke,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                             ::testing::ValuesIn(filterCPUInfoForDevice()));
+const auto params3_Smoke = ::testing::Combine(
+                             ::testing::Combine(
+                                 spParams3,
+                                 chParamsSingleGr,
+                                 defConvSpecificParams_Smoke,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                             ::testing::ValuesIn(filterCPUInfoForDevice()));
+const auto params4_Smoke = ::testing::Combine(
+                             ::testing::Combine(
+                                 spParams4,
+                                 chParamsSingleGr,
+                                 defConvSpecificParams_Smoke,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                             ::testing::ValuesIn(filterCPUInfoForDevice()));
+const auto params5_Smoke = ::testing::Combine(
+                             ::testing::Combine(
+                                 spParams4,
+                                 chParamsMulGr,
+                                 defConvSpecificParams_Smoke,
+                                 ::testing::ValuesIn(netPrecisions),
+                                 ::testing::Values(CommonTestUtils::DEVICE_CPU)),
+                             ::testing::ValuesIn(filterCPUInfoForDevice(true)));
+INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest1, DefConvLayerCPUTest, params1_Smoke, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest2, DefConvLayerCPUTest, params2_Smoke, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest3, DefConvLayerCPUTest, params3_Smoke, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest4, DefConvLayerCPUTest, params4_Smoke, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest5, DefConvLayerCPUTest, params5_Smoke, DefConvLayerCPUTest::getTestCaseName);
+
 const auto params1 = ::testing::Combine(
                          ::testing::Combine(
                              spParams1,
@@ -348,10 +408,11 @@ const auto params5 = ::testing::Combine(
                              ::testing::ValuesIn(netPrecisions),
                              ::testing::Values(CommonTestUtils::DEVICE_CPU)),
                          ::testing::ValuesIn(filterCPUInfoForDevice(true)));
-INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest1, DefConvLayerCPUTest, params1, DefConvLayerCPUTest::getTestCaseName);
-INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest2, DefConvLayerCPUTest, params2, DefConvLayerCPUTest::getTestCaseName);
-INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest3, DefConvLayerCPUTest, params3, DefConvLayerCPUTest::getTestCaseName);
-INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest4, DefConvLayerCPUTest, params4, DefConvLayerCPUTest::getTestCaseName);
-INSTANTIATE_TEST_SUITE_P(smoke_DefConvLayoutTest5, DefConvLayerCPUTest, params5, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(DefConvLayoutTest1, DefConvLayerCPUTest, params1, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(DefConvLayoutTest2, DefConvLayerCPUTest, params2, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(DefConvLayoutTest3, DefConvLayerCPUTest, params3, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(DefConvLayoutTest4, DefConvLayerCPUTest, params4, DefConvLayerCPUTest::getTestCaseName);
+INSTANTIATE_TEST_SUITE_P(DefConvLayoutTest5, DefConvLayerCPUTest, params5, DefConvLayerCPUTest::getTestCaseName);
+
 } // namespace
 } // namespace CPULayerTestsDefinitions

inference-engine/tests/functional/plugin/cpu/single_layer_tests/interpolate.cpp

@@ -289,7 +289,12 @@ std::vector<CPUSpecificParams> filterCPUInfoForDevice() {
     return resCPUParams;
 }
 /* ========== */
-const std::vector<ngraph::op::v4::Interpolate::CoordinateTransformMode> coordinateTransformModes = {
+const std::vector<ngraph::op::v4::Interpolate::CoordinateTransformMode> coordinateTransformModes_Smoke = {
+        ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL,
+        ngraph::op::v4::Interpolate::CoordinateTransformMode::ASYMMETRIC,
+};
+
+const std::vector<ngraph::op::v4::Interpolate::CoordinateTransformMode> coordinateTransformModes_Full = {
         ngraph::op::v4::Interpolate::CoordinateTransformMode::TF_HALF_PIXEL_FOR_NN,
         ngraph::op::v4::Interpolate::CoordinateTransformMode::PYTORCH_HALF_PIXEL,
         ngraph::op::v4::Interpolate::CoordinateTransformMode::HALF_PIXEL,
@@ -297,12 +302,13 @@ const std::vector<ngraph::op::v4::Interpolate::CoordinateTransformMode> coordina
         ngraph::op::v4::Interpolate::CoordinateTransformMode::ALIGN_CORNERS,
 };
 
-const std::vector<ngraph::op::v4::Interpolate::ShapeCalcMode> shapeCalculationMode = {
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+const std::vector<ngraph::op::v4::Interpolate::NearestMode> nearestModes_Smoke = {
+        ngraph::op::v4::Interpolate::NearestMode::SIMPLE,
+        ngraph::op::v4::Interpolate::NearestMode::ROUND_PREFER_FLOOR,
+        ngraph::op::v4::Interpolate::NearestMode::FLOOR,
 };
 
-const std::vector<ngraph::op::v4::Interpolate::NearestMode> nearestModes = {
+const std::vector<ngraph::op::v4::Interpolate::NearestMode> nearestModes_Full = {
         ngraph::op::v4::Interpolate::NearestMode::SIMPLE,
         ngraph::op::v4::Interpolate::NearestMode::ROUND_PREFER_FLOOR,
         ngraph::op::v4::Interpolate::NearestMode::FLOOR,
@@ -351,7 +357,7 @@ const std::vector<std::vector<int64_t>> defaultAxes4D = {
     {0, 1, 2, 3}
 };
 
-const std::vector<ShapeParams> shapeParams4D = {
+const std::vector<ShapeParams> shapeParams4D_Smoke = {
     ShapeParams{
         ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
         InputShape{{}, {{1, 11, 4, 4}}},
@@ -366,20 +372,6 @@ const std::vector<ShapeParams> shapeParams4D = {
         {{1, 11, 5, 6}},
         defaultAxes4D.front()
     },
-    ShapeParams{
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
-        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
-        ngraph::helpers::InputLayerType::CONSTANT,
-        {{1.f, 1.f, 1.25f, 1.5f}},
-        defaultAxes4D.front()
-    },
-    ShapeParams{
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
-        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {1, 11, 5, 5}}},
-        ngraph::helpers::InputLayerType::CONSTANT,
-        {{1, 11, 5, 6}},
-        defaultAxes4D.front()
-    },
     ShapeParams{
         ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
         InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
@@ -396,10 +388,36 @@ const std::vector<ShapeParams> shapeParams4D = {
     }
 };
 
-const auto interpolateCasesNN = ::testing::Combine(
+const std::vector<ShapeParams> shapeParams4D_Full = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {2, 7, 6, 5}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f}},
+        defaultAxes4D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1}, {{1, 11, 4, 4}, {1, 11, 5, 5}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6}},
+        defaultAxes4D.front()
+    }
+};
+
+const auto interpolateCasesNN_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(nearestModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(nearestModes_Smoke),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
+const auto interpolateCasesNN_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
+        ::testing::ValuesIn(nearestModes_Full),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads4D),
         ::testing::ValuesIn(pads4D),
@@ -407,8 +425,18 @@ const auto interpolateCasesNN = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesNN,
-            ::testing::ValuesIn(shapeParams4D),
+             interpolateCasesNN_Smoke,
+            ::testing::ValuesIn(shapeParams4D_Smoke),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(InterpolateNN_Layout_Test, InterpolateLayerCPUTest,
+         ::testing::Combine(
+             interpolateCasesNN_Full,
+             ::testing::ValuesIn(shapeParams4D_Full),
              ::testing::Values(ElementType::f32),
              ::testing::ValuesIn(filterCPUInfoForDevice()),
              ::testing::ValuesIn(interpolateFusingParamsSet),
@@ -434,7 +462,7 @@ const std::vector<ShapeParams> shapeParams4D_fixed_C = {
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN_Layout_PerChannelFuse_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesNN,
+            interpolateCasesNN_Smoke,
             ::testing::ValuesIn(shapeParams4D_fixed_C),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
@@ -442,9 +470,28 @@ INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN_Layout_PerChannelFuse_Test, Interpo
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
-const auto interpolateCasesLinearOnnx = ::testing::Combine(
+INSTANTIATE_TEST_SUITE_P(InterpolateNN_Layout_PerChannelFuse_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesNN_Full,
+            ::testing::ValuesIn(shapeParams4D_fixed_C),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::Values(fusingFakeQuantizePerChannelRelu),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+const auto interpolateCasesLinearOnnx_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
-        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
+const auto interpolateCasesLinearOnnx_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
         ::testing::ValuesIn(defNearestModes),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads4D),
@@ -453,17 +500,36 @@ const auto interpolateCasesLinearOnnx = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinearOnnx_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesLinearOnnx,
-            ::testing::ValuesIn(shapeParams4D),
+            interpolateCasesLinearOnnx_Smoke,
+            ::testing::ValuesIn(shapeParams4D_Smoke),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
-const auto interpolateCasesLinear = ::testing::Combine(
+INSTANTIATE_TEST_SUITE_P(InterpolateLinearOnnx_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesLinearOnnx_Full,
+            ::testing::ValuesIn(shapeParams4D_Full),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+const auto interpolateCasesLinear_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear),
-        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
+const auto interpolateCasesLinear_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
         ::testing::ValuesIn(defNearestModes),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads4D),
@@ -472,17 +538,36 @@ const auto interpolateCasesLinear = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinear_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesLinear,
-            ::testing::ValuesIn(shapeParams4D),
+            interpolateCasesLinear_Smoke,
+            ::testing::ValuesIn(shapeParams4D_Smoke),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
-const auto interpolateCasesCubic = ::testing::Combine(
+INSTANTIATE_TEST_SUITE_P(InterpolateLinear_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesLinear_Full,
+            ::testing::ValuesIn(shapeParams4D_Full),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+const auto interpolateCasesCubic_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::cubic),
-        ::testing::ValuesIn(coordinateTransformModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(pads4D),
+        ::testing::ValuesIn(cubeCoefs));
+
+const auto interpolateCasesCubic_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::cubic),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
         ::testing::ValuesIn(defNearestModes),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads4D),
@@ -491,8 +576,18 @@ const auto interpolateCasesCubic = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateCubic_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesCubic,
-            ::testing::ValuesIn(shapeParams4D),
+            interpolateCasesCubic_Smoke,
+            ::testing::ValuesIn(shapeParams4D_Smoke),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(InterpolateCubic_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesCubic_Full,
+            ::testing::ValuesIn(shapeParams4D_Full),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
@@ -526,7 +621,7 @@ const std::vector<std::vector<int64_t>> defaultAxes5D = {
     {0, 1, 2, 3, 4}
 };
 
-const std::vector<ShapeParams> shapeParams5D = {
+const std::vector<ShapeParams> shapeParams5D_Smoke = {
     ShapeParams{
         ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
         InputShape{{}, {{1, 11, 4, 4, 4}}},
@@ -541,20 +636,6 @@ const std::vector<ShapeParams> shapeParams5D = {
         {{1, 11, 5, 6, 2}},
         defaultAxes5D.front()
     },
-    ShapeParams{
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
-        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
-        ngraph::helpers::InputLayerType::CONSTANT,
-        {{1.f, 1.f, 1.25f, 1.5f, 0.5f}},
-        defaultAxes5D.front()
-    },
-    ShapeParams{
-        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
-        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {1, 11, 5, 5, 8}}},
-        ngraph::helpers::InputLayerType::CONSTANT,
-        {{1, 11, 5, 6, 4}},
-        defaultAxes5D.front()
-    },
     ShapeParams{
         ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
         InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
@@ -571,10 +652,35 @@ const std::vector<ShapeParams> shapeParams5D = {
     },
 };
 
-const auto interpolateCasesLinearOnnx5D = ::testing::Combine(
+const std::vector<ShapeParams> shapeParams5D_Full = {
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SCALES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {2, 7, 6, 5, 8}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1.f, 1.f, 1.25f, 1.5f, 0.5f}},
+        defaultAxes5D.front()
+    },
+    ShapeParams{
+        ngraph::op::v4::Interpolate::ShapeCalcMode::SIZES,
+        InputShape{{-1, {2, 20}, -1, -1, -1}, {{1, 11, 4, 4, 4}, {1, 11, 5, 5, 8}}},
+        ngraph::helpers::InputLayerType::CONSTANT,
+        {{1, 11, 5, 6, 4}},
+        defaultAxes5D.front()
+    }
+};
+
+const auto interpolateCasesLinearOnnx5D_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(nearestModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(cubeCoefs));
+const auto interpolateCasesLinearOnnx5D_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::linear_onnx),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
+        ::testing::ValuesIn(defNearestModes),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads5D),
         ::testing::ValuesIn(pads5D),
@@ -582,18 +688,37 @@ const auto interpolateCasesLinearOnnx5D = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateLinearOnnx5D_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesLinearOnnx5D,
-            ::testing::ValuesIn(shapeParams5D),
+            interpolateCasesLinearOnnx5D_Smoke,
+            ::testing::ValuesIn(shapeParams5D_Smoke),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice5D()),
             ::testing::ValuesIn(interpolateFusingParamsSet),
             ::testing::ValuesIn(filterAdditionalConfig())),
     InterpolateLayerCPUTest::getTestCaseName);
 
-const auto interpolateCasesNN5D = ::testing::Combine(
+INSTANTIATE_TEST_SUITE_P(InterpolateLinearOnnx5D_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesLinearOnnx5D_Full,
+            ::testing::ValuesIn(shapeParams5D_Full),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice5D()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+const auto interpolateCasesNN5D_Smoke = ::testing::Combine(
         ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
-        ::testing::ValuesIn(coordinateTransformModes),
-        ::testing::ValuesIn(defNearestModes),
+        ::testing::ValuesIn(coordinateTransformModes_Smoke),
+        ::testing::ValuesIn(nearestModes_Smoke),
+        ::testing::ValuesIn(antialias),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(pads5D),
+        ::testing::ValuesIn(cubeCoefs));
+
+const auto interpolateCasesNN5D_Full = ::testing::Combine(
+        ::testing::Values(ngraph::op::v4::Interpolate::InterpolateMode::nearest),
+        ::testing::ValuesIn(coordinateTransformModes_Full),
+        ::testing::ValuesIn(nearestModes_Full),
         ::testing::ValuesIn(antialias),
         ::testing::ValuesIn(pads5D),
         ::testing::ValuesIn(pads5D),
@@ -601,8 +726,18 @@ const auto interpolateCasesNN5D = ::testing::Combine(
 
 INSTANTIATE_TEST_SUITE_P(smoke_InterpolateNN5D_Layout_Test, InterpolateLayerCPUTest,
         ::testing::Combine(
-            interpolateCasesNN5D,
-            ::testing::ValuesIn(shapeParams5D),
+            interpolateCasesNN5D_Smoke,
+            ::testing::ValuesIn(shapeParams5D_Smoke),
+            ::testing::Values(ElementType::f32),
+            ::testing::ValuesIn(filterCPUInfoForDevice5D()),
+            ::testing::ValuesIn(interpolateFusingParamsSet),
+            ::testing::ValuesIn(filterAdditionalConfig())),
+    InterpolateLayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(InterpolateNN5D_Layout_Test, InterpolateLayerCPUTest,
+        ::testing::Combine(
+            interpolateCasesNN5D_Full,
+            ::testing::ValuesIn(shapeParams5D_Full),
             ::testing::Values(ElementType::f32),
             ::testing::ValuesIn(filterCPUInfoForDevice5D()),
             ::testing::ValuesIn(interpolateFusingParamsSet),

inference-engine/tests/functional/plugin/cpu/single_layer_tests/pad.cpp

@@ -102,7 +102,7 @@ const std::vector<ElementType> inputPrecisions = {
         ElementType::i8
 };
 
-const std::vector<float> argPadValue = {0.f, 1.f, 2.5f, -1.f};
+const std::vector<float> argPadValue = {0.f, 2.5f, -1.f};
 
 const std::vector<ngraph::helpers::PadMode> padMode = {
         ngraph::helpers::PadMode::EDGE,
@@ -112,14 +112,23 @@ const std::vector<ngraph::helpers::PadMode> padMode = {
 
 /* *======================* Static Shapes Tests 4D *======================* */
 
-const std::vector<std::vector<int64_t>> padsBegin4DConstBlocked = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 16, 1, 0}, {0, 0, 2, 0}};
-const std::vector<std::vector<int64_t>> padsEnd4DConstBlocked   = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 32, 2, 0}};
+const std::vector<std::vector<int64_t>> padsBegin4DConstBlocked_Smoke = {{0, 0, 1, 3}, {2, 16, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd4DConstBlocked_Smoke   = {{0, 0, 2, 1}, {2, 0, 0, 1}};
 
-const std::vector<std::vector<int64_t>> padsBegin4DBlocked = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 0, 1, 0}, {0, 0, 2, 0}};
-const std::vector<std::vector<int64_t>> padsEnd4DBlocked   = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 0, 2, 0}};
+const std::vector<std::vector<int64_t>> padsBegin4DBlocked_Smoke = {{0, 0, 1, 3}, {2, 0, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd4DBlocked_Smoke   = {{0, 0, 2, 1}, {2, 0, 0, 1}};
 
-const std::vector<std::vector<int64_t>> padsBegin4D = {{0, 0, 0, 0}, {0, 1, 1, 1}, {0, 2, 1, 0}, {0, 0, 0, 1}};
-const std::vector<std::vector<int64_t>> padsEnd4D   = {{0, 0, 0, 0}, {0, 2, 1, 1}, {0, 0, 2, 0}, {1, 1, 0, 0}};
+const std::vector<std::vector<int64_t>> padsBegin4D_Smoke = {{0, 1, 1, 1}, {0, 2, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd4D_Smoke   = {{0, 2, 1, 1}, {0, 0, 2, 0}};
+
+const std::vector<std::vector<int64_t>> padsBegin4DConstBlocked_Full = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 16, 1, 0}, {0, 0, 2, 0}};
+const std::vector<std::vector<int64_t>> padsEnd4DConstBlocked_Full   = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 32, 2, 0}};
+
+const std::vector<std::vector<int64_t>> padsBegin4DBlocked_Full = {{0, 0, 0, 0}, {0, 0, 1, 3}, {2, 0, 1, 0}, {0, 0, 2, 0}};
+const std::vector<std::vector<int64_t>> padsEnd4DBlocked_Full   = {{0, 0, 0, 0}, {0, 0, 2, 1}, {2, 0, 0, 1}, {1, 0, 2, 0}};
+
+const std::vector<std::vector<int64_t>> padsBegin4D_Full = {{0, 0, 0, 0}, {0, 1, 1, 1}, {0, 2, 1, 0}, {0, 0, 0, 1}};
+const std::vector<std::vector<int64_t>> padsEnd4D_Full   = {{0, 0, 0, 0}, {0, 2, 1, 1}, {0, 0, 2, 0}, {1, 1, 0, 0}};
 
 const std::vector<CPUSpecificParams> CPUParams4DBlocked = {
         cpuParams_nChw16c,
@@ -132,8 +141,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4DConstBlocked),
-                ::testing::ValuesIn(padsEnd4DConstBlocked),
+                ::testing::ValuesIn(padsBegin4DConstBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd4DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
@@ -146,8 +155,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4D),
-                ::testing::ValuesIn(padsEnd4D),
+                ::testing::ValuesIn(padsBegin4D_Smoke),
+                ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::Values(cpuParams_nhwc)),
@@ -160,8 +169,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4DBlocked),
-                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::ValuesIn(padsBegin4DBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
@@ -174,8 +183,64 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4DBlocked),
-                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::ValuesIn(padsBegin4DBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::Values(cpuParams_nhwc)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad4DConstBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DConstBlocked_Full),
+                ::testing::ValuesIn(padsEnd4DConstBlocked_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad4DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D_Full),
+                ::testing::ValuesIn(padsEnd4D_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::Values(cpuParams_nhwc)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad4DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked_Full),
+                ::testing::ValuesIn(padsEnd4DBlocked_Full),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad4D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 10, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked_Full),
+                ::testing::ValuesIn(padsEnd4DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::Values(cpuParams_nhwc)),
@@ -208,8 +273,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(inputShapesDynamic4D),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4D),
-                ::testing::ValuesIn(padsEnd4D),
+                ::testing::ValuesIn(padsBegin4D_Smoke),
+                ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams4DDynamic)),
@@ -222,8 +287,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::Values(inputShapesDynamic4D[1]),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4DConstBlocked),
-                ::testing::ValuesIn(padsEnd4DConstBlocked),
+                ::testing::ValuesIn(padsBegin4DConstBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd4DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
@@ -236,8 +301,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(inputShapesDynamic4D),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4D),
-                ::testing::ValuesIn(padsEnd4D),
+                ::testing::ValuesIn(padsBegin4D_Smoke),
+                ::testing::ValuesIn(padsEnd4D_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams4DDynamic)),
@@ -250,8 +315,64 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::Values(inputShapesDynamic4D[1]),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin4DBlocked),
-                ::testing::ValuesIn(padsEnd4DBlocked),
+                ::testing::ValuesIn(padsBegin4DBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd4DBlocked_Smoke),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic4DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic4D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D_Full),
+                ::testing::ValuesIn(padsEnd4D_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams4DDynamic)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic4DConstBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Values(inputShapesDynamic4D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DConstBlocked_Full),
+                ::testing::ValuesIn(padsEnd4DConstBlocked_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams4DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic4D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic4D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4D_Full),
+                ::testing::ValuesIn(padsEnd4D_Full),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams4DDynamic)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic4DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Values(inputShapesDynamic4D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin4DBlocked_Full),
+                ::testing::ValuesIn(padsEnd4DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams4DBlocked)),
@@ -262,14 +383,23 @@ INSTANTIATE_TEST_SUITE_P(
 
 /* *======================* Static Shapes Tests 5D *======================* */
 
-const std::vector<std::vector<int64_t>> padsBegin5DConstBlocked = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 32, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
-const std::vector<std::vector<int64_t>> padsEnd5DConstBlocked   = {{0, 0, 0, 0, 0}, {1, 16, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
+const std::vector<std::vector<int64_t>> padsBegin5DConstBlocked_Smoke = {{0, 0, 1, 1, 0}, {2, 32, 1, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd5DConstBlocked_Smoke   = {{1, 16, 1, 1, 0}, {0, 0, 0, 1, 0}};
 
-const std::vector<std::vector<int64_t>> padsBegin5DBlocked = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 0, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
-const std::vector<std::vector<int64_t>> padsEnd5DBlocked   = {{0, 0, 0, 0, 0}, {1, 0, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
+const std::vector<std::vector<int64_t>> padsBegin5DBlocked_Smoke = {{0, 0, 1, 1, 0}, {2, 0, 1, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd5DBlocked_Smoke   = {{1, 0, 1, 1, 0}, {0, 0, 0, 1, 0}};
 
-const std::vector<std::vector<int64_t>> padsBegin5D = {{0, 0, 0, 0, 0}, {0, 0, 2, 0, 0}, {1, 1, 1, 1, 0}, {2, 0, 1, 0, 1}, {0, 2, 1, 3, 1}};
-const std::vector<std::vector<int64_t>> padsEnd5D   = {{0, 0, 0, 0, 0}, {0, 0, 1, 0, 0}, {1, 0, 1, 1, 2}, {2, 2, 0, 1, 0}, {1, 1, 2, 0, 1}};
+const std::vector<std::vector<int64_t>> padsBegin5D_Smoke = {{0, 0, 2, 0, 0}, {1, 1, 1, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd5D_Smoke   = {{0, 0, 1, 0, 0}, {1, 0, 1, 1, 2}};
+
+const std::vector<std::vector<int64_t>> padsBegin5DConstBlocked_Full = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 32, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd5DConstBlocked_Full   = {{0, 0, 0, 0, 0}, {1, 16, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
+
+const std::vector<std::vector<int64_t>> padsBegin5DBlocked_Full = {{0, 0, 0, 0, 0}, {0, 0, 1, 1, 0}, {2, 0, 1, 1, 0}, {0, 0, 1, 3, 1}, {0, 0, 0, 1, 0}};
+const std::vector<std::vector<int64_t>> padsEnd5DBlocked_Full   = {{0, 0, 0, 0, 0}, {1, 0, 1, 1, 0}, {0, 0, 0, 1, 0}, {0, 0, 0, 1, 1}, {0, 0, 1, 0, 1}};
+
+const std::vector<std::vector<int64_t>> padsBegin5D_Full = {{0, 0, 0, 0, 0}, {0, 0, 2, 0, 0}, {1, 1, 1, 1, 0}, {2, 0, 1, 0, 1}, {0, 2, 1, 3, 1}};
+const std::vector<std::vector<int64_t>> padsEnd5D_Full   = {{0, 0, 0, 0, 0}, {0, 0, 1, 0, 0}, {1, 0, 1, 1, 2}, {2, 2, 0, 1, 0}, {1, 1, 2, 0, 1}};
 
 const std::vector<CPUSpecificParams> CPUParams5DBlocked = {
         cpuParams_nCdhw16c,
@@ -282,8 +412,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5DConstBlocked),
-                ::testing::ValuesIn(padsEnd5DConstBlocked),
+                ::testing::ValuesIn(padsBegin5DConstBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd5DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams5DBlocked)),
@@ -296,8 +426,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5D),
-                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(padsBegin5D_Smoke),
+                ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::Values(cpuParams_ndhwc)),
@@ -310,8 +440,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5DBlocked),
-                ::testing::ValuesIn(padsEnd5DBlocked),
+                ::testing::ValuesIn(padsBegin5DBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd5DBlocked_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams5DBlocked)),
@@ -324,8 +454,64 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5D),
-                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(padsBegin5D_Smoke),
+                ::testing::ValuesIn(padsEnd5D_Smoke),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::Values(cpuParams_ndhwc)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad5DConstBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DConstBlocked_Full),
+                ::testing::ValuesIn(padsEnd5DConstBlocked_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad5DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D_Full),
+                ::testing::ValuesIn(padsEnd5D_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::Values(cpuParams_ndhwc)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad5DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DBlocked_Full),
+                ::testing::ValuesIn(padsEnd5DBlocked_Full),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPad5D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(static_shapes_to_test_representation({{3, 16, 5, 5, 5}})),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D_Full),
+                ::testing::ValuesIn(padsEnd5D_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::Values(cpuParams_ndhwc)),
@@ -358,8 +544,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(inputShapesDynamic5D),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5D),
-                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(padsBegin5D_Smoke),
+                ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams5DDynamic)),
@@ -372,8 +558,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::Values(inputShapesDynamic5D[1]),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5DConstBlocked),
-                ::testing::ValuesIn(padsEnd5DConstBlocked),
+                ::testing::ValuesIn(padsBegin5DConstBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd5DConstBlocked_Smoke),
                 ::testing::ValuesIn(argPadValue),
                 ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
                 ::testing::ValuesIn(CPUParams5DBlocked)),
@@ -386,8 +572,8 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::ValuesIn(inputShapesDynamic5D),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5D),
-                ::testing::ValuesIn(padsEnd5D),
+                ::testing::ValuesIn(padsBegin5D_Smoke),
+                ::testing::ValuesIn(padsEnd5D_Smoke),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams5DDynamic)),
@@ -400,8 +586,64 @@ INSTANTIATE_TEST_SUITE_P(
         ::testing::Combine(
                 ::testing::Values(inputShapesDynamic5D[1]),
                 ::testing::ValuesIn(inputPrecisions),
-                ::testing::ValuesIn(padsBegin5DBlocked),
-                ::testing::ValuesIn(padsEnd5DBlocked),
+                ::testing::ValuesIn(padsBegin5DBlocked_Smoke),
+                ::testing::ValuesIn(padsEnd5DBlocked_Smoke),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic5DConst,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic5D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D_Full),
+                ::testing::ValuesIn(padsEnd5D_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams5DDynamic)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic5DConstBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Values(inputShapesDynamic5D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DConstBlocked_Full),
+                ::testing::ValuesIn(padsEnd5DConstBlocked_Full),
+                ::testing::ValuesIn(argPadValue),
+                ::testing::Values(ngraph::helpers::PadMode::CONSTANT),
+                ::testing::ValuesIn(CPUParams5DBlocked)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic5D,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::ValuesIn(inputShapesDynamic5D),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5D_Full),
+                ::testing::ValuesIn(padsEnd5D_Full),
+                ::testing::Values(0),
+                ::testing::ValuesIn(padMode),
+                ::testing::ValuesIn(CPUParams5DDynamic)),
+        PadLayerCPUTest::getTestCaseName
+);
+
+INSTANTIATE_TEST_SUITE_P(
+        CPUPadDynamic5DBlocked,
+        PadLayerCPUTest,
+        ::testing::Combine(
+                ::testing::Values(inputShapesDynamic5D[1]),
+                ::testing::ValuesIn(inputPrecisions),
+                ::testing::ValuesIn(padsBegin5DBlocked_Full),
+                ::testing::ValuesIn(padsEnd5DBlocked_Full),
                 ::testing::Values(0),
                 ::testing::ValuesIn(padMode),
                 ::testing::ValuesIn(CPUParams5DBlocked)),

inference-engine/tests/functional/plugin/cpu/single_layer_tests/pooling.cpp

@@ -20,6 +20,11 @@ using poolLayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::poolSpecific
                                              CPUSpecificParams,
                                              fusingSpecificParams>;
 
+using maxPoolV8LayerCpuTestParamsSet = std::tuple<LayerTestsDefinitions::maxPoolV8SpecificParams,
+        InputShape,
+        ElementType,
+        CPUSpecificParams>;
+
 class PoolingLayerCPUTest : public testing::WithParamInterface<poolLayerCpuTestParamsSet>,
                             virtual public SubgraphBaseTest, public CpuTestWithFusing {
 public:
@@ -68,8 +73,6 @@ public:
         results << CPUTestsBase::getTestCaseName(cpuParams);
         results << CpuTestWithFusing::getTestCaseName(fusingParams);
         return results.str();
-
-        return results.str();
     }
 
 protected:
@@ -98,7 +101,10 @@ protected:
         if (selectedType.empty()) {
             selectedType = getPrimitiveType();
         }
-        selectedType = selectedType + "_" + InferenceEngine::details::convertPrecision(inPrc).name();
+        if (isInt8)
+            selectedType = selectedType + "_I8";
+        else
+            selectedType = makeSelectedTypeStr(selectedType, inPrc);
 
         init_input_shapes({inputShapes});
 
@@ -124,11 +130,87 @@ protected:
     }
 };
 
+class MaxPoolingV8LayerCPUTest : public testing::WithParamInterface<maxPoolV8LayerCpuTestParamsSet>,
+                                 virtual public SubgraphBaseTest, public CPUTestsBase {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerCpuTestParamsSet>& obj) {
+        LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
+        InputShape inputShapes;
+        ElementType inPrc;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = obj.param;
+
+        std::vector<size_t> kernel, stride, dilation;
+        std::vector<size_t> padBegin, padEnd;
+        ngraph::op::PadType padType;
+        ngraph::op::RoundingType roundingType;
+        std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = basicParamsSet;
+
+        std::ostringstream results;
+        results << "IS=(";
+        results << CommonTestUtils::partialShape2str({inputShapes.first}) << ")_";
+        results << "TS=";
+        for (const auto& shape : inputShapes.second) {
+            results << CommonTestUtils::vec2str(shape) << "_";
+        }
+        results << "Prc=" << inPrc << "_";
+        results << "MaxPool_";
+        results << "K" << CommonTestUtils::vec2str(kernel) << "_";
+        results << "S" << CommonTestUtils::vec2str(stride) << "_";
+        results << "D" << CommonTestUtils::vec2str(dilation) << "_";
+        results << "PB" << CommonTestUtils::vec2str(padBegin) << "_";
+        results << "PE" << CommonTestUtils::vec2str(padEnd) << "_";
+        results << "Rounding=" << roundingType << "_";
+        results << "AutoPad=" << padType << "_";
+
+        results << CPUTestsBase::getTestCaseName(cpuParams);
+        return results.str();
+    }
+
+protected:
+    void SetUp() override {
+        targetDevice = CommonTestUtils::DEVICE_CPU;
+
+        LayerTestsDefinitions::maxPoolV8SpecificParams basicParamsSet;
+        InputShape inputShapes;
+        ElementType inPrc;
+        CPUSpecificParams cpuParams;
+        std::tie(basicParamsSet, inputShapes, inPrc, cpuParams) = this->GetParam();
+
+        std::vector<size_t> kernel, stride, dilation;
+        std::vector<size_t> padBegin, padEnd;
+        ngraph::op::PadType padType;
+        ngraph::op::RoundingType roundingType;
+        std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = basicParamsSet;
+        std::tie(inFmts, outFmts, priority, selectedType) = cpuParams;
+        if (selectedType.empty()) {
+            selectedType = getPrimitiveType();
+        }
+        selectedType = makeSelectedTypeStr(selectedType, inPrc);
+
+        init_input_shapes({inputShapes});
+
+        auto params = ngraph::builder::makeDynamicParams(inPrc, inputDynamicShapes);
+        std::shared_ptr<ngraph::Node> pooling = ngraph::builder::makeMaxPoolingV8(params[0], stride, dilation, padBegin, padEnd,
+                                                                                  kernel, roundingType, padType);
+        pooling->get_rt_info() = getCPUInfo();
+        ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pooling->output(0))};
+        function = std::make_shared<ngraph::Function>(results, params, "MaxPooling");
+    }
+};
+
 TEST_P(PoolingLayerCPUTest, CompareWithRefs) {
     SKIP_IF_CURRENT_TEST_IS_DISABLED()
 
     run();
-//     CheckPluginRelatedResults(executableNetwork, "Pooling");
+    CheckPluginRelatedResults(executableNetwork, "Pooling");
+}
+
+TEST_P(MaxPoolingV8LayerCPUTest, CompareWithRefs) {
+    SKIP_IF_CURRENT_TEST_IS_DISABLED()
+
+    run();
+    CheckPluginRelatedResults(executableNetwork, "Pooling");
 }
 
 namespace {
@@ -291,6 +373,20 @@ const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax4D = {
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {1, 1}, {0, 0}, {0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
+};
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV84D_ref = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2}, {2, 2}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 2}, {1, 2}, {0, 0}, {0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {4, 2}, {2, 1}, {2, 2}, {0, 0}, {0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+};
+
 const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg4D = {
         LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2}, {2, 2}, {1, 0}, {0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
@@ -321,6 +417,22 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_4D, PoolingLayerCPUTest,
                             ::testing::Values(emptyFusingSpec)),
                         PoolingLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV84D),
+                                 ::testing::ValuesIn(inputShapes4D),
+                                 ::testing::ValuesIn(inpOutPrecision),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs))),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_4D_ref, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV84D_ref),
+                                 ::testing::ValuesIn(inputShapes4D),
+                                 ::testing::ValuesIn(inpOutPrecision),
+                                 ::testing::Values(ref)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_4D, PoolingLayerCPUTest,
                         ::testing::Combine(
                             ::testing::ValuesIn(paramsAvg4D),
@@ -349,10 +461,24 @@ const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsMax5D = {
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER, false },
         LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {1, 1, 1},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {2, 3, 4}, {2, 2, 2}, {1, 1, 1}, {1, 2, 3},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::MAX, {3, 3, 3}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, false },
 };
 
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {1, 1, 1}, {0, 0, 0}, {0, 0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER },
+};
+
+const std::vector<LayerTestsDefinitions::maxPoolV8SpecificParams> paramsMaxV85D_ref = {
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {0, 0, 0}, {0, 0, 0},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_UPPER },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 2, 2}, {1, 1, 1}, {2, 2, 2}, {1, 1, 1}, {1, 1, 1},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+        LayerTestsDefinitions::maxPoolV8SpecificParams{ {2, 3, 4}, {2, 2, 2}, {2, 1, 1}, {1, 1, 1}, {1, 2, 2},
+                                                        ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT },
+};
+
 const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D = {
         LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {2, 2, 2}, {2, 2, 2}, {1, 0, 0}, {0, 0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::SAME_LOWER, true },
@@ -366,7 +492,7 @@ const std::vector<LayerTestsDefinitions::poolSpecificParams> paramsAvg5D = {
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
         LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {3, 3, 3}, {3, 3, 3}, {1, 1, 1}, {0, 0, 0},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
-        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {4, 4, 4}, {2, 2, 2}, {2, 2, 2},
+        LayerTestsDefinitions::poolSpecificParams{ ngraph::helpers::PoolingTypes::AVG, {4, 4, 4}, {2, 2, 2}, {2, 2, 2}, {2, 2, 2},
                             ngraph::op::RoundingType::CEIL, ngraph::op::PadType::EXPLICIT, true },
 };
 
@@ -385,6 +511,22 @@ INSTANTIATE_TEST_SUITE_P(smoke_MaxPool_CPU_5D, PoolingLayerCPUTest,
                              ::testing::Values(emptyFusingSpec)),
                          PoolingLayerCPUTest::getTestCaseName);
 
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV85D),
+                                 ::testing::ValuesIn(inputShapes5D),
+                                 ::testing::ValuesIn(inpOutPrecision),
+                                 ::testing::ValuesIn(filterCPUInfoForDevice(vecCpuConfigs))),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
+INSTANTIATE_TEST_SUITE_P(smoke_MaxPoolV8_CPU_5D_ref, MaxPoolingV8LayerCPUTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(paramsMaxV85D_ref),
+                                 ::testing::ValuesIn(inputShapes5D),
+                                 ::testing::ValuesIn(inpOutPrecision),
+                                 ::testing::Values(ref)),
+                         MaxPoolingV8LayerCPUTest::getTestCaseName);
+
 INSTANTIATE_TEST_SUITE_P(smoke_AvgPool_CPU_5D, PoolingLayerCPUTest,
                          ::testing::Combine(
                               ::testing::ValuesIn(paramsAvg5D),

inference-engine/tests/functional/plugin/cpu/single_layer_tests/reduce_ops.cpp

@@ -258,6 +258,12 @@ const std::vector<ngraph::helpers::ReductionType> reductionTypes = {
         ngraph::helpers::ReductionType::L2,
 };
 
+const std::vector<ngraph::helpers::ReductionType> reductionTypesFusing = {
+        ngraph::helpers::ReductionType::Mean,
+        ngraph::helpers::ReductionType::Max,
+        ngraph::helpers::ReductionType::L2,
+};
+
 const std::vector<ngraph::helpers::ReductionType> reductionLogicalTypes = {
         ngraph::helpers::ReductionType::LogicalOr,
         ngraph::helpers::ReductionType::LogicalAnd
@@ -315,13 +321,9 @@ std::vector<CPUSpecificParams> cpuParams_HybridLayout_5D = {
 
 const std::vector<fusingSpecificParams> fusingParamsSet {
         /* activations */
-        fusingRelu,
-        fusingElu,
-        fusingTanh,
         fusingSwish,
 
         /* FQ */
-        fusingFakeQuantizePerChannel,
         fusingFakeQuantizePerChannelRelu,
         fusingFakeQuantizePerTensorRelu,
         /* another patterns */
@@ -576,7 +578,7 @@ const auto params_OneAxis_fusing = testing::Combine(
             testing::ValuesIn(axes),
             testing::ValuesIn(opTypes),
             testing::Values(true),
-            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(reductionTypesFusing),
             testing::ValuesIn(inpOutPrc),
             testing::Values(ElementType::undefined),
             testing::Values(ElementType::undefined),
@@ -589,7 +591,7 @@ const auto params_MultiAxis_4D_fusing = testing::Combine(
                 testing::ValuesIn(axesND),
                 testing::Values(CommonTestUtils::OpType::VECTOR),
                 testing::Values(true),
-                testing::ValuesIn(reductionTypes),
+                testing::ValuesIn(reductionTypesFusing),
                 testing::ValuesIn(inpOutPrc),
                 testing::Values(ElementType::undefined),
                 testing::Values(ElementType::undefined),
@@ -602,7 +604,7 @@ const auto params_MultiAxis_5D_fusing = testing::Combine(
                 testing::ValuesIn(axes5D),
                 testing::Values(CommonTestUtils::OpType::VECTOR),
                 testing::Values(true),
-                testing::ValuesIn(reductionTypes),
+                testing::ValuesIn(reductionTypesFusing),
                 testing::ValuesIn(inpOutPrc),
                 testing::Values(ElementType::undefined),
                 testing::Values(ElementType::undefined),
@@ -637,7 +639,7 @@ const auto params_OneAxis_fusing_KeepNoDims = testing::Combine(
             testing::ValuesIn(axes),
             testing::ValuesIn(opTypes),
             testing::Values(false),
-            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(reductionTypesFusing),
             testing::ValuesIn(inpOutPrc),
             testing::Values(ElementType::undefined),
             testing::Values(ElementType::undefined),
@@ -650,7 +652,7 @@ const auto params_MultiAxis_4D_Hybrid_fusing_KeepNoDims = testing::Combine(
             testing::ValuesIn(axesNDFusing),
             testing::Values(CommonTestUtils::OpType::VECTOR),
             testing::Values(false),
-            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(reductionTypesFusing),
             testing::ValuesIn(inpOutPrc),
             testing::Values(ElementType::undefined),
             testing::Values(ElementType::undefined),
@@ -663,7 +665,7 @@ const auto params_MultiAxis_5D_Hybrid_fusing_KeepNoDims = testing::Combine(
             testing::ValuesIn(axes5DFusing),
             testing::Values(CommonTestUtils::OpType::VECTOR),
             testing::Values(false),
-            testing::ValuesIn(reductionTypes),
+            testing::ValuesIn(reductionTypesFusing),
             testing::ValuesIn(inpOutPrc),
             testing::Values(ElementType::undefined),
             testing::Values(ElementType::undefined),

inference-engine/tests/functional/plugin/cpu/test_utils/cpu_test_utils.cpp

@@ -265,18 +265,18 @@ CPUTestsBase::makeCPUInfo(std::vector<cpu_memory_format_t> inFmts, std::vector<c
     CPUInfo cpuInfo;
 
     if (!inFmts.empty()) {
-        cpuInfo.insert({std::string(ngraph::MLKDNNInputMemoryFormatsAttr),
-                std::make_shared<ngraph::MLKDNNInputMemoryFormats>(fmts2str(inFmts, "cpu:"))});
+        cpuInfo.insert({ngraph::MKLDNNInputMemoryFormats::get_type_info_static(),
+                ngraph::MKLDNNInputMemoryFormats(fmts2str(inFmts, "cpu:"))});
     }
     if (!outFmts.empty()) {
-        cpuInfo.insert({std::string(ngraph::MLKDNNOutputMemoryFormatsAttr),
-                std::make_shared<ngraph::MLKDNNOutputMemoryFormats>(fmts2str(outFmts, "cpu:"))});
+        cpuInfo.insert({ngraph::MKLDNNOutputMemoryFormats::get_type_info_static(),
+                ngraph::MKLDNNOutputMemoryFormats(fmts2str(outFmts, "cpu:"))});
     }
     if (!priority.empty()) {
         cpuInfo.insert({"PrimitivesPriority", std::make_shared<ngraph::VariantWrapper<std::string>>(impls2str(priority))});
     }
 
-    cpuInfo.insert({"enforceBF16evenForGraphTail", ov::make_variant<int64_t>(true)});
+    cpuInfo.insert({"enforceBF16evenForGraphTail", ov::make_runtime_attribute<int64_t>(true)});
 
     return cpuInfo;
 }

inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_roifeatureextractor.cpp

@@ -0,0 +1,37 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <vector>
+#include "single_layer_tests/experimental_detectron_roifeatureextractor.hpp"
+
+using namespace ov::test;
+using namespace ov::test::subgraph;
+
+namespace {
+const std::vector<int64_t> outputSize = {7, 14};
+const std::vector<int64_t> samplingRatio = {1, 2, 3};
+
+const std::vector<std::vector<int64_t>> pyramidScales = {
+        {8, 16, 32, 64},
+        {4, 8, 16, 32},
+        {2, 4, 8, 16}
+};
+
+const std::vector<std::vector<InputShape>> staticInputShape = {
+        static_shapes_to_test_representation({{1000, 4}, {1, 8, 200, 336}, {1, 8, 100, 168}, {1, 8, 50, 84}, {1, 8, 25, 42}}),
+        static_shapes_to_test_representation({{1000, 4}, {1, 16, 200, 336}, {1, 16, 100, 168}, {1, 16, 50, 84}, {1, 16, 25, 42}}),
+        static_shapes_to_test_representation({{1200, 4}, {1, 8, 200, 42}, {1, 8, 100, 336}, {1, 8, 50, 168}, {1, 8, 25, 84}})
+};
+
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalROI_static, ExperimentalDetectronROIFeatureExtractorLayerTest,
+                         ::testing::Combine(
+                                 ::testing::ValuesIn(staticInputShape),
+                                 ::testing::ValuesIn(outputSize),
+                                 ::testing::ValuesIn(samplingRatio),
+                                 ::testing::ValuesIn(pyramidScales),
+                                 ::testing::Values(false),
+                                 ::testing::Values(ov::element::Type_t::f32),
+                                 ::testing::Values(CommonTestUtils::DEVICE_GPU)),
+                         ExperimentalDetectronROIFeatureExtractorLayerTest::getTestCaseName);
+} // namespace

inference-engine/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/roi_align.cpp

@@ -0,0 +1,62 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#include <vector>
+
+#include "single_layer_tests/roi_align.hpp"
+#include "common_test_utils/test_constants.hpp"
+
+using namespace LayerTestsDefinitions;
+
+
+const std::vector<InferenceEngine::Precision> netPRCs = {
+    InferenceEngine::Precision::FP32,
+    // There is no possibility to test ROIAlign in fp16 precision,
+    // because on edge cases where in fp32 version ROI value is
+    // a little bit smaller than the nearest integer value,
+    // it would be bigger than the nearest integer in fp16 precision.
+    // Such behavior leads to completely different results of ROIAlign
+    // in fp32 and fp16 precisions.
+    // In real AI applications this problem is solved by precision-aware training.
+
+    // InferenceEngine::Precision::FP16,
+};
+
+const auto ROIAlignCases_average =
+    ::testing::Combine(
+                       ::testing::ValuesIn(
+                                           std::vector<std::vector<size_t>> {
+                                               { 3, 8, 16, 16 },
+                                               { 2, 1, 16, 16 },
+                                               { 2, 1, 8, 16 }}),
+                       ::testing::Values(std::vector<size_t>{ 2, 4 }),
+                       ::testing::Values(2),
+                       ::testing::Values(2),
+                       ::testing::ValuesIn(std::vector<float> { 1, 0.625 }),
+                       ::testing::Values(2),
+                       ::testing::Values("avg"),
+                       ::testing::ValuesIn(netPRCs),
+                       ::testing::Values(CommonTestUtils::DEVICE_GPU)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_TestsROIAlign_average, ROIAlignLayerTest, ROIAlignCases_average, ROIAlignLayerTest::getTestCaseName);
+
+const auto ROIAlignCases_max =
+    ::testing::Combine(
+                       ::testing::ValuesIn(
+                                           std::vector<std::vector<size_t>> {
+                                               { 2, 8, 20, 20 },
+                                               { 2, 1, 20, 20 },
+                                               { 2, 1, 10, 20 }
+                                           }),
+                       ::testing::Values(std::vector<size_t>{ 2, 4 }),
+                       ::testing::Values(2),
+                       ::testing::Values(2),
+                       ::testing::ValuesIn(std::vector<float> { 1, 0.625 }),
+                       ::testing::Values(2),
+                       ::testing::Values("max"),
+                       ::testing::ValuesIn(netPRCs),
+                       ::testing::Values(CommonTestUtils::DEVICE_GPU)
+);
+
+INSTANTIATE_TEST_SUITE_P(smoke_TestsROIAlign_max, ROIAlignLayerTest, ROIAlignCases_max, ROIAlignLayerTest::getTestCaseName);

inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_detection_output.hpp

@@ -0,0 +1,19 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <shared_test_classes/single_layer/experimental_detectron_detection_output.hpp>
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+TEST_P(ExperimentalDetectronDetectionOutputLayerTest, ExperimentalDetectronDetectionOutputLayerTests) {
+    run();
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_generate_proposals_single_image.hpp

@@ -0,0 +1,19 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <shared_test_classes/single_layer/experimental_detectron_generate_proposals_single_image.hpp>
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+TEST_P(ExperimentalDetectronGenerateProposalsSingleImageLayerTest, ExperimentalDetectronGenerateProposalsSingleImageLayerTests) {
+    run();
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/plugin/shared/include/single_layer_tests/experimental_detectron_prior_grid_generator.hpp

@@ -0,0 +1,19 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include <shared_test_classes/single_layer/experimental_detectron_prior_grid_generator.hpp>
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+TEST_P(ExperimentalDetectronPriorGridGeneratorLayerTest, ExperimentalDetectronPriorGridGeneratorLayerTests) {
+    run();
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/plugin/shared/include/single_layer_tests/pooling.hpp

@@ -19,4 +19,9 @@ TEST_P(GlobalPoolingLayerTest, CompareWithRefs) {
         PluginCache::get().reset();
     }
 }
+
+TEST_P(MaxPoolingV8LayerTest, CompareWithRefs) {
+    Run();
+}
+
 }  // namespace LayerTestsDefinitions

inference-engine/tests/functional/plugin/shared/src/behavior/caching/caching_tests.cpp

@@ -120,7 +120,7 @@ bool LoadNetworkCacheTestBase::importExportSupported(InferenceEngine::Core& ie)
     std::vector<std::string> supportedMetricKeys = ie.GetMetric(targetDevice, METRIC_KEY(SUPPORTED_METRICS));
     auto it = std::find(supportedMetricKeys.begin(), supportedMetricKeys.end(),
                         METRIC_KEY(IMPORT_EXPORT_SUPPORT));
-    bool supported = (it != supportedMetricKeys.end()) &&
+    auto supported = (it != supportedMetricKeys.end()) &&
                      ie.GetMetric(targetDevice, METRIC_KEY(IMPORT_EXPORT_SUPPORT)).as<bool>();
     return supported;
 }

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_detection_output.hpp

@@ -0,0 +1,39 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+typedef std::tuple<
+        std::vector<InputShape>,                // inputShapes
+        float,                                  // score_threshold
+        float,                                  // nms_threshol
+        float,                                  // max_delta_log_wh
+        int64_t,                                // num_classes
+        int64_t,                                // post_nms_count
+        size_t,                                 // max_detections_per_image
+        bool,                                   // class_agnostic_box_regression
+        std::vector<float>,                     // deltas_weights
+        ElementType,                            // Network precision
+        std::string                             // Device name
+> ExperimentalDetectronDetectionOutputTestParams;
+
+class ExperimentalDetectronDetectionOutputLayerTest :
+        public testing::WithParamInterface<ExperimentalDetectronDetectionOutputTestParams>,
+        virtual public SubgraphBaseTest {
+protected:
+    void SetUp() override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
+
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj);
+};
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_generate_proposals_single_image.hpp

@@ -0,0 +1,36 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+typedef std::tuple<
+        std::vector<InputShape>,                // Input shapes
+        float,                                  // min_size: minimum box width & height
+        float,                                  // nms_threshold: specifies NMS threshold
+        int64_t,                                // post_nms_count: number of top-n proposals after NMS
+        int64_t,                                // pre_nms_count: number of top-n proposals after NMS
+        std::pair<std::string, std::vector<ov::runtime::Tensor>>, // input tensors
+        ElementType,                            // Network precision
+        std::string                             // Device name>;
+> ExperimentalDetectronGenerateProposalsSingleImageTestParams;
+
+class ExperimentalDetectronGenerateProposalsSingleImageLayerTest :
+        public testing::WithParamInterface<ExperimentalDetectronGenerateProposalsSingleImageTestParams>,
+        virtual public SubgraphBaseTest {
+protected:
+    void SetUp() override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
+
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ExperimentalDetectronGenerateProposalsSingleImageTestParams>& obj);
+};
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/experimental_detectron_prior_grid_generator.hpp

@@ -0,0 +1,38 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "ngraph_functions/utils/ngraph_helpers.hpp"
+#include "common_test_utils/common_utils.hpp"
+#include "shared_test_classes/base/ov_subgraph.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+class ExperimentalDetectronPriorGridGeneratorTestParam {
+public:
+    ov::op::v6::ExperimentalDetectronPriorGridGenerator::Attributes attributes;
+    std::vector<InputShape> inputShapes;
+};
+
+typedef std::tuple<
+    ExperimentalDetectronPriorGridGeneratorTestParam,
+    std::pair<std::string, std::vector<ov::runtime::Tensor>>,
+    ElementType,                // Network precision
+    std::string                 // Device name>;
+> ExperimentalDetectronPriorGridGeneratorTestParams;
+
+class ExperimentalDetectronPriorGridGeneratorLayerTest :
+        public testing::WithParamInterface<ExperimentalDetectronPriorGridGeneratorTestParams>,
+        virtual public SubgraphBaseTest {
+protected:
+    void SetUp() override;
+    void generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) override;
+
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<ExperimentalDetectronPriorGridGeneratorTestParams>& obj);
+};
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/include/shared_test_classes/single_layer/pooling.hpp

@@ -48,6 +48,27 @@ typedef std::tuple<
         std::string                     // Device name
 > globalPoolLayerTestParamsSet;
 
+typedef std::tuple<
+        std::vector<size_t>,            // Kernel size
+        std::vector<size_t>,            // Stride
+        std::vector<size_t>,            // Dilation
+        std::vector<size_t>,            // Pad begin
+        std::vector<size_t>,            // Pad end
+        ngraph::op::RoundingType,       // Rounding type
+        ngraph::op::PadType             // Pad type
+> maxPoolV8SpecificParams;
+
+typedef std::tuple<
+        maxPoolV8SpecificParams,
+        InferenceEngine::Precision,     // Net precision
+        InferenceEngine::Precision,     // Input precision
+        InferenceEngine::Precision,     // Output precision
+        InferenceEngine::Layout,        // Input layout
+        InferenceEngine::Layout,        // Output layout
+        std::vector<size_t>,            // Input shape
+        std::string                     // Device name
+> maxPoolV8LayerTestParamsSet;
+
 class PoolingLayerTest : public testing::WithParamInterface<poolLayerTestParamsSet>,
                          virtual public LayerTestsUtils::LayerTestsCommon {
 public:
@@ -66,4 +87,13 @@ protected:
     void SetUp() override;
 };
 
+class MaxPoolingV8LayerTest : public testing::WithParamInterface<maxPoolV8LayerTestParamsSet>,
+                              virtual public LayerTestsUtils::LayerTestsCommon {
+public:
+    static std::string getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerTestParamsSet>& obj);
+
+protected:
+    void SetUp() override;
+};
+
 }  // namespace LayerTestsDefinitions

inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp

@@ -0,0 +1,144 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_layer/experimental_detectron_detection_output.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "common_test_utils/data_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+namespace {
+    std::ostream& operator <<(std::ostream& ss, const ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes& attributes) {
+    ss << "score_threshold=" << attributes.score_threshold << "_";
+    ss << "nms_threshold=" << attributes.nms_threshold << "_";
+    ss << "max_delta_log_wh=" << attributes.max_delta_log_wh << "_";
+    ss << "num_classes=" << attributes.num_classes << "_";
+    ss << "post_nms_count=" << attributes.post_nms_count << "_";
+    ss << "max_detections_per_image=" << attributes.max_detections_per_image << "_";
+    ss << "class_agnostic_box_regression=" << (attributes.class_agnostic_box_regression ? "true" : "false") << "_";
+    ss << "deltas_weights=" << CommonTestUtils::vec2str(attributes.deltas_weights);
+    return ss;
+}
+} // namespace
+
+std::string ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName(
+        const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj) {
+    std::vector<ov::test::InputShape> inputShapes;
+    ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes attributes;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(
+        inputShapes,
+        attributes.score_threshold,
+        attributes.nms_threshold,
+        attributes.max_delta_log_wh,
+        attributes.num_classes,
+        attributes.post_nms_count,
+        attributes.max_detections_per_image,
+        attributes.class_agnostic_box_regression,
+        attributes.deltas_weights,
+        netPrecision,
+        targetName) = obj.param;
+
+    std::ostringstream result;
+
+    using ov::test::operator<<;
+    result << "input_rois=" << inputShapes[0] << "_";
+    result << "input_deltas=" << inputShapes[1] << "_";
+    result << "input_scores=" << inputShapes[2] << "_";
+    result << "input_im_info=" << inputShapes[3] << "_";
+
+    using ov::test::subgraph::operator<<;
+    result << "attributes={" << attributes << "}_";
+    result << "netPRC=" << netPrecision << "_";
+    result << "trgDev=" << targetName;
+    return result.str();
+}
+
+void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
+    std::vector<InputShape> inputShapes;
+    ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes attributes;
+
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(
+        inputShapes,
+        attributes.score_threshold,
+        attributes.nms_threshold,
+        attributes.max_delta_log_wh,
+        attributes.num_classes,
+        attributes.post_nms_count,
+        attributes.max_detections_per_image,
+        attributes.class_agnostic_box_regression,
+        attributes.deltas_weights,
+        netPrecision,
+        targetName) = this->GetParam();
+
+    inType = outType = netPrecision;
+    targetDevice = targetName;
+
+    init_input_shapes(inputShapes);
+
+    auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
+    auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto experimentalDetectron = std::make_shared<ngraph::opset6::ExperimentalDetectronDetectionOutput>(
+        params[0], // input_rois
+        params[1], // input_deltas
+        params[2], // input_scores
+        params[3], // input_im_info
+        attributes);
+    function = std::make_shared<ov::Function>(
+        ov::OutputVector{experimentalDetectron->output(0), experimentalDetectron->output(1)},
+        "ExperimentalDetectronDetectionOutput");
+}
+
+void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    static const std::vector<ov::runtime::Tensor> inputTensors = {
+        // 16 x 4 = 64
+        CommonTestUtils::create_tensor<float>(ov::element::f32, Shape{16, 4}, {
+            1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  4.0f,  1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
+        // 16 x 8
+        CommonTestUtils::create_tensor<float>(ov::element::f32, Shape{16, 8}, {
+            5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
+        // 16 x 2 = 32
+        CommonTestUtils::create_tensor<float>(ov::element::f32, Shape{16, 2}, {
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
+        // 1 x 3 = 3
+        CommonTestUtils::create_tensor<float>(ov::element::f32, Shape{1, 3}, {1.0f, 1.0f, 1.0f})
+    };
+
+    inputs.clear();
+    const auto& funcInputs = function->inputs();
+    for (auto i = 0ul; i < funcInputs.size(); ++i) {
+        if (targetInputStaticShapes[i] != inputTensors[i].get_shape()) {
+            throw Exception("input shape is different from tensor shape");
+        }
+
+        inputs.insert({funcInputs[i].get_node_shared_ptr(), inputTensors[i]});
+    }
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_generate_proposals_single_image.cpp

@@ -0,0 +1,107 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_layer/experimental_detectron_generate_proposals_single_image.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+namespace {
+std::ostream& operator <<(
+        std::ostream& ss,
+        const ov::op::v6::ExperimentalDetectronGenerateProposalsSingleImage::Attributes& attributes) {
+    ss << "score_threshold=" << attributes.min_size << "_";
+    ss << "nms_threshold=" << attributes.nms_threshold << "_";
+    ss << "max_delta_log_wh=" << attributes.post_nms_count << "_";
+    ss << "num_classes=" << attributes.pre_nms_count;
+    return ss;
+}
+} // namespace
+
+std::string ExperimentalDetectronGenerateProposalsSingleImageLayerTest::getTestCaseName(
+        const testing::TestParamInfo<ExperimentalDetectronGenerateProposalsSingleImageTestParams>& obj) {
+    std::vector<InputShape> inputShapes;
+    ov::op::v6::ExperimentalDetectronGenerateProposalsSingleImage::Attributes attributes;
+    std::pair<std::string, std::vector<ov::runtime::Tensor>> inputTensors;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(
+        inputShapes,
+        attributes.min_size,
+        attributes.nms_threshold,
+        attributes.post_nms_count,
+        attributes.pre_nms_count,
+        inputTensors,
+        netPrecision,
+        targetName) = obj.param;
+
+    std::ostringstream result;
+    using ov::test::operator<<;
+    result << "im_info=" << inputShapes[0] << "_";
+    result << "anchors=" << inputShapes[1] << "_";
+    result << "deltas=" << inputShapes[2] << "_";
+    result << "scores=" << inputShapes[3] << "_";
+
+    using ov::test::subgraph::operator<<;
+    result << "attributes={" << attributes << "}_";
+    result << "inputTensors=" << inputTensors.first << "_";
+    result << "netPRC=" << netPrecision << "_";
+    result << "trgDev=" << targetName;
+    return result.str();
+}
+
+void ExperimentalDetectronGenerateProposalsSingleImageLayerTest::SetUp() {
+    std::vector<InputShape> inputShapes;
+    ov::op::v6::ExperimentalDetectronGenerateProposalsSingleImage::Attributes attributes;
+    std::pair<std::string, std::vector<ov::runtime::Tensor>> inputTensors;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(
+        inputShapes,
+        attributes.min_size,
+        attributes.nms_threshold,
+        attributes.post_nms_count,
+        attributes.pre_nms_count,
+        inputTensors,
+        netPrecision,
+        targetName) = this->GetParam();
+
+    inType = outType = netPrecision;
+    targetDevice = targetName;
+
+    init_input_shapes(inputShapes);
+
+    auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
+    auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto experimentalDetectron = std::make_shared<ov::op::v6::ExperimentalDetectronGenerateProposalsSingleImage>(
+        params[0], // im_info
+        params[1], // anchors
+        params[2], // deltas
+        params[3], // scores
+        attributes);
+    function = std::make_shared<ov::Function>(
+        ov::OutputVector{experimentalDetectron->output(0), experimentalDetectron->output(1)},
+        "ExperimentalDetectronGenerateProposalsSingleImage");
+}
+
+void ExperimentalDetectronGenerateProposalsSingleImageLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    auto inputTensors = std::get<5>(GetParam());
+
+    inputs.clear();
+    const auto& funcInputs = function->inputs();
+    for (auto i = 0ul; i < funcInputs.size(); ++i) {
+        if (targetInputStaticShapes[i] != inputTensors.second[i].get_shape()) {
+            throw Exception("input shape is different from tensor shape");
+        }
+
+        inputs.insert({funcInputs[i].get_node_shared_ptr(), inputTensors.second[i]});
+    }
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_prior_grid_generator.cpp

@@ -0,0 +1,100 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/single_layer/experimental_detectron_prior_grid_generator.hpp"
+#include "ngraph_functions/builders.hpp"
+#include "common_test_utils/data_utils.hpp"
+#include "functional_test_utils/ov_tensor_utils.hpp"
+
+namespace ov {
+namespace test {
+namespace subgraph {
+
+namespace {
+std::ostream& operator <<(
+        std::ostream& ss,
+        const ov::op::v6::ExperimentalDetectronPriorGridGenerator::Attributes& attributes) {
+    ss << "flatten=" << attributes.flatten << "_";
+    ss << "h=" << attributes.h << "_";
+    ss << "w=" << attributes.w << "_";
+    ss << "stride_x=" << attributes.stride_x << "_";
+    ss << "stride_y=" << attributes.stride_y;
+    return ss;
+}
+} // namespace
+
+std::string ExperimentalDetectronPriorGridGeneratorLayerTest::getTestCaseName(
+        const testing::TestParamInfo<ExperimentalDetectronPriorGridGeneratorTestParams>& obj) {
+    ExperimentalDetectronPriorGridGeneratorTestParam param;
+    std::pair<std::string, std::vector<ov::runtime::Tensor>> inputTensors;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(param, inputTensors, netPrecision, targetName) = obj.param;
+
+    std::ostringstream result;
+    using ov::test::operator<<;
+    result << "priors=" << param.inputShapes[0] << "_";
+    result << "feature_map=" << param.inputShapes[1] << "_";
+    result << "im_data=" << param.inputShapes[2] << "_";
+
+    using ov::test::subgraph::operator<<;
+    result << "attributes=" << param.attributes << "_";
+    result << "priorValues=" << inputTensors.first << "_";
+    result << "netPRC=" << netPrecision << "_";
+    result << "trgDev=" << targetName;
+    return result.str();
+}
+
+void ExperimentalDetectronPriorGridGeneratorLayerTest::SetUp() {
+    ExperimentalDetectronPriorGridGeneratorTestParam param;
+    std::pair<std::string, std::vector<ov::runtime::Tensor>> inputTensors;
+    ElementType netPrecision;
+    std::string targetName;
+    std::tie(param, inputTensors, netPrecision, targetName) = this->GetParam();
+
+    inType = outType = netPrecision;
+    targetDevice = targetName;
+
+    init_input_shapes(param.inputShapes);
+
+    auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
+    auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto experimentalDetectron = std::make_shared<op::v6::ExperimentalDetectronPriorGridGenerator>(
+        params[0], // priors
+        params[1], // feature_map
+        params[2], // im_data
+        param.attributes);
+    function = std::make_shared<ov::Function>(
+            ov::OutputVector{experimentalDetectron->output(0)},
+            "ExperimentalDetectronPriorGridGenerator");
+}
+
+void ExperimentalDetectronPriorGridGeneratorLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    auto inputTensors = std::get<1>(GetParam());
+
+    inputs.clear();
+    const auto& funcInputs = function->inputs();
+
+    auto i = 0ul;
+    for (; i < inputTensors.second.size(); ++i) {
+        if (targetInputStaticShapes[i] != inputTensors.second[i].get_shape()) {
+            throw Exception("input shape is different from tensor shape");
+        }
+
+        inputs.insert({funcInputs[i].get_node_shared_ptr(), inputTensors.second[i]});
+    }
+
+    for (auto j = i; j < funcInputs.size(); ++j) {
+        ov::runtime::Tensor inputTensor = CommonTestUtils::create_tensor<float>(
+            ov::element::f32,
+            targetInputStaticShapes[j],
+            std::vector<float>(0.f, shape_size(targetInputStaticShapes[j])));
+
+        inputs.insert({funcInputs[j].get_node_shared_ptr(), inputTensor});
+    }
+}
+
+} // namespace subgraph
+} // namespace test
+} // namespace ov

inference-engine/tests/functional/shared_test_classes/src/single_layer/memory.cpp

@@ -46,9 +46,9 @@ namespace LayerTestsDefinitions {
         }
 
         auto hostTensor = std::make_shared<HostTensor>(ngPrc, inputShape);
-        auto variable_context = std::make_shared<VariantWrapper<VariableContext>>(VariableContext());
+        auto variable_context = VariableContext();
         auto variable_value = std::make_shared<VariableValue>(hostTensor);
-        variable_context->get().set_variable_value(function->get_variable_by_id("v0"), variable_value);
+        variable_context.set_variable_value(function->get_variable_by_id("v0"), variable_value);
         eval_context["VariableContext"] = variable_context;
     }
 

inference-engine/tests/functional/shared_test_classes/src/single_layer/pooling.cpp

@@ -94,6 +94,38 @@ std::string GlobalPoolingLayerTest::getTestCaseName(const testing::TestParamInfo
     return result.str();
 }
 
+std::string MaxPoolingV8LayerTest::getTestCaseName(const testing::TestParamInfo<maxPoolV8LayerTestParamsSet>& obj) {
+    maxPoolV8SpecificParams poolParams;
+    InferenceEngine::Precision netPrecision;
+    InferenceEngine::Precision inPrc, outPrc;
+    InferenceEngine::Layout inLayout, outLayout;
+    std::vector<size_t> inputShapes;
+    std::string targetDevice;
+    std::tie(poolParams, netPrecision, inPrc, outPrc, inLayout, outLayout, inputShapes, targetDevice) = obj.param;
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = poolParams;
+
+    std::ostringstream result;
+    result << "IS=" << CommonTestUtils::vec2str(inputShapes) << "_";
+    result << "K" << CommonTestUtils::vec2str(kernel) << "_";
+    result << "S" << CommonTestUtils::vec2str(stride) << "_";
+    result << "D" << CommonTestUtils::vec2str(dilation) << "_";
+    result << "PB" << CommonTestUtils::vec2str(padBegin) << "_";
+    result << "PE" << CommonTestUtils::vec2str(padEnd) << "_";
+    result << "Rounding=" << roundingType << "_";
+    result << "AutoPad=" << padType << "_";
+    result << "netPRC=" << netPrecision.name() << "_";
+    result << "inPRC=" << inPrc.name() << "_";
+    result << "outPRC=" << outPrc.name() << "_";
+    result << "inL=" << inLayout << "_";
+    result << "outL=" << outLayout << "_";
+    result << "trgDev=" << targetDevice;
+    return result.str();
+}
+
 void PoolingLayerTest::SetUp() {
     poolSpecificParams poolParams;
     std::vector<size_t> inputShape;
@@ -159,4 +191,28 @@ void GlobalPoolingLayerTest::SetUp() {
     ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(pooling)};
     function = std::make_shared<ngraph::Function>(results, params, "pooling");
 }
+
+void MaxPoolingV8LayerTest::SetUp() {
+    maxPoolV8SpecificParams poolParams;
+    std::vector<size_t> inputShape;
+    InferenceEngine::Precision netPrecision;
+    std::tie(poolParams, netPrecision, inPrc, outPrc, inLayout, outLayout, inputShape, targetDevice) = this->GetParam();
+    std::vector<size_t> kernel, stride, dilation;
+    std::vector<size_t> padBegin, padEnd;
+    ngraph::op::PadType padType;
+    ngraph::op::RoundingType roundingType;
+    std::tie(kernel, stride, dilation, padBegin, padEnd, roundingType, padType) = poolParams;
+
+    auto ngPrc = FuncTestUtils::PrecisionUtils::convertIE2nGraphPrc(netPrecision);
+    auto params = ngraph::builder::makeParams(ngPrc, {inputShape});
+    auto paramOuts = ngraph::helpers::convert2OutputVector(
+            ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+
+    std::shared_ptr<ngraph::Node> maxPool = ngraph::builder::makeMaxPoolingV8(paramOuts[0], stride, dilation, padBegin, padEnd,
+                                                                              kernel, roundingType, padType);
+
+    ngraph::ResultVector results{std::make_shared<ngraph::opset3::Result>(maxPool->output(0))};
+    function = std::make_shared<ngraph::Function>(results, params, "MaxPoolV8");
+}
+
 }  // namespace LayerTestsDefinitions

inference-engine/tests/ie_test_utils/common_test_utils/data_utils.hpp

@@ -418,6 +418,18 @@ void inline fill_data_random<InferenceEngine::Precision::BF16>(InferenceEngine::
     fill_data_random_float<InferenceEngine::Precision::BF16>(blob, range, start_from, k, seed);
 }
 
+template <class T>
+static ov::runtime::Tensor create_tensor(
+        const ov::element::Type& element_type,
+        const ov::Shape& shape,
+        const std::vector<T>& values,
+        const size_t size = 0) {
+    const size_t real_size = size ? size : values.size() * sizeof(T) / element_type.size();
+    ov::runtime::Tensor tensor { element_type, shape };
+    std::memcpy(tensor.data(), values.data(), std::min(real_size * element_type.size(), sizeof(T) * values.size()));
+    return tensor;
+}
+
 template<typename T>
 typename std::enable_if<std::is_signed<T>::value, T>::type
 inline ie_abs(const T &val) {

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/builders.hpp

@@ -94,46 +94,7 @@ std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantizeTypeRelaxed(
     const ngraph::element::Type constantPrecision,
     const FakeQuantizeOnDataWithConstant& fqOnData);
 
-template <typename ... Args>
-void addAttribute(std::vector<std::shared_ptr<ngraph::Node>> nodes, Args&& ... args) {
-    const auto attribute = std::make_shared<ngraph::VariantWrapper<QuantizationAlignmentAttributePtr>>(
-        QuantizationAlignmentAttribute(std::forward<Args>(args)...));
-
-    for (const auto& node : nodes) {
-        node->get_rt_info()[ngraph::VariantWrapper<QuantizationAlignmentAttributePtr>::type_info.name] = attribute;
-    }
-}
-
-template <typename T>
-void addAttribute2(std::vector<std::shared_ptr<ngraph::Node>> nodes, T attribute) {
-    const std::string typeInfoName = attribute->get_type_info().name;
-    for (const auto& node : nodes) {
-        auto& rt = node->get_rt_info();
-        rt[typeInfoName] = attribute;
-    }
-}
-
-template <typename T, typename ... Args>
-void addAttribute3(std::vector<std::shared_ptr<ngraph::Node>> nodes, Args&& ... args) {
-    const auto attribute = std::make_shared<::ngraph::VariantWrapper<T>>(T(std::forward<Args>(args)...));
-    for (const auto& node : nodes) {
-        node->get_rt_info()[ngraph::VariantWrapper<T>::type_info.name] = attribute;
-    }
-}
-
-void addAttributes(std::vector<std::shared_ptr<ngraph::Node>> nodes, std::vector<std::shared_ptr<Variant>> attributes);
-
-template <typename T, typename ... Args>
-std::shared_ptr<Variant> make_shared_attribute(Args&& ... args) {
-    const auto attribute = std::make_shared<::ngraph::VariantWrapper<T>>(T(std::forward<Args>(args)...));
-    return attribute;
-}
-
-template <typename T, typename ... Args>
-std::shared_ptr<Variant> make_shared_attribute_ptr(Args&& ... args) {
-    const auto attribute = std::make_shared<::ngraph::VariantWrapper<std::shared_ptr<T>>>(std::make_shared<T>(std::forward<Args>(args)...));
-    return attribute;
-}
+void addAttributes(std::vector<std::shared_ptr<ngraph::Node>> nodes, std::vector<ov::Any> attributes);
 
 std::shared_ptr<Node> makeConvolution(
     const std::shared_ptr<Node>& parent,

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/common/fake_quantize_on_data.hpp

@@ -24,7 +24,7 @@ public:
         const std::vector<float>& outputLowValues,
         const std::vector<float>& outputHighValues,
         const ngraph::element::Type outputPrecision = ngraph::element::undefined,
-        const std::vector<std::shared_ptr<Variant>>& attributes = {});
+        const std::vector<ov::Any>& attributes = {});
 
     virtual ~FakeQuantizeOnData();
 
@@ -38,7 +38,7 @@ public:
     std::vector<float> outputLowValues;
     std::vector<float> outputHighValues;
     ngraph::element::Type outputPrecision;
-    std::vector<std::shared_ptr<Variant>> attributes;
+    std::vector<ov::Any> attributes;
 };
 
 inline std::ostream& operator<<(std::ostream& os, const std::vector<float>& values) {
@@ -71,8 +71,8 @@ public:
         const std::vector<float>& outputLowValues,
         const std::vector<float>& outputHighValues,
         const ngraph::element::Type outputPrecision = ngraph::element::undefined,
-        const std::vector<std::shared_ptr<Variant>>& attributes = {});
 
+        const std::vector<ov::Any>& attributes = {});
     virtual ~FakeQuantizeOnDataWithConstant();
 
     virtual bool empty() const;
@@ -84,7 +84,7 @@ public:
     std::vector<float> outputLowValues;
     std::vector<float> outputHighValues;
     ngraph::element::Type outputPrecision;
-    std::vector<std::shared_ptr<Variant>> attributes;
+    std::vector<ov::Any> attributes;
 };
 
 inline std::ostream& operator<<(std::ostream& out, const FakeQuantizeOnDataWithConstant& data) {

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/concat_function.hpp

@@ -123,7 +123,7 @@ public:
         const FakeQuantizeOnDataWithConstant& fakeQuantize2,
         const DequantizationOperations::Convert& convert2,
         const DequantizationOperations& dequantization2,
-        const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+        const std::vector<ov::Any>& concatAttributes,
         const ngraph::element::Type precisionAfterOperation,
         const DequantizationOperations& dequantizationAfter,
         const std::int64_t& axis,
@@ -141,7 +141,7 @@ public:
         const DequantizationOperations::Convert& convert2,
         const DequantizationOperations& dequantization2,
         const bool addReshape2,
-        const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+        const std::vector<ov::Any>& concatAttributes,
         const ngraph::element::Type precisionAfterOperation,
         const DequantizationOperations& dequantizationAfter,
         const std::int64_t& axis,

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/include/lpt_ngraph_functions/move_fake_quantize_function.hpp

@@ -30,7 +30,7 @@ public:
         const FakeQuantizeOnDataWithConstant& fqOnData3,
         const DequantizationOperations::Convert& convert3,
         const DequantizationOperations& dequantization3,
-        const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+        const std::vector<ov::Any>& concatAttributes,
         const ngraph::element::Type precisionAfterOperation,
         const DequantizationOperations& dequantizationAfter,
         const std::int64_t& axis);

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/common/builders.cpp

@@ -282,7 +282,9 @@ std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantize(
 
     auto& rt = fq->get_rt_info();
     for (auto& attribute : fqOnData.attributes) {
-        rt[attribute->get_type_info().name] = attribute;
+        if (attribute.is<ov::RuntimeAttribute>()) {
+            rt[attribute.as<ov::RuntimeAttribute>().get_type_info()] = attribute;
+        }
     }
 
     return fq;
@@ -298,12 +300,12 @@ std::shared_ptr<ngraph::opset1::FakeQuantize> makeFakeQuantizeTypeRelaxed(
         fqOnData.outputPrecision == ngraph::element::undefined ? constantPrecision : fqOnData.outputPrecision);
 }
 
-void addAttributes(std::vector<std::shared_ptr<ngraph::Node>> nodes, std::vector<std::shared_ptr<Variant>> attributes) {
+void addAttributes(std::vector<std::shared_ptr<ngraph::Node>> nodes, std::vector<ov::Any> attributes) {
     for (const auto& node : nodes) {
         for (const auto& attribute : attributes) {
-            auto& rt = node->get_rt_info();
-            const std::string typeInfoName = attribute->get_type_info().name;
-            rt[typeInfoName] = attribute;
+            if (attribute.is<ov::RuntimeAttribute>()) {
+                node->get_rt_info()[attribute.as<ov::RuntimeAttribute>().get_type_info()] = attribute;
+            }
         }
     }
 }

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/common/fake_quantize_on_data.cpp

@@ -19,7 +19,7 @@ FakeQuantizeOnData::FakeQuantizeOnData(
     const std::vector<float>& outputLowValues,
     const std::vector<float>& outputHighValues,
     const ngraph::element::Type outputPrecision,
-    const std::vector<std::shared_ptr<Variant>>& attributes) :
+    const std::vector<ov::Any>& attributes) :
     quantizationLevel(quantizationLevel),
     constantShape(constantShape),
     inputLowValues(inputLowValues),
@@ -58,7 +58,7 @@ FakeQuantizeOnDataWithConstant::FakeQuantizeOnDataWithConstant(
     const std::vector<float>& outputLowValues,
     const std::vector<float>& outputHighValues,
     const ngraph::element::Type outputPrecision,
-    const std::vector<std::shared_ptr<Variant>>& attributes) :
+    const std::vector<ov::Any>& attributes) :
     quantizationLevel(quantizationLevel),
     constantShapes(constantShapes),
     inputLowValues(inputLowValues),

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/concat_function.cpp

@@ -874,7 +874,7 @@ std::shared_ptr<ngraph::Function> ConcatFunction::get(
     const FakeQuantizeOnDataWithConstant& fqOnData2,
     const DequantizationOperations::Convert& convert2,
     const DequantizationOperations& dequantization2,
-    const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+    const std::vector<ov::Any>& concatAttributes,
     const ngraph::element::Type precisionAfterOperation,
     const DequantizationOperations& dequantizationAfter,
     const std::int64_t& axis,
@@ -960,7 +960,7 @@ std::shared_ptr<ngraph::Function> ConcatFunction::get(
     const DequantizationOperations::Convert& convert2,
     const DequantizationOperations& dequantization2,
     const bool addReshape2,
-    const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+    const std::vector<ov::Any>& concatAttributes,
     const ngraph::element::Type precisionAfterOperation,
     const DequantizationOperations& dequantizationAfter,
     const std::int64_t& axis,

inference-engine/tests/ngraph_helpers/lpt_ngraph_functions/src/move_fake_quantize_function.cpp

@@ -32,7 +32,7 @@ std::shared_ptr<ngraph::Function> MoveFakeQuantize::get(
     const FakeQuantizeOnDataWithConstant& fqOnData3,
     const DequantizationOperations::Convert& convert3,
     const DequantizationOperations& dequantization3,
-    const std::vector<std::shared_ptr<Variant>>& concatAttributes,
+    const std::vector<ov::Any>& concatAttributes,
     const ngraph::element::Type precisionAfterOperation,
     const DequantizationOperations& dequantizationAfter,
     const std::int64_t& axis) {

inference-engine/tests/ngraph_helpers/ngraph_functions/include/ngraph_functions/builders.hpp

@@ -428,6 +428,15 @@ std::shared_ptr<Node> makePooling(const ngraph::Output<Node> &in,
                                   bool excludePad,
                                   const ngraph::helpers::PoolingTypes &poolType);
 
+std::shared_ptr<Node> makeMaxPoolingV8(const ngraph::Output<Node> &in,
+                                       const std::vector<size_t> &strides,
+                                       const std::vector<size_t> &dilation,
+                                       const std::vector<size_t> &padsBegin,
+                                       const std::vector<size_t> &padsEnd,
+                                       const std::vector<size_t> &kernel,
+                                       const op::RoundingType &roundingType,
+                                       const op::PadType &padType);
+
 std::shared_ptr<Node> makeROIPooling(const Output<Node>& input,
                                      const Output<Node>& coords,
                                      const Shape& output_size,

inference-engine/tests/ngraph_helpers/ngraph_functions/src/pooling.cpp

@@ -35,5 +35,18 @@ std::shared_ptr<Node> makePooling(const ngraph::Output<Node> &in,
     return pooling;
 }
 
+std::shared_ptr<Node> makeMaxPoolingV8(const ngraph::Output<Node> &in,
+                                       const std::vector<size_t> &strides,
+                                       const std::vector<size_t> &dilation,
+                                       const std::vector<size_t> &padsBegin,
+                                       const std::vector<size_t> &padsEnd,
+                                       const std::vector<size_t> &kernel,
+                                       const op::RoundingType &roundingType,
+                                       const op::PadType &padType) {
+    std::shared_ptr<ngraph::Node> pooling = std::make_shared<ngraph::opset8::MaxPool>(in, strides, dilation, padsBegin, padsEnd,
+                                                                                      kernel, roundingType, padType);
+    return pooling;
+}
+
 }  // namespace builder
 }  // namespace ngraph

inference-engine/tests/unit/auto/auto_select_device_failed_test.cpp

@@ -17,6 +17,7 @@
 #include <gmock/gmock.h>
 #include "plugin/mock_auto_device_plugin.hpp"
 #include "cpp/ie_plugin.hpp"
+#include "mock_common.hpp"
 
 using ::testing::MatcherCast;
 using ::testing::AllOf;
@@ -33,15 +34,18 @@ using ::testing::AnyNumber;
 using Config = std::map<std::string, std::string>;
 using namespace MockMultiDevice;
 
-#define IE_SET_METRIC(key, name,  ...)                                                            \
-    typename ::InferenceEngine::Metrics::MetricType<::InferenceEngine::Metrics::key>::type name = \
-        __VA_ARGS__;
-
 using DeviceParams = std::tuple<std::string, bool>;
 
+enum MODEL {
+    GENERAL = 0,
+    LATENCY = 1,
+    THROUGHPUT = 2,
+};
+
 using ConfigParams = std::tuple<
         bool,                        // if can continue to run
         bool,                        // if select throw exception
+        MODEL,                       // config model general, latency, throughput
         std::vector<DeviceParams>,   // {device, loadSuccess}
         unsigned int,                // select count
         unsigned int,                // load count
@@ -72,7 +76,8 @@ public:
         std::vector<std::tuple<std::string, bool>> deviceConfigs;
         bool continueRun;
         bool thrExcWheSelect;
-        std::tie(continueRun, thrExcWheSelect, deviceConfigs,
+        MODEL configModel;
+        std::tie(continueRun, thrExcWheSelect, configModel, deviceConfigs,
                  selectCount, loadCount, loadSuccessCount) = obj.param;
         std::ostringstream result;
         for (auto& item : deviceConfigs) {
@@ -87,6 +92,22 @@ public:
         } else {
             result << "select_success_";
         }
+
+        switch (configModel) {
+            case GENERAL:
+                result << "GENERAL";
+                break;
+            case LATENCY:
+                result << "LATENCY";
+                break;
+            case THROUGHPUT:
+                result << "THROUGHPUT";
+                break;
+            default:
+                LOG_ERROR("should not come here");
+                break;
+        }
+
         result << "select_" << selectCount << "_loadCount_"
                << loadCount << "_loadSuccessCount_" << loadSuccessCount;
         return result.str();
@@ -142,7 +163,8 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) {
     std::vector<std::tuple<std::string, bool>> deviceConfigs;
     bool continueRun;
     bool thrExcWheSelect;
-    std::tie(continueRun, thrExcWheSelect, deviceConfigs, selectCount,
+    MODEL configModel;
+    std::tie(continueRun, thrExcWheSelect, configModel, deviceConfigs, selectCount,
              loadCount, loadSuccessCount) = this->GetParam();
 
     // test auto plugin
@@ -163,7 +185,24 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) {
                         ::testing::Matcher<const Config&>(_)))
                 .WillByDefault(Throw(InferenceEngine::GeneralError{""}));
         }
-        DeviceInformation devInfo = {deviceName, {}, 2, ""};
+        DeviceInformation devInfo;
+        switch (configModel) {
+            case GENERAL:
+                devInfo = {deviceName, {}, 2, ""};
+                break;
+            case LATENCY:
+                devInfo = {deviceName, {{CONFIG_KEY(PERFORMANCE_HINT),
+                    InferenceEngine::PluginConfigParams::LATENCY}}, 2, ""};
+                break;
+            case THROUGHPUT:
+                devInfo = {deviceName, {{CONFIG_KEY(PERFORMANCE_HINT),
+                    InferenceEngine::PluginConfigParams::THROUGHPUT}}, 2, ""};
+                break;
+            default:
+                LOG_ERROR("should not come here");
+                break;
+        }
+
         metaDevices.push_back(std::move(devInfo));
         // set the return value of SelectDevice
         // for example if there are three device, if will return GPU on the first call, and then MYRIAD
@@ -207,13 +246,13 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) {
 }
 
 // the test configure, for example
-// ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+// ConfigParams {true, false,  GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
 //               DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
 //                DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 3, 2},
 //
 // every element for ConfigParams
-// {continueRun, selectThrowException, deviceLoadsuccessVector, selectCount, loadCount, loadSuccessCount}
-// {       true,                false,                3 device,           2,         3,                2}
+// {continueRun, selectThrowException,  config model,  deviceLoadsuccessVector, selectCount, loadCount, loadSuccessCount}
+// {       true,                false,       GENERAL,                 3 device,           2,         3,                2}
 //
 // there are three devices for loading
 // CPU load for accelerator success, but GPU will load faild and then select MYRIAD and load again
@@ -223,52 +262,62 @@ TEST_P(AutoLoadFailedTest, LoadCNNetWork) {
 // the inference request num is loadSuccessCount * optimalNum, in this test case optimalNum is 2
 // so inference request num is 4 (CPU 2, MYRIAD 2)
 //
-const std::vector<ConfigParams> testConfigs = {ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+const std::vector<ConfigParams> testConfigs = {ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 2, 2},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 3, 2},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 2, 2},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 2, 1},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 2, 1},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 2, 3, 1},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 3, 4, 2},
+                                               ConfigParams {false, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 3, 4, 0},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 2, 2},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 3, 2},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 2, 1},
+                                               ConfigParams {false, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 2, 3, 0},
+                                               ConfigParams {false, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false}}, 1, 1, 0},
+                                               ConfigParams {false, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 1, 0},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true}}, 1, 1, 1},
+                                               ConfigParams {true, false, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 1, 1},
+                                               ConfigParams {false, true, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, true}}, 1, 0, 0},
+                                               ConfigParams {false, true, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 0, 0},
+                                               ConfigParams {true, true, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1},
+                                               ConfigParams {false, true, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 2, 2, 0},
+                                               ConfigParams {true, true, GENERAL, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1},
+                                               ConfigParams {true, false, LATENCY, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 3, 3, 1},
-                                               ConfigParams {false, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                               ConfigParams {true, false, LATENCY, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1},
+                                               ConfigParams {true, false, THROUGHPUT, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
                                                         DeviceParams {CommonTestUtils::DEVICE_MYRIAD, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 3, 3, 0},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 2, 2},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 2, 1},
-                                               ConfigParams {false, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 2, 2, 0},
-                                               ConfigParams {false, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, false}}, 1, 1, 0},
-                                               ConfigParams {false, false, {DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 1, 1, 0},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_GPU, true}}, 1, 1, 1},
-                                               ConfigParams {true, false, {DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 1, 1},
-                                               ConfigParams {false, true, {DeviceParams {CommonTestUtils::DEVICE_GPU, true}}, 1, 0, 0},
-                                               ConfigParams {false, true, {DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 1, 0, 0},
-                                               ConfigParams {true, true, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1},
-                                               ConfigParams {false, true, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_MYRIAD, true},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, false}}, 2, 2, 0},
-                                               ConfigParams {true, true, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
-                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 2, 1}
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 3, 4, 2},
+                                               ConfigParams {true, false, THROUGHPUT, {DeviceParams {CommonTestUtils::DEVICE_GPU, false},
+                                                        DeviceParams {CommonTestUtils::DEVICE_CPU, true}}, 2, 3, 2}
                                               };
 
 INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, AutoLoadFailedTest,

inference-engine/tests/unit/auto/exec_network_get_metrics.cpp

@@ -0,0 +1,269 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include <ie_metric_helpers.hpp>
+#include <common_test_utils/test_constants.hpp>
+#include "unit_test_utils/mocks/cpp_interfaces/interface/mock_icore.hpp"
+#include "unit_test_utils/mocks/mock_iinfer_request.hpp"
+#include "unit_test_utils/mocks/cpp_interfaces/impl/mock_inference_plugin_internal.hpp"
+#include "unit_test_utils/mocks/cpp_interfaces/interface/mock_iexecutable_network_internal.hpp"
+#include "unit_test_utils/mocks/cpp_interfaces/interface/mock_ivariable_state_internal.hpp"
+#include "unit_test_utils/mocks/cpp_interfaces/interface/mock_iinference_plugin.hpp"
+#include <ie_core.hpp>
+#include <multi-device/multi_device_config.hpp>
+#include <ngraph_functions/subgraph_builders.hpp>
+#include <gtest/gtest.h>
+#include <gmock/gmock.h>
+#include "plugin/mock_auto_device_plugin.hpp"
+#include "cpp/ie_plugin.hpp"
+#include <chrono>
+#include <thread>
+#include "mock_common.hpp"
+
+using ::testing::MatcherCast;
+using ::testing::AllOf;
+using ::testing::Throw;
+using ::testing::Matches;
+using ::testing::_;
+using ::testing::StrEq;
+using ::testing::Return;
+using ::testing::Property;
+using ::testing::Eq;
+using ::testing::ReturnRef;
+using ::testing::AtLeast;
+using ::testing::AnyNumber;
+using ::testing::InvokeWithoutArgs;
+using Config = std::map<std::string, std::string>;
+using namespace MockMultiDevice;
+
+using ConfigParams = std::tuple<
+        unsigned int,                // cpu OPTIMAL_NUMBER_OF_INFER_REQUESTS
+        int,                         // cpu infer requet num of customer want
+        bool,                        // if cpu sleep, cpu device will load slow
+        unsigned int,                // gpu OPTIMAL_NUMBER_OF_INFER_REQUESTS
+        int,                         // gpu infer requet num of customer want
+        bool,                        // if gpu sleep, cpu device will load slow
+        unsigned int                 // expect OPTIMAL_NUMBER_OF_INFER_REQUESTS
+        >;
+class ExecNetworkGetMetric : public ::testing::TestWithParam<ConfigParams> {
+public:
+    std::shared_ptr<ngraph::Function>               function;
+    InferenceEngine::CNNNetwork                     cnnNet;
+    std::shared_ptr<MockICore>                      core;
+    std::shared_ptr<MockMultiDeviceInferencePlugin> plugin;
+
+    //mock cpu exeNetwork
+    std::shared_ptr<MockIExecutableNetworkInternal> cpuMockIExeNet;
+    ov::runtime::SoPtr<IExecutableNetworkInternal>  cpuMockExeNetwork;
+    MockIInferencePlugin*                           cpuMockIPlugin;
+    InferenceEngine::InferencePlugin                cpuMockPlugin;
+    //mock gpu exeNetwork
+    std::shared_ptr<MockIExecutableNetworkInternal> gpuMockIExeNet;
+    ov::runtime::SoPtr<IExecutableNetworkInternal>  gpuMockExeNetwork;
+    MockIInferencePlugin*                           gpuMockIPlugin;
+    InferenceEngine::InferencePlugin                gpuMockPlugin;
+    // config for Auto device
+    std::map<std::string, std::string>              config;
+    std::vector<DeviceInformation>                  metaDevices;
+    std::shared_ptr<MockIInferRequestInternal>      inferReqInternal;
+
+public:
+    static std::string getTestCaseName(testing::TestParamInfo<ConfigParams> obj) {
+        unsigned int cpuOptimalNum;
+        int cpuCustomerNum;
+        unsigned int gpuOptimalNum;
+        int gpuCustomerNum;
+        unsigned int expectOptimalNum;
+        bool cpuSleep;
+        bool gpuSleep;
+        std::tie(cpuOptimalNum, cpuCustomerNum, cpuSleep,
+                 gpuOptimalNum, gpuCustomerNum, gpuSleep, expectOptimalNum) = obj.param;
+        std::ostringstream result;
+        result << "cpuOptimalNum_" << cpuOptimalNum << "cpuCustomerNum_" << cpuCustomerNum;
+        result << "gpuOptimalNum_" << gpuOptimalNum << "gpuCustomerNum_" << gpuCustomerNum;
+        result << "expectOptimalNum_" << expectOptimalNum;
+        if (cpuSleep) {
+            result << "_cpuSleep_" << "true";
+        } else {
+            result << "_cpuSleep_" << "false";
+        }
+
+        if (gpuSleep) {
+            result << "_gpuSleep_" << "true";
+        } else {
+            result << "_gpuSleep_" << "false";
+        }
+
+        return result.str();
+    }
+
+    void TearDown() override {
+        core.reset();
+        plugin.reset();
+        cpuMockIExeNet.reset();
+        cpuMockExeNetwork = {};
+        cpuMockPlugin = {};
+        gpuMockIExeNet.reset();
+        gpuMockExeNetwork = {};
+        gpuMockPlugin = {};
+        config.clear();
+        metaDevices.clear();
+        inferReqInternal.reset();
+    }
+
+    void SetUp() override {
+       // prepare cpuMockExeNetwork
+       cpuMockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
+       auto cpuMockIPluginPtr = std::make_shared<MockIInferencePlugin>();
+       ON_CALL(*cpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(cpuMockIExeNet));
+       cpuMockPlugin = InferenceEngine::InferencePlugin{{}, cpuMockIPluginPtr};
+       // remove annoying ON CALL message
+       EXPECT_CALL(*cpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).Times(1);
+       cpuMockExeNetwork = cpuMockPlugin.LoadNetwork(CNNNetwork{}, {});
+
+       // prepare gpuMockExeNetwork
+       gpuMockIExeNet = std::make_shared<MockIExecutableNetworkInternal>();
+       auto gpuMockIPluginPtr = std::make_shared<MockIInferencePlugin>();
+       ON_CALL(*gpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).WillByDefault(Return(gpuMockIExeNet));
+       gpuMockPlugin = InferenceEngine::InferencePlugin{{}, gpuMockIPluginPtr};
+       // remove annoying ON CALL message
+       EXPECT_CALL(*gpuMockIPluginPtr, LoadNetwork(MatcherCast<const CNNNetwork&>(_), _)).Times(1);
+       gpuMockExeNetwork = gpuMockPlugin.LoadNetwork(CNNNetwork{}, {});
+
+       // prepare mockicore and cnnNetwork for loading
+       core  = std::shared_ptr<MockICore>(new MockICore());
+       auto* origin_plugin = new MockMultiDeviceInferencePlugin();
+       plugin  = std::shared_ptr<MockMultiDeviceInferencePlugin>(origin_plugin);
+       function = ngraph::builder::subgraph::makeConvPoolRelu();
+       cnnNet = InferenceEngine::CNNNetwork(function);
+       // replace core with mock Icore
+       plugin->SetCore(core);
+       // mock execNetwork can work
+       inferReqInternal = std::make_shared<MockIInferRequestInternal>();
+       ON_CALL(*cpuMockIExeNet.get(), CreateInferRequest()).WillByDefault(Return(inferReqInternal));
+       ON_CALL(*gpuMockIExeNet.get(), CreateInferRequest()).WillByDefault(Return(inferReqInternal));
+       EXPECT_CALL(*inferReqInternal, SetCallback).Times(AtLeast(1));
+       IE_SET_METRIC(SUPPORTED_CONFIG_KEYS, supportConfigs, {});
+       ON_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _))
+           .WillByDefault(RETURN_MOCK_VALUE(supportConfigs));
+       EXPECT_CALL(*core, GetMetric(_, StrEq(METRIC_KEY(SUPPORTED_CONFIG_KEYS)), _)).Times(AnyNumber());
+
+       // test auto plugin
+       config.insert({CONFIG_KEY_INTERNAL(MULTI_WORK_MODE_AS_AUTO), InferenceEngine::PluginConfigParams::YES});
+       config.insert({InferenceEngine::MultiDeviceConfigParams::KEY_MULTI_DEVICE_PRIORITIES,
+               CommonTestUtils::DEVICE_CPU + std::string(",") + CommonTestUtils::DEVICE_GPU});
+
+       ON_CALL(*(HLogger), print(_)).WillByDefault([&](std::stringstream& stream) {
+                    std::cout << stream.str() << std::endl;
+               });
+    }
+};
+
+TEST_P(ExecNetworkGetMetric, OPTIMAL_NUMBER_OF_INFER_REQUESTS) {
+    unsigned int cpuOptimalNum;
+    int cpuCustomerNum;
+    unsigned int gpuOptimalNum;
+    int gpuCustomerNum;
+    unsigned int expectOptimalNum;
+    bool cpuSleep;
+    bool gpuSleep;
+    std::tie(cpuOptimalNum, cpuCustomerNum, cpuSleep,
+             gpuOptimalNum, gpuCustomerNum, gpuSleep, expectOptimalNum) = this->GetParam();
+
+    metaDevices.push_back({CommonTestUtils::DEVICE_CPU, {}, cpuCustomerNum, ""});
+    metaDevices.push_back({CommonTestUtils::DEVICE_GPU, {}, gpuCustomerNum, ""});
+    ON_CALL(*plugin, SelectDevice(_, _)).WillByDefault(Return(metaDevices[1]));
+    ON_CALL(*plugin, ParseMetaDevices(_, _)).WillByDefault(Return(metaDevices));
+    EXPECT_CALL(*plugin, ParseMetaDevices(_, _)).Times(1);
+    EXPECT_CALL(*plugin, SelectDevice(_, _)).Times(1);
+
+    if (cpuSleep) {
+        ON_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                    ::testing::Matcher<const std::string&>(StrEq(CommonTestUtils::DEVICE_CPU)),
+                    ::testing::Matcher<const Config&>(_))).WillByDefault(InvokeWithoutArgs([this]() {
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+                return cpuMockExeNetwork;
+                }));
+    } else {
+        ON_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                    ::testing::Matcher<const std::string&>(StrEq(CommonTestUtils::DEVICE_CPU)),
+                    ::testing::Matcher<const Config&>(_))).WillByDefault(Return(cpuMockExeNetwork));
+    }
+
+    if (gpuSleep) {
+        ON_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                    ::testing::Matcher<const std::string&>(StrEq(CommonTestUtils::DEVICE_GPU)),
+                    ::testing::Matcher<const Config&>(_))).WillByDefault(InvokeWithoutArgs([this]() {
+                std::this_thread::sleep_for(std::chrono::milliseconds(100));
+                return gpuMockExeNetwork;
+                }));
+    } else {
+        ON_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                    ::testing::Matcher<const std::string&>(StrEq(CommonTestUtils::DEVICE_GPU)),
+                    ::testing::Matcher<const Config&>(_))).WillByDefault(Return(gpuMockExeNetwork));
+    }
+
+    ON_CALL(*cpuMockIExeNet.get(), GetMetric(StrEq(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS))))
+           .WillByDefault(RETURN_MOCK_VALUE(cpuOptimalNum));
+    ON_CALL(*gpuMockIExeNet.get(), GetMetric(StrEq(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS))))
+           .WillByDefault(RETURN_MOCK_VALUE(gpuOptimalNum));
+
+    EXPECT_CALL(*cpuMockIExeNet.get(), GetMetric(StrEq(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS))))
+           .Times(AtLeast(1));
+
+    EXPECT_CALL(*gpuMockIExeNet.get(), GetMetric(StrEq(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS))))
+           .Times(AtLeast(1));
+
+    EXPECT_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                ::testing::Matcher<const std::string&>(CommonTestUtils::DEVICE_CPU),
+                ::testing::Matcher<const Config&>(_))).Times(1);
+
+    EXPECT_CALL(*core, LoadNetwork(::testing::Matcher<const InferenceEngine::CNNNetwork&>(_),
+                ::testing::Matcher<const std::string&>(CommonTestUtils::DEVICE_GPU),
+                ::testing::Matcher<const Config&>(_))).Times(1);
+
+    if (cpuCustomerNum == -1) {
+        EXPECT_CALL(*cpuMockIExeNet.get(), CreateInferRequest()).Times(cpuOptimalNum);
+    } else {
+        EXPECT_CALL(*cpuMockIExeNet.get(), CreateInferRequest()).Times(cpuCustomerNum);
+    }
+
+    if (gpuCustomerNum == -1) {
+        EXPECT_CALL(*gpuMockIExeNet.get(), CreateInferRequest()).Times(gpuOptimalNum);
+    } else {
+        EXPECT_CALL(*gpuMockIExeNet.get(), CreateInferRequest()).Times(gpuCustomerNum);
+    }
+
+    auto AutoExecNetwork =  plugin->LoadExeNetworkImpl(cnnNet, config);
+    auto result = AutoExecNetwork->GetMetric(METRIC_KEY(OPTIMAL_NUMBER_OF_INFER_REQUESTS)).as<unsigned int>();
+    EXPECT_EQ(result, expectOptimalNum);
+}
+
+
+// ConfigParams {unsigned int, int, bool,
+//               unsigned int, int, bool, unsigned int}
+//
+// every element for ConfigParams
+// {cpuOptimalNum, customer hope for cpu infer requset num, if cpu sleep when load,
+//  gpuOptimalNum, customer hope for gpu infer requset num, if gpu sleep when load,
+//  expectOptimalNum of Auto ExecNetwork}
+//
+const std::vector<ConfigParams> testConfigs = {
+                                               ConfigParams {1, -1, false, 2, -1, true, 8},
+                                               ConfigParams {1, -1, false, 10, -1, true, 8},
+                                               ConfigParams {12, -1, false, 2, -1, true, 12},
+                                               ConfigParams {12, -1, false, 10, -1, true, 12},
+                                               ConfigParams {1, -1, true, 2, -1, false, 8},
+                                               ConfigParams {1, -1, true, 10, -1, false, 10},
+                                               ConfigParams {6, -1, true, 2, -1, false, 8},
+                                               ConfigParams {6, -1, true, 10, -1, false, 10},
+                                               ConfigParams {6, 4, false, 2, 3, true, 8},
+                                               ConfigParams {6, 4, false, 10, 3, true, 8},
+                                               ConfigParams {1, 4, true, 2, 3, false, 8},
+                                               ConfigParams {1, 4, true, 10, 3, false, 10}
+                                              };
+
+INSTANTIATE_TEST_SUITE_P(smoke_Auto_BehaviorTests, ExecNetworkGetMetric,
+                ::testing::ValuesIn(testConfigs),
+            ExecNetworkGetMetric::getTestCaseName);

inference-engine/tests/unit/auto/mock_common.cpp

@@ -0,0 +1,16 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "mock_common.hpp"
+
+//  getMetric will return a fake ov::Any, gmock will call ostreamer << ov::Any
+//  it will cause core dump, so add this special implemented
+namespace testing {
+namespace internal {
+    template<>
+    void PrintTo<ov::Any>(const ov::Any& a, std::ostream* os) {
+        *os << "using custom PrintTo ov::Any";
+    }
+}
+}

inference-engine/tests/unit/auto/mock_common.hpp

@@ -0,0 +1,24 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+#include <gmock/gmock.h>
+#include <ie_metric_helpers.hpp>
+#include <ie_core.hpp>
+
+#define IE_SET_METRIC(key, name,  ...)                                                            \
+    typename ::InferenceEngine::Metrics::MetricType<::InferenceEngine::Metrics::key>::type name = \
+        __VA_ARGS__;
+
+#define RETURN_MOCK_VALUE(value) \
+    InvokeWithoutArgs([value](){return value;})
+
+//  getMetric will return a fake ov::Any, gmock will call ostreamer << ov::Any
+//  it will cause core dump, so add this special implemented
+namespace testing {
+namespace internal {
+    template<>
+    void PrintTo<ov::Any>(const ov::Any& a, std::ostream* os);
+}
+}

inference-engine/tests/unit/inference_engine/ie_compilation_context_test.cpp

@@ -134,13 +134,22 @@ static std::shared_ptr<ngraph::Function> create_simple_function() {
     data->get_output_tensor(0).set_names({"parameter"});
 
     auto mul_constant = ngraph::opset6::Constant::create(ngraph::element::i8, ngraph::Shape{1}, {3});
+    mul_constant->set_friendly_name("mul_constant");
+    mul_constant->get_output_tensor(0).set_names({"mul_constant"});
     auto mul = std::make_shared<ngraph::opset6::Multiply>(data, mul_constant);
+    mul->set_friendly_name("mul");
+    mul->get_output_tensor(0).set_names({"mul"});
 
     auto add_constant = ngraph::opset6::Constant::create(ngraph::element::i8, ngraph::Shape{1}, {2});
+    add_constant->set_friendly_name("add_constant");
+    add_constant->get_output_tensor(0).set_names({"add_constant"});
     auto add = std::make_shared<ngraph::opset6::Add>(mul, add_constant);
+    add->set_friendly_name("add");
+    add->get_output_tensor(0).set_names({"add"});
 
     // Create opset3::Result operation
     auto res = std::make_shared<ngraph::opset6::Result>(add);
+    res->set_friendly_name("res");
 
     // Create nGraph function
     auto func = std::make_shared<ngraph::Function>(ngraph::ResultVector{res}, ngraph::ParameterVector{data});
@@ -208,7 +217,7 @@ TEST(NetworkContext_CNNNetwork, HashWithPrimitivesPriority) {
     auto net2 = createNetwork();
     auto net3 = createNetwork();
     auto & op2 = net2.getFunction()->get_ops().front()->get_rt_info();
-    op2["PrimitivesPriority"] = std::make_shared<ngraph::VariantWrapper<std::string> > ("testPriority");
+    op2[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("testPriority");
 
     auto & op3 = net3.getFunction()->get_ops().front()->get_rt_info();
     op3["PrimitivesPriority"] = std::make_shared<ngraph::VariantWrapper<std::string> > ("testPriority");
@@ -222,24 +231,20 @@ TEST(NetworkContext_CNNNetwork, HashWithPrimitivesPriority) {
 
 TEST(NetworkContext_CNNNetwork, HashWithFusedNames) {
     auto setFusedEmpty = [&](Node::RTMap& rtInfo) {
-        rtInfo[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-                std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames());
+        rtInfo[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames();
     };
     auto setFused = [&](Node::RTMap& rtInfo, const std::string& name) {
-        rtInfo[VariantWrapper<ngraph::FusedNames>::get_type_info_static()] =
-                std::make_shared<VariantWrapper<ngraph::FusedNames>>(ngraph::FusedNames(name));
+        rtInfo[ngraph::FusedNames::get_type_info_static()] = ngraph::FusedNames(name);
     };
     checkCustomRt(setFusedEmpty, setFused);
 }
 
 TEST(NetworkContext_CNNNetwork, HashWithPrimitivesPriorityType) {
     auto setPrimEmpty = [&](Node::RTMap& rtInfo) {
-        rtInfo[ov::PrimitivesPriority::get_type_info_static()] =
-                std::make_shared<ov::PrimitivesPriority>("");
+        rtInfo[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority("");
     };
     auto setPrim = [&](Node::RTMap& rtInfo, const std::string& name) {
-        rtInfo[ov::PrimitivesPriority::get_type_info_static()] =
-                std::make_shared<ov::PrimitivesPriority>(name);
+        rtInfo[ov::PrimitivesPriority::get_type_info_static()] = ov::PrimitivesPriority(name);
     };
     checkCustomRt(setPrimEmpty, setPrim);
 }

inference-engine/thirdparty/clDNN/api/cldnn/primitives/experimental_detectron_roi_feature_extractor.hpp

@@ -0,0 +1,56 @@
+// Copyright (C) 2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+#pragma once
+#include "primitive.hpp"
+#include <vector>
+
+namespace cldnn {
+/// @addtogroup cpp_api C++ API
+/// @{
+/// @addtogroup cpp_topology Network Topology
+/// @{
+/// @addtogroup cpp_primitives Primitives
+/// @{
+
+/// @brief experimental detectron ROI feature extractor
+struct experimental_detectron_roi_feature_extractor : public primitive_base<experimental_detectron_roi_feature_extractor> {
+    CLDNN_DECLARE_PRIMITIVE(experimental_detectron_roi_feature_extractor)
+
+    /// @brief Constructs experimental_detectron_roi_feature_extractor primitive
+    /// @param id This primitive id
+    /// @param inputs Inputs for primitive id (ROIs, {pyramid levels, ...}, second_output)
+    /// @param output_dim Attribute specifies the width and height of the output tensor
+    /// @param pyramid_scales Scales of pyramid levels
+    /// @param sampling_ratio Attribute specifies the number of sampling points per the output value
+    /// @param aligned Attribute specifies add offset (-0.5) to ROIs sizes or not
+    experimental_detectron_roi_feature_extractor(const primitive_id& id,
+                                                 const std::vector<primitive_id>& inputs,
+                                                 int output_dim,
+                                                 const std::vector<int64_t>& pyramid_scales,
+                                                 int sampling_ratio,
+                                                 bool aligned,
+                                                 const primitive_id& ext_prim_id = "",
+                                                 const padding& output_padding = padding()) :
+            primitive_base(id, inputs, ext_prim_id, output_padding),
+            output_dim(output_dim),
+            pooled_height(output_dim),
+            pooled_width(output_dim),
+            pyramid_scales(pyramid_scales),
+            sampling_ratio(sampling_ratio),
+            aligned(aligned) {}
+
+    int output_dim = 0;
+    int pooled_height = 0;
+    int pooled_width = 0;
+    std::vector<int64_t> pyramid_scales;
+    int sampling_ratio = 0;
+    bool aligned = false;
+};
+
+/// @}
+/// @}
+/// @}
+}  // namespace cldnn

inference-engine/thirdparty/clDNN/api/cldnn/primitives/roi_align.hpp

@@ -0,0 +1,69 @@
+// Copyright (C) 2018-2021 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+#pragma once
+#include "primitive.hpp"
+#include <vector>
+
+namespace cldnn {
+/// @addtogroup cpp_api C++ API
+/// @{
+/// @addtogroup cpp_topology Network Topology
+/// @{
+/// @addtogroup cpp_primitives Primitives
+/// @{
+
+/// @brief ROIAlign is a pooling layer used over feature maps of
+/// non-uniform input sizes and outputs a feature map of a fixed size.
+struct roi_align : public primitive_base<roi_align> {
+    CLDNN_DECLARE_PRIMITIVE(roi_align)
+
+    /// @brief Pooling mode for the @ref roi_align
+    enum PoolingMode {
+        Max,
+        Avg
+    };
+
+    /// @brief Constructs roi_align primitive.
+    /// @param id This primitive id.
+    /// @param inputs Inputs data primitive ids.
+    /// @param pooled_h Height of the ROI output feature map.
+    /// @param pooled_w Width of the ROI output feature map.
+    /// @param sampling_ratio Number of bins over height and width to use to calculate each output feature map element.
+    /// @param spatial_scale multiplicative spatial scale factor to translate ROI coordinates
+    /// from their input spatial scale to the scale used when pooling.
+    /// @param mode Method to perform pooling to produce output feature map elements.
+    /// @param shrink_axis_mask Array of bits, that provide shrinks the dimensionality by 1, taking on the value at index begin[i].
+    roi_align(const primitive_id& id,
+              const std::vector<primitive_id>& inputs,
+              int pooled_h,
+              int pooled_w,
+              int sampling_ratio,
+              float spatial_scale,
+              PoolingMode mode,
+              const primitive_id& ext_prim_id = "",
+              const padding& output_padding = padding())
+        : primitive_base(id, inputs, ext_prim_id, output_padding),
+          pooled_h {pooled_h},
+          pooled_w {pooled_w},
+          sampling_ratio {sampling_ratio},
+          spatial_scale {spatial_scale},
+          mode {mode}
+    {}
+
+    /// @brief Height of the ROI output feature map.
+    int pooled_h;
+    /// @brief Width of the ROI output feature map.
+    int pooled_w;
+    /// @brief Number of bins over height and width to use to calculate each output feature map element.
+    int sampling_ratio;
+    /// @brief multiplicative spatial scale factor to translate ROI coordinates
+    /// from their input spatial scale to the scale used when pooling.
+    float spatial_scale;
+    /// @brief Method to perform pooling to produce output feature map elements.
+    PoolingMode mode;
+};
+/// @}
+/// @}
+/// @}
+}  // namespace cldnn

inference-engine/thirdparty/clDNN/api/cldnn/runtime/tensor.hpp

@@ -34,8 +34,6 @@ struct format_traits {
     size_t feature_num;
     /// @brief Number of spatial (x,y) dimensions in a format.
     size_t spatial_num;
-    /// @brief Number of local (x,y) dimensions in a format.
-    size_t local_num;
     /// @brief Number of groups in a format.
     size_t group_num;
     /// @brief Dimensions changing order from rare to often.
@@ -50,8 +48,6 @@ struct format_traits {
     static const char* feature_chars() { return "fic"; }
     /// @brief Characters representing spatial dimensions in an order.
     static const char* spatial_chars() { return "xyzhsw"; }
-    /// @brief Characters representing local dimensions in an order.
-    static const char* local_chars() { return "kl"; }
     /// @brief Characters representing group dimensions in an order.
     static const char* group_chars() { return "g"; }
     /// @brief Checks if @p c represents batch dimension.
@@ -60,8 +56,6 @@ struct format_traits {
     static bool is_feature_char(char c) { return std::string(feature_chars()).find_first_of(c) != std::string::npos; }
     /// @brief Checks if @p c represents spatial dimension.
     static bool is_spatial_char(char c) { return std::string(spatial_chars()).find_first_of(c) != std::string::npos; }
-    /// @brief Checks if @p c represents local dimensions.
-    static bool is_local_char(char c) { return std::string(local_chars()).find_first_of(c) != std::string::npos; }
     /// @brief Checks if @p c represents group dimensions.
     static bool is_group_char(char c) { return std::string(group_chars()).find_first_of(c) != std::string::npos; }
 };
@@ -235,139 +229,138 @@ struct format {
                 // B - number of Batch dimensions
                 // F - number of Feature dimensions
                 // S - number of Spatial dimensions
-                // L - number of Local dimensions
                 // G - number of Group dimensions
                 // Order - dims changing order from rare to often
                 // Inner order - dims order for internal storage in _sizes array
                 // Block sizes - vector of pairs of dimension number (by inner order) and block size ordered from rare to often
-                // Format                  B  F  S  L  G  Order  Inner order  Block sizes
-                { yxfb,                  { 1, 1, 2, 0, 0, "yxfb",   "bfxy?",  {}}},
-                { byxf,                  { 1, 1, 2, 0, 0, "byxf",   "bfxy?",  {}}},
-                { bfyx,                  { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {}}},
-                { fyxb,                  { 1, 1, 2, 0, 0, "fyxb",   "bfxy?",  {}}},
-                { b_fs_yx_fsv16,         { 1, 1, 2, 0, 0, "bfyx",   "bfxy",   {{1, 16}}}},
-                { b_fs_yx_fsv32,         { 1, 1, 2, 0, 0, "bfyx",   "bfxy",   {{1, 32}}}},
-                { b_fs_zyx_fsv32,        { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{1, 32}}}},
-                { bs_xs_xsv8_bsv8,       { 1, 1, 1, 0, 0, "bx",     "b?x??",  {{2, 8}, {0, 8}}}},
-                { bs_xs_xsv8_bsv16,      { 1, 1, 1, 0, 0, "bx",     "b?x??",  {{2, 8}, {0, 16}}}},
-                { bs_x_bsv16,            { 1, 1, 1, 0, 0, "bx",     "b?x??",  {{0, 16}}}},
-                { winograd_2x3_s1_data,  { 1, 1, 2, 0, 0, "bxyf",   "bfxy?",  {}}},
-                { b_fs_yx_fsv4,          { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{1, 4}}}},
-                { bfzyx,                 { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {}}},
-                { bfwzyx,                { 1, 1, 4, 0, 0, "bfwzyx", "bfxyzw", {}}},
-                { fs_b_yx_fsv32,         { 1, 1, 2, 0, 0, "fbyx",   "bfxy?",  {{1, 32}}}},
-                { b_fs_yx_32fp,          { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {}}},
-                { b_fs_zyx_fsv16,        { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{1, 16}}}},
-                { bs_fs_zyx_bsv16_fsv16, { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{0, 16 }, {1, 16}}}},
-                { bs_fs_yx_bsv16_fsv16,  { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{0, 16 }, {1, 16}}}},
-                { bs_fs_yx_bsv4_fsv4,    { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{0, 4 }, {1, 4}}}},
-                { bs_fs_yx_bsv4_fsv2,    { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{0, 4 }, {1, 2}}}},
-                { bs_fs_zyx_bsv4_fsv4,   { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{0, 4 }, {1, 4}}}},
-                { bs_fs_zyx_bsv4_fsv2,   { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{0, 4 }, {1, 2}}}},
-                { bs_fs_zyx_bsv32_fsv32, { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{0, 32 }, {1, 32}}}},
-                { bs_fs_zyx_bsv32_fsv16, { 1, 1, 3, 0, 0, "bfzyx",  "bfxyz",  {{0, 32 }, {1, 16}}}},
-                { bs_fs_yx_bsv32_fsv32,  { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{0, 32 }, {1, 32}}}},
-                { bs_fs_yx_bsv32_fsv16,  { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {{0, 32 }, {1, 16}}}},
-                { nv12,                  { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {}}},
-                { image_2d_rgba,         { 1, 1, 2, 0, 0, "bfyx",   "bfxy?",  {}}},
+                // Format                  B  F  S  G  Order  Inner order  Block sizes
+                { yxfb,                  { 1, 1, 2, 0, "yxfb",   "bfxy?",  {}}},
+                { byxf,                  { 1, 1, 2, 0, "byxf",   "bfxy?",  {}}},
+                { bfyx,                  { 1, 1, 2, 0, "bfyx",   "bfxy?",  {}}},
+                { fyxb,                  { 1, 1, 2, 0, "fyxb",   "bfxy?",  {}}},
+                { b_fs_yx_fsv16,         { 1, 1, 2, 0, "bfyx",   "bfxy",   {{1, 16}}}},
+                { b_fs_yx_fsv32,         { 1, 1, 2, 0, "bfyx",   "bfxy",   {{1, 32}}}},
+                { b_fs_zyx_fsv32,        { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{1, 32}}}},
+                { bs_xs_xsv8_bsv8,       { 1, 1, 1, 0, "bx",     "b?x??",  {{2, 8}, {0, 8}}}},
+                { bs_xs_xsv8_bsv16,      { 1, 1, 1, 0, "bx",     "b?x??",  {{2, 8}, {0, 16}}}},
+                { bs_x_bsv16,            { 1, 1, 1, 0, "bx",     "b?x??",  {{0, 16}}}},
+                { winograd_2x3_s1_data,  { 1, 1, 2, 0, "bxyf",   "bfxy?",  {}}},
+                { b_fs_yx_fsv4,          { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{1, 4}}}},
+                { bfzyx,                 { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {}}},
+                { bfwzyx,                { 1, 1, 4, 0, "bfwzyx", "bfxyzw", {}}},
+                { fs_b_yx_fsv32,         { 1, 1, 2, 0, "fbyx",   "bfxy?",  {{1, 32}}}},
+                { b_fs_yx_32fp,          { 1, 1, 2, 0, "bfyx",   "bfxy?",  {}}},
+                { b_fs_zyx_fsv16,        { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{1, 16}}}},
+                { bs_fs_zyx_bsv16_fsv16, { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{0, 16 }, {1, 16}}}},
+                { bs_fs_yx_bsv16_fsv16,  { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{0, 16 }, {1, 16}}}},
+                { bs_fs_yx_bsv4_fsv4,    { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{0, 4 }, {1, 4}}}},
+                { bs_fs_yx_bsv4_fsv2,    { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{0, 4 }, {1, 2}}}},
+                { bs_fs_zyx_bsv4_fsv4,   { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{0, 4 }, {1, 4}}}},
+                { bs_fs_zyx_bsv4_fsv2,   { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{0, 4 }, {1, 2}}}},
+                { bs_fs_zyx_bsv32_fsv32, { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{0, 32 }, {1, 32}}}},
+                { bs_fs_zyx_bsv32_fsv16, { 1, 1, 3, 0, "bfzyx",  "bfxyz",  {{0, 32 }, {1, 16}}}},
+                { bs_fs_yx_bsv32_fsv32,  { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{0, 32 }, {1, 32}}}},
+                { bs_fs_yx_bsv32_fsv16,  { 1, 1, 2, 0, "bfyx",   "bfxy?",  {{0, 32 }, {1, 16}}}},
+                { nv12,                  { 1, 1, 2, 0, "bfyx",   "bfxy?",  {}}},
+                { image_2d_rgba,         { 1, 1, 2, 0, "bfyx",   "bfxy?",  {}}},
 
-                { oiyx,                                        { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {}}},
-                { ioyx,                                        { 1, 1, 2, 0, 0, "ioyx",   "oixy",       {}}},
-                { iyxo,                                        { 1, 1, 2, 0, 0, "iyxo",   "oixy",       {}}},
-                { yxio,                                        { 1, 1, 2, 0, 0, "yxio",   "oixy?",      {}}},
-                { oizyx,                                       { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {}}},
-                { iozyx,                                       { 1, 1, 3, 0, 0, "iozyx",  "oixyz",      {}}},
-                { os_is_yx_isv16_osv16,                        { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{1, 16}, {0, 16}}}},
-                { o_is_yx_isv16,                               { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{1, 16}}}},
-                { os_yxi_osv16,                                { 1, 1, 2, 0, 0, "oyxi",   "oixy?",      {{0, 16}}}},
-                { os_iyx_osv16,                                { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{0, 16}}}},
-                { os_iyx_osv32,                                { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{0, 32}}}},
-                { os_iyx_osv64,                                { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{0, 64}}}},
-                { winograd_2x3_s1_weights,                     { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { winograd_2x3_s1_fused_weights,               { 1, 1, 2, 0, 0, "xyio",   "oixy?",      {}}},
-                { winograd_6x3_s1_fused_weights,               { 1, 1, 2, 0, 0, "xyio",   "oixy?",      {}}},
-                { image_2d_weights_winograd_6x3_s1_fbxyb,      { 1, 1, 2, 0, 0, "xyio",   "oixy?",      {}}},
-                { image_2d_weights_winograd_6x3_s1_xfbyb,      { 1, 1, 2, 0, 0, "xyio",   "oixy?",      {}}},
-                { image_2d_weights_c4_fyx_b,                   { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { image_2d_weights_c1_b_fyx,                   { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { lstm_weights_dio,                            { 1, 1, 2, 0, 0, "oixy",   "oixy?",      {}}},
-                { os_is_yx_isa8_osv8_isv4,                     { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { os_is_yx_isa8_osv16_isv4,                    { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { os_is_yx_isa8_osv8_isv4_swizzled_by_4,       { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {}}},
-                { os_is_yx_osa4_isa8_osv8_isv2,                { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{0, 32}, {1, 16}}}},
-                { os_is_yx_osa4_isa8_osv8_isv4,                { 1, 1, 2, 0, 0, "oiyx",   "oixy",      {{0, 32}, {1, 32}}}},
-                { os_is_zyx_osa4_isa8_osv8_isv2,               { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 16}}}},
-                { os_is_zyx_osa4_isa8_osv8_isv4,               { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 32}}}},
-                { os_is_yx_osa2_isa8_osv16_isv2,               { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{0, 32}, {1, 16}}}},
-                { os_is_yx_osa2_isa8_osv16_isv4,               { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{0, 32}, {1, 32}}}},
-                { os_is_zyx_isa8_osv8_isv4,                    { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 8}, {1, 4}}}},
-                { os_is_zyx_isa8_osv16_isv4,                   { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 4}}}},
-                { os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4,  { 1, 1, 2, 0, 0, "oiyx",   "oixy?",      {{0, 32}, {1, 32}}}},
-                { os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 32}}}},
-                { is_o_yx_isv32,                               { 1, 1, 2, 0, 0, "oyxi",   "oixy?",      {{1, 32}}}},
-                { is_o32_yx_isv32_swizzled_by_4,               { 1, 1, 2, 0, 0, "oyxi",   "oixy?",      {}}},
-                { os_is_y_x8_osv8_isv4,                        { 1, 1, 2, 0, 0, "oyxi",   "oixy?",      {}}},
-                { os_is_y_x8_osv8_isv4_swizzled_by_4,          { 1, 1, 2, 0, 0, "oyxi",   "oixy?",      {}}},
-                { os_is_yx_osv16_isv4,                         { 1, 1, 2, 0, 0, "oixy",   "oixy?",      {{0, 16}, {1, 4}}}},
-                { os_is_yx_osv8_isv4,                          { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{1, 4}, {0, 8}}}},
-                { os_is_yx_osv8_isv2,                          { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{1, 2}, {0, 8}}}},
-                { os_is_zyx_osv16_isv16,                       { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 16}, {1, 16}}}},
-                { os_is_yx_osv32_isv4_swizzled_by_2,           { 1, 1, 2, 0, 0, "oixy",   "oixy?",      {{0, 32}, {1, 4}}}},
-                { os_is_yx_osv32_isv4,                         { 1, 1, 2, 0, 0, "oixy",   "oixy?",      {{0, 32}, {1, 4}}}},
-                { os_is_zyx_osv32_isv4,                        { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 4}}}},
-                { os_is_yx_osv32_isv32p,                       { 1, 1, 1, 0, 0, "oixy",   "oixy?",      {}}},
-                { os_is_zyx_isv16_osv16,                       { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 16}, {1, 16}}}},
-                { is_os_zyx_isv16_osv16,                       { 1, 1, 3, 0, 0, "iozyx",  "oixyz",      {{1, 16}, {0, 16}}}},
-                { is_os_yx_isv16_osv16,                        { 1, 1, 2, 0, 0, "ioyx",   "oixyz",      {{1, 16}, {0, 16}}}},
-                { os_is_osv32_isv32_swizzled_by_4,             { 1, 1, 0, 0, 0, "oixy",   "oixy?",      {{0, 32}, {1, 32}}}},
-                { os_is_zyx_isv8_osv16_isv2,                   { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 2}}}},
-                { os_zyxi_osv16,                               { 1, 1, 3, 0, 0, "ozyxi",  "oixyz",      {{0, 16}}}},
-                { os_is_yx_isv8_osv16_isv2,                    { 1, 1, 2, 0, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 2}}}},
-                { os_is_yx_osv16_isv16,                        { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{1, 16}, {0, 16}}}},
-                { os_is_zyx_osv32_isv16,                       { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 16}}}},
-                { os_is_zyx_osv64_isv16,                       { 1, 1, 3, 0, 0, "oizyx",  "oixyz",      {{0, 64}, {1, 16}}}},
-                { os_iyx_osv32__ai32,                          { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{0, 32}}}},
-                { i_yxs_os_yxsv2_osv16,                        { 1, 1, 2, 0, 0, "iyxo",   "oixy",       {{0, 16}}}},
-                { iy_xs_os_xsv2_osv8__ao32,                    { 1, 1, 2, 0, 0, "iyxo",   "oixy",       {{2, 2}, {0, 8}}}},
-                { iy_xs_os_xsv2_osv16__ao32,                   { 1, 1, 2, 0, 0, "iyxo",   "oixy",       {{2, 2}, {0, 16}}}},
-                { os_i_yxs_osv4_yxsv4,                         { 1, 1, 2, 0, 0, "oiyx",   "oixy",       {{0, 4}}}},
+                { oiyx,                                        { 1, 1, 2, 0, "oiyx",   "oixy",       {}}},
+                { ioyx,                                        { 1, 1, 2, 0, "ioyx",   "oixy",       {}}},
+                { iyxo,                                        { 1, 1, 2, 0, "iyxo",   "oixy",       {}}},
+                { yxio,                                        { 1, 1, 2, 0, "yxio",   "oixy?",      {}}},
+                { oizyx,                                       { 1, 1, 3, 0, "oizyx",  "oixyz",      {}}},
+                { iozyx,                                       { 1, 1, 3, 0, "iozyx",  "oixyz",      {}}},
+                { os_is_yx_isv16_osv16,                        { 1, 1, 2, 0, "oiyx",   "oixy",       {{1, 16}, {0, 16}}}},
+                { o_is_yx_isv16,                               { 1, 1, 2, 0, "oiyx",   "oixy?",      {{1, 16}}}},
+                { os_yxi_osv16,                                { 1, 1, 2, 0, "oyxi",   "oixy?",      {{0, 16}}}},
+                { os_iyx_osv16,                                { 1, 1, 2, 0, "oiyx",   "oixy?",      {{0, 16}}}},
+                { os_iyx_osv32,                                { 1, 1, 2, 0, "oiyx",   "oixy?",      {{0, 32}}}},
+                { os_iyx_osv64,                                { 1, 1, 2, 0, "oiyx",   "oixy?",      {{0, 64}}}},
+                { winograd_2x3_s1_weights,                     { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { winograd_2x3_s1_fused_weights,               { 1, 1, 2, 0, "xyio",   "oixy?",      {}}},
+                { winograd_6x3_s1_fused_weights,               { 1, 1, 2, 0, "xyio",   "oixy?",      {}}},
+                { image_2d_weights_winograd_6x3_s1_fbxyb,      { 1, 1, 2, 0, "xyio",   "oixy?",      {}}},
+                { image_2d_weights_winograd_6x3_s1_xfbyb,      { 1, 1, 2, 0, "xyio",   "oixy?",      {}}},
+                { image_2d_weights_c4_fyx_b,                   { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { image_2d_weights_c1_b_fyx,                   { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { lstm_weights_dio,                            { 1, 1, 2, 0, "oixy",   "oixy?",      {}}},
+                { os_is_yx_isa8_osv8_isv4,                     { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { os_is_yx_isa8_osv16_isv4,                    { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { os_is_yx_isa8_osv8_isv4_swizzled_by_4,       { 1, 1, 2, 0, "oiyx",   "oixy?",      {}}},
+                { os_is_yx_osa4_isa8_osv8_isv2,                { 1, 1, 2, 0, "oiyx",   "oixy?",      {{0, 32}, {1, 16}}}},
+                { os_is_yx_osa4_isa8_osv8_isv4,                { 1, 1, 2, 0, "oiyx",   "oixy",      {{0, 32}, {1, 32}}}},
+                { os_is_zyx_osa4_isa8_osv8_isv2,               { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 16}}}},
+                { os_is_zyx_osa4_isa8_osv8_isv4,               { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 32}}}},
+                { os_is_yx_osa2_isa8_osv16_isv2,               { 1, 1, 2, 0, "oiyx",   "oixy",       {{0, 32}, {1, 16}}}},
+                { os_is_yx_osa2_isa8_osv16_isv4,               { 1, 1, 2, 0, "oiyx",   "oixy",       {{0, 32}, {1, 32}}}},
+                { os_is_zyx_isa8_osv8_isv4,                    { 1, 1, 3, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 8}, {1, 4}}}},
+                { os_is_zyx_isa8_osv16_isv4,                   { 1, 1, 3, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 4}}}},
+                { os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4,  { 1, 1, 2, 0, "oiyx",   "oixy?",      {{0, 32}, {1, 32}}}},
+                { os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 32}}}},
+                { is_o_yx_isv32,                               { 1, 1, 2, 0, "oyxi",   "oixy?",      {{1, 32}}}},
+                { is_o32_yx_isv32_swizzled_by_4,               { 1, 1, 2, 0, "oyxi",   "oixy?",      {}}},
+                { os_is_y_x8_osv8_isv4,                        { 1, 1, 2, 0, "oyxi",   "oixy?",      {}}},
+                { os_is_y_x8_osv8_isv4_swizzled_by_4,          { 1, 1, 2, 0, "oyxi",   "oixy?",      {}}},
+                { os_is_yx_osv16_isv4,                         { 1, 1, 2, 0, "oixy",   "oixy?",      {{0, 16}, {1, 4}}}},
+                { os_is_yx_osv8_isv4,                          { 1, 1, 2, 0, "oiyx",   "oixy",       {{1, 4}, {0, 8}}}},
+                { os_is_yx_osv8_isv2,                          { 1, 1, 2, 0, "oiyx",   "oixy",       {{1, 2}, {0, 8}}}},
+                { os_is_zyx_osv16_isv16,                       { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 16}, {1, 16}}}},
+                { os_is_yx_osv32_isv4_swizzled_by_2,           { 1, 1, 2, 0, "oixy",   "oixy?",      {{0, 32}, {1, 4}}}},
+                { os_is_yx_osv32_isv4,                         { 1, 1, 2, 0, "oixy",   "oixy?",      {{0, 32}, {1, 4}}}},
+                { os_is_zyx_osv32_isv4,                        { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 4}}}},
+                { os_is_yx_osv32_isv32p,                       { 1, 1, 1, 0, "oixy",   "oixy?",      {}}},
+                { os_is_zyx_isv16_osv16,                       { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 16}, {1, 16}}}},
+                { is_os_zyx_isv16_osv16,                       { 1, 1, 3, 0, "iozyx",  "oixyz",      {{1, 16}, {0, 16}}}},
+                { is_os_yx_isv16_osv16,                        { 1, 1, 2, 0, "ioyx",   "oixyz",      {{1, 16}, {0, 16}}}},
+                { os_is_osv32_isv32_swizzled_by_4,             { 1, 1, 0, 0, "oixy",   "oixy?",      {{0, 32}, {1, 32}}}},
+                { os_is_zyx_isv8_osv16_isv2,                   { 1, 1, 3, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 2}}}},
+                { os_zyxi_osv16,                               { 1, 1, 3, 0, "ozyxi",  "oixyz",      {{0, 16}}}},
+                { os_is_yx_isv8_osv16_isv2,                    { 1, 1, 2, 0, "oizyx",  "oixyz",      {{1, 8}, {0, 16}, {1, 2}}}},
+                { os_is_yx_osv16_isv16,                        { 1, 1, 2, 0, "oiyx",   "oixy",       {{1, 16}, {0, 16}}}},
+                { os_is_zyx_osv32_isv16,                       { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 32}, {1, 16}}}},
+                { os_is_zyx_osv64_isv16,                       { 1, 1, 3, 0, "oizyx",  "oixyz",      {{0, 64}, {1, 16}}}},
+                { os_iyx_osv32__ai32,                          { 1, 1, 2, 0, "oiyx",   "oixy",       {{0, 32}}}},
+                { i_yxs_os_yxsv2_osv16,                        { 1, 1, 2, 0, "iyxo",   "oixy",       {{0, 16}}}},
+                { iy_xs_os_xsv2_osv8__ao32,                    { 1, 1, 2, 0, "iyxo",   "oixy",       {{2, 2}, {0, 8}}}},
+                { iy_xs_os_xsv2_osv16__ao32,                   { 1, 1, 2, 0, "iyxo",   "oixy",       {{2, 2}, {0, 16}}}},
+                { os_i_yxs_osv4_yxsv4,                         { 1, 1, 2, 0, "oiyx",   "oixy",       {{0, 4}}}},
 
-                { goiyx,                                       { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {}}},
-                { gioyx,                                       { 1, 1, 2, 0, 1, "gioyx",  "oixy????g",  {}}},
-                { goizyx,                                      { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {}}},
-                { giozyx,                                      { 1, 1, 3, 0, 1, "giozyx", "oixyz???g",  {}}},
-                { g_os_iyx_osv16,                              { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 16}}}},
-                { g_os_iyx_osv32,                              { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 32}}}},
-                { gs_oiyx_gsv16,                               { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{8, 16}}}},
-                { gs_oizyx_gsv16,                              { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{8, 16}}}},
-                { gs_oiyx_gsv32,                               { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{8, 32}}}},
-                { gyxio,                                       { 1, 1, 2, 0, 1, "gyxio",  "oixy????g",  {}}},
-                { g_is_os_zyx_isv16_osv16,                     { 1, 1, 3, 0, 1, "giozyx", "oixyz???g",  {{1, 16}, {0, 16}}}},
-                { g_is_os_yx_isv16_osv16,                      { 1, 1, 2, 0, 1, "gioyx",  "oixy????g",  {{1, 16}, {0, 16}}}},
-                { g_os_is_zyx_isv8_osv16_isv2,                 { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{1, 8}, {0, 16}, {1, 2}}}},
-                { g_os_is_yx_isv8_osv16_isv2,                  { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{1, 8}, {0, 16}, {1, 2}}}},
-                { g_os_is_zyx_isv16_osv16,                     { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{0, 16}, {1, 16}}}},
-                { g_os_is_yx_osv16_isv4,                       { 1, 1, 2, 0, 1, "goixy",  "oixy????g",  {{0, 16}, {1, 4}}}},
-                { g_os_is_zyx_osv16_isv16,                     { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{0, 16}, {1, 16}}}},
-                { g_os_zyx_is_osv16_isv4,                      { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 16}, {1, 4}}}},
-                { g_os_zyx_is_osv16_isv16,                     { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 16}, {1, 16}}}},
-                { g_os_zyx_is_osv16_isv32,                     { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 16}, {1, 32}}}},
-                { g_os_zyx_is_osv32_isv4,                      { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 32}, {1, 4}}}},
-                { g_os_zyx_is_osv32_isv16,                     { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 32}, {1, 16}}}},
-                { g_os_zyx_is_osv32_isv32,                     { 1, 1, 3, 0, 1, "gozyxi", "oixyz???g",  {{0, 32}, {1, 32}}}},
-                { g_os_is_yx_osa4_isa8_osv8_isv4,              { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 32}, {1, 32}}}},
-                { g_os_is_zyx_osa4_isa8_osv8_isv4,             { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{0, 32}, {1, 32}}}},
-                { g_os_is_yx_osa4_isa8_osv8_isv2,              { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 32}, {1, 16}}}},
-                { g_os_is_zyx_osa4_isa8_osv8_isv2,             { 1, 1, 3, 0, 1, "goizyx", "oixyz???g",  {{0, 32}, {1, 16}}}},
-                { g_os_is_yx_osa2_isa8_osv16_isv4,             { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 32}, {1, 32}}}},
-                { g_os_is_yx_osa2_isa8_osv16_isv2,             { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{0, 32}, {1, 16}}}},
-                { gs_oi_yxs_gsv4_yxsv4,                        { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{8, 4}}}},
-                { gs_oi_yxs_gsv16_yxsv4,                       { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{8, 16}}}},
-                { gs_oi_yxs_gsv32_yxsv4,                       { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{8, 32}}}},
-                { g_os_is_yx_isv16_osv16,                      { 1, 1, 2, 0, 1, "goiyx",  "oixy????g",  {{1, 16}, {0, 16}}}},
-                { gi_yxs_os_yxsv2_osv16,                       { 1, 1, 2, 0, 1, "giyxo",  "oixy????g",  {{0, 16}}}},
-                { giy_xs_os_xsv2_osv8__ao32,                   { 1, 1, 2, 0, 1, "giyxo",  "oixy????g",  {{2, 2}, {0, 8}}}},
-                { giy_xs_os_xsv2_osv16__ao32,                  { 1, 1, 2, 0, 1, "giyxo",  "oixy????g",  {{2, 2}, {0, 16}}}},
+                { goiyx,                                       { 1, 1, 2, 1, "goiyx",  "oixy??g",  {}}},
+                { gioyx,                                       { 1, 1, 2, 1, "gioyx",  "oixy??g",  {}}},
+                { goizyx,                                      { 1, 1, 3, 1, "goizyx", "oixyz?g",  {}}},
+                { giozyx,                                      { 1, 1, 3, 1, "giozyx", "oixyz?g",  {}}},
+                { g_os_iyx_osv16,                              { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 16}}}},
+                { g_os_iyx_osv32,                              { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 32}}}},
+                { gs_oiyx_gsv16,                               { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{6, 16}}}},
+                { gs_oizyx_gsv16,                              { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{6, 16}}}},
+                { gs_oiyx_gsv32,                               { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{6, 32}}}},
+                { gyxio,                                       { 1, 1, 2, 1, "gyxio",  "oixy??g",  {}}},
+                { g_is_os_zyx_isv16_osv16,                     { 1, 1, 3, 1, "giozyx", "oixyz?g",  {{1, 16}, {0, 16}}}},
+                { g_is_os_yx_isv16_osv16,                      { 1, 1, 2, 1, "gioyx",  "oixy??g",  {{1, 16}, {0, 16}}}},
+                { g_os_is_zyx_isv8_osv16_isv2,                 { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{1, 8}, {0, 16}, {1, 2}}}},
+                { g_os_is_yx_isv8_osv16_isv2,                  { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{1, 8}, {0, 16}, {1, 2}}}},
+                { g_os_is_zyx_isv16_osv16,                     { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{0, 16}, {1, 16}}}},
+                { g_os_is_yx_osv16_isv4,                       { 1, 1, 2, 1, "goixy",  "oixy??g",  {{0, 16}, {1, 4}}}},
+                { g_os_is_zyx_osv16_isv16,                     { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{0, 16}, {1, 16}}}},
+                { g_os_zyx_is_osv16_isv4,                      { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 16}, {1, 4}}}},
+                { g_os_zyx_is_osv16_isv16,                     { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 16}, {1, 16}}}},
+                { g_os_zyx_is_osv16_isv32,                     { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 16}, {1, 32}}}},
+                { g_os_zyx_is_osv32_isv4,                      { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 32}, {1, 4}}}},
+                { g_os_zyx_is_osv32_isv16,                     { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 32}, {1, 16}}}},
+                { g_os_zyx_is_osv32_isv32,                     { 1, 1, 3, 1, "gozyxi", "oixyz?g",  {{0, 32}, {1, 32}}}},
+                { g_os_is_yx_osa4_isa8_osv8_isv4,              { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 32}, {1, 32}}}},
+                { g_os_is_zyx_osa4_isa8_osv8_isv4,             { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{0, 32}, {1, 32}}}},
+                { g_os_is_yx_osa4_isa8_osv8_isv2,              { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 32}, {1, 16}}}},
+                { g_os_is_zyx_osa4_isa8_osv8_isv2,             { 1, 1, 3, 1, "goizyx", "oixyz?g",  {{0, 32}, {1, 16}}}},
+                { g_os_is_yx_osa2_isa8_osv16_isv4,             { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 32}, {1, 32}}}},
+                { g_os_is_yx_osa2_isa8_osv16_isv2,             { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{0, 32}, {1, 16}}}},
+                { gs_oi_yxs_gsv4_yxsv4,                        { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{6, 4}}}},
+                { gs_oi_yxs_gsv16_yxsv4,                       { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{6, 16}}}},
+                { gs_oi_yxs_gsv32_yxsv4,                       { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{6, 32}}}},
+                { g_os_is_yx_isv16_osv16,                      { 1, 1, 2, 1, "goiyx",  "oixy??g",  {{1, 16}, {0, 16}}}},
+                { gi_yxs_os_yxsv2_osv16,                       { 1, 1, 2, 1, "giyxo",  "oixy??g",  {{0, 16}}}},
+                { giy_xs_os_xsv2_osv8__ao32,                   { 1, 1, 2, 1, "giyxo",  "oixy??g",  {{2, 2}, {0, 8}}}},
+                { giy_xs_os_xsv2_osv16__ao32,                  { 1, 1, 2, 1, "giyxo",  "oixy??g",  {{2, 2}, {0, 16}}}},
         };
         if (traits.find(fmt) == traits.end()) {
             throw std::runtime_error("[clDNN] Format description is missing in fmt traits");
@@ -381,8 +374,6 @@ struct format {
     static size_t feature_num(type fmt) { return traits(fmt).feature_num; }
     /// @brief Returns number of spatial dimensions for a @p format.
     static size_t spatial_num(type fmt) { return traits(fmt).spatial_num; }
-    /// @brief Returns number of local dimensions for a @p format.
-    static size_t local_num(type fmt) { return traits(fmt).local_num; }
     /// @brief Returns number of group dimensions for a @p format.
     static size_t group_num(type fmt) { return traits(fmt).group_num; }
     /// @brief Returns an order of dimensions for a @ format.
@@ -442,8 +433,6 @@ struct format {
     size_t feature_num() const { return traits(value).feature_num; }
     /// @brief Returns number of spatial dimensions.
     size_t spatial_num() const { return traits(value).spatial_num; }
-    /// @brief Returns number of local dimensions.
-    size_t local_num() const { return traits(value).local_num; }
     /// @brief Returns number of group dimensions.
     size_t group_num() const { return traits(value).group_num; }
     /// @brief Returns an order of dimensions in form of string.
@@ -483,9 +472,8 @@ struct format {
 constexpr int32_t tensor_batch_dim_max = 1;
 constexpr int32_t tensor_feature_dim_max = 1;
 constexpr int32_t tensor_spatial_dim_max = 4;
-constexpr int32_t tensor_local_dim_max = 2;
 constexpr int32_t tensor_group_dim_max = 1;
-constexpr int32_t tensor_dim_max = tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max + tensor_local_dim_max + tensor_group_dim_max;
+constexpr int32_t tensor_dim_max = tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max + tensor_group_dim_max;
 
 struct tensor;
 
@@ -496,7 +484,6 @@ enum class dim_vec_kind {
     batch,
     feature,
     spatial,
-    local,
     group
 };
 
@@ -524,16 +511,10 @@ struct dim_vec_limits<dim_vec_kind::spatial> {
     static constexpr int32_t dim_offset = tensor_batch_dim_max + tensor_feature_dim_max;
 };
 
-template <>
-struct dim_vec_limits<dim_vec_kind::local> {
-    static constexpr int32_t max_dimentionality = tensor_local_dim_max;
-    static constexpr int32_t dim_offset = tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max;
-};
-
 template <>
 struct dim_vec_limits<dim_vec_kind::group> {
     static constexpr int32_t max_dimentionality = tensor_group_dim_max;
-    static constexpr int32_t dim_offset = tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max + tensor_local_dim_max;
+    static constexpr int32_t dim_offset = tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max;
 };
 
 /// @brief Template class used in tensor constructor using dim_vec_kinds
@@ -570,11 +551,6 @@ details::dim_vec_kind_init<details::dim_vec_kind::spatial> spatial(InitTys&&...
     return details::dim_vec_kind_init<details::dim_vec_kind::spatial>(std::forward<InitTys>(inits)...);
 }
 
-template <typename... InitTys>
-details::dim_vec_kind_init<details::dim_vec_kind::local> local(InitTys&&... inits) {
-    return details::dim_vec_kind_init<details::dim_vec_kind::local>(std::forward<InitTys>(inits)...);
-}
-
 template <typename... InitTys>
 details::dim_vec_kind_init<details::dim_vec_kind::group> group(InitTys&&... inits) {
     return details::dim_vec_kind_init<details::dim_vec_kind::group>(std::forward<InitTys>(inits)...);
@@ -585,7 +561,6 @@ struct tensor {
     friend class details::dim_vec_kind_init<details::dim_vec_kind::batch>;
     friend class details::dim_vec_kind_init<details::dim_vec_kind::feature>;
     friend class details::dim_vec_kind_init<details::dim_vec_kind::spatial>;
-    friend class details::dim_vec_kind_init<details::dim_vec_kind::local>;
     friend class details::dim_vec_kind_init<details::dim_vec_kind::group>;
 
     typedef int32_t value_type;  ///< Values type stored in tensor.
@@ -594,7 +569,6 @@ struct tensor {
     mutable_array_ref<value_type> batch;    ///< Batch dimensions.
     mutable_array_ref<value_type> feature;  ///< Feature maps.
     mutable_array_ref<value_type> spatial;  ///< Spatial dimensions.
-    mutable_array_ref<value_type> local;    ///< Local dimensions.
     mutable_array_ref<value_type> group;    ///< Group dimensions.
 
 private:
@@ -606,8 +580,7 @@ public:
         batch(_sizes, tensor_batch_dim_max),
         feature(_sizes + tensor_batch_dim_max, tensor_feature_dim_max),
         spatial(_sizes + tensor_batch_dim_max + tensor_feature_dim_max, tensor_spatial_dim_max),
-        local(_sizes + tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max, tensor_local_dim_max),
-        group(_sizes + tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max + tensor_local_dim_max, tensor_group_dim_max) {
+        group(_sizes + tensor_batch_dim_max + tensor_feature_dim_max + tensor_spatial_dim_max, tensor_group_dim_max) {
         std::fill_n(_sizes, tensor_dim_max, default_size);
     }
 

inference-engine/thirdparty/clDNN/kernel_selector/common/common_types.h

@@ -21,6 +21,7 @@ enum class KernelType {
     NORMALIZE,
     POOLING,
     ROI_POOLING,
+    ROI_ALIGN,
     FULLY_CONNECTED,
     ACTIVATION,
     SOFT_MAX,
@@ -71,7 +72,8 @@ enum class KernelType {
     EXTRACT_IMAGE_PATCHES,
     LOOP,
     NON_MAX_SUPPRESSION,
-    DETECTION_OUTPUT
+    DETECTION_OUTPUT,
+    EXPERIMENTAL_DETECTRON_ROI_FEATURE_EXTRACTOR
 };
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.cpp

@@ -45,110 +45,110 @@ DataTensor::DataChannelArray DataTensor::dataChannelArray {{
 }};
 
 WeightsTensor::WeightsChannelArray WeightsTensor::weightsChannelArray {{
-    //                                                               X,  Y,  Z, IFM, OFM, LX, LY, G
-    { WeightsLayout::oi,                                          { -1, -1, -1,   0,   1, -1, -1, -1 } },
-    { WeightsLayout::io,                                          { -1, -1, -1,   1,   0, -1, -1, -1 } },
-    { WeightsLayout::oiyx,                                        {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::ioyx,                                        {  0,  1, -1,   3,   2, -1, -1, -1 } },
-    { WeightsLayout::oyxi,                                        {  1,  2, -1,   0,   3, -1, -1, -1 } },
-    { WeightsLayout::iyxo,                                        {  1,  2, -1,   3,   0, -1, -1, -1 } },
-    { WeightsLayout::yxio,                                        {  2,  3, -1,   1,   0, -1, -1, -1 } },
-    { WeightsLayout::os_iyx_osv16,                                {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_iyx_osv32,                                {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_iyx_osv32__ai32,                          {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_iyx_osv64,                                {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_iyx_osv16_rotate_180,                     {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::o_is_yx_isv16,                               {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_yxi_osv16,                                {  1,  2, -1,   0,   3, -1, -1, -1 } },
-    { WeightsLayout::os_i_osv8__ai8,                              { -1, -1, -1,   0,   1, -1, -1, -1 } },
-    { WeightsLayout::os_i_osv16__ai8,                             { -1, -1, -1,   0,   1, -1, -1, -1 } },
-    { WeightsLayout::os_i_osv16,                                  { -1, -1, -1,   0,   1, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv16_isv16,                        {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osv16_isv16,                       {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::g_os_is_zyx_osv16_isv16,                     {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::os_is_zyx_osv32_isv16,                       {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osv64_isv16,                       {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::i_yxs_os_yxsv2_osv16,                        {  1,  2, -1,   3,   0, -1, -1, -1 } },
-    { WeightsLayout::iy_xs_os_xsv2_osv16__ao32,                   {  1,  2, -1,   3,   0, -1, -1, -1 } },
-    { WeightsLayout::iy_xs_os_xsv2_osv8__ao32,                    {  1,  2, -1,   3,   0, -1, -1, -1 } },
-    { WeightsLayout::image_2d_weights_c4_fyx_b,                   {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::image_2d_weights_c1_b_fyx,                   {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::winograd_2x3_s1_weights,                     {  3,  2, -1,   1,   0, -1, -1, -1 } },
-    { WeightsLayout::winograd_2x3_s1_fused_weights,               {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::winograd_6x3_s1_fused_weights,               {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb,      {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb,      {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::dlstm_dir_io,                                {  1,  0, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_isa8_osv8_isv4,                     {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_isa8_osv16_isv4,                    {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_isa8_osv8_isv4_swizzled_by_4,       {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv4,                {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::g_os_is_yx_osa4_isa8_osv8_isv4,              {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_zyx_osa4_isa8_osv8_isv4,             {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv2,                {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv2,               {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv4,               {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::g_os_is_yx_osa4_isa8_osv8_isv2,              {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_zyx_osa4_isa8_osv8_isv2,             {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::os_is_yx_osa2_isa8_osv16_isv4,               {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::g_os_is_yx_osa2_isa8_osv16_isv4,             {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::os_is_yx_osa2_isa8_osv16_isv2,               {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::g_os_is_yx_osa2_isa8_osv16_isv2,             {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::os_is_zyx_isa8_osv8_isv4,                    {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_isa8_osv16_isv4,                   {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4,  {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::is_o_yx_isv32,                               {  1,  2, -1,   0,   3, -1, -1, -1 } },
-    { WeightsLayout::is_o32_yx_isv32_swizzled_by_4,               {  1,  2, -1,   0,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_y_x8_osv8_isv4,                        {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_y_x8_osv8_isv4_swizzled_by_4,          {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv8_isv4,                          {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv8_isv2,                          {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv16_isv4,                         {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv32_isv4_swizzled_by_2,           {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv32_isv4,                         {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_osv32_isv4,                        {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::oizyx,                                       {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::iozyx,                                       {  0,  1,  2,   4,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_osv32_isv32p,                       {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_isv16_osv16,                       {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_isv16_osv16,                        {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::is_os_zyx_isv16_osv16,                       {  0,  1,  2,   4,   3, -1, -1, -1 } },
-    { WeightsLayout::is_os_yx_isv16_osv16,                        {  0,  1, -1,   3,   2, -1, -1, -1 } },
-    { WeightsLayout::os_is_osv32_isv32_swizzled_by_4,             { -1, -1, -1,   0,   1, -1, -1, -1 } },
-    { WeightsLayout::os_is_zyx_isv8_osv16_isv2,                   {  0,  1,  2,   3,   4, -1, -1, -1 } },
-    { WeightsLayout::os_is_yx_isv8_osv16_isv2,                    {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::os_zyxi_osv16,                               {  1,  2,  3,   0,   4, -1, -1, -1 } },
-    { WeightsLayout::os_i_yxs_osv4_yxsv4,                         {  0,  1, -1,   2,   3, -1, -1, -1 } },
-    { WeightsLayout::goiyx,                                       {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gioyx,                                       {  0,  1, -1,   3,   2, -1, -1,  4 } },
-    { WeightsLayout::goizyx,                                      {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::giozyx,                                      {  0,  1,  2,   4,   3, -1, -1,  5 } },
-    { WeightsLayout::g_os_iyx_osv16,                              {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_iyx_osv32,                              {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gs_oiyx_gsv16,                               {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gs_oizyx_gsv16,                              {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::gs_oiyx_gsv32,                               {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gyxio,                                       {  2,  3, -1,   1,   0, -1, -1,  4 } },
-    { WeightsLayout::gi_yxs_os_yxsv2_osv16,                       {  1,  2, -1,   3,   0, -1, -1,  4 } },
-    { WeightsLayout::g_is_os_zyx_isv16_osv16,                     {  0,  1,  2,   4,   3, -1, -1,  5 } },
-    { WeightsLayout::g_is_os_yx_isv16_osv16,                      {  0,  1, -1,   3,   2, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_zyx_isv8_osv16_isv2,                 {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_is_yx_isv8_osv16_isv2,                  {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_zyx_isv16_osv16,                     {  0,  1,  2,   3,   4, -1, -1,  5 } },
-    { WeightsLayout::giy_xs_os_xsv2_osv16__ao32,                  {  1,  2, -1,   3,   0, -1, -1,  4 } },
-    { WeightsLayout::giy_xs_os_xsv2_osv8__ao32,                   {  1,  2, -1,   3,   0, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_yx_isv16_osv16,                      {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gs_oi_yxs_gsv4_yxsv4,                        {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gs_oi_yxs_gsv16_yxsv4,                       {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::gs_oi_yxs_gsv32_yxsv4,                       {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_is_yx_osv16_isv4,                       {  0,  1, -1,   2,   3, -1, -1,  4 } },
-    { WeightsLayout::g_os_zyx_is_osv16_isv4,                      {  1,  2,  3,   0,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_zyx_is_osv16_isv16,                     {  1,  2,  3,   0,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_zyx_is_osv16_isv32,                     {  1,  2,  3,   0,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_zyx_is_osv32_isv4,                      {  1,  2,  3,   0,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_zyx_is_osv32_isv16,                     {  1,  2,  3,   0,   4, -1, -1,  5 } },
-    { WeightsLayout::g_os_zyx_is_osv32_isv32,                     {  1,  2,  3,   0,   4, -1, -1,  5 } },
+    //                                                               X,  Y,  Z, IFM, OFM, G
+    { WeightsLayout::oi,                                          { -1, -1, -1,   0,   1, -1 } },
+    { WeightsLayout::io,                                          { -1, -1, -1,   1,   0, -1 } },
+    { WeightsLayout::oiyx,                                        {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::ioyx,                                        {  0,  1, -1,   3,   2, -1 } },
+    { WeightsLayout::oyxi,                                        {  1,  2, -1,   0,   3, -1 } },
+    { WeightsLayout::iyxo,                                        {  1,  2, -1,   3,   0, -1 } },
+    { WeightsLayout::yxio,                                        {  2,  3, -1,   1,   0, -1 } },
+    { WeightsLayout::os_iyx_osv16,                                {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_iyx_osv32,                                {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_iyx_osv32__ai32,                          {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_iyx_osv64,                                {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_iyx_osv16_rotate_180,                     {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::o_is_yx_isv16,                               {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_yxi_osv16,                                {  1,  2, -1,   0,   3, -1 } },
+    { WeightsLayout::os_i_osv8__ai8,                              { -1, -1, -1,   0,   1, -1 } },
+    { WeightsLayout::os_i_osv16__ai8,                             { -1, -1, -1,   0,   1, -1 } },
+    { WeightsLayout::os_i_osv16,                                  { -1, -1, -1,   0,   1, -1 } },
+    { WeightsLayout::os_is_yx_osv16_isv16,                        {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_zyx_osv16_isv16,                       {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::g_os_is_zyx_osv16_isv16,                     {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::os_is_zyx_osv32_isv16,                       {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_zyx_osv64_isv16,                       {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::i_yxs_os_yxsv2_osv16,                        {  1,  2, -1,   3,   0, -1 } },
+    { WeightsLayout::iy_xs_os_xsv2_osv16__ao32,                   {  1,  2, -1,   3,   0, -1 } },
+    { WeightsLayout::iy_xs_os_xsv2_osv8__ao32,                    {  1,  2, -1,   3,   0, -1 } },
+    { WeightsLayout::image_2d_weights_c4_fyx_b,                   {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::image_2d_weights_c1_b_fyx,                   {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::winograd_2x3_s1_weights,                     {  3,  2, -1,   1,   0, -1 } },
+    { WeightsLayout::winograd_2x3_s1_fused_weights,               {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::winograd_6x3_s1_fused_weights,               {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::image_2d_weights_winograd_6x3_s1_fbxyb,      {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::image_2d_weights_winograd_6x3_s1_xfbyb,      {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::dlstm_dir_io,                                {  1,  0, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_isa8_osv8_isv4,                     {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_isa8_osv16_isv4,                    {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_isa8_osv8_isv4_swizzled_by_4,       {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv4,                {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::g_os_is_yx_osa4_isa8_osv8_isv4,              {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_is_zyx_osa4_isa8_osv8_isv4,             {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv2,                {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv2,               {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv4,               {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::g_os_is_yx_osa4_isa8_osv8_isv2,              {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_is_zyx_osa4_isa8_osv8_isv2,             {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::os_is_yx_osa2_isa8_osv16_isv4,               {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::g_os_is_yx_osa2_isa8_osv16_isv4,             {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::os_is_yx_osa2_isa8_osv16_isv2,               {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::g_os_is_yx_osa2_isa8_osv16_isv2,             {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::os_is_zyx_isa8_osv8_isv4,                    {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_zyx_isa8_osv16_isv4,                   {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_yx_osa4_isa8_osv8_isv4_swizzled_by_4,  {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_zyx_osa4_isa8_osv8_isv4_swizzled_by_4, {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::is_o_yx_isv32,                               {  1,  2, -1,   0,   3, -1 } },
+    { WeightsLayout::is_o32_yx_isv32_swizzled_by_4,               {  1,  2, -1,   0,   3, -1 } },
+    { WeightsLayout::os_is_y_x8_osv8_isv4,                        {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_y_x8_osv8_isv4_swizzled_by_4,          {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv8_isv4,                          {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv8_isv2,                          {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv16_isv4,                         {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv32_isv4_swizzled_by_2,           {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv32_isv4,                         {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_zyx_osv32_isv4,                        {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::oizyx,                                       {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::iozyx,                                       {  0,  1,  2,   4,   3, -1 } },
+    { WeightsLayout::os_is_yx_osv32_isv32p,                       {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_is_zyx_isv16_osv16,                       {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_yx_isv16_osv16,                        {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::is_os_zyx_isv16_osv16,                       {  0,  1,  2,   4,   3, -1 } },
+    { WeightsLayout::is_os_yx_isv16_osv16,                        {  0,  1, -1,   3,   2, -1 } },
+    { WeightsLayout::os_is_osv32_isv32_swizzled_by_4,             { -1, -1, -1,   0,   1, -1 } },
+    { WeightsLayout::os_is_zyx_isv8_osv16_isv2,                   {  0,  1,  2,   3,   4, -1 } },
+    { WeightsLayout::os_is_yx_isv8_osv16_isv2,                    {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::os_zyxi_osv16,                               {  1,  2,  3,   0,   4, -1 } },
+    { WeightsLayout::os_i_yxs_osv4_yxsv4,                         {  0,  1, -1,   2,   3, -1 } },
+    { WeightsLayout::goiyx,                                       {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gioyx,                                       {  0,  1, -1,   3,   2,  4 } },
+    { WeightsLayout::goizyx,                                      {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::giozyx,                                      {  0,  1,  2,   4,   3,  5 } },
+    { WeightsLayout::g_os_iyx_osv16,                              {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_iyx_osv32,                              {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gs_oiyx_gsv16,                               {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gs_oizyx_gsv16,                              {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::gs_oiyx_gsv32,                               {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gyxio,                                       {  2,  3, -1,   1,   0,  4 } },
+    { WeightsLayout::gi_yxs_os_yxsv2_osv16,                       {  1,  2, -1,   3,   0,  4 } },
+    { WeightsLayout::g_is_os_zyx_isv16_osv16,                     {  0,  1,  2,   4,   3,  5 } },
+    { WeightsLayout::g_is_os_yx_isv16_osv16,                      {  0,  1, -1,   3,   2,  4 } },
+    { WeightsLayout::g_os_is_zyx_isv8_osv16_isv2,                 {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::g_os_is_yx_isv8_osv16_isv2,                  {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_is_zyx_isv16_osv16,                     {  0,  1,  2,   3,   4,  5 } },
+    { WeightsLayout::giy_xs_os_xsv2_osv16__ao32,                  {  1,  2, -1,   3,   0,  4 } },
+    { WeightsLayout::giy_xs_os_xsv2_osv8__ao32,                   {  1,  2, -1,   3,   0,  4 } },
+    { WeightsLayout::g_os_is_yx_isv16_osv16,                      {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gs_oi_yxs_gsv4_yxsv4,                        {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gs_oi_yxs_gsv16_yxsv4,                       {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::gs_oi_yxs_gsv32_yxsv4,                       {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_is_yx_osv16_isv4,                       {  0,  1, -1,   2,   3,  4 } },
+    { WeightsLayout::g_os_zyx_is_osv16_isv4,                      {  1,  2,  3,   0,   4,  5 } },
+    { WeightsLayout::g_os_zyx_is_osv16_isv16,                     {  1,  2,  3,   0,   4,  5 } },
+    { WeightsLayout::g_os_zyx_is_osv16_isv32,                     {  1,  2,  3,   0,   4,  5 } },
+    { WeightsLayout::g_os_zyx_is_osv32_isv4,                      {  1,  2,  3,   0,   4,  5 } },
+    { WeightsLayout::g_os_zyx_is_osv32_isv16,                     {  1,  2,  3,   0,   4,  5 } },
+    { WeightsLayout::g_os_zyx_is_osv32_isv32,                     {  1,  2,  3,   0,   4,  5 } },
 }};
 
 NDims DataTensor::GetSimpleDims(const std::vector<size_t>& d, DataLayout l) {
@@ -859,13 +859,6 @@ WeightsTensor WeightsTensor::TransformIgnorePadding(WeightsLayout l, WeightsType
         vec[Channelndex(l, WeightsChannelName::IFM)] = IFM().v;
         vec[Channelndex(l, WeightsChannelName::OFM)] = OFM().v;
         vec[Channelndex(l, WeightsChannelName::Z)] = Z().v;
-    } else if (src_channels == 6 && dst_channels == 6) {
-        vec[Channelndex(l, WeightsChannelName::X)] = X().v;
-        vec[Channelndex(l, WeightsChannelName::Y)] = Y().v;
-        vec[Channelndex(l, WeightsChannelName::IFM)] = IFM().v;
-        vec[Channelndex(l, WeightsChannelName::OFM)] = OFM().v;
-        vec[Channelndex(l, WeightsChannelName::LX)] = LX().v;
-        vec[Channelndex(l, WeightsChannelName::LY)] = LY().v;
     } else if (src_channels == 4 && dst_channels == 5) {
         vec[Channelndex(l, WeightsChannelName::X)] = X().v;
         vec[Channelndex(l, WeightsChannelName::Y)] = Y().v;

inference-engine/thirdparty/clDNN/kernel_selector/common/tensor_type.h

@@ -203,7 +203,7 @@ using NDims = std::vector<Dim>;
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 enum class DataChannelName { X = 0, Y = 1, Z = 2, W = 3, FEATURE = 4, BATCH = 5, COUNT = 6 };
 
-enum class WeightsChannelName { X = 0, Y = 1, Z = 2, IFM = 3, OFM = 4, LX = 5, LY = 6, G = 7, COUNT = 8 };
+enum class WeightsChannelName { X = 0, Y = 1, Z = 2, IFM = 3, OFM = 4, G = 5, COUNT = 6 };
 
 inline bool SimpleLayout(WeightsLayout l) {
     switch (l) {
@@ -568,8 +568,6 @@ struct WeightsTensor : TensorBaseT<WeightsType, WeightsLayout> {
     Dim Z() const { return Extract(layout, WeightsChannelName::Z, dims); }
     Dim IFM() const { return Extract(layout, WeightsChannelName::IFM, dims); }
     Dim OFM() const { return Extract(layout, WeightsChannelName::OFM, dims); }
-    Dim LX() const { return Extract(layout, WeightsChannelName::LX, dims); }
-    Dim LY() const { return Extract(layout, WeightsChannelName::LY, dims); }
     Dim G() const { return Extract(layout, WeightsChannelName::G, dims); }
 
     static inline Dim Extract(WeightsLayout l, WeightsChannelName channel, const NDims& d) {