Statically analyzed issues.

inference-engine/src/legacy_api/src/convert_function_to_cnn_network.cpp

@@ -669,6 +669,8 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
                        [](const std::shared_ptr<::ngraph::Node>& node, const std::map<std::string, std::string>& params) -> CNNLayerPtr {
         LayerParams attrs = {node->get_friendly_name(), node->description(), details::convertPrecision(node->get_output_element_type(0))};
         auto reduce_node = std::dynamic_pointer_cast<ngraph::op::util::ArithmeticReductionKeepDims>(node);
+        if (reduce_node == nullptr)
+            THROW_IE_EXCEPTION << "Node '" << node->get_name() << "' is not an instance of ArithmeticReductionKeepDims.";
         auto res = std::make_shared<InferenceEngine::ReduceLayer>(attrs);
         res->params = params;
         res->params["keep_dims"] = reduce_node->get_keep_dims() ? "True" : "False";
@@ -678,6 +680,8 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
     addSpecificCreator({"ReduceLogicalAnd"}, [](const std::shared_ptr<::ngraph::Node>& node, const std::map<std::string, std::string>& params) -> CNNLayerPtr {
         LayerParams attrs = {node->get_friendly_name(), "ReduceAnd", details::convertPrecision(node->get_output_element_type(0))};
         auto reduce_node = std::dynamic_pointer_cast<ngraph::op::util::LogicalReductionKeepDims>(node);
+        if (reduce_node == nullptr)
+            THROW_IE_EXCEPTION << "Node '" << node->get_name() << "' is not an instance of LogicalReductionKeepDims.";
         auto res = std::make_shared<InferenceEngine::ReduceLayer>(attrs);
         res->params = params;
         res->params["keep_dims"] = reduce_node->get_keep_dims() ? "True" : "False";
@@ -687,6 +691,8 @@ InferenceEngine::details::CNNLayerCreator::CNNLayerCreator(const std::shared_ptr
     addSpecificCreator({"ReduceLogicalOr"}, [](const std::shared_ptr<::ngraph::Node>& node, const std::map<std::string, std::string>& params) -> CNNLayerPtr {
         LayerParams attrs = {node->get_friendly_name(), "ReduceOr", details::convertPrecision(node->get_output_element_type(0))};
         auto reduce_node = std::dynamic_pointer_cast<ngraph::op::util::LogicalReductionKeepDims>(node);
+        if (reduce_node == nullptr)
+            THROW_IE_EXCEPTION << "Node '" << node->get_name() << "' is not an instance of LogicalReductionKeepDims.";
         auto res = std::make_shared<InferenceEngine::ReduceLayer>(attrs);
         res->params = params;
         res->params["keep_dims"] = reduce_node->get_keep_dims() ? "True" : "False";

inference-engine/src/legacy_api/src/net_pass.cpp

@@ -398,8 +398,10 @@ bool convertToRNNSeq(CNNLayerPtr cur, const N& net) {
     IE_ASSERT(cell->insData.size() == NS + 1);  // {data, state1, [state2]}
     IE_ASSERT(cell->outData.size() == NS);      // {state1, [state2]}
 
+    auto outData0InputsTo = getInputTo(cell->outData[0]);
     if (getCreatorLayer(cell->insData[0].lock()).lock() != rsp1 ||
-        getInputTo(cell->outData[0]).begin()->second != rsp2)
+            outData0InputsTo.empty() ||
+            outData0InputsTo.begin()->second != rsp2)
         return false;
 
     // Check port mapping

inference-engine/src/readers/ir_reader_v7/ie_layer_validators.cpp

@@ -269,7 +269,7 @@ void CropValidator::checkShapes(const CNNLayer* layer, const vector<SizeVector>&
             }
         } else if (!casted->dim.empty()) {
             int dim = casted->dim[i];
-            if (firstShape[axis] < static_cast<size_t>(offset + dim)) {
+            if (firstShape[axis] < (static_cast<size_t>(offset) + dim)) {
                 THROW_IE_EXCEPTION << "Incorrect crop data! Offset(" << offset << ") + result size of output(" << dim
                                    << ") should be less then input size(" << firstShape[axis] << ") for axis(" << axis
                                    << ")";

inference-engine/src/transformations/src/transformations/convert_opset1_to_legacy/fc_bias_fusion.cpp

@@ -26,6 +26,8 @@ ngraph::pass::FullyConnectedBiasFusion::FullyConnectedBiasFusion() {
 
         if (m_fc == nullptr) {
             m_fc = std::dynamic_pointer_cast<op::FullyConnected>(add_input_1);
+            if (m_fc == nullptr)
+                return false;
             m_bias = add_input_0;
         }
 

inference-engine/src/transformations/src/transformations/reduce_l1_decomposition.cpp

@@ -18,7 +18,7 @@ ngraph::pass::ReduceL1Decomposition::ReduceL1Decomposition() {
         auto &pattern_to_output = m.get_pattern_value_map();
         auto reduce_l1_node = std::dynamic_pointer_cast<ngraph::opset4::ReduceL1>(pattern_to_output.at(reduce_l1).get_node_shared_ptr());
 
-        if (m_transformation_callback(reduce_l1_node)) {
+        if (reduce_l1_node == nullptr || m_transformation_callback(reduce_l1_node)) {
             return false;
         }
 

inference-engine/src/transformations/src/transformations/reduce_l2_decomposition.cpp

@@ -18,7 +18,7 @@ ngraph::pass::ReduceL2Decomposition::ReduceL2Decomposition() {
         auto &pattern_to_output = m.get_pattern_value_map();
         auto reduce_l2_node = std::dynamic_pointer_cast<ngraph::opset4::ReduceL2>(pattern_to_output.at(reduce_l2).get_node_shared_ptr());
 
-        if (m_transformation_callback(reduce_l2_node)) {
+        if (reduce_l2_node == nullptr || m_transformation_callback(reduce_l2_node)) {
             return false;
         }
 

inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_reshape.cpp

@@ -23,6 +23,9 @@ void dynamicToStaticShapeReshape(std::shared_ptr<ngraph::Node> target) {
                      target->get_friendly_name(), target->get_type_info(), ngraph::vpu::op::DynamicShapeResolver::type_info, 0);
 
     const auto reshape = std::dynamic_pointer_cast<ngraph::opset3::Reshape>(target);
+    VPU_THROW_UNLESS(reshape != nullptr,
+                     "DynamicToStaticShape transformation for '{}' expects Reshape node",
+                     target->get_friendly_name());
     const auto outShapeDescriptor = reshape->input_value(1).get_node_shared_ptr();
 
     const auto replacement = ngraph::as_type_ptr<ngraph::opset3::Constant>(outShapeDescriptor)

inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_squeeze.cpp

@@ -28,6 +28,9 @@ void dynamicToStaticShapeSqueeze(std::shared_ptr<ngraph::Node> target) {
         target->get_friendly_name(), target->get_type_info(), ngraph::op::Constant::type_info, 1);
 
     const auto squeeze = std::dynamic_pointer_cast<ngraph::opset3::Squeeze>(target);
+    VPU_THROW_UNLESS(squeeze != nullptr,
+                     "DynamicToStaticShape transformation for '{}' expects Squeeze node",
+                     target->get_friendly_name());
     const auto copied = squeeze->clone_with_new_inputs(target->input_values());
     const auto shape = dsr->input(1).get_source_output();
 

inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_transpose.cpp

@@ -26,6 +26,9 @@ void dynamicToStaticShapeTranspose(std::shared_ptr<ngraph::Node> target) {
         target->get_friendly_name(), target->get_type_info(), ngraph::opset3::Constant::type_info, 1);
 
     const auto transpose = std::dynamic_pointer_cast<ngraph::opset3::Transpose>(target);
+    VPU_THROW_UNLESS(transpose != nullptr,
+                     "DynamicToStaticShape transformation for '{}' expects Transpose node",
+                     target->get_friendly_name());
     const auto copied = transpose->clone_with_new_inputs(target->input_values());
     const auto shape = dsr->input(1).get_source_output();
 

inference-engine/src/vpu/common/src/ngraph/transformations/dynamic_to_static_shape_unsqueeze.cpp

@@ -29,6 +29,9 @@ void dynamicToStaticShapeUnsqueeze(std::shared_ptr<ngraph::Node> target) {
         target->get_friendly_name(), target->get_type_info(), ngraph::op::Constant::type_info, 1);
 
     const auto unsqueeze = std::dynamic_pointer_cast<ngraph::opset3::Unsqueeze>(target);
+    VPU_THROW_UNLESS(unsqueeze != nullptr,
+                     "DynamicToStaticShape transformation for '{}' expects Unsqueeze node",
+                     target->get_friendly_name());
     const auto copied = unsqueeze->clone_with_new_inputs(target->input_values());
     const auto shape = dsr->input(1).get_source_output();
 

inference-engine/src/vpu/graph_transformer/src/frontend/ShaveElfMetadataParser.cpp

@@ -65,10 +65,12 @@ uint32_t md_parser_t::evaluate_expr(const md_expr_t *expression,
     case md_type_op_xor:
     case md_type_op_shl:
     case md_type_op_lshr:
-      uint32_t rhs = values.rbegin()[0];
-      uint32_t lhs = values.rbegin()[1];
-      values.pop_back();
-      values.back() = md_eval_expression_type_op_2(v.type, lhs, rhs);
+      if (!values.empty()) {
+        uint32_t rhs = values.rbegin()[0];
+        uint32_t lhs = values.rbegin()[1];
+        values.pop_back();
+        values.back() = md_eval_expression_type_op_2(v.type, lhs, rhs);
+      }
     }
       break;
     case md_type_global_size:

inference-engine/src/vpu/graph_transformer/src/frontend/custom_layer.cpp

@@ -184,6 +184,8 @@ CustomLayer::CustomLayer(std::string configDir, const pugi::xml_node& customLaye
             stageOrder.emplace(stageNum, CustomKernel{kernel, _configDir});
         }
 
+        VPU_THROW_UNLESS(!stageOrder.empty(),
+            "Error while binding %s custom layer: No stages.", _layerName);
         VPU_THROW_UNLESS(stageOrder.begin()->first == 0,
             "Error while binding %s custom layer: Stage 0 is not found.", _layerName);
         VPU_THROW_UNLESS(stageOrder.rbegin()->first == stageOrder.size() - 1,