Fixed static analysis issues (#3254)

diff --git a/inference-engine/samples/classification_sample_async/main.cpp b/inference-engine/samples/classification_sample_async/main.cpp
index 1e764ff..264d4ba 100644 (file)
--- a/inference-engine/samples/classification_sample_async/main.cpp
+++ b/inference-engine/samples/classification_sample_async/main.cpp
@@ -169,6 +169,8 @@ int main(int argc, char *argv[]) {
             auto minputHolder = minput->wmap();
 
             auto data = minputHolder.as<PrecisionTrait<Precision::U8>::value_type *>();
+            if (data == nullptr)
+                throw std::runtime_error("Input blob has not allocated buffer");
             /** Iterate over all input images **/
             for (size_t image_id = 0; image_id < imagesData.size(); ++image_id) {
                 /** Iterate over all pixel in image (b,g,r) **/
@@ -217,6 +219,8 @@ int main(int argc, char *argv[]) {
         // --------------------------- 8. Process output -------------------------------------------------------
         slog::info << "Processing output blobs" << slog::endl;
         OutputsDataMap outputInfo(network.getOutputsInfo());
+        if (outputInfo.empty())
+            throw std::runtime_error("Can't get output blobs");
         Blob::Ptr outputBlob = inferRequest.GetBlob(outputInfo.begin()->first);
 
         /** Validating -nt value **/

diff --git a/inference-engine/samples/style_transfer_sample/main.cpp b/inference-engine/samples/style_transfer_sample/main.cpp
index e410c71..6a036a9 100644 (file)
--- a/inference-engine/samples/style_transfer_sample/main.cpp
+++ b/inference-engine/samples/style_transfer_sample/main.cpp
@@ -177,7 +177,8 @@ int main(int argc, char *argv[]) {
             size_t image_size = minput->getTensorDesc().getDims()[3] * minput->getTensorDesc().getDims()[2];
 
             auto data = ilmHolder.as<PrecisionTrait<Precision::FP32>::value_type *>();
-
+            if (data == nullptr)
+                throw std::runtime_error("Input blob has not allocated buffer");
             /** Iterate over all input images **/
             for (size_t image_id = 0; image_id < imagesData.size(); ++image_id) {
                 /** Iterate over all pixel in image (b,g,r) **/

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
index e19708a..4bcd2d9 100644 (file)
--- a/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_exec_network.cpp
@@ -118,6 +118,8 @@ MKLDNNExecNetwork::MKLDNNExecNetwork(const InferenceEngine::ICNNNetwork &network
                 createConstInputTo(layer, shiftBlob, "biases");
             } else if (scalesBlob != nullptr) {
                 Blob::Ptr biases = make_shared_blob<float>(scalesBlob->getTensorDesc());
+                if (biases == nullptr)
+                    THROW_IE_EXCEPTION << "Cannot make 'biases' shared blob";
                 biases->allocate();
                 auto biasesPtr = biases->buffer().as<float*>();
                 for (size_t i = 0; i < biases->size(); i++)

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
index 32c9cff..7883277 100644 (file)
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph.cpp
@@ -1090,10 +1090,11 @@ void MKLDNNGraph::InsertReorder(MKLDNNEdgePtr edge, std::string layerName, const
                                     inDesc.getPrecision()}));
     MKLDNNNodePtr newReorder(new MKLDNNReorderNode(layer, getEngine(), weightsCache));
     auto *reorderPtr = dynamic_cast<MKLDNNReorderNode *>(newReorder.get());
-    if (reorderPtr) {
-        reorderPtr->setDescs(inDesc, outDesc);
-        reorderPtr->_scales = scales;
+    if (reorderPtr == nullptr) {
+        THROW_IE_EXCEPTION << "MKLDNNGraph::InsertReorder: Cannot cast to MKLDNNReorderNode";
     }
+    reorderPtr->setDescs(inDesc, outDesc);
+    reorderPtr->_scales = scales;
 
     auto oIndex = edge->getOutputNum();
     auto iIndex = edge->getInputNum();

diff --git a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
index 9ca7177..05cec5d 100644 (file)
--- a/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
+++ b/inference-engine/src/mkldnn_plugin/mkldnn_graph_optimizer.cpp
@@ -254,7 +254,12 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndZeroPoints(MKLDNNGraph &graph) {
                     return false;
 
                 auto zeroPointsBlob = dynamic_cast<TBlob<uint8_t>*>(arg0->getCnnLayer()->blobs["custom"].get());
+                if (zeroPointsBlob == nullptr)
+                    THROW_IE_EXCEPTION << "Cannot cast to TBlob internal zero points blob";
+
                 auto zeroPointsData = zeroPointsBlob->buffer().as<uint8_t*>();
+                if (zeroPointsData == nullptr)
+                    THROW_IE_EXCEPTION << "zeroPointsBlob has not allocated buffer";
 
                 for (int j = 0; j < parent0->getParentEdgesAtPort(1)[0]->getDims()[1]; j++) {
                     convNode->inputZeroPoints.push_back(zeroPointsData[j]);
@@ -302,7 +307,12 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndZeroPoints(MKLDNNGraph &graph) {
                     return false;
 
                 auto zeroPointsBlob = dynamic_cast<TBlob<int8_t>*>(arg0->getCnnLayer()->blobs["custom"].get());
+                if (zeroPointsBlob == nullptr)
+                    THROW_IE_EXCEPTION << "Cannot cast to TBlob internal zero points blob";
+
                 auto zeroPointsData = zeroPointsBlob->buffer().as<int8_t*>();
+                if (zeroPointsData == nullptr)
+                    THROW_IE_EXCEPTION << "zeroPointsBlob has not allocated buffer";
 
                 for (int j = 0; j < parent0->getParentEdgesAtPort(1)[0]->getDims()[0]; j++) {
                     convNode->weightsZeroPoints.push_back(static_cast<float>(zeroPointsData[j]));
@@ -338,8 +348,14 @@ void MKLDNNGraphOptimizer::FuseConvolutionAndZeroPoints(MKLDNNGraph &graph) {
             weightsLayer = getCreatorLayer(weightsLayer->insData[0].lock()).lock();
         }
 
+
         auto weightsBlob = dynamic_cast<TBlob<int8_t>*>(weightsLayer->blobs["custom"].get());
+        if (weightsBlob == nullptr)
+            THROW_IE_EXCEPTION << "Cannot cast to TBlob internal weights blob";
+
         auto weightsPtr = weightsBlob->buffer().as<int8_t*>();
+        if (weightsPtr == nullptr)
+            THROW_IE_EXCEPTION << "weightsBlob has not allocated buffer";
 
         ptrdiff_t G = convLayer->_group;
         ptrdiff_t OC = weightsLayer->outData[0]->getDims()[0] / G;

diff --git a/inference-engine/src/mkldnn_plugin/nodes/non_max_suppression.cpp b/inference-engine/src/mkldnn_plugin/nodes/non_max_suppression.cpp
index 9f8e326..9abad7b 100644 (file)
--- a/inference-engine/src/mkldnn_plugin/nodes/non_max_suppression.cpp
+++ b/inference-engine/src/mkldnn_plugin/nodes/non_max_suppression.cpp
@@ -184,7 +184,7 @@ public:
         int batch_index;
         int class_index;
         int box_index;
-        filteredBoxes() {}
+        filteredBoxes() = default;
         filteredBoxes(float _score, int _batch_index, int _class_index, int _box_index) :
                       score(_score), batch_index(_batch_index), class_index(_class_index), box_index(_box_index) {}
     };

diff --git a/inference-engine/src/multi_device/multi_device.cpp b/inference-engine/src/multi_device/multi_device.cpp
index 64ac092..72f95d6 100644 (file)
--- a/inference-engine/src/multi_device/multi_device.cpp
+++ b/inference-engine/src/multi_device/multi_device.cpp
@@ -551,6 +551,8 @@ QueryNetworkResult MultiDeviceInferencePlugin::QueryNetwork(const ICNNNetwork&
                     THROW_IE_EXCEPTION << NOT_IMPLEMENTED_str;
                 } else {
                     auto cnnNetworkImpl = std::make_shared<details::CNNNetworkImpl>(network);
+                    if (cnnNetworkImpl == nullptr)
+                        THROW_IE_EXCEPTION << "Cannot create CNNNetworkImpl shared_ptr";
                     queryNetwork(*cnnNetworkImpl);
                 }
             } else {