fix static check error

mindspore/lite/tools/anf_exporter/anf_exporter.cc

@@ -391,6 +391,10 @@ int AnfExporter::ConvertInputValueNode(const std::shared_ptr<AnfNode> &input_ano
   if (value->isa<tensor::Tensor>()) {
     auto valueAbstract = valueNode->abstract();
     auto abstractTensor = utils::cast<abstract::AbstractTensorPtr>(valueAbstract);
+    if (abstractTensor == nullptr || abstractTensor->element() == nullptr) {
+      MS_LOG(ERROR) << "abstractTensor or abstractTensor->element() is nullptr";
+      return RET_ERROR;
+    }
     auto typePtr = abstractTensor->element()->GetTypeTrack();
     paramTensor->dataType = typePtr->type_id();
     auto shape_vector = utils::cast<abstract::ShapePtr>(abstractTensor->BuildShape())->shape();
@@ -404,7 +408,11 @@ int AnfExporter::ConvertInputValueNode(const std::shared_ptr<AnfNode> &input_ano
     paramTensor->nodeType = schema::NodeType::NodeType_ValueNode;
     auto data = value->cast<tensor::TensorPtr>();
     paramTensor->data.resize(data->Size());
-    memcpy(paramTensor->data.data(), data->data_c(), data->Size());
+    auto ret = memcpy_s(paramTensor->data.data(), data->Size(), data->data_c(), data->Size());
+    if (ret != EOK) {
+      MS_LOG(ERROR) << "memcpy_s error.";
+      return RET_ERROR;
+    }
     node_id_map_[valueNode->fullname_with_scope()] = meta_graphT->allTensors.size();
     output_cnode->inputIndex.emplace_back(meta_graphT->allTensors.size());
     meta_graphT->allTensors.emplace_back(std::move(paramTensor));
@@ -417,7 +425,11 @@ int AnfExporter::ConvertInputValueNode(const std::shared_ptr<AnfNode> &input_ano
     paramTensor->nodeType = schema::NodeType::NodeType_ValueNode;
     int real_data = CastToInt(value, false).front();
     paramTensor->data.resize(sizeof(int32_t));
-    memcpy(paramTensor->data.data(), &real_data, sizeof(int32_t));
+    auto ret = memcpy_s(paramTensor->data.data(), sizeof(int32_t), &real_data, sizeof(int32_t));
+    if (ret != EOK) {
+      MS_LOG(ERROR) << "memcpy_s error.";
+      return RET_ERROR;
+    }
     node_id_map_[valueNode->fullname_with_scope()] = meta_graphT->allTensors.size();
     output_cnode->inputIndex.emplace_back(meta_graphT->allTensors.size());
     meta_graphT->allTensors.emplace_back(std::move(paramTensor));
@@ -526,6 +538,10 @@ void AnfExporter::SetOpOutputNode(const CNodePtr &cnode, const std::unique_ptr<s
     auto tuple = std::reinterpret_pointer_cast<abstract::AbstractTuple>(cnode->abstract());
     for (size_t i = 0; i < tuple->size(); i++) {
       auto msTensor = new (std::nothrow) schema::TensorT();
+      if (msTensor == nullptr) {
+        MS_LOG(ERROR) << "new msTensor failed";
+        return;
+      }
       msTensor->nodeType = schema::NodeType_CNode;
       fb_node->outputIndex.emplace_back(meta_graphT->allTensors.size());
 #ifdef SUPPORT_TRAIN
@@ -553,6 +569,10 @@ void AnfExporter::SetOpOutputNode(const CNodePtr &cnode, const std::unique_ptr<s
     }
   } else {
     auto ms_tensor = new (std::nothrow) schema::TensorT();
+    if (ms_tensor == nullptr) {
+      MS_LOG(ERROR) << "new tensor failed";
+      return;
+    }
     ms_tensor->nodeType = schema::NodeType_CNode;
     ms_tensor->dataType = TypeId::kNumberTypeFloat32;
     fb_node->outputIndex.emplace_back(meta_graphT->allTensors.size());

mindspore/lite/tools/converter/graphdef_transform.cc

@@ -154,6 +154,10 @@ int GraphDefTransform::Transform(const converter::Flags &ctx) {
   {
     Optimizer quantNodeOptimizer;
     auto dTypeTransPass = new (std::nothrow) DTypeTransPass();
+    if (dTypeTransPass == nullptr) {
+      MS_LOG(ERROR) << "new dTypeTransPass failed";
+      return RET_MEMORY_FAILED;
+    }
     dTypeTransPass->SetInputDataDType(ctx.inputDataType);
     dTypeTransPass->SetOutputDataDType(ctx.outputDataType);
     quantNodeOptimizer.AddPass(new (std::nothrow) TopologicalSortPass());

mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.cc

@@ -331,9 +331,17 @@ STATUS BatchNormFoldFusionPass::GenNewWeightTensor() {
   MS_ASSERT(muTensor->dataType == DataType_DT_FLOAT);
   void *miData = muTensor->data.data();
   auto *castedMiData = static_cast<float *>(miData);
+  if (channelOut == 0) {
+    MS_LOG(ERROR) << "divisor 'channelOut' cannot be 0";
+    return RET_ERROR;
+  }
   size_t stride = weightShapeSize / channelOut;
   for (int i = 0; i < channelOut; i++) {
     for (size_t j = 0; j < stride; j++) {
+      if (fabs(castedMiData[i]) <= 0.0f) {
+        MS_LOG(ERROR) << "divisor 'castedMiData' cannot be 0";
+        return RET_ERROR;
+      }
       castedNewWeightData[i * stride + j] = castedOldWeightData[i * stride + j] * castedGammaData[i] / castedMiData[i];
     }
   }
@@ -367,6 +375,10 @@ STATUS BatchNormFoldFusionPass::GenNewBiasTensor() {  // bias has no quant
   void *sigmaData = sigmaTensor->data.data();
   auto *castedSigmaData = static_cast<float *>(sigmaData);
   for (int i = 0; i < channelOut; i++) {
+    if (fabs(castedSigmaData[i]) <= 0.0f) {
+      MS_LOG(ERROR) << "divisor 'castedSigmaData' cannot be 0";
+      return RET_ERROR;
+    }
     castedNewBiasData[i] = castedBetaData[i] - castedGammaData[i] * castedMiData[i] / castedSigmaData[i];
   }
   return RET_OK;

mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.h

@@ -83,5 +83,4 @@ class BatchNormFoldFusionPass : public FusionPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // MINDSPORE_PREDICT_BATCHNORM_FOLD_FUSION_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/fusion/format_trans_fusion_pass.cc

@@ -63,7 +63,8 @@ STATUS FormatTransFusionPass::DefinePattern() {
 
     passOp->left = nc2nhOp;
     nh2ncOp->left = passOp;
-    std::unique_ptr<FusionPattern> nc2NhAndNh2NcPassFusionPattern(new FusionPattern(kNc2NhAndNh2NcPassFusionPattern));
+    std::unique_ptr<FusionPattern> nc2NhAndNh2NcPassFusionPattern(new (std::nothrow)
+                                                                    FusionPattern(kNc2NhAndNh2NcPassFusionPattern));
     if (nc2NhAndNh2NcPassFusionPattern == nullptr) {
       MS_LOG(ERROR) << "new " << kNc2NhAndNh2NcPassFusionPattern << "failed";
       return RET_ERROR;

mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.cc

@@ -34,6 +34,7 @@
 namespace mindspore {
 namespace lite {
 STATUS FusionPass::Run(schema::MetaGraphT *graph) {
+  MS_ASSERT(graph != nullptr);
   auto ret = DefinePattern();
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "DefinePattern Error " << ret;

mindspore/lite/tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.cc

@@ -94,7 +94,7 @@ STATUS MatMulBiasAddFusionPass::DoFusion(MetaGraphT *graph, const std::string &p
 
   // 2. change matmul to full connection op
   matMulNode->name += "-fc";
-  std::unique_ptr<FullConnectionT> fcAttr(new FullConnectionT());
+  std::unique_ptr<FullConnectionT> fcAttr(new (std::nothrow) FullConnectionT());
   if (fcAttr == nullptr) {
     MS_LOG(ERROR) << "new FullConnectionT node failed";
     return RET_ERROR;
@@ -151,7 +151,7 @@ STATUS MatMulBiasAddFusionPass::InsertTransposeNode(MetaGraphT *graph, const std
     }
     transNode->name = "transpose" + std::to_string(id++);
     transNode->primitive->value.type = schema::PrimitiveType_Transpose;
-    std::unique_ptr<TransposeT> transposeParam(new TransposeT());
+    std::unique_ptr<TransposeT> transposeParam(new (std::nothrow) TransposeT());
     if (transposeParam == nullptr) {
       MS_LOG(ERROR) << "new transposeParam failed";
       return RET_ERROR;

mindspore/lite/tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.cc

@@ -137,7 +137,7 @@ STATUS MulAddFusionPass::AddNewScaleNode(MetaGraphT *graph, const std::unique_pt
   MS_ASSERT(addNode != nullptr);
   // replace mulNode as scale
   mulNode->primitive->value.type = schema::PrimitiveType_Scale;
-  std::unique_ptr<ScaleT> scaleParam(new ScaleT());
+  std::unique_ptr<ScaleT> scaleParam(new (std::nothrow) ScaleT());
   if (scaleParam == nullptr) {
     MS_LOG(ERROR) << "new transposeParam failed";
     return RET_ERROR;

mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc

@@ -69,9 +69,9 @@ STATUS BatchNormConvertScalePass::ConvertBNToScale(MetaGraphT *graph, const std:
   MS_ASSERT(graph != nullptr);
   MS_ASSERT(bnNode != nullptr);
   bnNode->primitive->value.type = schema::PrimitiveType_Scale;
-  std::unique_ptr<ScaleT> scaleParam(new ScaleT());
+  std::unique_ptr<ScaleT> scaleParam(new (std::nothrow) ScaleT());
   if (scaleParam == nullptr) {
-    MS_LOG(ERROR) << "new transposeParam failed";
+    MS_LOG(ERROR) << "new scaleParam failed";
     return RET_ERROR;
   }
   scaleParam->axis = NCHW_DIM_C;
@@ -104,7 +104,7 @@ STATUS BatchNormConvertScalePass::GenNewScaleTensor(MetaGraphT *graph, const std
   newScaleWeightTensor->data.resize(weightShapeSize * sizeof(float));
   auto ret = memcpy_s(newScaleWeightTensor->data.data(), weightShapeSize * sizeof(float), transScale,
                       weightShapeSize * sizeof(float));
-  if (ret != RET_OK) {
+  if (ret != EOK) {
     MS_LOG(ERROR) << "memcpy error: " << ret;
     delete[] transScale;
     delete[] transBias;
@@ -127,7 +127,7 @@ STATUS BatchNormConvertScalePass::GenNewScaleTensor(MetaGraphT *graph, const std
   newScaleBiasTensor->data.resize(weightShapeSize * sizeof(float));
   ret = memcpy_s(newScaleBiasTensor->data.data(), weightShapeSize * sizeof(float), transBias,
                  weightShapeSize * sizeof(float));
-  if (ret != RET_OK) {
+  if (ret != EOK) {
     MS_LOG(ERROR) << "memcpy error: " << ret;
     delete[] transScale;
     delete[] transBias;
@@ -166,9 +166,17 @@ STATUS BatchNormConvertScalePass::GetTransParam(MetaGraphT *graph, const std::un
     return status;
   }
   this->transScale = new (std::nothrow) float[bnChannel];
+  if (this->transScale == nullptr) {
+    MS_LOG(ERROR) << "new transScale failed";
+    return RET_ERROR;
+  }
   this->transBias = new (std::nothrow) float[bnChannel];
+  if (this->transBias == nullptr) {
+    MS_LOG(ERROR) << "new transBias failed";
+    return RET_ERROR;
+  }
   // cal transScale, tf : scale/sqrt(variance + eps); caffe : 1/sqrt(variance + eps)
-  if (memcpy_s(transScale, bnChannel * sizeof(float), varianceData, bnChannel * sizeof(float)) != 0) {
+  if (memcpy_s(transScale, bnChannel * sizeof(float), varianceData, bnChannel * sizeof(float)) != EOK) {
     MS_LOG(ERROR) << "memcpy_s transScale error";
     delete[] transScale;
     delete[] transBias;
@@ -180,6 +188,10 @@ STATUS BatchNormConvertScalePass::GetTransParam(MetaGraphT *graph, const std::un
   for (uint32_t i = 0; i < bnChannel; i++) {
     float tmp = transScale[i] + eps;
     tmp = pow(tmp, POW_NUM);
+    if (tmp <= 0.0f) {
+      MS_LOG(ERROR) << "divisor 'tmp' cannot be 0";
+      return RET_ERROR;
+    }
     transScale[i] = 1 / tmp;
   }
 
@@ -278,6 +290,7 @@ STATUS BatchNormConvertScalePass::GetBnWeightTensors(MetaGraphT *graph, BNWeight
 STATUS BatchNormConvertScalePass::GetBnEpsilon(const std::unique_ptr<CNodeT> &bnNode) {
   MS_ASSERT(graph != nullptr);
   MS_ASSERT(bnNode != nullptr);
+  MS_ASSERT(bnNode->primitive != nullptr);
   if (bnNode->primitive->value.type == schema::PrimitiveType_FusedBatchNorm) {
     eps = bnNode->primitive->value.AsFusedBatchNorm()->epsilon;
   } else if (bnNode->primitive->value.type == schema::PrimitiveType_BatchNorm) {

mindspore/lite/tools/converter/legacy_optimizer/graph/dropout_node_remove_pass.cc

@@ -32,6 +32,10 @@ STATUS IsolateDropoutNode(schema::MetaGraphT *graphT, size_t nodeIdx) {
   }
 
   CNodeT *node = graphT->nodes.at(nodeIdx).get();
+  if (node == nullptr) {
+    MS_LOG(ERROR) << "node is nullptr";
+    return RET_ERROR;
+  }
   auto inputTensorIdxes = node->inputIndex;
   auto outputTensorIdxes = node->outputIndex;
   auto preNodeIdxes = GetInputNodeIdx(*graphT, nodeIdx);
@@ -103,6 +107,10 @@ STATUS DropoutNodeRemovePass::Run(schema::MetaGraphT *graph) {
   bool ifChanged = false;
   for (size_t i = 0; i < graph->nodes.size(); i++) {
     auto &node = graph->nodes.at(i);
+    if (node->primitive == nullptr) {
+      MS_LOG(ERROR) << "node->primitive is nullptr";
+      return RET_ERROR;
+    }
     if (node->primitive->value.type == schema::PrimitiveType_Dropout) {
       ifChanged = true;
       auto status = IsolateDropoutNode(graph, i);

mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.h

@@ -79,5 +79,4 @@ class DTypeTransPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // MINDSPORE_PREDICT_DTYPE_TRANS_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.cc

@@ -186,6 +186,7 @@ STATUS FormatTransPass::DoNodeInoutFormatTrans(schema::MetaGraphT *graph) {
 NodeIter FormatTransPass::InsertFormatTransNode(schema::MetaGraphT *graph, NodeIter existNodeIter, InsertPlace place,
                                                 size_t inoutIdx, FormatTransNodeType nodeType, STATUS *errorCode) {
   MS_ASSERT((*existNodeIter) != nullptr);
+  MS_ASSERT(graph != nullptr);
   auto existNodeName = (*existNodeIter)->name;
   std::string tileName;
   if (place == kBefore) {

mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.h

@@ -55,5 +55,4 @@ class FormatTransPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // MINDSPORE_PREDICT_FORMAT_TRANS_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/global_format_transform_pass.cc

@@ -105,6 +105,7 @@ STATUS ConvertNcTensor2Nh(TensorT *tensor, const std::vector<int> &pad_dims) {
   return RET_OK;
 }
 STATUS GlobalFormatTransformPass::TransWeightToNhwc(MetaGraphT *graph, const std::set<size_t> &pre_not_trans_nodes) {
+  MS_ASSERT(graph != nullptr);
   if (pre_not_trans_nodes.empty()) {
     return RET_OK;
   }

mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.cc

@@ -23,6 +23,7 @@
 
 namespace mindspore::lite {
 STATUS InferQuantParamPass::Run(schema::MetaGraphT *graph) {
+  MS_ASSERT(graph != nullptr);
   auto *quantParamRegister = QuantParamCalcRegister::GetInstance();
 
   for (auto iter = graph->nodes.begin(); iter != graph->nodes.end(); iter++) {
@@ -58,6 +59,7 @@ STATUS InferQuantParamPass::Run(schema::MetaGraphT *graph) {
 }
 
 void InferQuantParamPass::DetermineNodeQuantType(const schema::MetaGraphT &graph, schema::CNodeT *cnode) {
+  MS_ASSERT(graph != nullptr);
   MS_ASSERT(cnode != nullptr);
   bool canQuant = true;
   for (auto &inputTensorIdx : cnode->inputIndex) {

mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.h

@@ -35,5 +35,4 @@ class InferQuantParamPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // LITE_INFER_QUANT_PARAM_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.cc

@@ -31,6 +31,7 @@ constexpr int DEFAULT_DIM_VALUE = -1;
 namespace {
 std::vector<Tensor *> ConvertTensorToLiteTensor(MetaGraphT *graph, const std::vector<uint32_t> &tensor_indexs,
                                                 const schema::PrimitiveType node_type) {
+  MS_ASSERT(graph != nullptr);
   std::vector<Tensor *> lite_tensors;
   for (size_t i = 0; i < tensor_indexs.size(); i++) {
     auto &tensorT = graph->allTensors.at(tensor_indexs[i]);

mindspore/lite/tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.h

@@ -32,5 +32,4 @@ class IsolatedNodeRemovePass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // MINDSPORE_PREDICT_ISOLATED_NODE_REMOVE_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/set_unused_quant_param_to_default_pass.cc

@@ -19,6 +19,7 @@
 
 namespace mindspore::lite {
 STATUS SetUnusedQuantParamToDefaultPass::Run(schema::MetaGraphT *graph) {
+  MS_ASSERT(graph != nullptr);
   for (auto &tensor : graph->allTensors) {
     for (auto &quant_param : tensor->quantParams) {
       quant_param->min = 0;
@@ -29,5 +30,4 @@ STATUS SetUnusedQuantParamToDefaultPass::Run(schema::MetaGraphT *graph) {
   }
   return RET_OK;
 }
-
 }  // namespace mindspore::lite

mindspore/lite/tools/converter/legacy_optimizer/graph/set_unused_quant_param_to_default_pass.h

@@ -30,5 +30,4 @@ class SetUnusedQuantParamToDefaultPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // LITE_UNUSED_QUANT_PARAM_DATA_REMOVE_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/tensor_quant_pass.cc

@@ -23,7 +23,12 @@
 
 namespace mindspore::lite {
 STATUS TensorQuantPass::Run(schema::MetaGraphT *graph) {
+  MS_ASSERT(graph != nullptr);
   for (auto &node : graph->nodes) {
+    if (node == nullptr || node->primitive == nullptr) {
+      MS_LOG(ERROR) << " node or node->primitive is nullptr";
+      return RET_ERROR;
+    }
     if (node->primitive->value.type == PrimitiveType_QuantDTypeCast) {
       auto attr = node->primitive->value.AsQuantDTypeCast();
       auto &inputTensor = graph->allTensors.at(node->inputIndex.front());
@@ -97,6 +102,10 @@ STATUS TensorQuantPass::Run(schema::MetaGraphT *graph) {
         }
         void *biasData = tensor->data.data();
         auto *rawDatas = static_cast<float *>(biasData);
+        if (fabs(quantParam->scale) <= 0.0f) {
+          MS_LOG(ERROR) << "divisor 'scale' cannot be 0";
+          return RET_ERROR;
+        }
         for (size_t i = 0; i < bShapeSize; ++i) {
           qDatas[i] = (int32_t)std::round(rawDatas[i] / quantParam->scale);
         }
@@ -117,5 +126,4 @@ STATUS TensorQuantPass::Run(schema::MetaGraphT *graph) {
   }
   return RET_OK;
 }
-
 }  // namespace mindspore::lite

mindspore/lite/tools/converter/legacy_optimizer/graph/tensor_quant_pass.h

@@ -32,5 +32,4 @@ class TensorQuantPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // LITE_TENSOR_QUANT_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.cc

@@ -70,6 +70,7 @@ STATUS TopologicalSortPass::Run(schema::MetaGraphT *graph) {
 
 bool TopologicalSortPass::IsNodeNonDepend(const std::unique_ptr<schema::CNodeT> &node,
                                           const std::vector<size_t> &sinkedTensorIdxes) {
+  MS_ASSERT(node != nullptr);
   for (auto inputIdx : node->inputIndex) {
     if (!IsContain(sinkedTensorIdxes, size_t(inputIdx))) {
       return false;

mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.h

@@ -37,5 +37,4 @@ class TopologicalSortPass : public GraphPass {
 };
 }  // namespace lite
 }  // namespace mindspore
-
 #endif  // MINDSPORE_PREDICT_TOPOLOGICAL_SORT_PASS_H

mindspore/lite/tools/converter/legacy_optimizer/graph/trans_format_insert_pass.cc

@@ -26,6 +26,8 @@
 namespace mindspore {
 namespace lite {
 bool TransOpInsertPass::CanFusion(schema::MetaGraphT *graph, const std::unique_ptr<CNodeT> &node) {
+  MS_ASSERT(graph != nullptr);
+  MS_ASSERT(node != nullptr);
   auto input_node_indexes = GetInputNodeIdx(*graph, *node);
   pre_type_ = schema::PrimitiveType_NONE;
   size_t has_trans_count = 0;
@@ -34,6 +36,8 @@ bool TransOpInsertPass::CanFusion(schema::MetaGraphT *graph, const std::unique_p
     MS_ASSERT(graph->nodes.size() > input_node_index);
     auto &pre_node = graph->nodes.at(input_node_index);
     MS_ASSERT(pre_node != nullptr);
+    MS_ASSERT(pre_node->primitive != nullptr);
+    MS_ASSERT(pre_node->primitive->value != nullptr);
     if (pre_type_ == schema::PrimitiveType_NONE) {
       if (pre_node->primitive->value.type == schema::PrimitiveType_Nchw2Nhwc ||
           pre_node->primitive->value.type == schema::PrimitiveType_Nhwc2Nchw) {
@@ -61,6 +65,8 @@ bool TransOpInsertPass::CanFusion(schema::MetaGraphT *graph, const std::unique_p
     MS_ASSERT(graph->nodes.size() > output_node_index);
     auto &post_node = graph->nodes.at(output_node_index);
     MS_ASSERT(post_node != nullptr);
+    MS_ASSERT(post_node->primitive != nullptr);
+    MS_ASSERT(post_node->primitive->value != nullptr);
     if (post_type_ == schema::PrimitiveType_NONE) {
       if (post_node->primitive->value.type == schema::PrimitiveType_Nchw2Nhwc ||
           post_node->primitive->value.type == schema::PrimitiveType_Nhwc2Nchw) {