!4839 mem check fix

Merge pull request !4839 from wangchangkai/master

mindspore/lite/tools/anf_importer/anf_populater/anf_node_populater_registry.cc

@@ -18,6 +18,14 @@
 #include <string>
 namespace mindspore {
 namespace lite {
+AnfNodePopulaterRegistry::~AnfNodePopulaterRegistry() {
+  for (auto ite : populaters) {
+    if (ite.second != nullptr) {
+      delete ite.second;
+      ite.second = nullptr;
+    }
+  }
+}
 AnfNodePopulaterRegistry *AnfNodePopulaterRegistry::GetInstance() {
   static AnfNodePopulaterRegistry instance;
   return &instance;

mindspore/lite/tools/anf_importer/anf_populater/anf_node_populater_registry.h

@@ -23,7 +23,7 @@ namespace mindspore::lite {
 class AnfNodePopulaterRegistry {
  public:
   AnfNodePopulaterRegistry() = default;
-  virtual ~AnfNodePopulaterRegistry() = default;
+  virtual ~AnfNodePopulaterRegistry();
   static AnfNodePopulaterRegistry *GetInstance();
   AnfNodePopulater *GetNodePopulater(const std::string &name);
   void SetNodePopulater(const std::string &name, AnfNodePopulater *populater);

mindspore/lite/tools/anf_importer/import_from_protobuf.cc

@@ -140,6 +140,8 @@ bool AnfImporterFromProtobuf::BuildParameterForFuncGraph(const ParameterPtr &nod
     auto ret = memcpy_s(tensor_data_buf, tensor_info->Size(), initial_data.data(), initial_data.size());
     if (EOK != ret) {
       MS_LOG(ERROR) << "memcpy_s error";
+      delete tensor_data_buf;
+      delete tensor_info;
       return false;
     }
 

mindspore/lite/tools/converter/converter.cc

@@ -43,18 +43,10 @@ Converter::Converter() {
 }
 
 Converter::~Converter() {
-  if (nullptr != modelParser) {
-    delete modelParser;
-  }
-  if (nullptr != modelImporter) {
-    delete modelImporter;
-  }
-  if (nullptr != transform) {
-    delete transform;
-  }
-  if (nullptr != anfTransform) {
-    delete anfTransform;
-  }
+  delete modelParser;
+  delete modelImporter;
+  delete transform;
+  delete anfTransform;
 }
 
 class MindsporeImporter : public Converter {
@@ -154,7 +146,11 @@ void Converter::CreateQuantizer(FuncGraphPtr funcGraph, const converter::Flags *
   }
 }
 int RunConverter(int argc, const char **argv) {
-  auto flags = new converter::Flags;
+  std::unique_ptr<converter::Flags> flags(new (std::nothrow) converter::Flags);
+  if (flags == nullptr) {
+    MS_LOG(ERROR) << "new flags error ";
+    return RET_ERROR;
+  }
   auto status = flags->Init(argc, argv);
   if (status == RET_SUCCESS_EXIT) {
     return 0;
@@ -173,20 +169,20 @@ int RunConverter(int argc, const char **argv) {
       auto graph = std::make_shared<FuncGraph>();
       auto onnx_graph = AnfImporterFromProtobuf::ReadOnnxFromBinary(flags->modelFile);
       MindsporeImporter mindsporeImporter(onnx_graph, graph);
-      fb_graph = mindsporeImporter.Convert(flags);
+      fb_graph = mindsporeImporter.Convert(flags.get());
       break;
     }
     case FmkType::FmkType_CAFFE: {
       CaffeConverter caffeConverter;
-      fb_graph = caffeConverter.Convert(flags);
+      fb_graph = caffeConverter.Convert(flags.get());
     } break;
     case FmkType::FmkType_TFLITE: {
       TfliteConverter tfLiteConverter;
-      fb_graph = tfLiteConverter.Convert(flags);
+      fb_graph = tfLiteConverter.Convert(flags.get());
     } break;
     case FmkType::FmkType_ONNX: {
       OnnxConverter onnxConverter;
-      fb_graph = onnxConverter.Convert(flags);
+      fb_graph = onnxConverter.Convert(flags.get());
     } break;
     default: {
       MS_LOG(ERROR) << "Unsupported fmkType: " << flags->fmk;

mindspore/lite/tools/converter/parser/caffe/caffe_convolution_parser.cc

@@ -26,7 +26,7 @@ void CaffeConvolutionParser::ParseGroupConvolution(schema::CNodeT *op, schema::C
   }
   std::unique_ptr<schema::DepthwiseConv2DT> depthwiseConv2DParam = std::make_unique<schema::DepthwiseConv2DT>();
   if (depthwiseConv2DParam == nullptr) {
-    // MS_LOGW("new DepthwiseConv2DT failed");
+    MS_LOG(ERROR) << "new DepthwiseConv2DT failed";
     return;
   }
   depthwiseConv2DParam->format = attr->format;
@@ -53,8 +53,11 @@ void CaffeConvolutionParser::ParseGroupConvolution(schema::CNodeT *op, schema::C
 STATUS CaffeConvolutionParser::Parse(const caffe::LayerParameter &proto, const caffe::LayerParameter &weight,
                                      schema::CNodeT *op, std::vector<schema::TensorT *> *weightVec) {
   op->name = proto.name();
-  schema::Conv2DT *attr = new schema::Conv2DT();
-
+  std::unique_ptr<schema::Conv2DT> attr(new (std::nothrow) schema::Conv2DT());
+  if (attr == nullptr) {
+    MS_LOG(ERROR) << "new Conv2DT failed";
+    return RET_ERROR;
+  }
   attr->format = schema::Format_NCHW;
   const caffe::ConvolutionParameter convParam = proto.convolution_param();
 
@@ -118,9 +121,9 @@ STATUS CaffeConvolutionParser::Parse(const caffe::LayerParameter &proto, const c
   attr->padMode = schema::PadMode_CAFFE;
   op->primitive = std::make_unique<schema::PrimitiveT>();
   op->primitive->value.type = schema::PrimitiveType_Conv2D;
-  op->primitive->value.value = attr;
+  op->primitive->value.value = attr.get();
 
-  ParseGroupConvolution(op, attr);
+  ParseGroupConvolution(op, attr.release());
   status = convParser.ParseWeight(weight, weightVec);
   if (status != RET_OK) {
     MS_LOG(ERROR) << "ParseWeight for " << proto.name().c_str() << " failed";

mindspore/lite/tools/converter/parser/tflite/tflite_model_parser.cc

@@ -159,7 +159,7 @@ STATUS TfliteModelParser::ConvertTensor(const std::unique_ptr<tflite::SubGraphT>
     auto isConst = (!tensor_buffer->data.empty());
     if (isConst) {
       CopyConstTensorData(tflite_model_buffer, tflite_tensor.get(), tensor.get());
-    } else if (tensor->dataType ==TypeId::kNumberTypeUInt8) {
+    } else if (tensor->dataType == TypeId::kNumberTypeUInt8) {
       // set in/out tensor to int8 to fit ms-lite op
       tensor->dataType = TypeId::kNumberTypeInt8;
     }

mindspore/lite/tools/converter/quantizer/aware_quantizer.cc

@@ -103,11 +103,13 @@ AwareQuantizer::AwareQuantizer(schema::MetaGraphT *graph,
   const float stdValue = std::stof(stdValues, &sz);
   sz = 0;
   const float mean = std::stof(meanValues, &sz);
+  std::unique_ptr<InputArray> inArr = nullptr;
   if (inputInferType == "FLOAT") {
-    mInputArray = new InputArray(mean, stdValue);
+    inArr.reset(new (std::nothrow) InputArray(mean, stdValue));
   } else {
-    mInputArray = new InputArray(mean, stdValue, TypeId::kNumberTypeUInt8);
+    inArr.reset(new (std::nothrow) InputArray(mean, stdValue, TypeId::kNumberTypeUInt8));
   }
+  mInputArray = inArr.get();
   mInputArray->InitQuantParam();
 }
 
@@ -527,9 +529,9 @@ STATUS AwareQuantizer::QuantConvBias(const mindspore::schema::MetaGraphT *graph,
 
   // quant bias data
   auto bShapeSize = GetShapeSize(*(biasTensor.get()));
-  auto *qDatas = new (std::nothrow) int32_t[bShapeSize];
+  std::unique_ptr<int32_t[]> qDatas(new (std::nothrow) int32_t[bShapeSize]);
   if (qDatas == nullptr) {
-    //    MS_LOGE("new qDatas failed");
+    MS_LOG(ERROR) << "new qDatas failed";
     return RET_ERROR;
   }
   void *biasData = biasTensor->data.data();
@@ -541,13 +543,11 @@ STATUS AwareQuantizer::QuantConvBias(const mindspore::schema::MetaGraphT *graph,
   biasTensor->data.clear();
   biasTensor->data.resize(bShapeSize * sizeof(int32_t));
   auto ret = memcpy_s(biasTensor->data.data(), bShapeSize * sizeof(int32_t),
-                      qDatas, bShapeSize * sizeof(int32_t));
+                      qDatas.get(), bShapeSize * sizeof(int32_t));
   if (ret != EOK) {
-    //    MS_LOGE("memcpy_s failed: %d", ret);
-    delete[] qDatas;
+    MS_LOG(ERROR) << "memcpy_s failed: " << ret;
     return RET_ERROR;
   }
-  delete[] qDatas;
   return RET_OK;
 }
 

mindspore/lite/tools/converter/quantizer/calc_quant_param.cc

@@ -440,50 +440,62 @@ class CalcActivation : public QuantParamCalcer {
     }
   }
 };
-
+QuantParamCalcRegister::~QuantParamCalcRegister() {
+  for (auto ite : _registerMap) {
+    if (ite.second != nullptr) {
+      delete ite.second;
+      ite.second = nullptr;
+    }
+  }
+}
 QuantParamCalcRegister::QuantParamCalcRegister() {
   bool hasError = false;
-  auto baseCalcer = new (std::nothrow) QuantParamCalcer();
+  std::unique_ptr<QuantParamCalcer> baseCalcer(new (std::nothrow) QuantParamCalcer());
   if (baseCalcer == nullptr) {
-    // MS_LOGW("new QuantParamCalcer failed");
+    MS_LOG(ERROR) << "new QuantParamCalcer failed";
     hasError = true;
   }
-  auto commonCalcer = new (std::nothrow) CommonCalcer();
+  std::unique_ptr<CommonCalcer> commonCalcer(new (std::nothrow) CommonCalcer());
   if (commonCalcer == nullptr) {
-    // MS_LOGW("new commonCalcer failed");
+    MS_LOG(ERROR) << "new commonCalcer failed";
     hasError = true;
   }
-  auto linearCalcer = new (std::nothrow) LinearCalcer();
+
+  std::unique_ptr<LinearCalcer> linearCalcer(new (std::nothrow) LinearCalcer());
   if (linearCalcer == nullptr) {
-    // MS_LOGW("new linearCalcer failed");
+    MS_LOG(ERROR) << "new linearCalcer failed";
     hasError = true;
   }
   if (!hasError) {
     _registerMap[schema::PrimitiveType_Concat] = new CalcConcat();
     _registerMap[schema::PrimitiveType_Activation] = new CalcActivation();
     _registerMap[schema::PrimitiveType_Add] = new CalcAdd();
-    _registerMap[schema::PrimitiveType_Mul] = commonCalcer;
-    _registerMap[schema::PrimitiveType_Conv2D] = commonCalcer;
-    _registerMap[schema::PrimitiveType_DepthwiseConv2D] = commonCalcer;
-    _registerMap[schema::PrimitiveType_Pooling] = linearCalcer;
-    _registerMap[schema::PrimitiveType_Resize] = linearCalcer;
-    _registerMap[schema::PrimitiveType_Reshape] = linearCalcer;
-    _registerMap[schema::PrimitiveType_Shape] = linearCalcer;
+    _registerMap[schema::PrimitiveType_Mul] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_Conv2D] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_DepthwiseConv2D] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_Pooling] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_Resize] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_Reshape] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_Shape] = linearCalcer.get();
     _registerMap[schema::PrimitiveType_SoftMax] = new CalcToSet(0, 1);
-    _registerMap[schema::PrimitiveType_Squeeze] = linearCalcer;
+    _registerMap[schema::PrimitiveType_Squeeze] = linearCalcer.get();
     _registerMap[schema::PrimitiveType_RealDiv] = new CalcRealDiv();
-    _registerMap[schema::PrimitiveType_Reduce] = commonCalcer;
-    _registerMap[schema::PrimitiveType_BiasAdd] = commonCalcer;
-    _registerMap[schema::PrimitiveType_Mean] = linearCalcer;
-    _registerMap[schema::PrimitiveType_Transpose] = linearCalcer;
-    _registerMap[schema::PrimitiveType_MatMul] = commonCalcer;
-    _registerMap[schema::PrimitiveType_FullConnection] = commonCalcer;
-    _registerMap[schema::PrimitiveType_Nchw2Nhwc] = linearCalcer;
-    _registerMap[schema::PrimitiveType_Nhwc2Nchw] = linearCalcer;
+    _registerMap[schema::PrimitiveType_Reduce] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_BiasAdd] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_Mean] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_Transpose] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_MatMul] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_FullConnection] = commonCalcer.get();
+    _registerMap[schema::PrimitiveType_Nchw2Nhwc] = linearCalcer.get();
+    _registerMap[schema::PrimitiveType_Nhwc2Nchw] = linearCalcer.get();
+    // todo
     // detection_postprocess op's quant param will not infer only fetch from preNode or postNode
     // because we will not insert quantTransNode after this node in tflite_graph_8bit model if input data is float.
     // if quantTransNode is inserted after detection_postprocess node, there will be some errors
-    _registerMap[schema::PrimitiveType_DetectionPostProcess] = baseCalcer;
+    _registerMap[schema::PrimitiveType_DetectionPostProcess] = baseCalcer.get();
+    baseCalcer.release();
+    linearCalcer.release();
+    commonCalcer.release();
   }
 }
 

mindspore/lite/tools/converter/quantizer/calc_quant_param.h

@@ -55,7 +55,7 @@ class LinearCalcer : public QuantParamCalcer {
 
 class QuantParamCalcRegister {
  public:
-  virtual ~QuantParamCalcRegister() = default;
+  virtual ~QuantParamCalcRegister();
   QuantParamCalcer *GetQuantParamCalcer(schema::PrimitiveType opType);
   static QuantParamCalcRegister *GetInstance();
 

mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc

@@ -55,14 +55,18 @@ const std::vector<Tensor *> GetCNodeInputTensors(const CNodePtr &CNode) {
     if (lite_tensor_size == 0) {
       return input_tensors;
     }
-    auto tensor_data = new(std::nothrow)char[lite_tensor_size / sizeof(char)];
+    auto tensor_data = new (std::nothrow) char[lite_tensor_size / sizeof(char)];
     if (tensor_data == nullptr) {
       MS_LOG(ERROR) << "tensor_data is nullptr";
+      delete lite_tensor;
       return input_tensors;
     }
     auto ret = memcpy_s(tensor_data, lite_tensor_size, tensorT->data.data(), lite_tensor_size);
     if (ret != EOK) {
+      delete lite_tensor;
+      delete tensor_data;
       MS_LOG(EXCEPTION) << "memcpy error: " << ret;
+      return input_tensors;
     }
     lite_tensor->SetData(tensor_data);
     input_tensors.emplace_back(lite_tensor);
@@ -111,7 +115,9 @@ const ParameterPtr CreateNewParamter(const FuncGraphPtr &func_graph, Tensor *ten
     }
     auto ret = memcpy_s(tensor_data, size * sizeof(float), tensor->Data(), size * sizeof(float));
     if (ret != EOK) {
+      delete tensor_data;
       MS_LOG(EXCEPTION) << "memcpy error: " << ret;
+      return parameter;
     }
     param_value->set_tensor_addr(tensor_data);
     param_value->set_tensor_size(size * sizeof(float) / sizeof(uint8_t));
@@ -138,7 +144,17 @@ kernel::LiteKernel *GetLiteKernel(std::vector<Tensor *> inputs, std::vector<Tens
   return nullptr;
 }
 }  //  namespace
-
+void FreeInputTensor(std::vector<Tensor *> *input_tensor) {
+  MS_ASSERT(input_tensor != nullptr);
+  for (size_t i = 0; i < input_tensor->size(); i++) {
+    if ((*input_tensor)[i] == nullptr) {
+      continue;
+    }
+    delete (*input_tensor)[i];
+    (*input_tensor)[i] = nullptr;
+  }
+  return;
+}
 const AnfNodePtr ConstFoldPass::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
                                         const EquivPtr &) const {
   CheckIfFuncGraphIsNull(func_graph);
@@ -154,6 +170,7 @@ const AnfNodePtr ConstFoldPass::Process(const FuncGraphPtr &func_graph, const An
       auto input_cnode = input_node->cast<CNodePtr>();
       auto input_tensors = GetCNodeInputTensors(input_cnode);
       if (input_tensors.empty() || input_tensors.size() != input_cnode->inputs().size() - 1) {
+        FreeInputTensor(&input_tensors);
         return any_node;
       }
       MS_LOG(INFO) << "Begin fold node:" << input_node->fullname_with_scope();
@@ -163,21 +180,25 @@ const AnfNodePtr ConstFoldPass::Process(const FuncGraphPtr &func_graph, const An
       auto lite_primitive = mindspore::lite::PrimitiveC::CreatePrimitive(scheam_primitive);
       if (lite_primitive == nullptr) {
         MS_LOG(DEBUG) << "constant_folding schedule node lite primitive nullptr";
+        FreeInputTensor(&input_tensors);
         return nullptr;
       }
       lite_primitive->InferShape(input_tensors, output_tensors);
       auto lite_kernel = GetLiteKernel(input_tensors, output_tensors, lite_primitive);
       if (lite_kernel == nullptr) {
         MS_LOG(DEBUG) << "constant_folding schedule node lite kernel nullptr";
+        FreeInputTensor(&input_tensors);
         return nullptr;
       }
       auto ret = lite_kernel->Run();
       if (0 != ret) {
+        FreeInputTensor(&input_tensors);
         MS_LOG(EXCEPTION) << "run kernel failed, name: " << lite_kernel->name();
       }
       auto new_parameter = CreateNewParamter(func_graph, output_tensors.front());
       new_parameter->set_name(input_node->fullname_with_scope());
       any_node->set_input(i, new_parameter);
+      FreeInputTensor(&input_tensors);
     }
   }
   return any_node;

mindspore/lite/tools/optimizer/fusion/conv_transform_fusion.cc

@@ -73,7 +73,7 @@ const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, co
   auto abstr = transform_node->abstract();
   int kernel_nums = Get_Kenrnel_nums(conv_node);
   if (kernel_nums <= 0) {
-    MS_LOG(ERROR) << "Unsupported conv node, " << conv_node->DebugString();
+    MS_LOG(INFO) << "Unsupported conv node, " << conv_node->DebugString();
     return node;
   }
   auto trans_scale = new(std::nothrow) float[kernel_nums];
@@ -84,6 +84,7 @@ const AnfNodePtr ConvTransformFusion::Process(const FuncGraphPtr &func_graph, co
   auto trans_bias = new(std::nothrow) float[kernel_nums];
   if (trans_bias == nullptr) {
     MS_LOG(ERROR) << "tensor_data is nullptr";
+    delete trans_scale;
     return nullptr;
   }
   GenTransParam(transform_node, kernel_nums, trans_scale, trans_bias);
@@ -164,7 +165,7 @@ const void ConvTransformFusion::GenNewConvTensor(const FuncGraphPtr &func_graph,
     bias_flag = true;
   } else {
     bias_data = new(std::nothrow) float[kernel_num];
-    if (trans_scale == nullptr) {
+    if (bias_data == nullptr) {
       MS_LOG(ERROR) << "tensor_data is nullptr";
       return;
     }