!5561 Fix C++ coding standard problem

Merge pull request !5561 from yeyunpeng2020/r0.7

mindspore/lite/java/native/runtime/ms_tensor.cpp

@@ -227,7 +227,7 @@ extern "C" JNIEXPORT jboolean JNICALL Java_com_mindspore_lite_MSTensor_setByteBu
                                                                                          jobject buffer) {
   jbyte *p_data = reinterpret_cast<jbyte *>(env->GetDirectBufferAddress(buffer));  // get buffer poiter
   jlong data_len = env->GetDirectBufferCapacity(buffer);                           // get buffer capacity
-  if (!p_data) {
+  if (p_data == nullptr) {
     MS_LOGE("GetDirectBufferAddress return null");
     return NULL;
   }

mindspore/lite/src/common/file_utils.h

@@ -34,7 +34,7 @@ char *ReadFile(const char *file, size_t *size);
 std::string RealPath(const char *path);
 
 template <typename T>
-void WriteToTxt(const std::string& file_path, void *data, size_t element_size) {
+void WriteToTxt(const std::string &file_path, void *data, size_t element_size) {
   std::ofstream out_file;
   out_file.open(file_path, std::ios::out);
   auto real_data = reinterpret_cast<T *>(data);
@@ -44,7 +44,7 @@ void WriteToTxt(const std::string& file_path, void *data, size_t element_size) {
   out_file.close();
 }
 
-int WriteToBin(const std::string& file_path, void *data, size_t size);
+int WriteToBin(const std::string &file_path, void *data, size_t size);
 
 int CompareOutputData(float *output_data, float *correct_data, int data_size);
 void CompareOutput(float *output_data, std::string file_path);
@@ -55,4 +55,3 @@ std::string GetAndroidPackagePath();
 }  // namespace mindspore
 
 #endif  // MINDSPORE_LITE_COMMON_FILE_UTILS_H_
-

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

@@ -126,14 +126,10 @@ std::map<tflite::ActivationFunctionType, schema::ActivationType> tfMsActivationF
 };
 
 std::map<int, TypeId> type_map = {
-  {tflite::TensorType_FLOAT64, TypeId::kNumberTypeFloat64},
-  {tflite::TensorType_FLOAT32, TypeId::kNumberTypeFloat32},
-  {tflite::TensorType_FLOAT16, TypeId::kNumberTypeFloat16},
-  {tflite::TensorType_INT32, TypeId::kNumberTypeInt32},
-  {tflite::TensorType_INT16, TypeId::kNumberTypeInt16},
-  {tflite::TensorType_INT8, TypeId::kNumberTypeInt8},
-  {tflite::TensorType_INT64, TypeId::kNumberTypeInt64},
-  {tflite::TensorType_UINT8, TypeId::kNumberTypeUInt8},
+  {tflite::TensorType_FLOAT64, TypeId::kNumberTypeFloat64}, {tflite::TensorType_FLOAT32, TypeId::kNumberTypeFloat32},
+  {tflite::TensorType_FLOAT16, TypeId::kNumberTypeFloat16}, {tflite::TensorType_INT32, TypeId::kNumberTypeInt32},
+  {tflite::TensorType_INT16, TypeId::kNumberTypeInt16},     {tflite::TensorType_INT8, TypeId::kNumberTypeInt8},
+  {tflite::TensorType_INT64, TypeId::kNumberTypeInt64},     {tflite::TensorType_UINT8, TypeId::kNumberTypeUInt8},
   {tflite::TensorType_BOOL, TypeId::kNumberTypeBool},
 };
 
@@ -190,11 +186,8 @@ size_t GetDataTypeSize(const TypeId &data_type) {
   }
 }
 
-STATUS getPaddingParam(const std::unique_ptr<tflite::TensorT> &tensor,
-                       schema::PadMode pad_mode,
-                       int strideH, int strideW,
-                       int windowH, int windowW,
-                       std::vector<int> *params) {
+STATUS getPaddingParam(const std::unique_ptr<tflite::TensorT> &tensor, schema::PadMode pad_mode, int strideH,
+                       int strideW, int windowH, int windowW, std::vector<int> *params) {
   if (tensor == nullptr) {
     MS_LOG(ERROR) << "the input tensor is null";
     return RET_ERROR;
@@ -208,12 +201,18 @@ STATUS getPaddingParam(const std::unique_ptr<tflite::TensorT> &tensor,
     auto shape = tensor->shape;
     int H_input = shape.at(1);
     int W_input = shape.at(2);
-
+    if (strideH == 0) {
+      MS_LOG(ERROR) << "strideH is zero";
+      return RET_ERROR;
+    }
     int H_output = ceil(H_input * 1.0 / strideH);
     int pad_needed_H = (H_output - 1) * strideH + windowH - H_input;
     padUp = floor(pad_needed_H / 2.0);
     padDown = pad_needed_H - padUp;
-
+    if (strideW == 0) {
+      MS_LOG(ERROR) << "strideW is zero";
+      return RET_ERROR;
+    }
     int W_output = ceil(W_input * 1.0 / strideW);
     int pad_needed_W = (W_output - 1) * strideW + windowW - W_input;
     padLeft = floor(pad_needed_W / 2.0);
@@ -227,9 +226,7 @@ STATUS getPaddingParam(const std::unique_ptr<tflite::TensorT> &tensor,
   return RET_OK;
 }
 
-void Split(const std::string &src_str,
-           std::vector<std::string> *dst_str,
-           const std::string &chr) {
+void Split(const std::string &src_str, std::vector<std::string> *dst_str, const std::string &chr) {
   std::string ::size_type p1 = 0, p2 = src_str.find(chr);
   while (std::string::npos != p2) {
     dst_str->push_back(src_str.substr(p1, p2 - p1));

model_zoo/official/lite/image_classification/app/src/main/cpp/MSNetWork.h

@@ -32,7 +32,6 @@
 #include <memory>
 #include <utility>
 
-
 struct ImgDims {
   int channel = 0;
   int width = 0;
@@ -43,8 +42,6 @@ struct ImgDims {
     std::shared_ptr<mindspore::session::LiteSession> sess = nullptr;
 };*/
 
-
-
 class MSNetWork {
  public:
   MSNetWork();
@@ -55,6 +52,7 @@ class MSNetWork {
 
   int ReleaseNets(void);
 
+ protected:
   mindspore::session::LiteSession *session;
   mindspore::lite::Model *model;
   static const int RET_CATEGORY_SUM = 601;

model_zoo/official/lite/image_classification/app/src/main/cpp/MindSporeNetnative.cpp

@@ -76,10 +76,10 @@ cv::Mat PreProcessImageData(cv::Mat input) {
 
   imgFloatTmp.convertTo(imgResized256, CV_32FC3, normalizMin / normalizMax);
 
-  int offsetX = 16;
-  int offsetY = 16;
-  int cropWidth = 224;
-  int cropHeight = 224;
+  const int offsetX = 16;
+  const int offsetY = 16;
+  const int cropWidth = 224;
+  const int cropHeight = 224;
 
   // Standardization processing.
   float meanR = 0.485;