fix code style

core/general-server/op/general_infer_op.cpp

@@ -47,7 +47,7 @@ int GeneralInferOp::inference() {
   const std::string pre_name = pre_node_names[0];
 
   const GeneralBlob *input_blob = get_depend_argument<GeneralBlob>(pre_name);
-  if(!input_blob){
+  if (!input_blob) {
     LOG(ERROR) << "input_blob is nullptr,error";
       return -1;
   }
@@ -55,7 +55,7 @@ int GeneralInferOp::inference() {
   VLOG(2) << "(logid=" << log_id << ") Get precedent op name: " << pre_name;
 
   GeneralBlob *output_blob = mutable_data<GeneralBlob>();
-  if(!output_blob){
+  if (!output_blob) {
     LOG(ERROR) << "output_blob is nullptr,error";
       return -1;
   }

core/general-server/op/general_reader_op.cpp

@@ -32,7 +32,7 @@ using baidu::paddle_serving::predictor::general_model::Tensor;
 using baidu::paddle_serving::predictor::general_model::Request;
 using baidu::paddle_serving::predictor::general_model::FeedInst;
 using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
-enum ProtoDataType { P_INT64,P_FLOAT32,P_INT32 };
+enum ProtoDataType { P_INT64, P_FLOAT32, P_INT32 };
 int conf_check(const Request *req,
                const std::shared_ptr<PaddleGeneralModelConfig> &model_config) {
   int var_num = req->insts(0).tensor_array_size();
@@ -79,13 +79,9 @@ int GeneralReaderOp::inference() {
   std::vector<int64_t> capacity;
 
   GeneralBlob *res = mutable_data<GeneralBlob>();
-  TensorVector *out = &res->tensor_vector;
-  if(!res){
-      LOG(ERROR) << "res is nullptr,error";
-      return -1;
-  }
-  res->SetLogId(log_id);
+  TensorVector *out = &(res->tensor_vector);
   
+  res->SetLogId(log_id);
   if (!res) {
     LOG(ERROR) << "(logid=" << log_id
                << ") Failed get op tls reader object output";
@@ -94,9 +90,8 @@ int GeneralReaderOp::inference() {
   Timer timeline;
   int64_t start = timeline.TimeStampUS();
   int var_num = req->insts(0).tensor_array_size();
-  VLOG(2) << "(logid=" << log_id << ") var num: " << var_num;
 
-  VLOG(2) << "(logid=" << log_id
+  VLOG(2) << "(logid=" << log_id << ") var num: " << var_num
           << ") start to call load general model_conf op";
 
   baidu::paddle_serving::predictor::Resource &resource =
@@ -106,8 +101,6 @@ int GeneralReaderOp::inference() {
   std::shared_ptr<PaddleGeneralModelConfig> model_config =
       resource.get_general_model_config();
 
-  VLOG(2) << "(logid=" << log_id << ") print general model config done.";
-
   // TODO(guru4elephant): how to do conditional check?
   /*
   int ret = conf_check(req, model_config);
@@ -170,11 +163,13 @@ int GeneralReaderOp::inference() {
     out->push_back(lod_tensor);
   }
   // specify the memory needed for output tensor_vector
+  int tensor_size = 0;
+  int data_len = 0;
   for (int i = 0; i < var_num; ++i) {
     if (out->at(i).lod.size() == 1) {
-      int tensor_size = 0;
+      tensor_size = 0;
       const Tensor &tensor = req->insts(0).tensor_array(i);
-      int data_len = 0;
+      data_len = 0;
       if (tensor.int64_data_size() > 0) {
         data_len = tensor.int64_data_size();
       } else if (tensor.float_data_size() > 0) {
@@ -207,14 +202,16 @@ int GeneralReaderOp::inference() {
   }
 
   // fill the data into output general_blob
+  int offset = 0;
+  int elem_num = 0;
   for (int i = 0; i < var_num; ++i) {
     if (elem_type[i] == P_INT64) {
       int64_t *dst_ptr = static_cast<int64_t *>(out->at(i).data.data());
       VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
               << "] is " << req->insts(0).tensor_array(i).int64_data(0);
-      int offset = 0;
-      int elem_num = req->insts(0).tensor_array(i).int64_data_size();
-      if(!dst_ptr){
+      offset = 0;
+      elem_num = req->insts(0).tensor_array(i).int64_data_size();
+      if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
@@ -225,9 +222,9 @@ int GeneralReaderOp::inference() {
       float *dst_ptr = static_cast<float *>(out->at(i).data.data());
       VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
               << "] is " << req->insts(0).tensor_array(i).float_data(0);
-      int offset = 0;
-      int elem_num = req->insts(0).tensor_array(i).float_data_size();
-      if(!dst_ptr){
+      offset = 0;
+      elem_num = req->insts(0).tensor_array(i).float_data_size();
+      if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
@@ -238,9 +235,9 @@ int GeneralReaderOp::inference() {
       int32_t *dst_ptr = static_cast<int32_t *>(out->at(i).data.data());
       VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
               << "] is " << req->insts(0).tensor_array(i).int_data(0);
-      int offset = 0;
-      int elem_num = req->insts(0).tensor_array(i).int_data_size();
-      if(!dst_ptr){
+      offset = 0;
+      elem_num = req->insts(0).tensor_array(i).int_data_size();
+      if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }

core/general-server/op/general_response_op.cpp

@@ -42,7 +42,9 @@ using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 int GeneralResponseOp::inference() {
   const std::vector<std::string> pre_node_names = pre_names();
   VLOG(2) << "pre node names size: " << pre_node_names.size();
-  const GeneralBlob *input_blob;
+  const GeneralBlob *input_blob = nullptr;
+  int var_idx = 0;
+  int cap = 1;
   uint64_t log_id =
       get_depend_argument<GeneralBlob>(pre_node_names[0])->GetLogId();
 
@@ -116,9 +118,9 @@ int GeneralResponseOp::inference() {
       }
     }
 
-    int var_idx = 0;
+    var_idx = 0;
     for (auto &idx : fetch_index) {
-      int cap = 1;
+      cap = 1;
       for (int j = 0; j < in->at(idx).shape.size(); ++j) {
         cap *= in->at(idx).shape[j];
       }

core/predictor/framework/infer.h

@@ -612,13 +612,13 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
       void* origin_data = (*tensorVector_in_pointer)[i].data.data();
       //Because the core needs to determine the size of memory space according to the data type passed in.
       //The pointer type of data must be one of float *,int64_t*,int32_t* instead void*.
-      if((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::FLOAT32){
+      if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::FLOAT32) {
         float* data = static_cast<float*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
-      }else if((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT64){
+      }else if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT64) {
         int64_t* data = static_cast<int64_t*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
-      }else if((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT32){
+      }else if ((*tensorVector_in_pointer)[i].dtype == paddle::PaddleDType::INT32) {
         int32_t* data = static_cast<int32_t*>(origin_data);
         lod_tensor_in->CopyFromCpu(data);
       }
@@ -639,7 +639,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
     char* databuf_char = NULL;
     size_t databuf_size = 0;
     TensorVector* tensorVector_out_pointer = reinterpret_cast<TensorVector*>(out);
-    if(!tensorVector_out_pointer){
+    if (!tensorVector_out_pointer) {
       LOG(ERROR) << "tensorVector_out_pointer is nullptr,error";
       return -1;
     }
@@ -650,7 +650,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
       output_shape = lod_tensor_out->shape();
       out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
       dataType = lod_tensor_out->type();
-      if(dataType == paddle::PaddleDType::FLOAT32){
+      if (dataType == paddle::PaddleDType::FLOAT32) {
         databuf_size = out_num*sizeof(float);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {
@@ -660,7 +660,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
         float* data_out = reinterpret_cast<float*>(databuf_data);
         lod_tensor_out->CopyToCpu(data_out);
         databuf_char = reinterpret_cast<char*>(data_out);
-      }else if(dataType == paddle::PaddleDType::INT64){
+      }else if (dataType == paddle::PaddleDType::INT64) {
         databuf_size = out_num*sizeof(int64_t);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {
@@ -670,7 +670,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<FluidFamilyCore> {
         int64_t* data_out = reinterpret_cast<int64_t*>(databuf_data);
         lod_tensor_out->CopyToCpu(data_out);
         databuf_char = reinterpret_cast<char*>(data_out);
-      }else if(dataType == paddle::PaddleDType::INT32){
+      }else if (dataType == paddle::PaddleDType::INT32) {
         databuf_size = out_num*sizeof(int32_t);
         databuf_data = MempoolWrapper::instance().malloc(databuf_size);
         if (!databuf_data) {

core/predictor/framework/infer_data.h

@@ -80,9 +80,9 @@ struct Tensor {
   size_t ele_byte() const {
     if (type == INT64) {
       return sizeof(int64_t);
-    } else if(type == FLOAT32){
+    } else if (type == FLOAT32) {
       return sizeof(float);
-    }else{
+    } else {
       return sizeof(int32_t);
     }
   }