fix code style

CMakeLists.txt

@@ -37,6 +37,8 @@ if(NOT CMAKE_BUILD_TYPE)
       "Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
       FORCE)
 endif()
+SET(CMAKE_CXX_FLAGS_DEBUG "$ENV{CXXFLAGS} -O0 -Wall -g2 -ggdb")
+SET(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O3 -Wall")
 
 set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
   "A path setting third party libraries download & build directories.")
@@ -57,13 +59,11 @@ option(PACK                 "Compile for whl"
 option(WITH_TRT             "Compile Paddle Serving with TRT"                   OFF)
 option(PADDLE_ON_INFERENCE  "Compile for encryption"                             ON)
 option(WITH_OPENCV	    "Compile Paddle Serving with OPENCV"                    OFF)
-option(WITH_GDB             "Compile Paddle Serving with GDB"                   OFF)
 
-if (WITH_GDB)
-    SET(CMAKE_BUILD_TYPE "Debug")
-    SET(CMAKE_CXX_FLAGS_DEBUG "$ENV{CXXFLAGS} -O0 -Wall -g2 -ggdb")
-    SET(CMAKE_CXX_FLAGS_RELEASE "$ENV{CXXFLAGS} -O3 -Wall")
-endif()
+
+
+
+
 
 if (WITH_OPENCV)
     SET(OPENCV_DIR "" CACHE PATH "Location of libraries")

cmake/external/zlib.cmake

@@ -54,8 +54,7 @@ ELSE(WIN32)
   SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
 ENDIF(WIN32)
 
-IF(WITH_OPENCV)
-ELSE()
+IF(NOT WITH_OPENCV)
   ADD_LIBRARY(zlib STATIC IMPORTED GLOBAL)
 ENDIF()
 

core/general-client/src/general_model.cpp

@@ -88,7 +88,7 @@ int PredictorClient::init(const std::vector<std::string> &conf_file) {
       _shape.push_back(tmp_feed_shape);
     }
 
-    if (conf_file.size()>1) {
+    if (conf_file.size() > 1) {
       model_config.Clear();
       if (configure::read_proto_conf(conf_file[conf_file.size()-1].c_str(), &model_config) != 0) {
         LOG(ERROR) << "Failed to load general model config"
@@ -162,8 +162,8 @@ int PredictorClient::numpy_predict(
     PredictorRes &predict_res_batch,
     const int &pid,
     const uint64_t log_id) {
-  int batch_size = std::max( float_feed_batch.size(), int_feed_batch.size() );
-  batch_size = batch_size>string_feed_batch.size()? batch_size : string_feed_batch.size();
+  int batch_size = std::max(float_feed_batch.size(), int_feed_batch.size());
+  batch_size = batch_size > string_feed_batch.size() ? batch_size : string_feed_batch.size();
   VLOG(2) << "batch size: " << batch_size;
   predict_res_batch.clear();
   Timer timeline;
@@ -186,6 +186,8 @@ int PredictorClient::numpy_predict(
     req.add_fetch_var_names(name);
   }
 
+  int vec_idx = 0;
+
   for (int bi = 0; bi < batch_size; bi++) {
     VLOG(2) << "prepare batch " << bi;
     std::vector<Tensor *> tensor_vec;
@@ -207,9 +209,13 @@ int PredictorClient::numpy_predict(
 
     VLOG(2) << "batch [" << bi << "] " << "prepared";
 
-    int vec_idx = 0;
+    vec_idx = 0;
     for (auto &name : float_feed_name) {
       int idx = _feed_name_to_idx[name];
+      if (idx >= tensor_vec.size()) {
+        LOG(ERROR) << "idx > tensor_vec.size()";
+        return -1;
+      }
       Tensor *tensor = tensor_vec[idx];
       VLOG(2) << "prepare float feed " << name << " shape size "
               << float_shape[vec_idx].size();
@@ -272,6 +278,10 @@ int PredictorClient::numpy_predict(
     vec_idx = 0;
     for (auto &name : int_feed_name) {
       int idx = _feed_name_to_idx[name];
+      if (idx >= tensor_vec.size()) {
+        LOG(ERROR) << "idx > tensor_vec.size()";
+        return -1;
+      }
       Tensor *tensor = tensor_vec[idx];
 
       for (uint32_t j = 0; j < int_shape[vec_idx].size(); ++j) {
@@ -358,6 +368,10 @@ int PredictorClient::numpy_predict(
     vec_idx = 0;
     for (auto &name : string_feed_name) {
       int idx = _feed_name_to_idx[name];
+      if (idx >= tensor_vec.size()) {
+        LOG(ERROR) << "idx > tensor_vec.size()";
+        return -1;
+      }
       Tensor *tensor = tensor_vec[idx];
 
       for (uint32_t j = 0; j < string_shape[vec_idx].size(); ++j) {
@@ -371,7 +385,7 @@ int PredictorClient::numpy_predict(
       const int string_shape_size = string_shape[vec_idx].size();
       //string_shape[vec_idx] = [1];cause numpy has no datatype of string.
       //we pass string via vector<vector<string> >.
-      if( string_shape_size!= 1 ){
+      if (string_shape_size != 1) {
         LOG(ERROR) << "string_shape_size should be 1-D, but received is : " << string_shape_size;
         return -1;
       }

core/general-server/op/general_infer_op.cpp

@@ -71,10 +71,7 @@ int GeneralInferOp::inference() {
   TensorVector *out = &output_blob->tensor_vector;
 
   int batch_size = input_blob->_batch_size;
-  VLOG(2) << "(logid=" << log_id << ") input batch size: " << batch_size;
-
   output_blob->_batch_size = batch_size;
-
   VLOG(2) << "(logid=" << log_id << ") infer batch size: " << batch_size;
 
   Timer timeline;

core/general-server/op/general_reader_op.cpp

@@ -46,17 +46,18 @@ int conf_check(const Request *req,
   VLOG(2) << "fetch var num in reader op: " << req->fetch_var_names_size();
 
   for (int i = 0; i < var_num; ++i) {
+    const Tensor &tensor = req->insts(0).tensor_array(i);
     if (model_config->_feed_type[i] !=
-        req->insts(0).tensor_array(i).elem_type()) {
+        tensor.elem_type()) {
       LOG(ERROR) << "feed type not match.";
       return -1;
     }
     if (model_config->_feed_shape[i].size() ==
-        req->insts(0).tensor_array(i).shape_size()) {
+        tensor.shape_size()) {
       for (int j = 0; j < model_config->_feed_shape[i].size(); ++j) {
-        req->insts(0).tensor_array(i).shape(j);
+        tensor.shape(j);
         if (model_config->_feed_shape[i][j] !=
-            req->insts(0).tensor_array(i).shape(j)) {
+            tensor.shape(j)) {
           LOG(ERROR) << "feed shape not match.";
           return -1;
         }
@@ -124,7 +125,7 @@ int GeneralReaderOp::inference() {
     paddle::PaddleTensor lod_tensor;
     const Tensor &tensor = req->insts(0).tensor_array(i);
     data_len = 0;
-    elem_type[i] = req->insts(0).tensor_array(i).elem_type();
+    elem_type[i] = tensor.elem_type();
     VLOG(2) << "var[" << i << "] has elem type: " << elem_type[i];
     if (elem_type[i] == P_INT64) {  // int64
       elem_size[i] = sizeof(int64_t);
@@ -150,16 +151,16 @@ int GeneralReaderOp::inference() {
     // implement lod tensor here
     // only support 1-D lod
     // TODO:support 2-D lod
-    if (req->insts(0).tensor_array(i).lod_size() > 0) {
+    if (tensor.lod_size() > 0) {
       VLOG(2) << "(logid=" << log_id << ") var[" << i << "] is lod_tensor";
       lod_tensor.lod.resize(1);
-      for (int k = 0; k < req->insts(0).tensor_array(i).lod_size(); ++k) {
-        lod_tensor.lod[0].push_back(req->insts(0).tensor_array(i).lod(k));
+      for (int k = 0; k < tensor.lod_size(); ++k) {
+        lod_tensor.lod[0].push_back(tensor.lod(k));
       }
     }
 
-    for (int k = 0; k < req->insts(0).tensor_array(i).shape_size(); ++k) {
-      int dim = req->insts(0).tensor_array(i).shape(k);
+    for (int k = 0; k < tensor.shape_size(); ++k) {
+      int dim = tensor.shape(k);
       VLOG(2) << "(logid=" << log_id << ") shape for var[" << i
               << "]: " << dim;
       lod_tensor.shape.push_back(dim);
@@ -178,57 +179,57 @@ int GeneralReaderOp::inference() {
     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);
+              << "] is " << tensor.int64_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
-      memcpy(dst_ptr, req->insts(0).tensor_array(i).int64_data().data(),databuf_size[i]);
+      memcpy(dst_ptr, tensor.int64_data().data(),databuf_size[i]);
       /*
-      int elem_num = req->insts(0).tensor_array(i).int64_data_size();
+      int elem_num = tensor.int64_data_size();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[k] = req->insts(0).tensor_array(i).int64_data(k);
+        dst_ptr[k] = tensor.int64_data(k);
       }
       */
     } else if (elem_type[i] == P_FLOAT32) {
       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);
+              << "] is " << tensor.float_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
-      memcpy(dst_ptr, req->insts(0).tensor_array(i).float_data().data(),databuf_size[i]);
-      /*int elem_num = req->insts(0).tensor_array(i).float_data_size();
+      memcpy(dst_ptr, tensor.float_data().data(),databuf_size[i]);
+      /*int elem_num = tensor.float_data_size();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[k] = req->insts(0).tensor_array(i).float_data(k);
+        dst_ptr[k] = tensor.float_data(k);
       }*/
     } else if (elem_type[i] == P_INT32) {
       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);
+              << "] is " << tensor.int_data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
-      memcpy(dst_ptr, req->insts(0).tensor_array(i).int_data().data(),databuf_size[i]);
+      memcpy(dst_ptr, tensor.int_data().data(),databuf_size[i]);
       /*
-      int elem_num = req->insts(0).tensor_array(i).int_data_size();
+      int elem_num = tensor.int_data_size();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[k] = req->insts(0).tensor_array(i).int_data(k);
+        dst_ptr[k] = tensor.int_data(k);
       }
       */
     } else if (elem_type[i] == P_STRING) {
       std::string *dst_ptr = static_cast<std::string *>(out->at(i).data.data());
       VLOG(2) << "(logid=" << log_id << ") first element data in var[" << i
-              << "] is " << req->insts(0).tensor_array(i).data(0);
+              << "] is " << tensor.data(0);
       if (!dst_ptr) {
         LOG(ERROR) << "dst_ptr is nullptr";
             return -1;
       }
-      int elem_num = req->insts(0).tensor_array(i).data_size();
+      int elem_num = tensor.data_size();
       for (int k = 0; k < elem_num; ++k) {
-        dst_ptr[k] = req->insts(0).tensor_array(i).data(k);
+        dst_ptr[k] = tensor.data(k);
       }
     }
   }

core/predictor/framework/infer.h

@@ -512,7 +512,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<PaddleInferenceCore
     //Inside each for loop, use the in[i]->name as inputName and call 'core->GetInputHandle(inputName)' to get the pointer of InputData.
     //Set the lod and shape information of InputData first. then copy data from cpu to the core.
     const TensorVector* tensorVector_in_pointer = reinterpret_cast<const TensorVector*>(in);
-    for(int i =0; i< tensorVector_in_pointer->size();++i){
+    for (int i=0; i < tensorVector_in_pointer->size(); ++i) {
       auto lod_tensor_in = core->GetInputHandle((*tensorVector_in_pointer)[i].name);
       lod_tensor_in->SetLoD((*tensorVector_in_pointer)[i].lod);
       lod_tensor_in->Reshape((*tensorVector_in_pointer)[i].shape);
@@ -552,7 +552,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<PaddleInferenceCore
     }
     //Get the type and shape information of OutputData first. then copy data to cpu from the core.
     //The pointer type of data_out must be one of float *,int64_t*,int32_t* instead void*.
-    for (int i = 0; i < outnames.size(); ++i){
+    for (int i=0; i < outnames.size(); ++i) {
       auto lod_tensor_out = core->GetOutputHandle(outnames[i]);
       output_shape = lod_tensor_out->shape();
       out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1, std::multiplies<int>());
@@ -596,7 +596,7 @@ class FluidInferEngine : public CloneDBReloadableInferEngine<PaddleInferenceCore
       tensor_out.dtype = paddle::PaddleDType(dataType);
       tensor_out.shape.assign(output_shape.begin(), output_shape.end());
       std::vector<std::vector<size_t>> out_lod = lod_tensor_out->lod();
-      for (int li = 0; li < out_lod.size(); ++li) {
+      for (int li=0; li < out_lod.size(); ++li) {
         std::vector<size_t> lod_element;
         lod_element.assign(out_lod[li].begin(), out_lod[li].end());
         tensor_out.lod.push_back(lod_element);

core/predictor/framework/resource.cpp

@@ -150,10 +150,8 @@ int Resource::initialize(const std::string& path, const std::string& file) {
 
   if (FLAGS_enable_model_toolkit) {
     size_t model_toolkit_num = resource_conf.model_toolkit_path_size();
-    for (size_t mi = 0; mi < model_toolkit_num; ++mi) {
-      
+    for (size_t mi=0; mi < model_toolkit_num; ++mi) {
       std::string model_toolkit_path = resource_conf.model_toolkit_path(mi);
-
       std::string model_toolkit_file = resource_conf.model_toolkit_file(mi);
 
       if (InferManager::instance().proc_initialize(
@@ -227,7 +225,7 @@ int Resource::general_model_initialize(const std::string& path,
     return -1;
   }
   size_t general_model_num = resource_conf.general_model_path_size();
-  for (size_t gi = 0; gi < general_model_num; ++gi) {
+  for (size_t gi=0; gi < general_model_num; ++gi) {
 
 
     std::string general_model_path = resource_conf.general_model_path(gi);
@@ -251,7 +249,7 @@ int Resource::general_model_initialize(const std::string& path,
     _config->_is_lod_feed.resize(feed_var_num);
     _config->_capacity.resize(feed_var_num);
     _config->_feed_shape.resize(feed_var_num);
-    for (int i = 0; i < feed_var_num; ++i) {
+    for (int i=0; i < feed_var_num; ++i) {
       _config->_feed_name[i] = model_config.feed_var(i).name();
       _config->_feed_alias_name[i] = model_config.feed_var(i).alias_name();
       VLOG(2) << "feed var[" << i << "]: " << _config->_feed_name[i];
@@ -267,7 +265,7 @@ int Resource::general_model_initialize(const std::string& path,
         VLOG(2) << "var[" << i << "] is tensor";
         _config->_capacity[i] = 1;
         _config->_is_lod_feed[i] = false;
-        for (int j = 0; j < model_config.feed_var(i).shape_size(); ++j) {
+        for (int j=0; j < model_config.feed_var(i).shape_size(); ++j) {
           int32_t dim = model_config.feed_var(i).shape(j);
           VLOG(2) << "var[" << i << "].shape[" << i << "]: " << dim;
           _config->_feed_shape[i].push_back(dim);
@@ -281,7 +279,7 @@ int Resource::general_model_initialize(const std::string& path,
     _config->_fetch_name.resize(fetch_var_num);
     _config->_fetch_alias_name.resize(fetch_var_num);
     _config->_fetch_shape.resize(fetch_var_num);
-    for (int i = 0; i < fetch_var_num; ++i) {
+    for (int i=0; i < fetch_var_num; ++i) {
       _config->_fetch_name[i] = model_config.fetch_var(i).name();
       _config->_fetch_alias_name[i] = model_config.fetch_var(i).alias_name();
       _config->_fetch_name_to_index[_config->_fetch_name[i]] = i;
@@ -292,7 +290,7 @@ int Resource::general_model_initialize(const std::string& path,
         _config->_is_lod_fetch[i] = true;
       } else {
         _config->_is_lod_fetch[i] = false;
-        for (int j = 0; j < model_config.fetch_var(i).shape_size(); ++j) {
+        for (int j=0; j < model_config.fetch_var(i).shape_size(); ++j) {
           int dim = model_config.fetch_var(i).shape(j);
           _config->_fetch_shape[i].push_back(dim);
         }

java/examples/src/main/java/PipelineClientExample.java

@@ -32,7 +32,7 @@ public class PipelineClientExample {
         System.out.println(fetch);
         
         if (StaticPipelineClient.succ != true) {
-            if(!StaticPipelineClient.initClient("127.0.0.1","18070")){
+            if (!StaticPipelineClient.initClient("127.0.0.1","18070")) {
                 System.out.println("connect failed.");
                 return false;
             }
@@ -57,7 +57,7 @@ public class PipelineClientExample {
         List<String> fetch = Arrays.asList("prediction");
         System.out.println(fetch);
         if (StaticPipelineClient.succ != true) {
-            if(!StaticPipelineClient.initClient("127.0.0.1","18070")){
+            if (!StaticPipelineClient.initClient("127.0.0.1","18070")) {
                 System.out.println("connect failed.");
                 return false;
             }
@@ -86,7 +86,7 @@ public class PipelineClientExample {
             }};
         List<String> fetch = Arrays.asList("prediction");
         if (StaticPipelineClient.succ != true) {
-            if(!StaticPipelineClient.initClient("127.0.0.1","9998")){
+            if (!StaticPipelineClient.initClient("127.0.0.1","9998")) {
                 System.out.println("connect failed.");
                 return false;
             }
@@ -105,7 +105,7 @@ public class PipelineClientExample {
    * @param npdata INDArray type(The input data).
    * @return String (specified String type for python Numpy eval method).
    */
-    String convertINDArrayToString(INDArray npdata){
+    String convertINDArrayToString(INDArray npdata) {
         return "array("+npdata.toString()+")";
     }
 

java/examples/src/main/java/StaticPipelineClient.java

@@ -30,10 +30,10 @@ public class StaticPipelineClient {
    * @param strPort String type(The server port) such as "8891".
    * @return boolean (the sign of connect status).
    */
-    public static boolean initClient(String strIp,String strPort){
+    public static boolean initClient(String strIp,String strPort) {
         String target = strIp+ ":"+ strPort;//"172.17.0.2:18070";
         System.out.println("initial connect.");
-        if(succ){
+        if (succ) {
             System.out.println("already connect.");
             return true;
         }

java/src/main/java/io/paddle/serving/client/PipelineClient.java

@@ -88,7 +88,7 @@ public class PipelineClient {
             keys.add(entry.getKey());
             values.add(entry.getValue());
         }
-        if(profile){
+        if (profile) {
             keys.add(_profile_key);
             values.add(_profile_value);
         }

python/paddle_serving_client/client.py

@@ -31,13 +31,18 @@ sys.path.append(
     os.path.join(os.path.abspath(os.path.dirname(__file__)), 'proto'))
 from .proto import multi_lang_general_model_service_pb2_grpc
 
+#param 'type'(which is in feed_var or fetch_var) = 0 means dataType is int64
+#param 'type'(which is in feed_var or fetch_var) = 1 means dataType is float32
+#param 'type'(which is in feed_var or fetch_var) = 2 means dataType is int32
+#param 'type'(which is in feed_var or fetch_var) = 3 means dataType is string(also called bytes in proto)
 int64_type = 0
 float32_type = 1
 int32_type = 2
 bytes_type = 3
+#int_type,float_type,string_type are the set of each subdivision classes.
 int_type = set([int64_type, int32_type])
 float_type = set([float32_type])
-string_type= set([bytes_type])
+string_type = set([bytes_type])
 
 
 class _NOPProfiler(object):
@@ -172,9 +177,9 @@ class Client(object):
         self.client_handle_.init_gflags([sys.argv[
             0]] + ["--tryfromenv=" + ",".join(read_env_flags)])
         self.feed_names_ = [var.alias_name for var in model_conf.feed_var]
-        self.feed_names_to_idx_ = {}#this is not useful
+        self.feed_names_to_idx_ = {}  #this is not useful
         self.lod_tensor_set = set()
-        self.feed_tensor_len = {}#this is only used for shape check
+        self.feed_tensor_len = {}  #this is only used for shape check
         self.key = None
 
         for i, var in enumerate(model_conf.feed_var):
@@ -420,9 +425,9 @@ class Client(object):
             res = self.client_handle_.numpy_predict(
                 float_slot_batch, float_feed_names, float_shape,
                 float_lod_slot_batch, int_slot_batch, int_feed_names, int_shape,
-                int_lod_slot_batch, string_slot_batch, string_feed_names, string_shape,
-                string_lod_slot_batch, fetch_names, result_batch_handle, self.pid,
-                log_id)
+                int_lod_slot_batch, string_slot_batch, string_feed_names,
+                string_shape, string_lod_slot_batch, fetch_names,
+                result_batch_handle, self.pid, log_id)
         elif self.has_numpy_input == False:
             raise ValueError(
                 "Please make sure all of your inputs are numpy array")