c++ client support uint8&int8

core/general-client/include/client.h

@@ -88,7 +88,7 @@ class PredictorData {
                        const std::string& name,
                        const std::vector<int>& shape,
                        const std::vector<int>& lod,
-                       const int datatype = 3);
+                       const int datatype = 20);
 
   const std::map<std::string, std::vector<float>>& float_data_map() const {
     return _float_data_map;
@@ -140,6 +140,8 @@ class PredictorData {
 
   int get_datatype(std::string name) const;
 
+  void set_datatype(std::string name, int type);
+
   std::string print();
 
  private:
@@ -159,6 +161,7 @@ class PredictorData {
       oss << "{";
       oss << it->first << key_seg;
       const std::vector<T2>& v = it->second;
+      oss << v.size() << key_seg;
       for (size_t i = 0; i < v.size(); ++i) {
         if (i != v.size() - 1) {
           oss << v[i] << val_seg;
@@ -184,7 +187,9 @@ class PredictorData {
     typename std::map<T1, T2>::const_iterator itEnd = map.end();
     for (; it != itEnd; it++) {
       oss << "{";
-      oss << it->first << key_seg << it->second;
+      oss << it->first << key_seg
+          << "size=" << it->second.size() << key_seg
+          << "type=" << this->get_datatype(it->first);
       oss << "}";
     }
     return oss.str();

core/general-client/src/client.cpp

@@ -172,6 +172,10 @@ int PredictorData::get_datatype(std::string name) const {
   return 0;
 }
 
+void PredictorData::set_datatype(std::string name, int type) {
+  _datatype_map[name] = type;
+}
+
 std::string PredictorData::print() {
   std::string res;
   res.append(map2string<std::string, float>(_float_data_map));
@@ -325,20 +329,25 @@ int PredictorInputs::GenProto(const PredictorInputs& inputs,
     tensor->set_name(feed_name[idx]);
     tensor->set_alias_name(name);
 
-    const int string_shape_size = string_shape.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) {
-      LOG(ERROR) << "string_shape_size should be 1-D, but received is : "
-                 << string_shape_size;
-      return -1;
-    }
-    switch (string_shape_size) {
-      case 1: {
-        tensor->add_data(string_data);
-        break;
+    if (datatype == P_STRING) {
+      const int string_shape_size = string_shape.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) {
+        LOG(ERROR) << "string_shape_size should be 1-D, but received is : "
+                   << string_shape_size;
+        return -1;
+      }
+      switch (string_shape_size) {
+        case 1: {
+          tensor->add_data(string_data);
+          break;
+        }
       }
+    } else {
+      tensor->set_tensor_content(string_data);
     }
+    
   }
   return 0;
 }
@@ -371,6 +380,8 @@ int PredictorOutputs::ParseProto(const Response& res,
     std::shared_ptr<PredictorOutputs::PredictorOutput> predictor_output =
         std::make_shared<PredictorOutputs::PredictorOutput>();
     predictor_output->engine_name = output.engine_name();
+
+    PredictorData& predictor_data = predictor_output->data;
     std::map<std::string, std::vector<float>>& float_data_map = *predictor_output->data.mutable_float_data_map();
     std::map<std::string, std::vector<int64_t>>& int64_data_map = *predictor_output->data.mutable_int64_data_map();
     std::map<std::string, std::vector<int32_t>>& int32_data_map = *predictor_output->data.mutable_int_data_map();
@@ -419,7 +430,13 @@ int PredictorOutputs::ParseProto(const Response& res,
         int32_data_map[name] = std::vector<int32_t>(
             output.tensor(idx).int_data().begin(),
             output.tensor(idx).int_data().begin() + size);
+      } else if (fetch_name_to_type[name] == P_UINT8
+                || fetch_name_to_type[name] == P_INT8) {
+        VLOG(2) << "fetch var [" << name << "]type="
+                << fetch_name_to_type[name];
+        string_data_map[name] = output.tensor(idx).tensor_content();
       }
+      predictor_data.set_datatype(name, output.tensor(idx).elem_type());
       idx += 1;
     }
     outputs.add_data(predictor_output);