make it run successful

core/general-client/include/general_model.h

@@ -76,7 +76,7 @@ class PredictorRes {
     _variant_tag = variant_tag;
   }
   const std::string& variant_tag() { return _variant_tag; }
-  int models_num() {return _models.size();}
+  int model_num() {return _models.size();}
 
   std::vector<ModelRes> _models;
 

core/general-client/src/pybind_general_model.cpp

@@ -42,8 +42,8 @@ PYBIND11_MODULE(serving_client, m) {
            py::return_value_policy::reference)
       .def("variant_tag",
            [](PredictorRes &self) { return self.variant_tag(); })
-      .def("models_num",
-           [](PredictorRes &self) {return self.models_num(); });
+      .def("model_num",
+           [](PredictorRes &self) {return self.model_num(); });
 
   py::class_<PredictorClient>(m, "PredictorClient", py::buffer_protocol())
       .def(py::init())

core/general-server/op/general_response_op.cpp

@@ -66,14 +66,14 @@ int GeneralResponseOp::inference() {
   }
 
   const GeneralBlob *input_blob;
-  for (uint32_t i = 0; i < pre_node_names.size(); ++i) {
-    VLOG(2) << "pre names[" << i << "]: "
-            << pre_node_names[i] << " ("
+  for (uint32_t pi = 0; pi < pre_node_names.size(); ++pi) {
+    VLOG(2) << "pre names[" << pi << "]: "
+            << pre_node_names[pi] << " ("
             << pre_node_names.size() << ")";
-    input_blob = get_depend_argument<GeneralBlob>(pre_node_names[i]);
-    fprintf(stderr, "input(%s) blob address %x\n", pre_node_names[i].c_str(), input_blob);
+    input_blob = get_depend_argument<GeneralBlob>(pre_node_names[pi]);
+    fprintf(stderr, "input(%s) blob address %x\n", pre_node_names[pi].c_str(), input_blob);
     if (!input_blob) {
-      LOG(ERROR) << "Failed mutable depended argument, op: " << pre_node_names[0];
+      LOG(ERROR) << "Failed mutable depended argument, op: " << pre_node_names[pi];
       return -1;
     }
 
@@ -81,7 +81,6 @@ int GeneralResponseOp::inference() {
     int batch_size = input_blob->GetBatchSize();
     VLOG(2) << "input batch size: " << batch_size;
 
-    //TODO
     ModelOutput *output = res->add_outputs();
     for (int i = 0; i < batch_size; ++i) {
       FetchInst *fetch_inst = output->add_insts();

python/paddle_serving_client/__init__.py

@@ -248,14 +248,14 @@ class Client(object):
 
         multi_result_map_batch = []
         model_num = result_batch.model_num()
-        for i in range(model_num):
+        for mi in range(model_num):
             result_map_batch = []
             result_map = {}
             for i, name in enumerate(fetch_names):
                 if self.fetch_names_to_type_[name] == int_type:
-                    result_map[name] = result_batch.get_int64_by_name(i, name)
+                    result_map[name] = result_batch.get_int64_by_name(mi, name)
                 elif self.fetch_names_to_type_[name] == float_type:
-                    result_map[name] = result_batch.get_float_by_name(i, name)
+                    result_map[name] = result_batch.get_float_by_name(mi, name)
             for i in range(batch_size):
                 single_result = {}
                 for key in result_map: