update proto && PredictorRes in client

core/configure/proto/multi_lang_general_model_service.proto

@@ -32,7 +32,7 @@ message Request {
   repeated FeedInst insts = 1;
   repeated string feed_var_names = 2;
   repeated string fetch_var_names = 3;
-  required bool is_python = 4 [ default = true ];
+  required bool is_python = 4 [ default = false ];
 };
 
 message Response {

python/paddle_serving_client/__init__.py

@@ -21,7 +21,6 @@ import google.protobuf.text_format
 import numpy as np
 import time
 import sys
-from .serving_client import PredictorRes
 
 import grpc
 from .proto import multi_lang_general_model_service_pb2
@@ -129,6 +128,8 @@ class Client(object):
         self.all_numpy_input = True
         self.has_numpy_input = False
         self.rpc_timeout_ms = 20000
+        from .serving_client import PredictorRes
+        self.predictorres_constructor = PredictorRes
 
     def load_client_config(self, path):
         from .serving_client import PredictorClient
@@ -308,7 +309,7 @@ class Client(object):
         self.profile_.record('py_prepro_1')
         self.profile_.record('py_client_infer_0')
 
-        result_batch_handle = PredictorRes()
+        result_batch_handle = self.predictorres_constructor()
         if self.all_numpy_input:
             res = self.client_handle_.numpy_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
@@ -495,9 +496,11 @@ class MultiLangClient(object):
                     raise Exception("error type.")
             else:
                 if v_type == 0:  # int64
-                    result_map[name] = np.array(list(var.int64_data))
+                    result_map[name] = np.array(
+                        list(var.int64_data), dtype="int64")
                 elif v_type == 1:  # float32
-                    result_map[name] = np.array(list(var.float_data))
+                    result_map[name] = np.array(
+                        list(var.float_data), dtype="float32")
                 else:
                     raise Exception("error type.")
             result_map[name].shape = list(var.shape)