fix: result_handle_ not thread-safe in client

python/paddle_serving_client/__init__.py

@@ -21,6 +21,7 @@ import google.protobuf.text_format
 import numpy as np
 import time
 import sys
+from .serving_client import PredictorRes
 
 int_type = 0
 float_type = 1
@@ -108,7 +109,6 @@ class Client(object):
         self.feed_names_ = []
         self.fetch_names_ = []
         self.client_handle_ = None
-        self.result_handle_ = None
         self.feed_shapes_ = {}
         self.feed_types_ = {}
         self.feed_names_to_idx_ = {}
@@ -122,7 +122,6 @@ class Client(object):
 
     def load_client_config(self, path):
         from .serving_client import PredictorClient
-        from .serving_client import PredictorRes
         model_conf = m_config.GeneralModelConfig()
         f = open(path, 'r')
         model_conf = google.protobuf.text_format.Merge(
@@ -132,7 +131,6 @@ class Client(object):
         # get feed vars, fetch vars
         # get feed shapes, feed types
         # map feed names to index
-        self.result_handle_ = PredictorRes()
         self.client_handle_ = PredictorClient()
         self.client_handle_.init(path)
         if "FLAGS_max_body_size" not in os.environ:
@@ -302,15 +300,17 @@ class Client(object):
         self.profile_.record('py_prepro_1')
         self.profile_.record('py_client_infer_0')
 
-        result_batch = self.result_handle_
+        result_batch_handle = PredictorRes()
         if self.all_numpy_input:
             res = self.client_handle_.numpy_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
-                int_feed_names, int_shape, fetch_names, result_batch, self.pid)
+                int_feed_names, int_shape, fetch_names, result_batch_handle,
+                self.pid)
         elif self.has_numpy_input == False:
             res = self.client_handle_.batch_predict(
                 float_slot_batch, float_feed_names, float_shape, int_slot_batch,
-                int_feed_names, int_shape, fetch_names, result_batch, self.pid)
+                int_feed_names, int_shape, fetch_names, result_batch_handle,
+                self.pid)
         else:
             raise SystemExit(
                 "Please make sure the inputs are all in list type or all in numpy.array type"
@@ -323,26 +323,28 @@ class Client(object):
             return None
 
         multi_result_map = []
-        model_engine_names = result_batch.get_engine_names()
+        model_engine_names = result_batch_handle.get_engine_names()
         for mi, engine_name in enumerate(model_engine_names):
             result_map = {}
             # result map needs to be a numpy array
             for i, name in enumerate(fetch_names):
                 if self.fetch_names_to_type_[name] == int_type:
                     # result_map[name] will be py::array(numpy array)
-                    result_map[name] = result_batch.get_int64_by_name(mi, name)
-                    shape = result_batch.get_shape(mi, name)
+                    result_map[name] = result_batch_handle.get_int64_by_name(
+                        mi, name)
+                    shape = result_batch_handle.get_shape(mi, name)
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
-                        result_map["{}.lod".format(name)] = np.array(
-                            result_batch.get_lod(mi, name))
+                        result_map["{}.lod".format(
+                            name)] = result_batch_handle.get_lod(mi, name)
                 elif self.fetch_names_to_type_[name] == float_type:
-                    result_map[name] = result_batch.get_float_by_name(mi, name)
-                    shape = result_batch.get_shape(mi, name)
+                    result_map[name] = result_batch_handle.get_float_by_name(
+                        mi, name)
+                    shape = result_batch_handle.get_shape(mi, name)
                     result_map[name].shape = shape
                     if name in self.lod_tensor_set:
-                        result_map["{}.lod".format(name)] = np.array(
-                            result_batch.get_lod(mi, name))
+                        result_map["{}.lod".format(
+                            name)] = result_batch_handle.get_lod(mi, name)
             multi_result_map.append(result_map)
         ret = None
         if len(model_engine_names) == 1:
@@ -360,7 +362,7 @@ class Client(object):
 
         # When using the A/B test, the tag of variant needs to be returned
         return ret if not need_variant_tag else [
-            ret, self.result_handle_.variant_tag()
+            ret, result_batch_handle.variant_tag()
         ]
 
     def release(self):