add shape check

python/paddle_serving_client/__init__.py

@@ -118,7 +118,8 @@ class Client(object):
         self.producers = []
         self.consumer = None
         self.profile_ = _Profiler()
-        self.numpy_input = True
+        self.all_numpy_input = True
+        self.has_numpy_input = False
 
     def rpath(self):
         lib_path = os.path.dirname(paddle_serving_client.__file__)
@@ -272,9 +273,10 @@ class Client(object):
                     if isinstance(feed_i[key], np.ndarray):
                         #int_slot.append(np.reshape(feed_i[key], (-1)).tolist())
                         int_slot.append(feed_i[key])
+                        self.has_numpy_input = True
                     else:
                         int_slot.append(feed_i[key])
-                        self.numpy_input = False
+                        self.all_numpy_input = False
                 elif self.feed_types_[key] == float_type:
                     if i == 0:
                         float_feed_names.append(key)
@@ -285,9 +287,10 @@ class Client(object):
                     if isinstance(feed_i[key], np.ndarray):
                         #float_slot.append(np.reshape(feed_i[key], (-1)).tolist())
                         float_slot.append(feed_i[key])
+                        self.has_numpy_input = True
                     else:
                         float_slot.append(feed_i[key])
-                        self.numpy_input = False
+                        self.all_numpy_input = False
             int_slot_batch.append(int_slot)
             float_slot_batch.append(float_slot)
 
@@ -295,14 +298,18 @@ class Client(object):
         self.profile_.record('py_client_infer_0')
 
         result_batch = self.result_handle_
-        if self.numpy_input:
+        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)
-        else:
+        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)
+        else:
+            raise SystemExit(
+                "Please make sure the inputs are all in list type or all in numpy.array type"
+            )
 
         self.profile_.record('py_client_infer_1')
         self.profile_.record('py_postpro_0')