supports string input export

python/paddle_serving_client/io/__init__.py

@@ -211,8 +211,10 @@ def save_model(server_model_folder,
         new_params_path = os.path.join(server_model_folder, params_filename)
 
         with open(new_model_path, "wb") as new_model_file:
-            new_model_file.write(main_program._remove_training_info(False).desc.serialize_to_string())
-        
+            new_model_file.write(
+                main_program._remove_training_info(False)
+                .desc.serialize_to_string())
+
         paddle.static.save_vars(
             executor=executor,
             dirname=server_model_folder,
@@ -231,7 +233,8 @@ def save_model(server_model_folder,
         key = CipherUtils.gen_key_to_file(128, "key")
         params = fluid.io.save_persistables(
             executor=executor, dirname=None, main_program=main_program)
-        model = main_program._remove_training_info(False).desc.serialize_to_string()
+        model = main_program._remove_training_info(
+            False).desc.serialize_to_string()
         if not os.path.exists(server_model_folder):
             os.makedirs(server_model_folder)
         os.chdir(server_model_folder)
@@ -248,15 +251,20 @@ def save_model(server_model_folder,
         fetch_alias = target_var_names
     else:
         fetch_alias = fetch_alias_names.split(',')
-    if len(feed_alias) != len(feed_var_dict.keys()) or len(fetch_alias) != len(target_var_names):
-        raise ValueError("please check the input --feed_alias_names and --fetch_alias_names, should be same size with feed_vars and fetch_vars") 
+    if len(feed_alias) != len(feed_var_dict.keys()) or len(fetch_alias) != len(
+            target_var_names):
+        raise ValueError(
+            "please check the input --feed_alias_names and --fetch_alias_names, should be same size with feed_vars and fetch_vars"
+        )
     for i, key in enumerate(feed_var_dict):
         feed_var = model_conf.FeedVar()
         feed_var.alias_name = feed_alias[i]
         feed_var.name = feed_var_dict[key].name
         feed_var.feed_type = var_type_conversion(feed_var_dict[key].dtype)
 
-        feed_var.is_lod_tensor = feed_var_dict[key].lod_level >= 1
+        feed_var.is_lod_tensor = feed_var_dict[
+            key].lod_level >= 1 if feed_var_dict[
+                key].lod_level is not None else False
         if feed_var.is_lod_tensor:
             feed_var.shape.extend([-1])
         else:
@@ -331,7 +339,8 @@ def inference_model_to_serving(dirname,
     fetch_dict = {x.name: x for x in fetch_targets}
     save_model(serving_server, serving_client, feed_dict, fetch_dict,
                inference_program, encryption, key_len, encrypt_conf,
-               model_filename, params_filename, show_proto, feed_alias_names, fetch_alias_names)
+               model_filename, params_filename, show_proto, feed_alias_names,
+               fetch_alias_names)
     feed_names = feed_dict.keys()
     fetch_names = fetch_dict.keys()
     return feed_names, fetch_names