fix code style

core/general-server/op/general_copy_op.cpp

@@ -37,7 +37,9 @@ int GeneralCopyOp::inference() {
   // reade request from client
   const std::vector<std::string> pre_node_names = pre_names();
   if (pre_node_names.size() != 1) {
-    LOG(ERROR) << "This op(" << op_name() <<") can only have one predecessor op, but received " << pre_node_names.size();
+    LOG(ERROR) << "This op(" << op_name()
+               << ") can only have one predecessor op, but received "
+               << pre_node_names.size();
     return -1;
   }
   const std::string pre_name = pre_node_names[0];

core/general-server/op/general_dist_kv_infer_op.cpp

@@ -41,7 +41,9 @@ using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 int GeneralDistKVInferOp::inference() {
   VLOG(2) << "Going to run inference";
   if (pre_node_names.size() != 1) {
-    LOG(ERROR) << "This op(" << op_name() <<") can only have one predecessor op, but received " << pre_node_names.size();
+    LOG(ERROR) << "This op(" << op_name()
+               << ") can only have one predecessor op, but received "
+               << pre_node_names.size();
     return -1;
   }
   const std::string pre_name = pre_node_names[0];

core/general-server/op/general_dist_kv_quant_infer_op.cpp

@@ -42,7 +42,9 @@ using baidu::paddle_serving::predictor::PaddleGeneralModelConfig;
 int GeneralDistKVQuantInferOp::inference() {
   VLOG(2) << "Going to run inference";
   if (pre_node_names.size() != 1) {
-    LOG(ERROR) << "This op(" << op_name() <<") can only have one predecessor op, but received " << pre_node_names.size();
+    LOG(ERROR) << "This op(" << op_name()
+               << ") can only have one predecessor op, but received "
+               << pre_node_names.size();
     return -1;
   }
   const std::string pre_name = pre_node_names[0];

core/predictor/framework/infer.h

@@ -765,7 +765,8 @@ class InferManager {
     }
     size_t engine_num = model_toolkit_conf.engines_size();
     for (size_t ei = 0; ei < engine_num; ++ei) {
-      LOG(INFO) << "model_toolkit_conf.engines(" << ei << ").name: " << model_toolkit_conf.engines(ei).name();
+      LOG(INFO) << "model_toolkit_conf.engines(" << ei
+                << ").name: " << model_toolkit_conf.engines(ei).name();
       std::string engine_name = model_toolkit_conf.engines(ei).name();
       VersionedInferEngine* engine = new (std::nothrow) VersionedInferEngine();
       if (!engine) {

ensemble-demo/client.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-from paddle_serving_client import Client
-from imdb_reader import IMDBDataset
-import sys
-
-client = Client()
-client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt')
-client.connect(["127.0.0.1:9393"])
-
-# you can define any english sentence or dataset here
-# This example reuses imdb reader in training, you
-# can define your own data preprocessing easily.
-imdb_dataset = IMDBDataset()
-imdb_dataset.load_resource('imdb.vocab')
-
-for i in range(400):
-    line = 'i am very sad | 0'
-    word_ids, label = imdb_dataset.get_words_and_label(line)
-    feed = {"words": word_ids}
-    fetch = ["acc", "cost", "prediction"]
-    fetch_maps = client.predict(feed=feed, fetch=fetch)
-    if len(fetch_maps) == 1:
-        print("step: {}, res: {}".format(i, fetch_maps['prediction'][1]))
-    else:
-        for mi, fetch_map in enumerate(fetch_maps):
-            print("step: {}, model: {}, res: {}".format(i, mi, fetch_map['prediction'][1]))
-    # print('bow: 0.633530199528, cnn: 0.560272455215')
-    # exit(0)

ensemble-demo/server.py

-# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# pylint: disable=doc-string-missing
-
-import os
-import sys
-from paddle_serving_server import OpMaker
-from paddle_serving_server import OpSeqMaker
-from paddle_serving_server import Server
-
-op_maker = OpMaker()
-read_op = op_maker.create('general_reader')
-g1_infer_op = op_maker.create('general_infer', node_name='g1')
-g2_infer_op = op_maker.create('general_infer', node_name='g2')
-# add_op = op_maker.create('general_add')
-response_op = op_maker.create('general_response')
-
-op_seq_maker = OpSeqMaker()
-op_seq_maker.add_op(read_op)
-op_seq_maker.add_op(g1_infer_op, dependent_nodes=[read_op])
-op_seq_maker.add_op(g2_infer_op, dependent_nodes=[read_op])
-# op_seq_maker.add_op(add_op, dependent_nodes=[g1_infer_op, g2_infer_op])
-# op_seq_maker.add_op(response_op, dependent_nodes=[add_op])
-op_seq_maker.add_op(response_op, dependent_nodes=[g1_infer_op, g2_infer_op])
-
-server = Server()
-server.set_op_sequence(op_seq_maker.get_op_sequence())
-# server.load_model_config(sys.argv[1])
-model_configs = {'g1': 'imdb_cnn_model', 'g2': 'imdb_bow_model'}
-server.load_model_config(model_configs)
-server.prepare_server(workdir="work_dir1", port=9393, device="cpu")
-server.run_server()

python/paddle_serving_client/__init__.py

@@ -277,8 +277,8 @@ class Client(object):
             else:
                 for mi, result_map_batch in enumerate(multi_result_map_batch):
                     ret[model_engine_names[mi]] = result_map_batch
-        return [ret, self.result_handle_.variant_tag()
-                ] if need_variant_tag else ret
+        return [ret,
+                self.result_handle_.variant_tag()] if need_variant_tag else ret
 
     def release(self):
         self.client_handle_.destroy_predictor()

python/paddle_serving_server/__init__.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+# pylint: disable=doc-string-missing
 
 import os
 from .proto import server_configure_pb2 as server_sdk