# 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. import sys from paddle_serving_client import Client from paddle_serving_client.metric import auc from paddle_serving_client.utils import MultiThreadRunner import time from test_bert_client import BertService def predict(thr_id, resource): bc = BertService( model_name="bert_chinese_L-12_H-768_A-12", max_seq_len=20, do_lower_case=True) bc.load_client(resource["conf_file"], resource["server_endpoint"]) thread_num = resource["thread_num"] file_list = resource["filelist"] line_id = 0 result = [] label_list = [] dataset = [] for fn in file_list: fin = open(fn) for line in fin: if line_id % thread_num == thr_id - 1: dataset.append(line.strip()) line_id += 1 fin.close() start = time.time() fetch = ["pooled_output"] for inst in dataset: fetch_map = bc.run_general([[inst]], fetch) result.append(fetch_map["pooled_output"]) end = time.time() return [result, label_list, [end - start]] if __name__ == '__main__': conf_file = sys.argv[1] data_file = sys.argv[2] thread_num = sys.argv[3] resource = {} resource["conf_file"] = conf_file resource["server_endpoint"] = ["127.0.0.1:9293"] resource["filelist"] = [data_file] resource["thread_num"] = int(thread_num) thread_runner = MultiThreadRunner() result = thread_runner.run(predict, int(sys.argv[3]), resource) print("total time {} s".format(sum(result[-1]) / len(result[-1])))