refine benchmark scripts

python/examples/imdb/benchmark.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.
+
+import sys
+from paddle_serving_client import Client
+from paddle_serving_client.metric import auc
+from paddle_serving_client.utils import MultiThreadRunner
+import time
+
+def predict(thr_id, resource):
+    client = Client()
+    client.load_client_config(resource["conf_file"])
+    client.connect(resource["server_endpoint"])
+    thread_num = resource["thread_num"]
+    file_list = resource["filelist"]
+    line_id = 0
+    prob = []
+    label_list = []
+    dataset = []
+    for fn in file_list:
+        fin = open(fn)
+        for line in fin:
+            if line_id % thread_num == thr_id - 1:
+                group = line.strip().split()
+                words = [int(x) for x in group[1:int(group[0])]]
+                label = [int(group[-1])]
+                feed = {"words": words, "label": label}
+                dataset.append(feed)
+            line_id += 1
+        fin.close()
+
+    start = time.time()
+    fetch = ["acc", "cost", "prediction"]
+    infer_time_list = []
+    for inst in dataset:
+        fetch_map = client.predict(feed=inst, fetch=fetch, debug=True)
+        prob.append(fetch_map["prediction"][1])
+        label_list.append(label[0])
+        infer_time_list.append(fetch_map["infer_time"])
+    end = time.time()
+    client.release()
+    return [prob, label_list, [sum(infer_time_list)], [end - start]]
+
+if __name__ == '__main__':
+    conf_file = sys.argv[1]
+    data_file = sys.argv[2]
+    resource = {}
+    resource["conf_file"] = conf_file
+    resource["server_endpoint"] = ["127.0.0.1:9292"]
+    resource["filelist"] = [data_file]
+    resource["thread_num"] = int(sys.argv[3])
+
+    thread_runner = MultiThreadRunner()
+    result = thread_runner.run(predict, int(sys.argv[3]), resource)
+    print(result[-1])
+    print("{}\t{}".format(sys.argv[3], sum(result[-1]) / len(result[-1])))
+    print("{}\t{}".format(sys.argv[3], sum(result[2]) / 1000.0 / 1000.0 / len(result[2])))