fix imagenet benchmark

python/examples/imagenet/benchmark.py

+# -*- coding: utf-8 -*-
+#
 # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -13,13 +15,17 @@
 # limitations under the License.
 # pylint: disable=doc-string-missing
 
+from __future__ import unicode_literals, absolute_import
+import os
 import sys
-from image_reader import ImageReader
+import time
 from paddle_serving_client import Client
 from paddle_serving_client.utils import MultiThreadRunner
 from paddle_serving_client.utils import benchmark_args
-import time
-import os
+import requests
+import json
+import base64
+from image_reader import ImageReader
 
 args = benchmark_args()
 
@@ -31,30 +37,61 @@ def single_func(idx, resource):
     img_list = []
     for i in range(1000):
         img_list.append(open("./image_data/n01440764/" + file_list[i]).read())
+    profile_flags = False
+    if "FLAGS_profile_client" in os.environ and os.environ[
+            "FLAGS_profile_client"]:
+        profile_flags = True
     if args.request == "rpc":
         reader = ImageReader()
         fetch = ["score"]
         client = Client()
         client.load_client_config(args.model)
         client.connect([resource["endpoint"][idx % len(resource["endpoint"])]])
-
         start = time.time()
-        for i in range(100):
+        for i in range(1000):
+            if args.batch_size >= 1:
+                feed_batch = []
+                i_start = time.time()
+                for bi in range(args.batch_size):
                     img = reader.process_image(img_list[i])
-            fetch_map = client.predict(feed={"image": img}, fetch=["score"])
+                    feed_batch.append({"image": img})
+                i_end = time.time()
+                if profile_flags:
+                    print("PROFILE\tpid:{}\timage_pre_0:{} image_pre_1:{}".
+                          format(os.getpid(),
+                                 int(round(i_start * 1000000)),
+                                 int(round(i_end * 1000000))))
+
+                result = client.predict(feed=feed_batch, fetch=fetch)
+            else:
+                print("unsupport batch size {}".format(args.batch_size))
+
+    elif args.request == "http":
+        py_version = 2
+        server = "http://" + resource["endpoint"][idx % len(resource[
+            "endpoint"])] + "/image/prediction"
+        start = time.time()
+        for i in range(1000):
+            if py_version == 2:
+                image = base64.b64encode(
+                    open("./image_data/n01440764/" + file_list[i]).read())
+            else:
+                image = base64.b64encode(open(image_path, "rb").read()).decode(
+                    "utf-8")
+            req = json.dumps({"feed": [{"image": image}], "fetch": ["score"]})
+            r = requests.post(
+                server, data=req, headers={"Content-Type": "application/json"})
     end = time.time()
     return [[end - start]]
-    return [[end - start]]
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     multi_thread_runner = MultiThreadRunner()
-    endpoint_list = ["127.0.0.1:9292"]
-    #card_num = 4
-    #for i in range(args.thread):
-    #    endpoint_list.append("127.0.0.1:{}".format(9295 + i % card_num))
+    endpoint_list = ["127.0.0.1:9696"]
+    #endpoint_list = endpoint_list + endpoint_list + endpoint_list
     result = multi_thread_runner.run(single_func, args.thread,
                                      {"endpoint": endpoint_list})
+    #result = single_func(0, {"endpoint": endpoint_list})
     avg_cost = 0
     for i in range(args.thread):
         avg_cost += result[0][i]

python/examples/imagenet/benchmark.sh

 rm profile_log
-for thread_num in 1 2 4 8 16
+for thread_num in 1 2 4 8
 do
-    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --model ResNet101_vd_client_config/serving_client_conf.prototxt --request rpc > profile 2>&1
+for batch_size in 1 2 4 8 16 32 64 128
+do
+    $PYTHONROOT/bin/python benchmark.py --thread $thread_num --batch_size $batch_size --model ResNet50_vd_client_config/serving_client_conf.prototxt --request rpc > profile 2>&1
     echo "========================================"
     echo "batch size : $batch_size" >> profile_log
     $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
     tail -n 1 profile >> profile_log
 done
+done

python/examples/imagenet/benchmark_batch.py

-# -*- coding: utf-8 -*-
-#
-# 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 __future__ import unicode_literals, absolute_import
-import os
-import sys
-import time
-from paddle_serving_client import Client
-from paddle_serving_client.utils import MultiThreadRunner
-from paddle_serving_client.utils import benchmark_args
-import requests
-import json
-import base64
-from image_reader import ImageReader
-
-args = benchmark_args()
-
-
-def single_func(idx, resource):
-    file_list = []
-    for file_name in os.listdir("./image_data/n01440764"):
-        file_list.append(file_name)
-    img_list = []
-    for i in range(1000):
-        img_list.append(open("./image_data/n01440764/" + file_list[i]).read())
-    profile_flags = False
-    if "FLAGS_profile_client" in os.environ and os.environ[
-            "FLAGS_profile_client"]:
-        profile_flags = True
-    if args.request == "rpc":
-        reader = ImageReader()
-        fetch = ["score"]
-        client = Client()
-        client.load_client_config(args.model)
-        client.connect([resource["endpoint"][idx % len(resource["endpoint"])]])
-        start = time.time()
-        for i in range(1000):
-            if args.batch_size >= 1:
-                feed_batch = []
-                i_start = time.time()
-                for bi in range(args.batch_size):
-                    img = reader.process_image(img_list[i])
-                    feed_batch.append({"image": img})
-                i_end = time.time()
-                if profile_flags:
-                    print("PROFILE\tpid:{}\timage_pre_0:{} image_pre_1:{}".
-                          format(os.getpid(),
-                                 int(round(i_start * 1000000)),
-                                 int(round(i_end * 1000000))))
-
-                result = client.predict(feed=feed_batch, fetch=fetch)
-            else:
-                print("unsupport batch size {}".format(args.batch_size))
-
-    elif args.request == "http":
-        py_version = 2
-        server = "http://" + resource["endpoint"][idx % len(resource[
-            "endpoint"])] + "/image/prediction"
-        start = time.time()
-        for i in range(1000):
-            if py_version == 2:
-                image = base64.b64encode(
-                    open("./image_data/n01440764/" + file_list[i]).read())
-            else:
-                image = base64.b64encode(open(image_path, "rb").read()).decode(
-                    "utf-8")
-            req = json.dumps({"feed": [{"image": image}], "fetch": ["score"]})
-            r = requests.post(
-                server, data=req, headers={"Content-Type": "application/json"})
-    end = time.time()
-    return [[end - start]]
-
-
-if __name__ == '__main__':
-    multi_thread_runner = MultiThreadRunner()
-    endpoint_list = ["127.0.0.1:9292"]
-    #endpoint_list = endpoint_list + endpoint_list + endpoint_list
-    result = multi_thread_runner.run(single_func, args.thread,
-                                     {"endpoint": endpoint_list})
-    #result = single_func(0, {"endpoint": endpoint_list})
-    avg_cost = 0
-    for i in range(args.thread):
-        avg_cost += result[0][i]
-    avg_cost = avg_cost / args.thread
-    print("average total cost {} s.".format(avg_cost))

python/examples/imagenet/benchmark_batch.sh

-rm profile_log
-for thread_num in 1 2 4 8 16
-do
-for batch_size in 1 2 4 8 16 32 64 128 256 512
-do
-    $PYTHONROOT/bin/python benchmark_batch.py --thread $thread_num --batch_size $batch_size --model ResNet101_vd_client_config/serving_client_conf.prototxt --request rpc > profile 2>&1
-    echo "========================================"
-    echo "batch size : $batch_size" >> profile_log
-    $PYTHONROOT/bin/python ../util/show_profile.py profile $thread_num >> profile_log
-    tail -n 1 profile >> profile_log
-done
-done