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提交 3374c504 编写于 作者: M MRXLT
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fix imagenet benchmark

上级 0699b50e
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Showing with 55 addition and 126 deletion
python/examples/imagenet/benchmark.py
浏览文件 @ 3374c504
# -*- 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
浏览文件 @ 3374c504
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 已删除 100644 → 0
浏览文件 @ 0699b50e
# -*- 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 已删除 100644 → 0
浏览文件 @ 0699b50e
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
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