提交 f906f849 编写于 作者: L LDOUBLEV

opt benchmark

上级 d89c6b43
# 提供可稳定复现性能的脚本,默认在标准docker环境内py37执行: paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 py=37
# 执行目录:需说明
cd PaddleOCR
#cd PaddleOCR
# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
python3.7 -m pip install -r requirements.txt
# 2 拷贝该模型需要数据、预训练模型
wget -p ./tain_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../
wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
#wget -p ./tain_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../
#wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
# 3 批量运行(如不方便批量,1,2需放到单个模型中)
model_mode_list=(det_mv3_db det_r50_vd_east)
fp_item_list=(fp32)
bs_list=(256 128)
bs_list=(4 8)
for model_mode in ${model_mode_list[@]}; do
for fp_item in ${fp_item_list[@]}; do
for bs_item in ${bs_list[@]}; do
echo "index is speed, 1gpus, begin, ${model_name}"
run_mode=sp
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
CUDA_VISIBLE_DEVICES=3 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
sleep 60
echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
run_mode=mp
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
sleep 60
#run_mode=mp
#CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
#sleep 60
done
done
done
......
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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
import paddle
# A global variable to record the number of calling times for profiler
# functions. It is used to specify the tracing range of training steps.
_profiler_step_id = 0
# A global variable to avoid parsing from string every time.
_profiler_options = None
class ProfilerOptions(object):
'''
Use a string to initialize a ProfilerOptions.
The string should be in the format: "key1=value1;key2=value;key3=value3".
For example:
"profile_path=model.profile"
"batch_range=[50, 60]; profile_path=model.profile"
"batch_range=[50, 60]; tracer_option=OpDetail; profile_path=model.profile"
ProfilerOptions supports following key-value pair:
batch_range - a integer list, e.g. [100, 110].
state - a string, the optional values are 'CPU', 'GPU' or 'All'.
sorted_key - a string, the optional values are 'calls', 'total',
'max', 'min' or 'ave.
tracer_option - a string, the optional values are 'Default', 'OpDetail',
'AllOpDetail'.
profile_path - a string, the path to save the serialized profile data,
which can be used to generate a timeline.
exit_on_finished - a boolean.
'''
def __init__(self, options_str):
assert isinstance(options_str, str)
self._options = {
'batch_range': [10, 20],
'state': 'All',
'sorted_key': 'total',
'tracer_option': 'Default',
'profile_path': '/tmp/profile',
'exit_on_finished': True
}
self._parse_from_string(options_str)
def _parse_from_string(self, options_str):
for kv in options_str.replace(' ', '').split(';'):
key, value = kv.split('=')
if key == 'batch_range':
value_list = value.replace('[', '').replace(']', '').split(',')
value_list = list(map(int, value_list))
if len(value_list) >= 2 and value_list[0] >= 0 and value_list[
1] > value_list[0]:
self._options[key] = value_list
elif key == 'exit_on_finished':
self._options[key] = value.lower() in ("yes", "true", "t", "1")
elif key in [
'state', 'sorted_key', 'tracer_option', 'profile_path'
]:
self._options[key] = value
def __getitem__(self, name):
if self._options.get(name, None) is None:
raise ValueError(
"ProfilerOptions does not have an option named %s." % name)
return self._options[name]
def add_profiler_step(options_str=None):
'''
Enable the operator-level timing using PaddlePaddle's profiler.
The profiler uses a independent variable to count the profiler steps.
One call of this function is treated as a profiler step.
Args:
profiler_options - a string to initialize the ProfilerOptions.
Default is None, and the profiler is disabled.
'''
if options_str is None:
return
global _profiler_step_id
global _profiler_options
if _profiler_options is None:
_profiler_options = ProfilerOptions(options_str)
if _profiler_step_id == _profiler_options['batch_range'][0]:
paddle.utils.profiler.start_profiler(
_profiler_options['state'], _profiler_options['tracer_option'])
elif _profiler_step_id == _profiler_options['batch_range'][1]:
paddle.utils.profiler.stop_profiler(_profiler_options['sorted_key'],
_profiler_options['profile_path'])
if _profiler_options['exit_on_finished']:
sys.exit(0)
_profiler_step_id += 1
......@@ -31,6 +31,7 @@ from ppocr.utils.stats import TrainingStats
from ppocr.utils.save_load import save_model
from ppocr.utils.utility import print_dict
from ppocr.utils.logging import get_logger
from ppocr.utils import profiler
from ppocr.data import build_dataloader
import numpy as np
......
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