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移动paddle_detection

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liuyebo
2024-09-24 17:02:56 +08:00
parent 90a6d5ec75
commit 3438cf6e0e
2025 changed files with 11 additions and 11 deletions
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47
services/paddle_services/paddle_detection/benchmark/README.md Normal file
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# 通用检测benchmark测试脚本说明
```
├── benchmark
│ ├── analysis_log.py
│ ├── prepare.sh
│ ├── README.md
│ ├── run_all.sh
│ ├── run_benchmark.sh
```
## 脚本说明
### prepare.sh
相关数据准备脚本，完成数据、模型的自动下载
### run_all.sh
主要运行脚本，可完成所有相关模型的测试方案
### run_benchmark.sh
单模型运行脚本，可完成指定模型的测试方案
## Docker 运行环境
* docker image: registry.baidubce.com/paddlepaddle/paddle:2.1.2-gpu-cuda10.2-cudnn7
* paddle = 2.1.2
* python = 3.7
## 运行benchmark测试
### 运行所有模型
```
git clone https://github.com/PaddlePaddle/PaddleDetection.git
cd PaddleDetection
bash benchmark/run_all.sh
```
### 运行指定模型
* Usage：bash run_benchmark.sh ${run_mode} ${batch_size} ${fp_item} ${max_epoch} ${model_name}
* model_name: faster_rcnn, fcos, deformable_detr, gfl, hrnet, higherhrnet, solov2, jde, fairmot
```
git clone https://github.com/PaddlePaddle/PaddleDetection.git
cd PaddleDetection
bash benchmark/prepare.sh
# 单卡
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh sp 2 fp32 1 faster_rcnn
# 多卡
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh mp 2 fp32 1 faster_rcnn
```

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services/paddle_services/paddle_detection/benchmark/configs/faster_rcnn_r50_fpn_1x_coco.yml Normal file
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_BASE_: [
'../../configs/datasets/coco_detection.yml',
'../../configs/runtime.yml',
'../../configs/faster_rcnn/_base_/optimizer_1x.yml',
'../../configs/faster_rcnn/_base_/faster_rcnn_r50_fpn.yml',
]
weights: output/faster_rcnn_r50_fpn_1x_coco/model_final
worker_num: 2
TrainReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- RandomFlip: {prob: 0.5}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
shuffle: true
drop_last: true
collate_batch: false
EvalReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
shuffle: false
drop_last: false
TestReader:
sample_transforms:
- Decode: {}
- Resize: {interp: 2, target_size: [800, 1333], keep_ratio: True}
- NormalizeImage: {is_scale: true, mean: [0.485,0.456,0.406], std: [0.229, 0.224,0.225]}
- Permute: {}
batch_transforms:
- PadBatch: {pad_to_stride: 32}
batch_size: 1
shuffle: false
drop_last: false

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services/paddle_services/paddle_detection/benchmark/prepare.sh Normal file
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#!/usr/bin/env bash
pip install -U pip Cython
pip install -r requirements.txt
mv ./dataset/coco/download_coco.py . && rm -rf ./dataset/coco/* && mv ./download_coco.py ./dataset/coco/
# prepare lite train data
wget -nc -P ./dataset/coco/ https://paddledet.bj.bcebos.com/data/coco_benchmark.tar
cd ./dataset/coco/ && tar -xvf coco_benchmark.tar && mv -u coco_benchmark/* .
rm -rf coco_benchmark/
cd ../../
rm -rf ./dataset/mot/*
# prepare mot mini train data
wget -nc -P ./dataset/mot/ https://paddledet.bj.bcebos.com/data/mot_benchmark.tar
cd ./dataset/mot/ && tar -xvf mot_benchmark.tar && mv -u mot_benchmark/* .
rm -rf mot_benchmark/

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services/paddle_services/paddle_detection/benchmark/run_all.sh Normal file
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# Use docker: paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7 paddle=2.1.2 python3.7
#
# Usage:
# git clone https://github.com/PaddlePaddle/PaddleDetection.git
# cd PaddleDetection
# bash benchmark/run_all.sh
log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} # benchmark系统指定该参数,不需要跑profile时,log_path指向存speed的目录
# run prepare.sh
bash benchmark/prepare.sh
model_name_list=(faster_rcnn fcos deformable_detr gfl hrnet higherhrnet solov2 jde fairmot)
fp_item_list=(fp32)
max_epoch=2
for model_item in ${model_name_list[@]}; do
for fp_item in ${fp_item_list[@]}; do
case ${model_item} in
faster_rcnn) bs_list=(1 8) ;;
fcos) bs_list=(2) ;;
deformable_detr) bs_list=(2) ;;
gfl) bs_list=(2) ;;
hrnet) bs_list=(64) ;;
higherhrnet) bs_list=(20) ;;
solov2) bs_list=(2) ;;
jde) bs_list=(4) ;;
fairmot) bs_list=(6) ;;
*) echo "wrong model_name"; exit 1;
esac
for bs_item in ${bs_list[@]}
do
run_mode=sp
log_name=detection_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
echo "index is speed, 1gpus, begin, ${log_name}"
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} \
${fp_item} ${max_epoch} ${model_item} | tee ${log_path}/${log_name}_speed_1gpus 2>&1
sleep 60
run_mode=mp
log_name=detection_${model_item}_bs${bs_item}_${fp_item} # 如:clas_MobileNetv1_mp_bs32_fp32_8
echo "index is speed, 8gpus, run_mode is multi_process, begin, ${log_name}"
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} \
${bs_item} ${fp_item} ${max_epoch} ${model_item}| tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
sleep 60
done
done
done

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services/paddle_services/paddle_detection/benchmark/run_benchmark.sh Normal file
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#!/usr/bin/env bash
set -xe
# Usage：CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${batch_size} ${fp_item} ${max_epoch} ${model_name}
python="python3.7"
# Parameter description
function _set_params(){
run_mode=${1:-"sp"} # sp|mp
batch_size=${2:-"2"}
fp_item=${3:-"fp32"} # fp32|fp16
max_epoch=${4:-"1"}
model_item=${5:-"model_item"}
run_log_path=${TRAIN_LOG_DIR:-$(pwd)}
# 添加日志解析需要的参数
base_batch_size=${batch_size}
mission_name="目标检测"
direction_id="0"
ips_unit="images/s"
skip_steps=10 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
index="1"
model_name=${model_item}_bs${batch_size}_${fp_item}
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
num_gpu_devices=${#arr[*]}
log_file=${run_log_path}/${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
}
function _train(){
echo "Train on ${num_gpu_devices} GPUs"
echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
# set runtime params
set_optimizer_lr_sp=" "
set_optimizer_lr_mp=" "
# parse model_item
case ${model_item} in
faster_rcnn) model_yml="benchmark/configs/faster_rcnn_r50_fpn_1x_coco.yml"
set_optimizer_lr_sp="LearningRate.base_lr=0.001" ;;
fcos) model_yml="configs/fcos/fcos_r50_fpn_1x_coco.yml"
set_optimizer_lr_sp="LearningRate.base_lr=0.001" ;;
deformable_detr) model_yml="configs/deformable_detr/deformable_detr_r50_1x_coco.yml" ;;
gfl) model_yml="configs/gfl/gfl_r50_fpn_1x_coco.yml"
set_optimizer_lr_sp="LearningRate.base_lr=0.001" ;;
hrnet) model_yml="configs/keypoint/hrnet/hrnet_w32_256x192.yml" ;;
higherhrnet) model_yml="configs/keypoint/higherhrnet/higherhrnet_hrnet_w32_512.yml" ;;
solov2) model_yml="configs/solov2/solov2_r50_fpn_1x_coco.yml" ;;
jde) model_yml="configs/mot/jde/jde_darknet53_30e_1088x608.yml" ;;
fairmot) model_yml="configs/mot/fairmot/fairmot_dla34_30e_1088x608.yml" ;;
*) echo "Undefined model_item"; exit 1;
esac
set_batch_size="TrainReader.batch_size=${batch_size}"
set_max_epoch="epoch=${max_epoch}"
set_log_iter="log_iter=1"
if [ ${fp_item} = "fp16" ]; then
set_fp_item="--fp16"
else
set_fp_item=" "
fi
case ${run_mode} in
sp) train_cmd="${python} -u tools/train.py -c ${model_yml} ${set_fp_item} \
-o ${set_batch_size} ${set_max_epoch} ${set_log_iter} ${set_optimizer_lr_sp}" ;;
mp) rm -rf mylog
train_cmd="${python} -m paddle.distributed.launch --log_dir=./mylog \
--gpus=${CUDA_VISIBLE_DEVICES} tools/train.py -c ${model_yml} ${set_fp_item} \
-o ${set_batch_size} ${set_max_epoch} ${set_log_iter} ${set_optimizer_lr_mp}"
log_parse_file="mylog/workerlog.0" ;;
*) echo "choose run_mode(sp or mp)"; exit 1;
esac
timeout 15m ${train_cmd} > ${log_file} 2>&1
if [ $? -ne 0 ];then
echo -e "${train_cmd}, FAIL"
export job_fail_flag=1
else
echo -e "${train_cmd}, SUCCESS"
export job_fail_flag=0
fi
kill -9 `ps -ef|grep 'python'|awk '{print $2}'`
if [ $run_mode = "mp" -a -d mylog ]; then
rm ${log_file}
cp mylog/workerlog.0 ${log_file}
fi
}
source ${BENCHMARK_ROOT}/scripts/run_model.sh # 在该脚本中会对符合benchmark规范的log使用analysis.py 脚本进行性能数据解析;该脚本在联调时可从benchmark repo中下载https://github.com/PaddlePaddle/benchmark/blob/master/scripts/run_model.sh;如果不联调只想要产出训练log可以注掉本行,提交时需打开
_set_params $@
# _train # 如果只想产出训练log,不解析,可取消注释
_run # 该函数在run_model.sh中,执行时会调用_train; 如果不联调只想要产出训练log可以注掉本行,提交时需打开