提交 3b2e50a1 编写于 作者: L LDOUBLEV

Merge branch 'add_det_benchmark' of https://github.com/LDOUBLEV/PaddleOCR into fix_eval

#!/usr/bin/env bash
set -xe
# 运行示例:CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
# 参数说明
function _set_params(){
run_mode=${1:-"sp"} # 单卡sp|多卡mp
batch_size=${2:-"64"}
fp_item=${3:-"fp32"} # fp32|fp16
max_iter=${4:-"500"} # 可选,如果需要修改代码提前中断
model_name=${5:-"model_name"}
run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
# 以下不用修改
device=${CUDA_VISIBLE_DEVICES//,/ }
arr=(${device})
num_gpu_devices=${#arr[*]}
log_file=${run_log_path}/${model_name}_${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"
train_cmd="-c configs/det/${model_name}.yml
-o Train.loader.batch_size_per_card=${batch_size}
-o Global.epoch_num=${max_iter} "
case ${run_mode} in
sp)
train_cmd="python3.7 tools/train.py "${train_cmd}""
;;
mp)
train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
;;
*) echo "choose run_mode(sp or mp)"; exit 1;
esac
# 以下不用修改
timeout 15m ${train_cmd} > ${log_file} 2>&1
if [ $? -ne 0 ];then
echo -e "${model_name}, FAIL"
export job_fail_flag=1
else
echo -e "${model_name}, SUCCESS"
export job_fail_flag=0
fi
kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
if [ $run_mode = "mp" -a -d mylog ]; then
rm ${log_file}
cp mylog/workerlog.0 ${log_file}
fi
}
_set_params $@
_train
# 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
# 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
# 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)
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)
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
done
done
done
...@@ -8,7 +8,7 @@ Global: ...@@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration # evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000] eval_batch_step: [4000, 5000]
cal_metric_during_train: False cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/ pretrained_model: ./pretrain_models/ResNet50_vd_pretrained
checkpoints: checkpoints:
save_inference_dir: save_inference_dir:
use_visualdl: False use_visualdl: False
......
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