From 7e28ae8c78f7d25099a4f3968673c191aa1e1d9b Mon Sep 17 00:00:00 2001 From: hysunflower Date: Wed, 17 Nov 2021 06:35:29 +0000 Subject: [PATCH] update ocr scripts for benchmark --- benchmark/run_benchmark_det.sh | 42 ++++++++++++++++------------------ benchmark/run_det.sh | 10 ++++---- 2 files changed, 26 insertions(+), 26 deletions(-) diff --git a/benchmark/run_benchmark_det.sh b/benchmark/run_benchmark_det.sh index 46144b43..0439f7c1 100644 --- a/benchmark/run_benchmark_det.sh +++ b/benchmark/run_benchmark_det.sh @@ -1,32 +1,40 @@ #!/usr/bin/env bash -set -xe +set -x # 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=${4:-"10"} # 可选，如果需要修改代码提前中断 - model_name=${5:-"model_name"} + fp_item=${3:-"fp32"} # fp32|fp16 + max_epoch=${4:-"10"} # 可选，如果需要修改代码提前中断 + model_item=${5:-"model_item"} run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数 - +# 日志解析所需参数 + base_batch_size=${batch_size} + mission_name="OCR" + direction_id="0" + ips_unit="instance/sec" + skip_steps=2 # 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=${model_item}_${run_mode}_bs${batch_size}_${fp_item} # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示 # 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}_${num_gpu_devices} + log_file=${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" - train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} Global.eval_batch_step=[0,20000] Global.print_batch_step=2" + train_cmd="-c configs/det/${model_item}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_epoch} Global.eval_batch_step=[0,20000] Global.print_batch_step=2" case ${run_mode} in sp) - train_cmd="python3.7 tools/train.py "${train_cmd}"" + train_cmd="python tools/train.py "${train_cmd}"" ;; mp) - train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}" + train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}" ;; *) echo "choose run_mode(sp or mp)"; exit 1; esac @@ -46,17 +54,7 @@ function _train(){ fi } -function _analysis_log(){ - analysis_cmd="python3.7 benchmark/analysis.py --filename ${log_file} --mission_name ${model_name} --run_mode ${run_mode} --direction_id 0 --keyword 'ips:' --base_batch_size ${batch_size} --skip_steps 1 --gpu_num ${num_gpu_devices} --index 1 --model_mode=-1 --ips_unit=samples/sec" - eval $analysis_cmd -} - -function _kill_process(){ - kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'` -} - - +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;该脚本在连调时可从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 -_analysis_log -_kill_process \ No newline at end of file +#_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 diff --git a/benchmark/run_det.sh b/benchmark/run_det.sh index 68109b3a..5272d5dc 100644 --- a/benchmark/run_det.sh +++ b/benchmark/run_det.sh @@ -1,10 +1,12 @@ +#!/bin/bash # 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: ./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 +python -m pip install -r requirements.txt # 2 拷贝该模型需要数据、预训练模型 wget -P ./train_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_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse) @@ -15,12 +17,12 @@ for model_mode in ${model_mode_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_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} # (5min) - sleep 60 + CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} # (5min) + sleep 6 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_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} - sleep 60 + sleep 6 done done done -- GitLab