Merge pull request #4670 from hysunflower/update_ocr_scripts_for_benchmark
Update ocr scripts for benchmarkpull/4760/head
Double_V
GitHub
commit
caa7799b67
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@ -5,28 +5,36 @@ set -xe
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function _set_params(){
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run_mode=${1:-"sp"} # 单卡sp|多卡mp
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batch_size=${2:-"64"}
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fp_item=${3:-"fp32"} # fp32|fp16
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max_iter=${4:-"10"} # 可选，如果需要修改代码提前中断
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model_name=${5:-"model_name"}
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fp_item=${3:-"fp32"} # fp32|fp16
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max_epoch=${4:-"10"} # 可选，如果需要修改代码提前中断
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model_item=${5:-"model_item"}
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run_log_path=${TRAIN_LOG_DIR:-$(pwd)} # TRAIN_LOG_DIR 后续QA设置该参数
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# 日志解析所需参数
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base_batch_size=${batch_size}
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mission_name="OCR"
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direction_id="0"
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ips_unit="images/sec"
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skip_steps=2 # 解析日志，有些模型前几个step耗时长，需要跳过 (必填)
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keyword="ips:" # 解析日志，筛选出数据所在行的关键字 (必填)
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index="1"
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model_name=${model_item}_${run_mode}_bs${batch_size}_${fp_item} # model_item 用于yml文件名匹配，model_name 用于数据入库前端展示
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# 以下不用修改
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device=${CUDA_VISIBLE_DEVICES//,/ }
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arr=(${device})
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num_gpu_devices=${#arr[*]}
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log_file=${run_log_path}/${model_name}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
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log_file=${run_log_path}/${model_item}_${run_mode}_bs${batch_size}_${fp_item}_${num_gpu_devices}
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}
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function _train(){
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echo "Train on ${num_gpu_devices} GPUs"
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echo "current CUDA_VISIBLE_DEVICES=$CUDA_VISIBLE_DEVICES, gpus=$num_gpu_devices, batch_size=$batch_size"
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train_cmd="-c configs/det/${model_name}.yml -o Train.loader.batch_size_per_card=${batch_size} Global.epoch_num=${max_iter} Global.eval_batch_step=[0,20000] Global.print_batch_step=2"
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train_cmd="-c configs/det/${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"
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case ${run_mode} in
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sp)
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train_cmd="python3.7 tools/train.py "${train_cmd}""
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train_cmd="python tools/train.py "${train_cmd}""
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;;
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mp)
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train_cmd="python3.7 -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
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train_cmd="python -m paddle.distributed.launch --log_dir=./mylog --gpus=$CUDA_VISIBLE_DEVICES tools/train.py ${train_cmd}"
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;;
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*) echo "choose run_mode(sp or mp)"; exit 1;
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esac
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@ -46,17 +54,7 @@ function _train(){
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fi
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}
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function _analysis_log(){
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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"
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eval $analysis_cmd
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}
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function _kill_process(){
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kill -9 `ps -ef|grep 'python3.7'|awk '{print $2}'`
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}
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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
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_set_params $@
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_train
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_analysis_log
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_kill_process
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#_train # 如果只想产出训练log,不解析,可取消注释
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_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
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@ -1,7 +1,8 @@
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#!/bin/bash
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# 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
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# 执行目录: ./PaddleOCR
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# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
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python3.7 -m pip install -r requirements.txt
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python -m pip install -r requirements.txt
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# 2 拷贝该模型需要数据、预训练模型
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wget -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data && tar xf icdar2015.tar && cd ../
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wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
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@ -17,11 +18,13 @@ for model_mode in ${model_mode_list[@]}; do
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for bs_item in ${bs_list[@]}; do
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echo "index is speed, 1gpus, begin, ${model_name}"
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run_mode=sp
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CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} # (5min)
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log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
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CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (5min)
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sleep 60
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echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
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run_mode=mp
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CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode}
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log_name=ocr_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}
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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} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1
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sleep 60
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done
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done
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