#!/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) log_path=${LOG_PATH_INDEX_DIR:-$(pwd)} 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 wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_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) fp_item_list=(fp32) for model_mode in ${model_mode_list[@]}; do for fp_item in ${fp_item_list[@]}; do if [ ${model_mode} == "det_r50_vd_east" ]; then bs_list=(16) else bs_list=(8 16) fi for bs_item in ${bs_list[@]}; do log_name=ocr_${model_mode}_bs${bs_item}_${fp_item} 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} 1 ${model_mode} | tee ${log_path}/${log_name}_speed_1gpus 2>&1 # (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_det.sh ${run_mode} ${bs_item} ${fp_item} 2 ${model_mode} | tee ${log_path}/${log_name}_speed_8gpus8p 2>&1 sleep 60 done done done