30 lines
1.4 KiB
Bash
30 lines
1.4 KiB
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
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# 执行目录：需说明
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cd 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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# 2 拷贝该模型需要数据、预训练模型
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wget -p ./tain_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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# 3 批量运行（如不方便批量，1，2需放到单个模型中）
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model_mode_list=(det_mv3_db det_r50_vd_east)
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fp_item_list=(fp32)
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bs_list=(256 128)
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for model_mode in ${model_mode_list[@]}; do
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for fp_item in ${fp_item_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.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (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.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
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sleep 60
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done
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done
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done
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