40 lines
2.1 KiB
Bash
40 lines
2.1 KiB
Bash
#!/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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log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}
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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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wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet18_vd_pretrained.pdparams
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wget -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_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_res18_db_v2.0 det_r50_vd_east det_r50_vd_pse)
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fp_item_list=(fp32)
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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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if [ ${model_mode} == "det_r50_vd_east" ]; then
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bs_list=(16)
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else
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bs_list=(8 16)
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fi
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for bs_item in ${bs_list[@]}; do
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echo "index is speed, 1gpus, begin, ${model_name}"
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run_mode=sp
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log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
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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, ${model_name}"
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run_mode=mp
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log_name=ocr_${model_mode}_bs${bs_item}_${fp_item}_${run_mode}
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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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done
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