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LDOUBLEV committed Sep 23, 2021
1 parent 07006b8 commit 49fba3e
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Showing 2 changed files with 7 additions and 6 deletions.
11 changes: 6 additions & 5 deletions benchmark/run_det.sh
Original file line number Diff line number Diff line change
@@ -1,10 +1,11 @@
# 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:需说明
# cd PaddleOCR
cd 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
python3.7 -m pip install -r requirements.txt
# 2 拷贝该模型需要数据、预训练模型
# wget -p ./tain_data/ xxxxx
wget -p ./tain_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_mv3_db det_r50_vd_east)
Expand All @@ -15,11 +16,11 @@ 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=7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} # (5min)
sleep 60
echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
run_mode=mp
CUDA_VISIBLE_DEVICES=6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}
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}
sleep 60
done
done
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2 changes: 1 addition & 1 deletion configs/det/det_r50_vd_east.yml
Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@ Global:
# evaluation is run every 5000 iterations after the 4000th iteration
eval_batch_step: [4000, 5000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet50_vd_pretrained/
pretrained_model: ./pretrain_models/ResNet50_vd_pretrained
checkpoints:
save_inference_dir:
use_visualdl: False
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