PaddleClas/benchmark/run_all.sh

# 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 **
# 1 安装该模型需要的依赖 (如需开启优化策略请注明)
# pip install ...
# 2 拷贝该模型需要数据、预训练模型
# 3 批量运行（如不方便批量，1，2需放到单个模型中）
log_path=${LOG_PATH_INDEX_DIR:-$(pwd)}  # LOG_PATH_INDEX_DIR 后续QA设置参数
model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 ShuffleNetV2_x1_0 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base)    # benchmark 监控模型列表
#model_mode_list=(MobileNetV1 MobileNetV2 MobileNetV3_large_x1_0 EfficientNetB0 ShuffleNetV2_x1_0 DenseNet121 HRNet_W48_C SwinTransformer_tiny_patch4_window7_224 alt_gvt_base)   # 该脚本支持列表
fp_item_list=(fp32)
#bs_list=(32 64 96 128)
for model_mode in ${model_mode_list[@]}; do
      for fp_item in ${fp_item_list[@]}; do
          if [ ${model_mode} = MobileNetV3_large_x1_0 ] || [ ${model_mode} = ShuffleNetV2_x1_0 ]; then
              bs_list=(256)
          else
              bs_list=(64)
          fi
          for bs_item in ${bs_list[@]};do
	    echo "index is speed, 1gpus, begin, ${model_name}"
	    run_mode=sp
	    CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode} | tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_1gpus 2>&1    #  (5min)
	    sleep 10
            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.sh ${run_mode} ${bs_item} ${fp_item} 1 ${model_mode}| tee ${log_path}/clas_${model_mode}_${run_mode}_bs${bs_item}_${fp_item}_8gpus8p 2>&1
            sleep 10
            done
      done
done