update_scripts_benchmark

benchmark/run_all.sh

@@ -5,13 +5,15 @@
 # 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）
- 
 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)
+bs_list=(64)     #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)
+          fi
           for bs_item in ${bs_list[@]};do
 	    echo "index is speed, 1gpus, begin, ${model_name}"
 	    run_mode=sp

benchmark/run_benchmark.sh

@@ -45,7 +45,7 @@ function _train(){
     esac
     rm -rf mylog
 # 以下不用修改
-    timeout 15m ${train_cmd} > ${log_file} 2>&1
+    timeout 5m ${train_cmd} > ${log_file} 2>&1
     if [ $? -ne 0 ];then
         echo -e "${model_name}, FAIL"
         export job_fail_flag=1