update_scripts_benchmark

benchmark/run_all.sh

@@ -5,6 +5,7 @@
 # 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=./benchmark
 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)

benchmark/run_benchmark.sh

 #!/usr/bin/env bash
-set -xe
+set -x
 # 运行示例：CUDA_VISIBLE_DEVICES=0 bash run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 500 ${model_mode}
 # 参数说明
 function _set_params(){
@@ -35,7 +35,7 @@ function _train(){
         model_config=`find ppcls/configs/ImageNet -name ${model_name}_fp16.yaml` 
     fi
 
-    train_cmd="-c ${model_config} -o DataLoader.Train.sampler.batch_size=${batch_size} -o Global.epochs=${epochs}"   
+    train_cmd="-c ${model_config} -o DataLoader.Train.sampler.batch_size=${batch_size} -o Global.epochs=${epochs}  -o Global.eval_during_train=False"
     case ${run_mode} in
     sp) train_cmd="python -u tools/train.py ${train_cmd}" ;;
     mp)