diff --git a/test/ocr_det_params.txt b/test/ocr_det_params.txt
index 01ac82d3d7d459ca324ec61cfcaac2386660a211..da7e034bdd1ef1f593020d2dd5e809b472651b54 100644
--- a/test/ocr_det_params.txt
+++ b/test/ocr_det_params.txt
@@ -1,13 +1,12 @@
 model_name:ocr_det
 python:python3.7
 gpu_list:0|0,1
-Global.auto_cast:False
+Global.auto_cast:null
 Global.epoch_num:10
 Global.save_model_dir:./output/
-Global.save_inference_dir:./output/
 Train.loader.batch_size_per_card:
-Global.use_gpu
-Global.pretrained_model
+Global.use_gpu:
+Global.pretrained_model:null
 
 trainer:norm|pact
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
@@ -17,6 +16,8 @@ distill_train:null
 
 eval:tools/eval.py -c configs/det/det_mv3_db.yml -o 
 
+Global.save_inference_dir:./output/
+Global.checkpoints:
 norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o 
 quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o 
 fpgm_export:deploy/slim/prune/export_prune_model.py
@@ -29,7 +30,6 @@ inference:tools/infer/predict_det.py
 --rec_batch_num:1
 --use_tensorrt:True|False
 --precision:fp32|fp16|int8
---det_model_dir
---image_dir
---save_log_path
-
+--det_model_dir:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:./test/output/
diff --git a/test/prepare.sh b/test/prepare.sh
index 150682469641a784f641313d361bb921d6d9dfb8..14b62383c2d9fd426bc84d3f58e557f2b3269353 100644
--- a/test/prepare.sh
+++ b/test/prepare.sh
@@ -26,8 +26,10 @@ IFS=$'\n'
 # The training params
 model_name=$(func_parser_value "${lines[0]}")
 train_model_list=$(func_parser_value "${lines[0]}")
+
 trainer_list=$(func_parser_value "${lines[10]}")
 
+
 # MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
 MODE=$2
 # prepare pretrained weights and dataset 
@@ -62,8 +64,8 @@ else
     rm -rf ./train_data/icdar2015
     wget -nc -P ./train_data https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
     if [ ${model_name} = "ocr_det" ]; then
-        eval_model_name="ch_ppocr_mobile_v2.0_det_train"
-        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
+        eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar
         cd ./inference && tar xf ${eval_model_name}.tar && cd ../
     else 
         eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
@@ -94,7 +96,7 @@ for train_model in ${train_model_list[*]}; do
     # eval 
     for slim_trainer in ${trainer_list[*]}; do 
         if [ ${slim_trainer} = "norm" ]; then
-            if [ ${model_name} = "ocr_det" ]; then
+            if [ ${model_name} = "det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -104,7 +106,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi 
         elif [ ${slim_trainer} = "pact" ]; then
-            if [ ${model_name} = "ocr_det" ]; then
+            if [ ${model_name} = "det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -114,7 +116,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi
         elif [ ${slim_trainer} = "distill" ]; then
-            if [ ${model_name} = "ocr_det" ]; then
+            if [ ${model_name} = "det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
@@ -124,7 +126,7 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi 
         elif [ ${slim_trainer} = "fpgm" ]; then
-            if [ ${model_name} = "ocr_det" ]; then
+            if [ ${model_name} = "det" ]; then
                 eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
                 wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
diff --git a/test/test.sh b/test/test.sh
index 2a27563ffaa2b1b96b58cbff89546acf7a286210..a75aed426988755e395f27520fc76de10b4676d1 100644
--- a/test/test.sh
+++ b/test/test.sh
@@ -41,59 +41,51 @@ gpu_list=$(func_parser_value "${lines[2]}")
 autocast_list=$(func_parser_value "${lines[3]}")
 autocast_key=$(func_parser_key "${lines[3]}")
 epoch_key=$(func_parser_key "${lines[4]}")
+epoch_num=$(func_parser_value "${lines[4]}")
 save_model_key=$(func_parser_key "${lines[5]}")
-save_infer_key=$(func_parser_key "${lines[6]}")
-train_batch_key=$(func_parser_key "${lines[7]}")
-train_use_gpu_key=$(func_parser_key "${lines[8]}")
-pretrain_model_key=$(func_parser_key "${lines[9]}")
-
-trainer_list=$(func_parser_value "${lines[10]}")
-norm_trainer=$(func_parser_value "${lines[11]}")
-pact_trainer=$(func_parser_value "${lines[12]}")
-fpgm_trainer=$(func_parser_value "${lines[13]}")
-distill_trainer=$(func_parser_value "${lines[14]}")
-
-eval_py=$(func_parser_value "${lines[15]}")
-norm_export=$(func_parser_value "${lines[16]}")
-pact_export=$(func_parser_value "${lines[17]}")
-fpgm_export=$(func_parser_value "${lines[18]}")
-distill_export=$(func_parser_value "${lines[19]}")
-
-inference_py=$(func_parser_value "${lines[20]}")
-use_gpu_key=$(func_parser_key "${lines[21]}")
-use_gpu_list=$(func_parser_value "${lines[21]}")
-use_mkldnn_key=$(func_parser_key "${lines[22]}")
-use_mkldnn_list=$(func_parser_value "${lines[22]}")
-cpu_threads_key=$(func_parser_key "${lines[23]}")
-cpu_threads_list=$(func_parser_value "${lines[23]}")
-batch_size_key=$(func_parser_key "${lines[24]}")
-batch_size_list=$(func_parser_value "${lines[24]}")
-use_trt_key=$(func_parser_key "${lines[25]}")
-use_trt_list=$(func_parser_value "${lines[25]}")
-precision_key=$(func_parser_key "${lines[26]}")
-precision_list=$(func_parser_value "${lines[26]}")
-model_dir_key=$(func_parser_key "${lines[27]}")
-image_dir_key=$(func_parser_key "${lines[28]}")
-save_log_key=$(func_parser_key "${lines[29]}")
+train_batch_key=$(func_parser_key "${lines[6]}")
+train_use_gpu_key=$(func_parser_key "${lines[7]}")
+pretrain_model_key=$(func_parser_key "${lines[8]}")
+pretrain_model_value=$(func_parser_value "${lines[8]}")
+
+trainer_list=$(func_parser_value "${lines[9]}")
+norm_trainer=$(func_parser_value "${lines[10]}")
+pact_trainer=$(func_parser_value "${lines[11]}")
+fpgm_trainer=$(func_parser_value "${lines[12]}")
+distill_trainer=$(func_parser_value "${lines[13]}")
+
+eval_py=$(func_parser_value "${lines[14]}")
+
+save_infer_key=$(func_parser_key "${lines[15]}")
+export_weight=$(func_parser_key "${lines[16]}")
+norm_export=$(func_parser_value "${lines[17]}")
+pact_export=$(func_parser_value "${lines[18]}")
+fpgm_export=$(func_parser_value "${lines[19]}")
+distill_export=$(func_parser_value "${lines[20]}")
+
+inference_py=$(func_parser_value "${lines[21]}")
+use_gpu_key=$(func_parser_key "${lines[22]}")
+use_gpu_list=$(func_parser_value "${lines[22]}")
+use_mkldnn_key=$(func_parser_key "${lines[23]}")
+use_mkldnn_list=$(func_parser_value "${lines[23]}")
+cpu_threads_key=$(func_parser_key "${lines[24]}")
+cpu_threads_list=$(func_parser_value "${lines[24]}")
+batch_size_key=$(func_parser_key "${lines[25]}")
+batch_size_list=$(func_parser_value "${lines[25]}")
+use_trt_key=$(func_parser_key "${lines[26]}")
+use_trt_list=$(func_parser_value "${lines[26]}")
+precision_key=$(func_parser_key "${lines[27]}")
+precision_list=$(func_parser_value "${lines[27]}")
+infer_model_key=$(func_parser_key "${lines[28]}")
+infer_model=$(func_parser_value "${lines[28]}")
+image_dir_key=$(func_parser_key "${lines[29]}")
+infer_img_dir=$(func_parser_value "${lines[29]}")
+save_log_key=$(func_parser_key "${lines[30]}")
 
 LOG_PATH="./test/output"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results.log"
 
-if [ ${MODE} = "lite_train_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=10
-elif [ ${MODE} = "whole_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=10
-elif [ ${MODE} = "whole_train_infer" ]; then
-    export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-    export epoch_num=300
-else
-    export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
-    export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
-fi
-
 
 function func_inference(){
     IFS='|'
@@ -110,7 +102,7 @@ function func_inference(){
                 for threads in ${cpu_threads_list[*]}; do
                     for batch_size in ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_mkldnn_key}=${use_mkldnn} ${cpu_threads_key}=${threads} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -124,7 +116,7 @@ function func_inference(){
                     fi
                     for batch_size in ${batch_size_list[*]}; do
                         _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
-                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${model_dir_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True"
+                        command="${_python} ${_script} ${use_gpu_key}=${use_gpu} ${use_trt_key}=${use_trt} ${precision_key}=${precision} ${infer_model_key}=${_model_dir} ${batch_size_key}=${batch_size} ${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path}  --benchmark=True"
                         eval $command
                         status_check $? "${command}" "${status_log}"
                     done
@@ -138,9 +130,9 @@ if [ ${MODE} != "infer" ]; then
 
 IFS="|"
 for gpu in ${gpu_list[*]}; do
-    train_use_gpu=True
+    use_gpu=True
     if [ ${gpu} = "-1" ];then
-        train_use_gpu=False
+        use_gpu=False
         env=""
     elif [ ${#gpu} -le 1 ];then
         env="export CUDA_VISIBLE_DEVICES=${gpu}"
@@ -155,6 +147,7 @@ for gpu in ${gpu_list[*]}; do
         ips=${array[0]}
         gpu=${array[1]}
         IFS="|"
+        env=" "
     fi
     for autocast in ${autocast_list[*]}; do 
         for trainer in ${trainer_list[*]}; do 
@@ -179,13 +172,32 @@ for gpu in ${gpu_list[*]}; do
                 continue
             fi
 
-            save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
-            if [ ${#gpu} -le 2 ];then  # epoch_num #TODO
-                cmd="${python} ${run_train} ${train_use_gpu_key}=${train_use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
-            elif [ ${#gpu} -le 15 ];then
-                cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num}  ${save_model_key}=${save_log}"
+            # not set autocast when autocast is null
+            if [ ${autocast} = "null" ]; then
+                set_autocast=" "
+            else
+                set_autocast="${autocast_key}=${autocast}"
+            fi
+            # not set epoch when whole_train_infer
+            if [ ${MODE} != "whole_train_infer" ]; then
+                set_epoch="${epoch_key}=${epoch_num}"
+            else
+                set_epoch=" "
+            fi
+            # set pretrain
+            if [ ${pretrain_model_value} != "null" ]; then
+                set_pretrain="${pretrain_model_key}=${pretrain_model_value}"
             else
-                cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
+                set_pretrain=" "
+            fi
+
+            save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+            if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
+                cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu}  ${save_model_key}=${save_log} ${set_epoch} ${set_pretrain} ${set_autocast}"
+            elif [ ${#gpu} -le 15 ];then  # train with multi-gpu
+                cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${save_model_key}=${save_log}  ${set_epoch} ${set_pretrain} ${set_autocast}"
+            else     # train with multi-machine
+                cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast}"
             fi
             # run train
             eval $cmd
@@ -198,11 +210,12 @@ for gpu in ${gpu_list[*]}; do
 
             # run export model
             save_infer_path="${save_log}"
-            export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
+            export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
             eval $export_cmd
             status_check $? "${export_cmd}" "${status_log}"
 
             #run inference
+            echo $env
             save_infer_path="${save_log}"
             func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
         done
@@ -210,12 +223,13 @@ for gpu in ${gpu_list[*]}; do
 done
 
 else
-    save_infer_path="${LOG_PATH}/${MODE}"
-    run_export=${norm_export}
-    export_cmd="${python} ${run_export} ${save_model_key}=${save_infer_path} ${pretrain_model_key}=${infer_model_dir} ${save_infer_key}=${save_infer_path}"
-    eval $export_cmd
-    status_check $? "${export_cmd}" "${status_log}"
-
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    echo $env
     #run inference
-    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
+    func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}"
 fi