test to test_v5

test/ocr_det_params.txt

+model_name:ocr_det
+python:python3.7
+gpu_list:-1|0|0,1
+Global.auto_cast:False|True
+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
+
+trainer:norm|pact|fpgm
+norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+quant_train:deploy/slim/quantization/quant.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/det_mv3_db_v2.0_train/best_accuracy
+fpgm_train:null
+distill_train:null
+
+eval:tools/eval.py -c configs/det/det_mv3_db.yml -o 
+
+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
+distill_export:null
+
+inference:tools/infer/predict_det.py
+--use_gpu:True|False
+--enable_mkldnn:True|False
+--cpu_threads:1|6
+--rec_batch_num:1
+--use_tensorrt:True|False
+--precision:fp32|fp16|int8
+--det_model_dir
+--image_dir
+--save_log_path
+

test/paddleocr_ci_params.txt

-train_model_list: ocr_det
-gpu_list: -1|0|0,1
-auto_cast_list: False|True
-trainer_list: norm|pact|fpgm
-python: python3.7
-
-inference: python
-devices: cpu|gpu
-use_mkldnn_list: True|False
-cpu_threads_list: 1|6
-rec_batch_size_list: 1|6
-gpu_trt_list: True|False
-gpu_precision_list: fp32|fp16|int8
-infer_gpu_id: 0
-log_path: ./output

test/infer.sh → test/prepare.sh

 #!/bin/bash
 FILENAME=$1
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
+MODE=$2
+
 dataline=$(cat ${FILENAME})
+
 # parser params
 IFS=$'\n'
 lines=(${dataline})
-function func_parser(){
+function func_parser_key(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[0]}
+    echo ${tmp}
+}
+function func_parser_value(){
     strs=$1
-    IFS=": "
+    IFS=":"
     array=(${strs})
     tmp=${array[1]}
     echo ${tmp}
 }
 IFS=$'\n'
 # The training params
-train_model_list=$(func_parser "${lines[0]}")
-slim_trainer_list=$(func_parser "${lines[3]}")
-python=$(func_parser "${lines[4]}")
-# inference params
-# inference=$(func_parser "${lines[5]}")
-devices=$(func_parser "${lines[6]}")
-use_mkldnn_list=$(func_parser "${lines[7]}")
-cpu_threads_list=$(func_parser "${lines[8]}")
-rec_batch_size_list=$(func_parser "${lines[9]}")
-gpu_trt_list=$(func_parser "${lines[10]}")
-gpu_precision_list=$(func_parser "${lines[11]}")
+model_name=$(func_parser_value "${lines[0]}")
+train_model_list=$(func_parser_value "${lines[0]}")
+slim_trainer_list=$(func_parser_value "${lines[12]}")
 
-infer_gpu_id=$(func_parser "${lines[12]}")
-log_path=$(func_parser "${lines[13]}")
-status_log="${log_path}/result.log"
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
+MODE=$2
+# prepare pretrained weights and dataset 
+wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
+wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
+cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
 
-# install requirments
-${python} -m pip install pynvml;
-${python} -m pip install psutil;
-${python} -m pip install GPUtil;
+if [ ${MODE} = "lite_train_infer" ];then
+    # pretrain lite train data
+    rm -rf ./train_data/icdar2015
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
+    cd ./train_data/ && tar xf icdar2015_lite.tar
+    ln -s ./icdar2015_lite ./icdar2015
+    cd ../
+    epoch=10
+    eval_batch_step=10
+elif [ ${MODE} = "whole_train_infer" ];then
+    rm -rf ./train_data/icdar2015
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
+    cd ./train_data/ && tar xf icdar2015.tar && cd ../
+    epoch=500
+    eval_batch_step=200
+elif [ ${MODE} = "whole_infer" ];then
+    rm -rf ./train_data/icdar2015
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
+    cd ./train_data/ && tar xf icdar2015_infer.tar
+    ln -s ./icdar2015_infer ./icdar2015
+    cd ../
+    epoch=10
+    eval_batch_step=10
+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
+        cd ./inference && tar xf ${eval_model_name}.tar && cd ../
+    else 
+        eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
+        wget -nc  -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
+        cd ./inference && tar xf ${eval_model_name}.tar && cd ../
+    fi 
+fi
 
-paddle_info="$(${python} -c "import paddle;print(f'paddle_version:{paddle.__version__}');print(f'paddle_commit:{paddle.__git_commit__}')")"
-echo -e "\033[33m $paddle_info \033[0m" | tee -a ${status_log}
-cpu_model=`cat /proc/cpuinfo | grep "model name" | awk -F ':' '{print $2}' | sort | uniq`
-echo -e "\033[33m cpu_info:$cpu_model \033[0m" | tee -a ${status_log}
-ip=`ifconfig| grep -A 1 'eth0'|grep 'inet'|awk -F ':' '{print $2}'|awk '{print $1}'`
-echo -e "\033[33m ip_info:$ip \033[0m" | tee -a ${status_log}
 
-function status_check(){
-    last_status=$1   # the exit code
-    run_model=$2
-    run_command=$3
-    run_log=$4
-    if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m $run_model successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    else
-        echo -e "\033[33m $case failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
-    fi
-}
 IFS='|'
 for train_model in ${train_model_list[*]}; do 
     if [ ${train_model} = "ocr_det" ];then
@@ -113,61 +134,5 @@ for train_model in ${train_model_list[*]}; do
                 cd ./inference && tar xf ${eval_model_name}.tar && cd ../
             fi
         fi
-
-        save_log_path="${log_path}/${eval_model_name}"
-        command="${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model='./inference/${eval_model_name}/best_accuracy' Global.save_model_dir=${save_log_path} Eval.dataset.data_dir=${data_dir}  Eval.dataset.label_file_list=${data_label_file}"
-        ${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model=./inference/${eval_model_name}/best_accuracy Global.save_model_dir=${save_log_path} Eval.dataset.data_dir=${data_dir}  Eval.dataset.label_file_list=${data_label_file}
-        status_check $? "${trainer}" "${command}" "${status_log}"
-
-        command="${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model="${eval_model_name}/best_accuracy" Global.save_inference_dir=${log_path}/${eval_model_name}_infer Global.save_model_dir=${save_log_path}"
-        ${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model="./inference/${eval_model_name}/best_accuracy" Global.save_inference_dir="${log_path}/${eval_model_name}_infer" Global.save_model_dir=${save_log_path}
-        status_check $? "${trainer}" "${command}" "${status_log}"
-
-        if [ $? -eq 0 ]; then
-            echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${save_log}/train.log
-        else
-            cat ${save_log}/train.log
-            echo -e "\033[33m training of $model_name failed!\033[0m" | tee -a ${save_log}/train.log
-        fi
-        if [ "${model_name}" = "det" ]; then 
-            export rec_batch_size_list=( "1" )
-            inference="tools/infer/predict_det.py"
-            det_model_dir="${log_path}/${eval_model_name}_infer"
-            rec_model_dir=""
-        elif [ "${model_name}" = "rec" ]; then
-            inference="tools/infer/predict_rec.py"
-            rec_model_dir="${log_path}/${eval_model_name}_infer"
-            det_model_dir=""
-        fi
-        # inference 
-        for device in ${devices[*]}; do 
-            if [ ${device} = "cpu" ]; then
-                for use_mkldnn in ${use_mkldnn_list[*]}; do
-                    for threads in ${cpu_threads_list[*]}; do
-                        for rec_batch_size in ${rec_batch_size_list[*]}; do    
-                            save_log_path="${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log"
-                            command="${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  --save_log_path=${save_log_path}"
-                            ${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  --save_log_path=${save_log_path}
-                            status_check $? "${trainer}" "${command}" "${status_log}"
-                        done
-                    done
-                done
-            else
-                # env="export CUDA_VISIBLE_DEVICES=${infer_gpu_id}"
-                for use_trt in ${gpu_trt_list[*]}; do
-                    for precision in ${gpu_precision_list[*]}; do
-                        if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
-                            continue
-                        fi
-                        for rec_batch_size in ${rec_batch_size_list[*]}; do
-                            save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
-                            command="${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt}  --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
-                            ${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt}  --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
-                            status_check $? "${trainer}" "${command}" "${status_log}"
-                        done
-                    done
-                done
-            fi
-        done
     done
 done

test/test.sh

-#!/bin/bash 
-# Usage:
-# bash test/test.sh ./test/paddleocr_ci_params.txt 'lite_train_infer'
-
+#!/bin/bash
 FILENAME=$1
-
-# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
+# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer', 'infer']
 MODE=$2
-# prepare pretrained weights and dataset 
-wget -nc -P  ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
-wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
-cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
-
-if [ ${MODE} = "lite_train_infer" ];then
-    # pretrain lite train data
-    rm -rf ./train_data/icdar2015
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-    cd ./train_data/ && tar xf icdar2015_lite.tar
-    ln -s ./icdar2015_lite ./icdar2015
-    cd ../
-    epoch=10
-    eval_batch_step=10
-elif [ ${MODE} = "whole_train_infer" ];then
-    rm -rf ./train_data/icdar2015
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
-    cd ./train_data/ && tar xf icdar2015.tar && cd ../
-    epoch=500
-    eval_batch_step=200
-else
-    rm -rf ./train_data/icdar2015
-    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
-    cd ./train_data/ && tar xf icdar2015_infer.tar
-    ln -s ./icdar2015_infer ./icdar2015
-    cd ../
-    epoch=10
-    eval_batch_step=10
-fi
-
-img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
-
 
 dataline=$(cat ${FILENAME})
+
 # parser params
 IFS=$'\n'
 lines=(${dataline})
-function func_parser(){
+function func_parser_key(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[0]}
+    echo ${tmp}
+}
+function func_parser_value(){
     strs=$1
-    IFS=": "
+    IFS=":"
     array=(${strs})
     tmp=${array[1]}
     echo ${tmp}
 }
-IFS=$'\n'
-# The training params
-train_model_list=$(func_parser "${lines[0]}")
-gpu_list=$(func_parser "${lines[1]}")
-auto_cast_list=$(func_parser "${lines[2]}")
-slim_trainer_list=$(func_parser "${lines[3]}")
-python=$(func_parser "${lines[4]}")
-# inference params
-inference=$(func_parser "${lines[5]}")
-devices=$(func_parser "${lines[6]}")
-use_mkldnn_list=$(func_parser "${lines[7]}")
-cpu_threads_list=$(func_parser "${lines[8]}")
-rec_batch_size_list=$(func_parser "${lines[9]}")
-gpu_trt_list=$(func_parser "${lines[10]}")
-gpu_precision_list=$(func_parser "${lines[11]}")
-
-log_path=$(func_parser "${lines[13]}")
-status_log="${log_path}/result.log"
-
-# install requirments
-${python} -m pip install pynvml;
-${python} -m pip install psutil;
-${python} -m pip install GPUtil;
-${python} -m pip install paddlesim==2.0.0
-
-paddle_info="$(${python} -c "import paddle;print(f'paddle_version:{paddle.__version__}');print(f'paddle_commit:{paddle.__git_commit__}')")"
-echo -e "\033[33m $paddle_info \033[0m" | tee -a ${status_log}
-cpu_model=`cat /proc/cpuinfo | grep "model name" | awk -F ':' '{print $2}' | sort | uniq`
-echo -e "\033[33m cpu_info:$cpu_model \033[0m" | tee -a ${status_log}
-ip=`ifconfig| grep -A 1 'eth0'|grep 'inet'|awk -F ':' '{print $2}'|awk '{print $1}'`
-echo -e "\033[33m ip_info:$ip \033[0m" | tee -a ${status_log}
-
 function status_check(){
     last_status=$1   # the exit code
-    run_model=$2
-    run_command=$3
-    run_log=$4
+    run_command=$2
+    run_log=$3
     if [ $last_status -eq 0 ]; then
-        echo -e "\033[33m $run_model successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
     else
-        echo -e "\033[33m $case failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
     fi
 }
 
-IFS="|"
-for train_model in ${train_model_list[*]}; do 
-    if [ ${train_model} = "ocr_det" ];then
-        model_name="det"
-        yml_file="configs/det/det_mv3_db.yml"
-    elif [ ${train_model} = "ocr_rec" ];then
-        model_name="rec"
-        yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
-    else
-        model_name="det"
-        yml_file="configs/det/det_mv3_db.yml"
-    fi
-    IFS="|"
-    for gpu in ${gpu_list[*]}; do
-        use_gpu=True
-        if [ ${gpu} = "-1" ];then
-            use_gpu=False
-            env=""
-        elif [ ${#gpu} -le 1 ];then
-            env="CUDA_VISIBLE_DEVICES=${gpu}"
+IFS=$'\n'
+# The training params
+model_name=$(func_parser_value "${lines[0]}")
+python=$(func_parser_value "${lines[1]}")
+gpu_list=$(func_parser_value "${lines[2]}")
+autocast_list=$(func_parser_value "${lines[3]}")
+autocast_key=$(func_parser_key "${lines[3]}")
+epoch_key=$(func_parser_key "${lines[4]}")
+save_model_key=$(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]}")
+
+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='|'
+    _python=$1
+    _script=$2
+    _model_dir=$3
+    _log_path=$4
+    _img_dir=$5
+    
+    # inference 
+    for use_gpu in ${use_gpu_list[*]}; do 
+        if [ ${use_gpu} = "False" ]; then
+            for use_mkldnn in ${use_mkldnn_list[*]}; do
+                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}"
+                        eval $command
+                        status_check $? "${command}" "${status_log}"
+                    done
+                done
+            done
         else
-            IFS=","
-            array=(${gpu})
-            env="CUDA_VISIBLE_DEVICES=${array[0]}"
-            IFS="|"
-        fi
-        for auto_cast in ${auto_cast_list[*]}; do 
-            for slim_trainer in ${slim_trainer_list[*]}; do 
-                if [ ${slim_trainer} = "norm" ]; then
-                    trainer="tools/train.py"
-                    export_model="tools/export_model.py"
-                    pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
-                elif [ ${slim_trainer} = "pact" ]; then
-                    trainer="deploy/slim/quantization/quant.py"
-                    export_model="deploy/slim/quantization/export_model.py"
-                    pretrain="./pretrain_models/det_mv3_db_v2.0_train/best_accuracy"
-                elif [ ${slim_trainer} = "fpgm" ]; then
-                    trainer="deploy/slim/prune/sensitivity_anal.py"
-                    export_model="deploy/slim/prune/export_prune_model.py"
-                    pretrain="./pretrain_models/det_mv3_db_v2.0_train/best_accuracy"
-                    wget -nc -P https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/sen.pickle
-                elif [ ${slim_trainer} = "distill" ]; then
-                    trainer="deploy/slim/distill/train_dml.py"
-                    export_model="deploy/slim/distill/export_distill_model.py"
-                    pretrain=""
-                else
-                    trainer="tools/train.py"
-                    export_model="tools/export_model.py"
-                    pretrain="./pretrain_models/MobileNetV3_large_x0_5_pretrained"
-                fi
-                save_log="${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}"
-                if [ ${#gpu} -le 2 ];then
-                    command="${python} ${trainer}  -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain}  Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
-                    ${python} ${trainer}  -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain}  Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu}  Train.loader.batch_size_per_card=2
-                else 
-                    command="${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer}  -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain}  Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
-                    ${python} -m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu} ${trainer}  -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.pretrained_model=${pretrain}  Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu}  Train.loader.batch_size_per_card=2
-                fi
-                status_check $? "${trainer}" "${command}" "${status_log}"
-
-                command="${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest Global.save_inference_dir=${save_log}_infer/ Global.save_model_dir=${save_log}"
-                ${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest Global.save_inference_dir=${save_log}_infer/ Global.save_model_dir=${save_log} 
-                status_check $? "${trainer}" "${command}" "${status_log}"
-               
-                if [ "${model_name}" = "det" ]; then 
-                    export rec_batch_size_list=( "1" )
-                    inference="tools/infer/predict_det.py"
-                    det_model_dir=${save_log}_infer
-                    rec_model_dir=""
-                elif [ "${model_name}" = "rec" ]; then
-                    inference="tools/infer/predict_rec.py"
-                    rec_model_dir=${save_log}_infer
-                    det_model_dir=""
-                fi
-                # inference 
-                for device in ${devices[*]}; do 
-                    if [ ${device} = "cpu" ]; then
-                        for use_mkldnn in ${use_mkldnn_list[*]}; do
-                            for threads in ${cpu_threads_list[*]}; do
-                                for rec_batch_size in ${rec_batch_size_list[*]}; do    
-                                    save_log_path="${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log"
-                                    command="${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  --save_log_path=${save_log_path}"
-                                    ${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  --save_log_path=${save_log_path}
-                                    status_check $? "${inference}" "${command}" "${status_log}"
-                                done
-                            done
-                        done
-                    else 
-                        for use_trt in ${gpu_trt_list[*]}; do
-                            for precision in ${gpu_precision_list[*]}; do
-                                if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
-                                    continue
-                                fi
-                                for rec_batch_size in ${rec_batch_size_list[*]}; do
-                                    save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
-                                    command="${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt}  --precision=${precision} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
-                                    ${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt}  --precision=${precision} --benchmark=True --det_model_dir=${det_model_dir} --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
-                                    status_check $? "${inference}" "${command}" "${status_log}"
-                                done
-                            done
-                        done
+            for use_trt in ${use_trt_list[*]}; do
+                for precision in ${precision_list[*]}; do
+                    if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
+                        continue
                     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}"
+                        eval $command
+                        status_check $? "${command}" "${status_log}"
+                    done
                 done
             done
+        fi
+    done
+}
+
+if [ ${MODE} != "infer" ]; then
+
+IFS="|"
+for gpu in ${gpu_list[*]}; do
+    use_gpu=True
+    if [ ${gpu} = "-1" ];then
+        use_gpu=False
+        env=""
+    elif [ ${#gpu} -le 1 ];then
+        env="export CUDA_VISIBLE_DEVICES=${gpu}"
+    elif [ ${#gpu} -le 15 ];then
+        IFS=","
+        array=(${gpu})
+        env="export CUDA_VISIBLE_DEVICES=${array[0]}"
+        IFS="|"
+    else
+        IFS=";"
+        array=(${gpu})
+        ips=${array[0]}
+        gpu=${array[1]}
+        IFS="|"
+    fi
+    for autocast in ${autocast_list[*]}; do 
+        for trainer in ${trainer_list[*]}; do 
+            if [ ${trainer} = "pact" ]; then
+                run_train=${pact_trainer}
+                run_export=${pact_export}
+            elif [ ${trainer} = "fpgm" ]; then
+                run_train=${fpgm_trainer}
+                run_export=${fpgm_export}
+            elif [ ${trainer} = "distill" ]; then
+                run_train=${distill_trainer}
+                run_export=${distill_export}
+            else
+                run_train=${norm_trainer}
+                run_export=${norm_export}
+            fi
+
+            if [ ${run_train} = "null" ]; then
+                continue
+            fi
+            if [ ${run_export} = "null" ]; then
+                continue
+            fi
+
+            save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
+            if [ ${#gpu} -le 2 ];then  # epoch_num #TODO
+                cmd="${python} ${run_train} ${train_use_gpu_key}=${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}"
+            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}"
+            fi
+            # run train
+            eval $cmd
+            status_check $? "${cmd}" "${status_log}"
+
+            # run eval
+            eval_cmd="${python} ${eval_py} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest" 
+            eval $eval_cmd
+            status_check $? "${eval_cmd}" "${status_log}"
+
+            # run export model
+            save_infer_path="${save_log}"
+            export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/latest ${save_infer_key}=${save_infer_path}"
+            eval $export_cmd
+            status_check $? "${export_cmd}" "${status_log}"
+
+            #run inference
+            save_infer_path="${save_log}"
+            func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
         done
     done
 done
+
+else
+    save_infer_path="${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}" "${status_log}"
+
+    #run inference
+    func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${infer_img_dir}"
+fi