add test shell

test/params.txt

+train_model_list: det;benchmark/benchmark_det.yml
+gpu_list: -1|0|0,1
+auto_cast_list: False|True
+trainer_list: norm|quant|prune
+python: python3.7
+
+inference: python|C++
+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
+img_dir: /paddle/OCR/test_set/benchmark_eval
+
+epoch: 10
+checkpoints: None
+

test/test.sh

+#!/bin/bash 
+FILENAME=$1
+
+# 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
+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 ../
+elif [ ${MODE} = "whole_train_infer" ];then
+    rm -rf ./train_data/icdar2015
+    wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
+    cd ./train_data/ && tar xf icdar2015.tar && cd ../
+else
+    echo "Do Nothing"
+fi
+
+
+dataline=$(cat ${FILENAME})
+# parser params
+IFS=$'\n'
+lines=(${dataline})
+function func_parser(){
+    strs=$1
+    IFS=":"
+    array=(${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]}")
+img_dir=$(func_parser "${lines[12]}")
+
+# train superparameters
+epoch=$(func_parser "${lines[13]}")
+checkpoints=$(func_parser "${lines[14]}")
+
+
+for train_model in ${train_model_list[*]}; do 
+    if [ ${train_model} = "det" ];then
+        model_name="det"
+        yml_file="configs/det/det_mv3_db.yml"
+    elif [ ${train_model} = "rec" ];then
+        model_name="rec"
+        yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
+    else
+        model_name="det"
+        yml_file="configs/det/det_mv3_db.yml"
+    fi
+    # array=(${train_model})
+    # for j in "${!array[@]}"; do
+    #     model_name=${array[0]}
+    #     yml_file=${array[1]}
+    # done
+    IFS="|"
+    for gpu in ${gpu_list[*]}; do
+        use_gpu=True
+        if [ ${gpu} = "-1" ];then
+            lanuch=""
+            use_gpu=False
+        elif [ ${#gpu} -le 1 ];then
+            launch=""
+        else
+            launch="-m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu}"
+        fi
+        # echo "model_name: ${model_name}  yml_file: ${yml_file}   launch: ${launch}   gpu: ${gpu}" 
+        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"
+                elif [ ${slim_trainer} = "quant" ]; then
+                    trainer="deploy/slim/quantization/quant.py"
+                    export_model="deploy/slim/quantization/export_model.py"
+                elif [ ${slim_trainer} = "prune" ]; then
+                    trainer="deploy/slim/prune/sensitivity_anal.py"
+                    export_model="deploy/slim/prune/export_prune_model.py"
+                elif [ ${slim_trainer} = "distill" ]; then
+                    trainer="deploy/slim/distill/train_dml.py"
+                    export_model="deploy/slim/distill/export_distill_model.py"
+                else
+                    trainer="tools/train.py"
+                    export_model="tools/export_model.py"
+                fi
+                # dataset="Train.dataset.data_dir=${train_dir}  Train.dataset.label_file_list=${train_label_file}  Eval.dataset.data_dir=${eval_dir} Eval.dataset.label_file_list=${eval_label_file}"
+                save_log=${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}
+                echo ${python}  ${launch}  ${trainer}  -c ${yml_file} -o Global.auto_cast=${auto_cast}  Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu}  Global.epoch=${epoch}
+                echo ${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/best_accuracy Global.save_inference_dir=${save_log}/export_inference/ 
+                if [ "${model_name}" = "det" ]; then 
+                    export rec_batch_size_list=( "1" )
+                    inference="tools/infer/predict_det.py"
+                elif [ "${model_name}" = "rec" ]; then
+                    inference="tools/infer/predict_rec.py"
+                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    
+                                    echo ${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${save_log}/export_inference/ --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  --save_log_path=${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
+                                    # ${python} ${inference} --enable_mkldnn=${use_mkldnn} --use_gpu=False --cpu_threads=${threads} --benchmark=True --det_model_dir=${save_log}/export_inference/ --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir}  --image_dir=${img_dir}  2>&1 | tee ${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.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 ${rec_batch_size_list[*]}; do
+                                    # echo "${model_name}  ${det_model_dir} ${rec_model_dir}, use_trt: ${use_trt}   use_fp16: ${use_fp16}"
+                                    echo ${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt}  --precision=${precision} --benchmark=True --det_model_dir=${save_log}/export_inference/ --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log
+                                done
+                            done
+                        done
+                    fi
+                done
+            done
+        done
+    done
+done