add test_v7

tests/params.txt

+===========================train_params=========================== 
+model_name:ocr_det
+python:python3.7
+gpu_list:0|0,1
+Global.auto_cast:null
+Global.epoch_num:2
+Global.save_model_dir:./output/
+Train.loader.batch_size_per_card:2
+Global.use_gpu:
+Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./train_data/icdar2015/text_localization/ch4_test_images/
+null:null
+##
+trainer:norm_train|pact_train
+norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
+pact_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
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:null
+null:null
+##
+===========================infer_params===========================
+Global.save_inference_dir:./output/
+Global.pretrained_model:
+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
+null:null
+null: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:./inference/ch_ppocr_mobile_v2.0_det_infer/
+--image_dir:./inference/ch_det_data_50/all-sum-510/
+--save_log_path:null
+--benchmark:True
+null:null

tests/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_key(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[0]}
+    echo ${tmp}
+}
+function func_parser_value(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+IFS=$'\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 
+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
+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_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"
+        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
+
+

tests/test.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_key(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[0]}
+    echo ${tmp}
+}
+function func_parser_value(){
+    strs=$1
+    IFS=":"
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+function func_set_params(){
+    key=$1
+    value=$2
+    if [ ${key} = "null" ];then
+        echo " "
+    elif [[ ${value} = "null" ]] || [[ ${value} = " " ]] || [ ${#value} -le 0 ];then
+        echo " "
+    else 
+        echo "${key}=${value}"
+    fi
+}
+function status_check(){
+    last_status=$1   # the exit code
+    run_command=$2
+    run_log=$3
+    if [ $last_status -eq 0 ]; then
+        echo -e "\033[33m Run successfully with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+    else
+        echo -e "\033[33m Run failed with command - ${run_command}!  \033[0m" | tee -a ${run_log}
+    fi
+}
+
+IFS=$'\n'
+# The training params
+model_name=$(func_parser_value "${lines[1]}")
+python=$(func_parser_value "${lines[2]}")
+gpu_list=$(func_parser_value "${lines[3]}")
+autocast_list=$(func_parser_value "${lines[4]}")
+autocast_key=$(func_parser_key "${lines[4]}")
+epoch_key=$(func_parser_key "${lines[5]}")
+epoch_num=$(func_parser_value "${lines[5]}")
+save_model_key=$(func_parser_key "${lines[6]}")
+train_batch_key=$(func_parser_key "${lines[7]}")
+train_batch_value=$(func_parser_value "${lines[7]}")
+train_use_gpu_key=$(func_parser_key "${lines[8]}")
+pretrain_model_key=$(func_parser_key "${lines[9]}")
+pretrain_model_value=$(func_parser_value "${lines[9]}")
+train_model_name=$(func_parser_value "${lines[10]}")
+train_infer_img_dir=$(func_parser_value "${lines[11]}")
+train_param_key1=$(func_parser_key "${lines[12]}")
+train_param_value1=$(func_parser_value "${lines[12]}")
+
+trainer_list=$(func_parser_value "${lines[14]}")
+trainer_norm=$(func_parser_key "${lines[15]}")
+norm_trainer=$(func_parser_value "${lines[15]}")
+pact_key=$(func_parser_key "${lines[16]}")
+pact_trainer=$(func_parser_value "${lines[16]}")
+fpgm_key=$(func_parser_key "${lines[17]}")
+fpgm_trainer=$(func_parser_value "${lines[17]}")
+distill_key=$(func_parser_key "${lines[18]}")
+distill_trainer=$(func_parser_value "${lines[18]}")
+trainer_key1=$(func_parser_key "${lines[19]}")
+trainer_value1=$(func_parser_value "${lines[19]}")
+trainer_key1=$(func_parser_key "${lines[20]}")
+trainer_value2=$(func_parser_value "${lines[20]}")
+
+eval_py=$(func_parser_value "${lines[23]}")
+eval_key1=$(func_parser_key "${lines[24]}")
+eval_value1=$(func_parser_value "${lines[24]}")
+
+save_infer_key=$(func_parser_key "${lines[27]}")
+export_weight=$(func_parser_key "${lines[28]}")
+norm_export=$(func_parser_value "${lines[29]}")
+pact_export=$(func_parser_value "${lines[30]}")
+fpgm_export=$(func_parser_value "${lines[31]}")
+distill_export=$(func_parser_value "${lines[32]}")
+export_key1=$(func_parser_key "${lines[33]}")
+export_value1=$(func_parser_value "${lines[33]}")
+export_key2=$(func_parser_key "${lines[34]}")
+export_value2=$(func_parser_value "${lines[34]}")
+
+inference_py=$(func_parser_value "${lines[36]}")
+use_gpu_key=$(func_parser_key "${lines[37]}")
+use_gpu_list=$(func_parser_value "${lines[37]}")
+use_mkldnn_key=$(func_parser_key "${lines[38]}")
+use_mkldnn_list=$(func_parser_value "${lines[38]}")
+cpu_threads_key=$(func_parser_key "${lines[39]}")
+cpu_threads_list=$(func_parser_value "${lines[39]}")
+batch_size_key=$(func_parser_key "${lines[40]}")
+batch_size_list=$(func_parser_value "${lines[40]}")
+use_trt_key=$(func_parser_key "${lines[41]}")
+use_trt_list=$(func_parser_value "${lines[41]}")
+precision_key=$(func_parser_key "${lines[42]}")
+precision_list=$(func_parser_value "${lines[42]}")
+infer_model_key=$(func_parser_key "${lines[43]}")
+infer_model=$(func_parser_value "${lines[43]}")
+image_dir_key=$(func_parser_key "${lines[44]}")
+infer_img_dir=$(func_parser_value "${lines[44]}")
+save_log_key=$(func_parser_key "${lines[45]}")
+benchmark_key=$(func_parser_key "${lines[46]}")
+benchmark_value=$(func_parser_value "${lines[46]}")
+infer_key2=$(func_parser_key "${lines[47]}")
+infer_value2=$(func_parser_value "${lines[47]}")
+
+LOG_PATH="./tests/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results.log"
+
+
+function func_inference(){
+    IFS='|'
+    _python=$1
+    _script=$2
+    _model_dir=$3
+    _log_path=$4
+    _img_dir=$5
+    _flag_quant=$6
+    # inference 
+    for use_gpu in ${use_gpu_list[*]}; do
+        if [ ${use_gpu} = "False" ] && [ ${_flag_quant} = "True" ]; then
+            continue
+        fi
+        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_${batch_size}.log"
+                        #${image_dir_key}=${_img_dir}  ${save_log_key}=${_save_log_path} --benchmark=True
+                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                        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} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        eval $command
+                        status_check $? "${command}" "${status_log}"
+                    done
+                done
+            done
+        else
+            for use_trt in ${use_trt_list[*]}; do
+                for precision in ${precision_list[*]}; do
+                    if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
+                        continue
+                    fi
+                    if [ ${use_trt} = "False" ] && [ ${_flag_quant} = "True" ]; then
+                        continue
+                    fi
+                    if [ ${precision} != "int8" ] && [ ${_flag_quant} = "True" ]; then
+                        continue
+                    fi
+                    for batch_size in ${batch_size_list[*]}; do
+                        _save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}.log"
+                        set_infer_data=$(func_set_params "${image_dir_key}" "${_img_dir}")
+                        set_benchmark=$(func_set_params "${benchmark_key}" "${benchmark_value}")
+                        command="${_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} ${set_infer_data} ${set_benchmark} > ${_save_log_path} 2>&1 "
+                        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}"
+        eval ${env}
+    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="|"
+        env=" "
+    fi
+    for autocast in ${autocast_list[*]}; do 
+        for trainer in ${trainer_list[*]}; do 
+            flag_quant=False
+            if [ ${trainer} = ${pact_key} ]; then
+                run_train=${pact_trainer}
+                run_export=${pact_export}
+                flag_quant=True
+            elif [ ${trainer} = "${fpgm_key}" ]; then
+                run_train=${fpgm_trainer}
+                run_export=${fpgm_export}
+            elif [ ${trainer} = "${distill_key}" ]; then
+                run_train=${distill_trainer}
+                run_export=${distill_export}
+            elif [ ${trainer} = ${trainer_key1} ]; then
+                run_train=${trainer_value1}
+                run_export=${export_value1}
+            elif [[ ${trainer} = ${trainer_key2} ]]; then
+                run_train=${trainer_value2}
+                run_export=${export_value2}
+            else
+                run_train=${norm_trainer}
+                run_export=${norm_export}
+            fi
+
+            if [ ${run_train} = "null" ]; then
+                continue
+            fi
+            
+            set_autocast=$(func_set_params "${autocast_key}" "${autocast}")
+            set_autocast=$(func_set_params "${epoch_key}" "${epoch_num}")
+            set_pretrain=$(func_set_params "${pretrain_model_key}" "${pretrain_model_value}")
+            set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
+            set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
+
+            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} ${set_batchsize} ${set_train_params1} "
+            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} ${set_batchsize} ${set_train_params1}"
+            else     # train with multi-machine
+                cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${save_model_key}=${save_log} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
+            fi
+            # run train
+            eval $cmd
+            status_check $? "${cmd}" "${status_log}"
+
+            # run eval 
+            if [ ${eval_py} != "null" ]; then
+                eval_cmd="${python} ${eval_py} ${save_model_key}=${save_log} ${pretrain_model_key}=${save_log}/${train_model_name}" 
+                eval $eval_cmd
+                status_check $? "${eval_cmd}" "${status_log}"
+            fi
+
+            if [ ${run_export} != "null" ]; then 
+                # run export model
+                save_infer_path="${save_log}"
+                export_cmd="${python} ${run_export} ${save_model_key}=${save_log} ${export_weight}=${save_log}/${train_model_name} ${save_infer_key}=${save_infer_path}"
+                eval $export_cmd
+                status_check $? "${export_cmd}" "${status_log}"
+
+                #run inference
+                eval $env
+                save_infer_path="${save_log}"
+                func_inference "${python}" "${inference_py}" "${save_infer_path}" "${LOG_PATH}" "${train_infer_img_dir}" "${flag_quant}"
+                eval "unset CUDA_VISIBLE_DEVICES"
+            fi
+        done
+    done
+done
+
+else
+    GPUID=$3
+    if [ ${#GPUID} -le 0 ];then
+        env=" "
+    else
+        env="export CUDA_VISIBLE_DEVICES=${GPUID}"
+    fi
+    echo $env
+    #run inference
+    func_inference "${python}" "${inference_py}" "${infer_model}" "${LOG_PATH}" "${infer_img_dir}" "False"
+fi