#!/bin/bash # Usage: # bash test/test.sh ./test/params.txt 'lite_train_infer' 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 ../ epoch=10 eval_batch_step=10 elif [ ${MODE} = "whole_train_infer" ];then rm -rf ./train_data/icdar2015 wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar cd ./train_data/ && tar xf icdar2015.tar && cd ../ epoch=300 eval_batch_step=200 else echo "Do Nothing" fi dataline=$(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="./train_data/icdar2015/text_localization/ch4_test_images/" # train superparameters #epoch=$(func_parser "${lines[12]}") #checkpoints=$(func_parser "${lines[13]}") 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 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} ${python} ${launch} ${trainer} -c ${yml_file} -o Global.epoch_num=${epoch} Global.eval_batch_step=${eval_batch_step} Global.auto_cast=${auto_cast} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} ${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}" ${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