add test shell
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LDOUBLEV
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commit
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train_model_list: det;benchmark/benchmark_det.yml
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gpu_list: -1|0|0,1
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auto_cast_list: False|True
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trainer_list: norm|quant|prune
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python: python3.7
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inference: python|C++
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devices: cpu|gpu
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use_mkldnn_list: True|False
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cpu_threads_list: 1|6
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rec_batch_size_list: 1|6
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gpu_trt_list: True|False
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gpu_precision_list: fp32|fp16|int8
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img_dir: /paddle/OCR/test_set/benchmark_eval
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epoch: 10
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checkpoints: None
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#!/bin/bash
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FILENAME=$1
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# MODE be one of ['lite_train_infer' 'whole_infer' 'whole_train_infer']
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MODE=$2
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# prepare pretrained weights and dataset
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wget -nc -P ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x0_5_pretrained.pdparams
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if [ ${MODE} = "lite_train_infer" ];then
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# pretrain lite train data
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
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cd ./train_data/ && tar xf icdar2015_lite.tar &&
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ln -s ./icdar2015_lite ./icdar2015
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cd ../
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elif [ ${MODE} = "whole_train_infer" ];then
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rm -rf ./train_data/icdar2015
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wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
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cd ./train_data/ && tar xf icdar2015.tar && cd ../
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else
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echo "Do Nothing"
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fi
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dataline=$(cat ${FILENAME})
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# parser params
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IFS=$'\n'
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lines=(${dataline})
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function func_parser(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[1]}
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echo ${tmp}
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}
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IFS=$'\n'
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# The training params
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train_model_list=$(func_parser "${lines[0]}")
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gpu_list=$(func_parser "${lines[1]}")
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auto_cast_list=$(func_parser "${lines[2]}")
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slim_trainer_list=$(func_parser "${lines[3]}")
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python=$(func_parser "${lines[4]}")
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# inference params
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inference=$(func_parser "${lines[5]}")
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devices=$(func_parser "${lines[6]}")
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use_mkldnn_list=$(func_parser "${lines[7]}")
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cpu_threads_list=$(func_parser "${lines[8]}")
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rec_batch_size_list=$(func_parser "${lines[9]}")
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gpu_trt_list=$(func_parser "${lines[10]}")
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gpu_precision_list=$(func_parser "${lines[11]}")
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img_dir=$(func_parser "${lines[12]}")
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# train superparameters
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epoch=$(func_parser "${lines[13]}")
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checkpoints=$(func_parser "${lines[14]}")
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for train_model in ${train_model_list[*]}; do
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if [ ${train_model} = "det" ];then
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model_name="det"
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yml_file="configs/det/det_mv3_db.yml"
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elif [ ${train_model} = "rec" ];then
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model_name="rec"
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yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
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else
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model_name="det"
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yml_file="configs/det/det_mv3_db.yml"
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fi
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# array=(${train_model})
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# for j in "${!array[@]}"; do
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# model_name=${array[0]}
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# yml_file=${array[1]}
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# done
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IFS="|"
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for gpu in ${gpu_list[*]}; do
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use_gpu=True
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if [ ${gpu} = "-1" ];then
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lanuch=""
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use_gpu=False
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elif [ ${#gpu} -le 1 ];then
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launch=""
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else
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launch="-m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu}"
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fi
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# echo "model_name: ${model_name} yml_file: ${yml_file} launch: ${launch} gpu: ${gpu}"
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for auto_cast in ${auto_cast_list[*]}; do
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for slim_trainer in ${slim_trainer_list[*]}; do
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if [ ${slim_trainer} = "norm" ]; then
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trainer="tools/train.py"
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export_model="tools/export_model.py"
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elif [ ${slim_trainer} = "quant" ]; then
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trainer="deploy/slim/quantization/quant.py"
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export_model="deploy/slim/quantization/export_model.py"
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elif [ ${slim_trainer} = "prune" ]; then
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trainer="deploy/slim/prune/sensitivity_anal.py"
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export_model="deploy/slim/prune/export_prune_model.py"
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elif [ ${slim_trainer} = "distill" ]; then
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trainer="deploy/slim/distill/train_dml.py"
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export_model="deploy/slim/distill/export_distill_model.py"
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else
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trainer="tools/train.py"
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export_model="tools/export_model.py"
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fi
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# dataset="Train.dataset.data_dir=${train_dir} Train.dataset.label_file_list=${train_label_file} Eval.dataset.data_dir=${eval_dir} Eval.dataset.label_file_list=${eval_label_file}"
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save_log=${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}
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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}
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echo ${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/best_accuracy Global.save_inference_dir=${save_log}/export_inference/
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if [ "${model_name}" = "det" ]; then
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export rec_batch_size_list=( "1" )
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inference="tools/infer/predict_det.py"
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elif [ "${model_name}" = "rec" ]; then
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inference="tools/infer/predict_rec.py"
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fi
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# inference
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for device in ${devices[*]}; do
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if [ ${device} = "cpu" ]; then
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for use_mkldnn in ${use_mkldnn_list[*]}; do
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for threads in ${cpu_threads_list[*]}; do
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for rec_batch_size in ${rec_batch_size_list[*]}; do
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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
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# ${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
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done
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done
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done
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else
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for use_trt in ${gpu_trt_list[*]}; do
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for precision in ${gpu_precision_list[*]}; do
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if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
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continue
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fi
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for rec_batch_size in ${rec_batch_size_list[*]}; do
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# echo "${model_name} ${det_model_dir} ${rec_model_dir}, use_trt: ${use_trt} use_fp16: ${use_fp16}"
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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
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done
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done
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done
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fi
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done
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done
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done
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done
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done
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