Merge pull request #3047 from LDOUBLEV/test
add ocr test ci shell script
This commit is contained in:
Double_V
GitHub
commit
f8f7a0863e
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model_name:ocr_det
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python:python3.7
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gpu_list:-1|0|0,1
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Global.auto_cast:False|True
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Global.epoch_num:10
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Global.save_model_dir:./output/
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Global.save_inference_dir:./output/
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Train.loader.batch_size_per_card:
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Global.use_gpu
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Global.pretrained_model
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trainer:norm|pact|fpgm
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norm_train:tools/train.py -c configs/det/det_mv3_db.yml -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained
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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
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fpgm_train:null
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distill_train:null
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eval:tools/eval.py -c configs/det/det_mv3_db.yml -o
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norm_export:tools/export_model.py -c configs/det/det_mv3_db.yml -o
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quant_export:deploy/slim/quantization/export_model.py -c configs/det/det_mv3_db.yml -o
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fpgm_export:deploy/slim/prune/export_prune_model.py
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distill_export:null
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inference:tools/infer/predict_det.py
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--use_gpu:True|False
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--enable_mkldnn:True|False
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--cpu_threads:1|6
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--rec_batch_num:1
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--use_tensorrt:True|False
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--precision:fp32|fp16|int8
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--det_model_dir
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--image_dir
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--save_log_path
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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', 'infer']
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MODE=$2
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dataline=$(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_key(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[0]}
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echo ${tmp}
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}
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function func_parser_value(){
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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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model_name=$(func_parser_value "${lines[0]}")
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train_model_list=$(func_parser_value "${lines[0]}")
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trainer_list=$(func_parser_value "${lines[10]}")
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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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wget -nc -P ./pretrain_models/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/en/det_mv3_db_v2.0_train.tar
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cd pretrain_models && tar xf det_mv3_db_v2.0_train.tar && cd ../
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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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epoch=10
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eval_batch_step=10
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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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epoch=500
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eval_batch_step=200
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elif [ ${MODE} = "whole_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_infer.tar
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cd ./train_data/ && tar xf icdar2015_infer.tar
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ln -s ./icdar2015_infer ./icdar2015
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cd ../
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epoch=10
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eval_batch_step=10
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else
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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/ch_det_data_50.tar
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if [ ${model_name} = "ocr_det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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fi
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IFS='|'
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for train_model in ${train_model_list[*]}; do
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if [ ${train_model} = "ocr_det" ];then
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model_name="ocr_det"
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yml_file="configs/det/ch_ppocr_v2.0/ch_det_mv3_db_v2.0.yml"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar
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cd ./inference && tar xf ch_det_data_50.tar && cd ../
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img_dir="./inference/ch_det_data_50/all-sum-510"
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data_dir=./inference/ch_det_data_50/
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data_label_file=[./inference/ch_det_data_50/test_gt_50.txt]
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elif [ ${train_model} = "ocr_rec" ];then
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model_name="ocr_rec"
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yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_rec_data_200.tar
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cd ./inference && tar xf ch_rec_data_200.tar && cd ../
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img_dir="./inference/ch_rec_data_200/"
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fi
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# eval
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for slim_trainer in ${trainer_list[*]}; do
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if [ ${slim_trainer} = "norm" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "pact" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_quant_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_quant_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_quant_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "distill" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_distill_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_distill_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_distill_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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elif [ ${slim_trainer} = "fpgm" ]; then
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if [ ${model_name} = "det" ]; then
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eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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else
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eval_model_name="ch_ppocr_mobile_v2.0_rec_prune_train"
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wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_prune_train.tar
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cd ./inference && tar xf ${eval_model_name}.tar && cd ../
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fi
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fi
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done
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done
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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', 'infer']
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MODE=$2
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dataline=$(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_key(){
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strs=$1
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IFS=":"
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array=(${strs})
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tmp=${array[0]}
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echo ${tmp}
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}
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function func_parser_value(){
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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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function status_check(){
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last_status=$1 # the exit code
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run_command=$2
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run_log=$3
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if [ $last_status -eq 0 ]; then
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echo -e "\033[33m Run successfully with command - ${run_command}! \033[0m" | tee -a ${run_log}
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else
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echo -e "\033[33m Run failed with command - ${run_command}! \033[0m" | tee -a ${run_log}
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fi
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}
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IFS=$'\n'
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# The training params
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model_name=$(func_parser_value "${lines[0]}")
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python=$(func_parser_value "${lines[1]}")
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gpu_list=$(func_parser_value "${lines[2]}")
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autocast_list=$(func_parser_value "${lines[3]}")
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autocast_key=$(func_parser_key "${lines[3]}")
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epoch_key=$(func_parser_key "${lines[4]}")
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save_model_key=$(func_parser_key "${lines[5]}")
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save_infer_key=$(func_parser_key "${lines[6]}")
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train_batch_key=$(func_parser_key "${lines[7]}")
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train_use_gpu_key=$(func_parser_key "${lines[8]}")
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pretrain_model_key=$(func_parser_key "${lines[9]}")
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trainer_list=$(func_parser_value "${lines[10]}")
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norm_trainer=$(func_parser_value "${lines[11]}")
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pact_trainer=$(func_parser_value "${lines[12]}")
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fpgm_trainer=$(func_parser_value "${lines[13]}")
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distill_trainer=$(func_parser_value "${lines[14]}")
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eval_py=$(func_parser_value "${lines[15]}")
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norm_export=$(func_parser_value "${lines[16]}")
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pact_export=$(func_parser_value "${lines[17]}")
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fpgm_export=$(func_parser_value "${lines[18]}")
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distill_export=$(func_parser_value "${lines[19]}")
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inference_py=$(func_parser_value "${lines[20]}")
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use_gpu_key=$(func_parser_key "${lines[21]}")
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use_gpu_list=$(func_parser_value "${lines[21]}")
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use_mkldnn_key=$(func_parser_key "${lines[22]}")
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use_mkldnn_list=$(func_parser_value "${lines[22]}")
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cpu_threads_key=$(func_parser_key "${lines[23]}")
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cpu_threads_list=$(func_parser_value "${lines[23]}")
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batch_size_key=$(func_parser_key "${lines[24]}")
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batch_size_list=$(func_parser_value "${lines[24]}")
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use_trt_key=$(func_parser_key "${lines[25]}")
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use_trt_list=$(func_parser_value "${lines[25]}")
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precision_key=$(func_parser_key "${lines[26]}")
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precision_list=$(func_parser_value "${lines[26]}")
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model_dir_key=$(func_parser_key "${lines[27]}")
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image_dir_key=$(func_parser_key "${lines[28]}")
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save_log_key=$(func_parser_key "${lines[29]}")
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LOG_PATH="./test/output"
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mkdir -p ${LOG_PATH}
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status_log="${LOG_PATH}/results.log"
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if [ ${MODE} = "lite_train_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=10
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elif [ ${MODE} = "whole_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=10
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elif [ ${MODE} = "whole_train_infer" ]; then
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export infer_img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
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export epoch_num=300
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else
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export infer_img_dir="./inference/ch_det_data_50/all-sum-510"
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export infer_model_dir="./inference/ch_ppocr_mobile_v2.0_det_train/best_accuracy"
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fi
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function func_inference(){
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IFS='|'
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_python=$1
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_script=$2
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_model_dir=$3
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_log_path=$4
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_img_dir=$5
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# inference
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for use_gpu in ${use_gpu_list[*]}; do
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if [ ${use_gpu} = "False" ]; 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 batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_cpu_usemkldnn_${use_mkldnn}_threads_${threads}_batchsize_${batch_size}"
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command="${_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}"
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eval $command
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status_check $? "${command}" "${status_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 ${use_trt_list[*]}; do
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for precision in ${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 batch_size in ${batch_size_list[*]}; do
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_save_log_path="${_log_path}/infer_gpu_usetrt_${use_trt}_precision_${precision}_batchsize_${batch_size}"
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command="${_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}"
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eval $command
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status_check $? "${command}" "${status_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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}
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if [ ${MODE} != "infer" ]; then
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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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use_gpu=False
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env=""
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elif [ ${#gpu} -le 1 ];then
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env="export CUDA_VISIBLE_DEVICES=${gpu}"
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elif [ ${#gpu} -le 15 ];then
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IFS=","
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array=(${gpu})
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env="export CUDA_VISIBLE_DEVICES=${array[0]}"
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IFS="|"
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else
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IFS=";"
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array=(${gpu})
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ips=${array[0]}
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gpu=${array[1]}
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IFS="|"
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fi
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for autocast in ${autocast_list[*]}; do
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for trainer in ${trainer_list[*]}; do
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if [ ${trainer} = "pact" ]; then
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run_train=${pact_trainer}
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run_export=${pact_export}
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elif [ ${trainer} = "fpgm" ]; then
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run_train=${fpgm_trainer}
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run_export=${fpgm_export}
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elif [ ${trainer} = "distill" ]; then
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run_train=${distill_trainer}
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run_export=${distill_export}
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else
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run_train=${norm_trainer}
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run_export=${norm_export}
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fi
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if [ ${run_train} = "null" ]; then
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continue
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fi
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if [ ${run_export} = "null" ]; then
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continue
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fi
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save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
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if [ ${#gpu} -le 2 ];then # epoch_num #TODO
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cmd="${python} ${run_train} ${train_use_gpu_key}=${use_gpu} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log} "
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elif [ ${#gpu} -le 15 ];then
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cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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else
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cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${autocast_key}=${autocast} ${epoch_key}=${epoch_num} ${save_model_key}=${save_log}"
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fi
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# run train
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eval $cmd
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status_check $? "${cmd}" "${status_log}"
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# run eval
|
||||
eval_cmd="${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
|
||||
Loading…
Reference in New Issue