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PaddleOCR
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add log_path to params.txt

This commit is contained in:
LDOUBLEV 2021-06-09 10:12:32 +08:00
parent 4a179f5fc0
commit 04fb6148e1
2 changed files with 24 additions and 14 deletions
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4
test/params.txt
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@ -4,7 +4,7 @@ auto_cast_list: False
trainer_list: norm|quant|prune
python: python3.7
inference: python|C++
inference: python
devices: cpu|gpu
use_mkldnn_list: True|False
cpu_threads_list: 1|6
@ -12,4 +12,4 @@ rec_batch_size_list: 1|6
gpu_trt_list: True|False
gpu_precision_list: fp32|fp16|int8
log_path: ./output

34
test/test.sh
View File

@ -8,11 +8,12 @@ FILENAME=$1
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 &&
cd ./train_data/ && tar xf icdar2015_lite.tar
ln -s ./icdar2015_lite ./icdar2015
cd ../
epoch=10
@ -24,9 +25,17 @@ elif [ ${MODE} = "whole_train_infer" ];then
epoch=500
eval_batch_step=200
else
echo "Do Nothing"
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_infer.tar
cd ./train_data/ && tar xf icdar2015_infer.tar
ln -s ./icdar2015_infer ./icdar2015
cd ../
epoch=10
eval_batch_step=10
fi
img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
dataline=$(cat ${FILENAME})
# parser params
@ -34,7 +43,7 @@ IFS=$'\n'
lines=(${dataline})
function func_parser(){
strs=$1
IFS=":"
IFS=": "
array=(${strs})
tmp=${array[1]}
echo ${tmp}
@ -54,7 +63,8 @@ cpu_threads_list=$(func_parser "${lines[8]}")
rec_batch_size_list=$(func_parser "${lines[9]}")
gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
img_dir="./train_data/icdar2015/text_localization/ch4_test_images/"
log_path=$(func_parser "${lines[12]}")
function status_check(){
last_status=$1 # the exit code
@ -113,12 +123,12 @@ for train_model in ${train_model_list[*]}; do
fi
save_log=${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}
command="${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} ${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}
status_check $? "${trainer}" "${command}" "${save_log}/train.log"
echo ${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}
# status_check $? "${trainer}" "${command}" "${save_log}/train.log"
command="${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/best_accuracy Global.save_inference_dir=${save_log}/export_inference/ Global.save_model_dir=${save_log}"
${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/best_accuracy Global.save_inference_dir=${save_log}/export_inference/ Global.save_model_dir=${save_log}
status_check $? "${trainer}" "${command}" "${save_log}/train.log"
echo ${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/best_accuracy Global.save_inference_dir=${save_log}/export_inference/ Global.save_model_dir=${save_log}
# status_check $? "${trainer}" "${command}" "${save_log}/train.log"
if [ "${model_name}" = "det" ]; then
export rec_batch_size_list=( "1" )
@ -138,8 +148,8 @@ for train_model in ${train_model_list[*]}; do
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log"
command="${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=${img_dir} --save_log_path=${save_log_path}"
${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=${img_dir} --save_log_path=${save_log_path}
status_check $? "${inference}" "${command}" "${save_log}"
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=${img_dir} --save_log_path=${save_log_path}
# status_check $? "${inference}" "${command}" "${save_log}"
done
done
done
@ -151,8 +161,8 @@ for train_model in ${train_model_list[*]}; do
fi
for rec_batch_size in ${rec_batch_size_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
${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=${img_dir} --save_log_path=${save_log_path}
status_check $? "${inference}" "${command}" "${save_log}"
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=${img_dir} --save_log_path=${save_log_path}
# status_check $? "${inference}" "${command}" "${save_log}"
done
done
done