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PaddleOCR
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add env, add infer det imgs, add infer_gpu_id to params.txt

This commit is contained in:
LDOUBLEV 2021-06-09 11:22:29 +08:00
parent 04fb6148e1
commit 1203276fbd
3 changed files with 29 additions and 20 deletions
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17
test/infer.sh
View File

@ -14,8 +14,6 @@ function func_parser(){
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 "${lines[4]}")
# inference params
@ -27,13 +25,15 @@ rec_batch_size_list=$(func_parser "${lines[9]}")
gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
infer_gpu_id=$(func_parser "${lines[12]}")
log_path=$(func_parser "${lines[13]}")
function status_check(){
last_status=$1 # the exit code
run_model=$2
run_command=$3
save_log=$4
echo ${case3}
if [ $last_status -eq 0 ]; then
echo -e "\033[33m $run_model successfully with command - ${run_command}! \033[0m" | tee -a ${save_log}
else
@ -45,11 +45,13 @@ for train_model in ${train_model_list[*]}; do
if [ ${train_model} = "det" ];then
model_name="det"
yml_file="configs/det/det_mv3_db.yml"
img_dir=""
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_det_data_50.tar && tar xf ./inference/ch_det_data_50.tar
img_dir="./inference/ch_det_data_50/"
elif [ ${train_model} = "rec" ];then
model_name="rec"
yml_file="configs/rec/rec_mv3_none_bilstm_ctc.yml"
img_dir=""
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/ch_rec_data_200.tar && tar xf ./inference/ch_rec_data_200.tar
img_dir="./inference/ch_rec_data_200/"
fi
# eval
@ -126,6 +128,7 @@ for train_model in ${train_model_list[*]}; do
done
done
else
env="CUDA_VISIBLE_DEVICES=${infer_gpu_id}"
for use_trt in ${gpu_trt_list[*]}; do
for precision in ${gpu_precision_list[*]}; do
if [ ${use_trt} = "False" ] && [ ${precision} != "fp32" ]; then
@ -133,8 +136,8 @@ 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_${rec_batch_size}_infer.log"
command="${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
command="${env} ${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}"
${env} ${python} ${inference} --use_gpu=True --use_tensorrt=${use_trt} --precision=${precision} --benchmark=True --det_model_dir=${log_path}/${eval_model_name}_infer --rec_batch_num=${rec_batch_size} --rec_model_dir=${rec_model_dir} --image_dir=${img_dir} --save_log_path=${save_log_path}
status_check $? "${trainer}" "${command}" "${save_log_path}"
done
done

2
test/params.txt
View File

@ -11,5 +11,5 @@ cpu_threads_list: 1|6
rec_batch_size_list: 1|6
gpu_trt_list: True|False
gpu_precision_list: fp32|fp16|int8
infer_gpu_id: 0
log_path: ./output

30
test/test.sh
View File

@ -64,14 +64,13 @@ rec_batch_size_list=$(func_parser "${lines[9]}")
gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
log_path=$(func_parser "${lines[12]}")
log_path=$(func_parser "${lines[13]}")
function status_check(){
last_status=$1 # the exit code
run_model=$2
run_command=$3
save_log=$4
echo ${case3}
if [ $last_status -eq 0 ]; then
echo -e "\033[33m $run_model successfully with command - ${run_command}! \033[0m" | tee -a ${save_log}
else
@ -97,10 +96,16 @@ for train_model in ${train_model_list[*]}; do
if [ ${gpu} = "-1" ];then
lanuch=""
use_gpu=False
env=""
elif [ ${#gpu} -le 1 ];then
launch=""
env="CUDA_VISIBLE_DEVICES=${gpu}"
else
launch="-m paddle.distributed.launch --log_dir=./debug/ --gpus ${gpu}"
IFS=","
array=(${gpu})
env="CUDA_VISIBLE_DEVICES=${array[0]}"
IFS="|"
fi
for auto_cast in ${auto_cast_list[*]}; do
@ -122,13 +127,13 @@ for train_model in ${train_model_list[*]}; do
export_model="tools/export_model.py"
fi
save_log=${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}"
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="${env} ${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}"
${env} ${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}"
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"
command="${env} ${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}"
${env} ${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" )
@ -148,8 +153,8 @@ 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}"
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}"
${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
@ -161,8 +166,9 @@ 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"
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}"
command="${env} ${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}"
${env} ${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