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refine status check

This commit is contained in:
LDOUBLEV 2021-06-08 21:22:54 +08:00
parent f7a554c2af
commit 58437e6448
2 changed files with 59 additions and 33 deletions
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43
test/infer.sh
View File

@ -28,6 +28,19 @@ gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
function status_check(){
last_status=$1 # 上个阶段的退出码
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
echo -e "\033[33m $case failed with command - ${run_command}! \033[0m" | tee -a ${save_log}
fi
}
for train_model in ${train_model_list[*]}; do
if [ ${train_model} = "det" ];then
model_name="det"
@ -42,7 +55,7 @@ for train_model in ${train_model_list[*]}; do
# eval
for slim_trainer in ${slim_trainer_list[*]}; do
if [ ${slim_trainer} = "norm" ]; then
if [ ${model_name} = "model_name" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar
else
@ -50,7 +63,7 @@ for train_model in ${train_model_list[*]}; do
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_train.tar
fi
elif [ ${slim_trainer} = "quant" ]; then
if [ ${model_name} = "model_name" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_quant_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_quant_train.tar
else
@ -58,7 +71,7 @@ for train_model in ${train_model_list[*]}; do
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_quant_train.tar
fi
elif [ ${slim_trainer} = "distill" ]; then
if [ ${model_name} = "model_name" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_distill_infer"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_distill_train.tar
else
@ -66,7 +79,7 @@ for train_model in ${train_model_list[*]}; do
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_distill_train.tar
fi
elif [ ${slim_trainer} = "prune" ]; then
if [ ${model_name} = "model_name" ]; then
if [ ${model_name} = "det" ]; then
eval_model_name="ch_ppocr_mobile_v2.0_det_prune_train"
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_det_prune_train.tar
else
@ -74,9 +87,15 @@ for train_model in ${train_model_list[*]}; do
wget -nc -P ./inference https://paddleocr.bj.bcebos.com/dygraph_v2.0/slim/ch_ppocr_mobile_v2.0_rec_prune_train.tar
fi
fi
save_log_path="${log_path}/${eval_model_name}"
command="${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_model_dir=${save_log_path}"
${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_model_dir=${save_log_path}
status_check $? "${trainer}" "${command}" "${save_log_path}/train.log"
command="${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_inference_dir=${log_path}/${eval_model_name}_infer Global.save_model_dir=${save_log_path}"
${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_inference_dir=${log_path}/${eval_model_name}_infer Global.save_model_dir=${save_log_path}
status_check $? "${trainer}" "${command}" "${save_log_path}/train.log"
echo ${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_model_dir=${log_path}/${model_name}
echo ${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model=${eval_model_name} Global.save_inference_dir=${log_path}/${eval_model_name}_infer
if [ $? -eq 0 ]; then
echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${save_log}/train.log
else
@ -99,8 +118,10 @@ for train_model in ${train_model_list[*]}; do
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=${threads} --benchmark=True --det_model_dir=${det_model_dir} --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
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=${threads} --benchmark=True --det_model_dir=${det_model_dir} --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=${threads} --benchmark=True --det_model_dir=${det_model_dir} --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
done
@ -111,8 +132,10 @@ for train_model in ${train_model_list[*]}; do
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}"
echo ${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=${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log
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}
status_check $? "${trainer}" "${command}" "${save_log_path}"
done
done
done

49
test/test.sh
View File

@ -21,7 +21,7 @@ elif [ ${MODE} = "whole_train_infer" ];then
rm -rf ./train_data/icdar2015
wget -nc -P ./train_data/ https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar
cd ./train_data/ && tar xf icdar2015.tar && cd ../
epoch=300
epoch=500
eval_batch_step=200
else
echo "Do Nothing"
@ -55,9 +55,19 @@ rec_batch_size_list=$(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]}")
function status_check(){
last_status=$1 # 上个阶段的退出码
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
echo -e "\033[33m $case failed with command - ${run_command}! \033[0m" | tee -a ${save_log}
fi
}
for train_model in ${train_model_list[*]}; do
@ -101,17 +111,14 @@ for train_model in ${train_model_list[*]}; do
trainer="tools/train.py"
export_model="tools/export_model.py"
fi
# dataset="Train.dataset.data_dir=${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}
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"
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}
if [ $? -eq 0 ]; then
echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${save_log}/train.log
else
cat ${save_log}/train.log
echo -e "\033[33m training of $model_name failed!\033[0m" | tee -a ${save_log}/train.log
fi
status_check $? "${trainer}" "${command}" "${save_log}/train.log"
if [ "${model_name}" = "det" ]; then
export rec_batch_size_list=( "1" )
@ -129,15 +136,10 @@ for train_model in ${train_model_list[*]}; do
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=${img_dir} --save_log_path=${log_path}/${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
if [ $? -eq 0 ]; then
echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${log_path}${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
else
cat ${log_path}${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
echo -e "\033[33m training of $model_name failed!\033[0m" | tee -a ${log_path}${model_name}_${slim_trainer}_cpu_usemkldnn_${use_mkldnn}_cputhreads_${threads}_recbatchnum_${rec_batch_size}_infer.log
fi
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}"
done
done
done
@ -148,8 +150,9 @@ for train_model in ${train_model_list[*]}; do
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_${rec_batch_size}_infer.log
save_log_path="${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}"
done
done
done