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PaddleOCR
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return status to log_path/results.log

pull/3047/head
LDOUBLEV 2021-06-09 14:48:56 +08:00
parent 90454c767e
commit 9372741adf
2 changed files with 11 additions and 9 deletions
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9
test/infer.sh
View File

@ -27,6 +27,7 @@ gpu_precision_list=$(func_parser "${lines[11]}")
infer_gpu_id=$(func_parser "${lines[12]}")
log_path=$(func_parser "${lines[13]}")
status_log="${log_path}/result.log"
function status_check(){
@ -103,11 +104,11 @@ for train_model in ${train_model_list[*]}; do
save_log_path="${log_path}/${eval_model_name}"
command="${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model="${eval_model_name}/best_accuracy" Global.save_model_dir=${save_log_path}"
${python} tools/eval.py -c ${yml_file} -o Global.pretrained_model="${eval_model_name}/best_accuracy" Global.save_model_dir=${save_log_path}
status_check $? "${trainer}" "${command}" "${save_log_path}/train.log"
status_check $? "${trainer}" "${command}" "${status_log}"
command="${python} tools/export_model.py -c ${yml_file} -o Global.pretrained_model="${eval_model_name}/best_accuracy" 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}/best_accuracy" 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"
status_check $? "${trainer}" "${command}" "${status_log}"
if [ $? -eq 0 ]; then
echo -e "\033[33m training of $model_name successfully!\033[0m" | tee -a ${save_log}/train.log
@ -134,7 +135,7 @@ for train_model in ${train_model_list[*]}; do
save_log_path="${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=${threads} --benchmark=True --det_model_dir=${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}"
status_check $? "${trainer}" "${command}" "${status_log}"
done
done
done
@ -149,7 +150,7 @@ for train_model in ${train_model_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
command="${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}"
status_check $? "${trainer}" "${command}" "${status_log}"
done
done
done

11
test/test.sh
View File

@ -1,6 +1,6 @@
#!/bin/bash
# Usage:
# bash test/test.sh ./test/params.txt 'lite_train_infer'
# bash test/test.sh ./test/paddleocr_ci_params.txt 'lite_train_infer'
FILENAME=$1
@ -67,6 +67,7 @@ gpu_trt_list=$(func_parser "${lines[10]}")
gpu_precision_list=$(func_parser "${lines[11]}")
log_path=$(func_parser "${lines[13]}")
status_log="${log_path}/result.log"
function status_check(){
last_status=$1 # the exit code
@ -135,11 +136,11 @@ for train_model in ${train_model_list[*]}; do
save_log="${log_path}/${model_name}_${slim_trainer}_autocast_${auto_cast}_gpuid_${gpu}"
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.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2"
${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.pretrained_model=${pretrain} Global.save_model_dir=${save_log} Global.use_gpu=${use_gpu} Train.loader.batch_size_per_card=2
status_check $? "${trainer}" "${command}" "${save_log}/train.log"
status_check $? "${trainer}" "${command}" "${status_log}"
command="${env} ${python} ${export_model} -c ${yml_file} -o Global.pretrained_model=${save_log}/latest 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}/latest Global.save_inference_dir=${save_log}/export_inference/ Global.save_model_dir=${save_log}
status_check $? "${trainer}" "${command}" "${save_log}/train.log"
status_check $? "${trainer}" "${command}" "${status_log}"
if [ "${model_name}" = "det" ]; then
export rec_batch_size_list=( "1" )
@ -160,7 +161,7 @@ for train_model in ${train_model_list[*]}; do
save_log_path="${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}"
status_check $? "${inference}" "${command}" "${status_log}"
done
done
done
@ -174,7 +175,7 @@ for train_model in ${train_model_list[*]}; do
save_log_path="${log_path}/${model_name}_${slim_trainer}_gpu_usetensorrt_${use_trt}_usefp16_${precision}_recbatchnum_${rec_batch_size}_infer.log"
command="${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}"
status_check $? "${inference}" "${command}" "${status_log}"
done
done
done