return status to log_path/results.log

test/infer.sh

@@ -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

test/test.sh

 #!/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