format test_train_inference_python.sh

test_tipc/test_train_inference_python.sh

@@ -88,17 +88,17 @@ benchmark_value=$(func_parser_value "${lines[49]}")
 infer_key1=$(func_parser_key "${lines[50]}")
 infer_value1=$(func_parser_value "${lines[50]}")
 if [ ! $epoch_num ]; then
-  epoch_num=2
+    epoch_num=2
 fi
 if [[ $MODE = 'benchmark_train' ]]; then
-  epoch_num=1
+    epoch_num=1
 fi
 
 LOG_PATH="./test_tipc/output/${model_name}/${MODE}"
 mkdir -p ${LOG_PATH}
 status_log="${LOG_PATH}/results_python.log"
 
-function func_inference(){
+function func_inference() {
     IFS='|'
     _python=$1
     _script=$2
@@ -216,12 +216,12 @@ else
         train_use_gpu=${USE_GPU_KEY[Count]}
         Count=$(($Count + 1))
         ips=""
-        if [ ${gpu} = "-1" ];then
+        if [ ${gpu} = "-1" ]; then
             env=""
-        elif [ ${#gpu} -le 1 ];then
+        elif [ ${#gpu} -le 1 ]; then
             env="export CUDA_VISIBLE_DEVICES=${gpu}"
             eval ${env}
-        elif [ ${#gpu} -le 15 ];then
+        elif [ ${#gpu} -le 15 ]; then
             IFS=","
             array=(${gpu})
             env="export CUDA_VISIBLE_DEVICES=${array[0]}"
@@ -271,7 +271,7 @@ else
                 set_batchsize=$(func_set_params "${train_batch_key}" "${train_batch_value}")
                 set_train_params1=$(func_set_params "${train_param_key1}" "${train_param_value1}")
                 set_use_gpu=$(func_set_params "${train_use_gpu_key}" "${train_use_gpu_value}")
-                if [ ${#ips} -le 15 ];then
+                if [ ${#ips} -le 15 ]; then
                     # if length of ips >= 15, then it is seen as multi-machine
                     # 15 is the min length of ips info for multi-machine: 0.0.0.0,0.0.0.0
                     save_log="${LOG_PATH}/${trainer}_gpus_${gpu}_autocast_${autocast}"
@@ -290,26 +290,26 @@ else
                 # fi
 
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
-                if [ ${#gpu} -le 2 ];then  # train with cpu or single gpu
+                if [ ${#gpu} -le 2 ]; then # train with cpu or single gpu
                     cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} "
-                elif [ ${#ips} -le 15 ];then  # train with multi-gpu
+                elif [ ${#ips} -le 15 ]; then # train with multi-gpu
                     cmd="${python} -m paddle.distributed.launch --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1}"
-                else     # train with multi-machine
+                else # train with multi-machine
                     cmd="${python} -m paddle.distributed.launch --ips=${ips} --gpus=${gpu} ${run_train} ${set_use_gpu} ${set_save_model} ${set_pretrain} ${set_epoch} ${set_autocast} ${set_batchsize} ${set_train_params1}"
                 fi
                 # run train
-		eval "unset CUDA_VISIBLE_DEVICES"
-		# export FLAGS_cudnn_deterministic=True
-		sleep 5
+                eval "unset CUDA_VISIBLE_DEVICES"
+                # export FLAGS_cudnn_deterministic=True
+                sleep 5
                 eval $cmd
                 status_check $? "${cmd}" "${status_log}" "${model_name}"
                 sleep 5
 
-		if [[ $FILENAME == *GeneralRecognition* ]]; then
-		    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
-		else
+                if [[ $FILENAME == *GeneralRecognition* ]]; then
+                    set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+                else
                     set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/${model_name}/${train_model_name}")
-		fi
+                fi
                 # save norm trained models to set pretrain for pact training and fpgm training
                 if [ ${trainer} = ${trainer_norm} ]; then
                     load_norm_train_model=${set_eval_pretrain}
@@ -326,11 +326,11 @@ else
                 if [ ${run_export} != "null" ]; then
                     # run export model
                     save_infer_path="${save_log}"
-		    if [[ $FILENAME == *GeneralRecognition* ]]; then
-		        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
-		    else
-		        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
-		    fi
+                    if [[ $FILENAME == *GeneralRecognition* ]]; then
+                        set_eval_pretrain=$(func_set_params "${pretrain_model_key}" "${save_log}/RecModel/${train_model_name}")
+                    else
+                        set_export_weight=$(func_set_params "${export_weight}" "${save_log}/${model_name}/${train_model_name}")
+                    fi
                     set_save_infer_key=$(func_set_params "${save_infer_key}" "${save_infer_path}")
                     export_cmd="${python} ${run_export} ${set_export_weight} ${set_save_infer_key}"
                     eval $export_cmd
@@ -339,12 +339,12 @@ else
                     #run inference
                     eval $env
                     save_infer_path="${save_log}"
-		    cd deploy
+                    cd deploy
                     func_inference "${python}" "${inference_py}" "../${save_infer_path}" "../${LOG_PATH}" "${infer_img_dir}" "${flag_quant}"
-		    cd ..
+                    cd ..
                 fi
                 eval "unset CUDA_VISIBLE_DEVICES"
-            done  # done with:    for trainer in ${trainer_list[*]}; do
-        done      # done with:    for autocast in ${autocast_list[*]}; do
-    done          # done with:    for gpu in ${gpu_list[*]}; do
-fi  # end if [ ${MODE} = "infer" ]; then
+            done # done with:    for trainer in ${trainer_list[*]}; do
+        done     # done with:    for autocast in ${autocast_list[*]}; do
+    done         # done with:    for gpu in ${gpu_list[*]}; do
+fi               # end if [ ${MODE} = "infer" ]; then