format test_train_inference_python.sh

test_tipc/test_train_inference_python.sh

@@ -98,7 +98,7 @@ 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}"
@@ -290,9 +290,9 @@ 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} ${set_train_params1}"
                 else # train with multi-machine
                     cmd="${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}"