delete extra params and set profile_option as bool

test_tipc/configs/det_mv3_db_v2.0/train_benchmark.txt

@@ -10,7 +10,7 @@ Train.loader.batch_size_per_card:benchmark_train=16
 Global.pretrained_model:null
 train_model_name:latest
 train_infer_img_dir:null
-null:null
+--profiler_options:True
 ##
 trainer:norm_train
 norm_train:tools/train.py -c configs/det/det_mv3_db.yml  -o Global.pretrained_model=./pretrain_models/MobileNetV3_large_x0_5_pretrained

test_tipc/test_train_inference_python.sh

@@ -5,12 +5,6 @@ FILENAME=$1
 # MODE be one of ['lite_train_lite_infer' 'lite_train_whole_infer' 'whole_train_whole_infer', 'whole_infer', 'klquant_whole_infer']
 MODE=$2
 
-if [ $# -eq 3 ] ; then
-    extra_train_params=$3
-else
-    extra_train_params=""
-fi
-
 dataline=$(awk 'NR==1, NR==51{print}'  $FILENAME)
 
 # parser params
@@ -336,11 +330,11 @@ else
 
                 set_save_model=$(func_set_params "${save_model_key}" "${save_log}")
                 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} ${set_amp_config}  ${extra_train_params}"
+                    cmd="${python} ${run_train} ${set_use_gpu}  ${set_save_model} ${set_epoch} ${set_pretrain} ${set_autocast} ${set_batchsize} ${set_train_params1} ${set_amp_config} "
                 elif [ ${#ips} -le 26 ];then  # train with multi-gpu
-                    cmd="${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} ${set_amp_config} ${extra_train_params}"
+                    cmd="${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} ${set_amp_config}"
                 else     # train with multi-machine
-                    cmd="${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} ${set_amp_config} ${extra_train_params}"
+                    cmd="${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} ${set_amp_config}"
                 fi
                 # run train
                 eval $cmd

tools/program.py

@@ -46,8 +46,8 @@ class ArgsParser(ArgumentParser):
         self.add_argument(
             '-p',
             '--profiler_options',
-            type=str,
-            default=None,
+            type=bool,
+            default=False,
             help='The option of profiler, which should be in format \"key1=value1;key2=value2;key3=value3\".'
         )
 
@@ -150,6 +150,10 @@ def train(config,
     print_batch_step = config['Global']['print_batch_step']
     eval_batch_step = config['Global']['eval_batch_step']
     profiler_options = config['profiler_options']
+    if profiler_options is True:
+        profiler_options = "batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile"
+    else:
+        profiler_options = None
 
     global_step = 0
     if 'global_step' in pre_best_model_dict: