diff --git a/model_zoo/official/cv/centerface/README.md b/model_zoo/official/cv/centerface/README.md
index db8f2ba0558..854ae800e6e 100644
--- a/model_zoo/official/cv/centerface/README.md
+++ b/model_zoo/official/cv/centerface/README.md
@@ -175,7 +175,7 @@ step6: eval
 # cd ../dependency/evaluate;
 # python setup.py install;
 # cd -; #cd ../../scripts;
-sh eval_all.sh
+sh eval_all.sh [ground_truth_path]
 ```
 
 # [Script Description](#contents)
@@ -192,6 +192,7 @@ sh eval_all.sh
         ├── postprocess.py               // 310infer postprocess scripts
         ├── README.md                    // descriptions about CenterFace
         ├── ascend310_infer              // application for 310 inference
+        ├─default_config.yaml            // Training parameter profile
         ├── scripts
         │   ├──run_infer_310.sh          // shell script for infer on ascend310
         │   ├──eval.sh                   // evaluate a single testing result
@@ -205,7 +206,6 @@ sh eval_all.sh
         │   ├──__init__.py
         │   ├──centerface.py             // centerface networks, training entry
         │   ├──dataset.py                // generate dataloader and data processing entry
-        │   ├──config.py                 // centerface unique configs
         │   ├──losses.py                 // losses for centerface
         │   ├──lr_scheduler.py           // learning rate scheduler
         │   ├──mobile_v2.py              // modified mobilenet_v2 backbone
@@ -213,6 +213,11 @@ sh eval_all.sh
         │   ├──var_init.py               // weight initialization
         │   ├──convert_weight_mobilenetv2.py   // convert pretrained backbone to mindspore
         │   ├──convert_weight.py               // CenterFace model convert to mindspore
+        |   └──model_utils
+        |      ├──config.py          // Processing configuration parameters
+        |      ├──device_adapter.py  // Get cloud ID
+        |      ├──local_adapter.py   // Get local ID
+        |     └ ──moxing_adapter.py  // Parameter processing
         └── dependency                   // third party codes: MIT License
             ├──extd                      // training dependency: data augmentation
             │   ├──utils
@@ -451,7 +456,7 @@ cd ../../../scripts;
     ```python
     # you need to change the parameter in eval.sh
     # default eval the ckpt saved in ./scripts/output/centerface/999
-    sh eval.sh
+    sh eval.sh [ground_truth_path]
     ```
 
 2. eval many testing output for user to choose the best one
@@ -459,7 +464,7 @@ cd ../../../scripts;
     ```python
     # you need to change the parameter in eval_all.sh
     # default eval the ckpt saved in ./scripts/output/centerface/[89-140]
-    sh eval_all.sh
+    sh eval_all.sh [ground_truth_path]
     ```
 
 3. test+eval
@@ -475,7 +480,7 @@ sh test_and_eval.sh [MODEL_PATH] [DATASET] [GROUND_TRUTH_MAT] [SAVE_PATH] [CKPT]
 you can see the MAP below by eval.sh
 
 ```log
-(ci3.7) [root@bms-aiserver scripts]# ./eval.sh
+(ci3.7) [root@bms-aiserver scripts]# ./eval.sh ./ground_truth_path
 start eval
 ==================== Results = ==================== ./scripts/output/centerface/999
 Easy   Val AP: 0.923914407045363
@@ -488,7 +493,7 @@ end eval
 you can see the MAP below by eval_all.sh
 
 ```log
-(ci3.7) [root@bms-aiserver scripts]# ./eval_all.sh
+(ci3.7) [root@bms-aiserver scripts]# ./eval_all.sh ./ground_truth_path
 ==================== Results = ==================== ./scripts/output/centerface/89
 Easy   Val AP: 0.8884892849068273
 Medium Val AP: 0.8928813452811216
diff --git a/model_zoo/official/cv/centerface/default_config.yaml b/model_zoo/official/cv/centerface/default_config.yaml
new file mode 100644
index 00000000000..0df4c4ce09c
--- /dev/null
+++ b/model_zoo/official/cv/centerface/default_config.yaml
@@ -0,0 +1,234 @@
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+enable_modelarts: False
+data_url: ""
+train_url: ""
+checkpoint_url: ""
+data_path: "/cache/data"
+output_path: "/cache/train"
+load_path: "/cache/checkpoint_path"
+checkpoint_path: './checkpoint/'
+device_target: Ascend
+enable_profiling: False
+
+# ==============================================================================
+# Config setup
+flip_idx: [[0, 1], [3, 4]]
+default_resolution: [512, 512]
+heads: {'hm': 1, 'wh': 2, 'hm_offset': 2, 'landmarks': 5 * 2}
+head_conv: 64
+max_objs: 64
+
+rand_crop: True
+scale: 0.4
+shift: 0.1
+aug_rot: 0
+color_aug: True
+flip: 0.5
+input_res: 512 #768 #800
+output_res: 128 #192 #200
+num_classes: 1
+num_joints: 5
+reg_offset: True
+hm_hp: True
+reg_hp_offset: True
+dense_hp: False
+hm_weight: 1.0
+wh_weight: 0.1
+off_weight: 1.0
+lm_weight: 0.1
+rotate: 0
+
+# for test
+mean: [0.408, 0.447, 0.470]
+std: [0.289, 0.274, 0.278]
+test_scales: [0.999,]
+nms: 1
+flip_test: 0
+fix_res: True
+input_h: 832 #800
+input_w: 832 #800
+K: 200
+down_ratio: 4
+test_batch_size: 1
+
+master_batch_size: 8
+num_workers: 8
+not_rand_crop: False
+no_color_aug: False
+
+# ==============================================================================
+# train.py mindspore coco training
+# dataset related
+data_dir: '/cache/data/'
+annot_path: '/cache/data/annotations/train_wider_face.json'
+img_dir: '/cache/data/images/WIDER_train/images/'
+per_batch_size: 8
+
+# network related
+pretrained_backbone: '/cache/data/centerface/mobilenet_v2.ckpt'
+resume: ''
+
+# optimizer and lr related
+lr_scheduler: 'multistep'
+lr: 0.004 # 4e-3
+lr_epochs: '90,120'
+lr_gamma: 0.1
+eta_min: 0.
+t_max: 140
+max_epoch: 140
+warmup_epochs: 0
+weight_decay: 0.0005
+momentum: 0.9
+optimizer: 'adam'
+
+# loss related
+loss_scale: 1024
+label_smooth: 0
+label_smooth_factor: 0.1
+
+ # logging related
+log_interval: 100
+ckpt_path: './output'
+ckpt_interval: 0 # None
+
+is_save_on_master: 1
+
+# distributed related
+is_distributed: 1
+rank: 0
+group_size: 1
+
+# roma obs
+#train_url: ""
+
+# profiler init, can open when you debug. if train, donot open, since it cost memory and disk space
+need_profiler: 0
+
+# reset default config
+training_shape: ""
+resize_rate: 0 # None
+
+# test.py mindspore coco training'
+test_model: ''
+ground_truth_mat: ''
+save_dir: ''
+ground_truth_path: ''
+eval: 0
+eval_script_path: ''
+ckpt_name: ""
+device_num: 1
+steps_per_epoch: 198
+start: 0
+end: 18
+
+# export.py centerface export'
+device_id: 0
+batch_size: 1
+ckpt_file: ''
+file_name: "centerface"
+file_format: 'AIR'
+
+# centerface preprocess"
+dataset_path: ''
+preprocess_path: ''
+
+# postprocess / centerface calcul AP
+result_path: ''
+label_file: ''
+meta_file: ''
+save_path: ''
+
+# ==============================================================================
+# src/convert_weight.py
+ckpt_fn: '/model_path/centerface.ckpt'
+pt_fn: '/model_path/centerface.pth'
+out_fn: '/model_path/centerface_out.ckpt'
+pt2ckpt: 1
+
+# src/convert_weight_mobilenetv2.py
+ckpt_fn_v2: '/model_path/mobilenet_v2_key.ckpt'
+pt_fn_v2: '/model_path/mobilenet_v2-b0353104.pth'
+out_ckpt_fn: '/model_path/mobilenet_v2-b0353104.ckpt'
+
+
+---
+# Config description for each option
+enable_modelarts: 'Whether training on modelarts, default: False'
+data_url: 'Dataset url for obs'
+train_url: 'Training output url for obs'
+data_path: 'Dataset path for local'
+output_path: 'Training output path for local'
+
+data_dir: 'train data dir'
+annot_path: 'train data annotation path'
+img_dir: 'train data img dir'
+per_batch_size: 'batch size for per gpu'
+pretrained_backbone: 'model_path, local pretrained backbone model to load'
+resume: 'path of pretrained centerface_model'
+lr_scheduler: 'lr-scheduler, option type: exponential, cosine_annealing'
+lr: 'learning rate of the training'
+lr_epochs: 'epoch of lr changing'
+lr_gamma: 'decrease lr by a factor of exponential lr_scheduler'
+eta_min: 'eta_min in cosine_annealing scheduler'
+t_max: 'T-max in cosine_annealing scheduler'
+max_epoch: 'max epoch num to train the model'
+warmup_epochs: 'warmup epoch'
+weight_decay: 'weight decay'
+momentum: 'momentum'
+optimizer: 'optimizer type, default: adam'
+loss_scale: 'static loss scale'
+label_smooth: 'whether to use label smooth in CE'
+label_smooth_factor: 'smooth strength of original one-hot'
+log_interval: 'logging interval'
+ckpt_path: 'checkpoint save location'
+ckpt_interval: 'ckpt_interval'
+is_save_on_master: 'save ckpt on master or all rank'
+is_distributed: 'if multi device'
+rank: 'local rank of distributed'
+group_size: 'world size of distributed'
+need_profiler: 'whether use profiler'
+training_shape: 'fix training shape'
+resize_rate: 'resize rate for multi-scale training'
+test_model: 'test model dir'
+ground_truth_mat: 'ground_truth, mat type'
+save_dir: 'save_path for evaluate'
+ground_truth_path: 'ground_truth path, contain all mat file'
+eval: 'if do eval after test'
+eval_script_path: 'evaluate script path'
+ckpt_name: 'input model name'
+steps_per_epoch: 'steps for each epoch'
+start: 'start loop number, used to calculate first epoch number'
+end: 'end loop number, used to calculate last epoch number'
+batch_size: "batch size"
+ckpt_file: "Checkpoint file path."
+
+ckpt_fn: 'ckpt for user to get cell/module name'
+pt_fn: 'checkpoint filename to convert'
+out_fn: 'convert output ckpt/pth path'
+pt2ckpt: '1 : pt2ckpt; 0 : ckpt2pt'
+
+enable_profiling: 'Whether enable profiling while training, default: False'
+only_create_dataset: 'If set it true, only create Mindrecord, default is false.'
+run_distribute: 'Run distribute, default is false.'
+do_train: 'Do train or not, default is true.'
+do_eval: 'Do eval or not, default is false.'
+dataset: 'Dataset, default is coco.'
+pre_trained: 'Pretrain file path.'
+device_id: 'Device id, default is 0.'
+device_num: 'Use device nums, default is 1.'
+rank_id: 'Rank id, default is 0.'
+file_name: "output file name."
+file_format: 'file format'
+img_path: "image file path."
+result_path: "result file path."
+
+dataset_path: "dataset path."
+preprocess_path: "preprocess path."
+label_file: "label file"
+meta_file: "label file"
+save_path: "label file"
+
+---
+platform: ['Ascend', 'GPU', 'CPU']
+file_format: ["AIR", "MINDIR"]
+freeze_layer: ["", "none", "backbone"]
\ No newline at end of file
diff --git a/model_zoo/official/cv/centerface/export.py b/model_zoo/official/cv/centerface/export.py
index 46def3cb0a9..0f5f4bd6c8f 100644
--- a/model_zoo/official/cv/centerface/export.py
+++ b/model_zoo/official/cv/centerface/export.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 """export checkpoint file into air, onnx, mindir models"""
-import argparse
 import numpy as np
 
 import mindspore
@@ -21,27 +20,22 @@ from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
 
 from src.centerface import CenterfaceMobilev2, CenterFaceWithNms
-from src.config import ConfigCenterface
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+from src.model_utils.moxing_adapter import moxing_wrapper
 
-parser = argparse.ArgumentParser(description='centerface export')
-parser.add_argument("--device_id", type=int, default=0, help="Device id")
-parser.add_argument("--batch_size", type=int, default=1, help="batch size")
-parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
-parser.add_argument("--file_name", type=str, default="centerface", help="output file name.")
-parser.add_argument('--file_format', type=str, choices=["AIR", "MINDIR"], default='AIR', help='file format')
-parser.add_argument("--device_target", type=str, choices=["Ascend", "GPU", "CPU"], default="Ascend",
-                    help="device target")
-args = parser.parse_args()
+context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+if config.device_target == "Ascend":
+    context.set_context(device_id=get_device_id())
 
-context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
-if args.device_target == "Ascend":
-    context.set_context(device_id=args.device_id)
+def modelarts_process():
+    pass
 
-if __name__ == '__main__':
-    config = ConfigCenterface()
+@moxing_wrapper(pre_process=modelarts_process)
+def export_centerface():
     net = CenterfaceMobilev2()
 
-    param_dict = load_checkpoint(args.ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     param_dict_new = {}
     for key, values in param_dict.items():
         if key.startswith('moments.') or key.startswith('moment1.') or key.startswith('moment2.'):
@@ -55,5 +49,8 @@ if __name__ == '__main__':
     net = CenterFaceWithNms(net)
     net.set_train(False)
 
-    input_data = Tensor(np.zeros([args.batch_size, 3, config.input_h, config.input_w]), mindspore.float32)
-    export(net, input_data, file_name=args.file_name, file_format=args.file_format)
+    input_data = Tensor(np.zeros([config.batch_size, 3, config.input_h, config.input_w]), mindspore.float32)
+    export(net, input_data, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    export_centerface()
diff --git a/model_zoo/official/cv/centerface/postprocess.py b/model_zoo/official/cv/centerface/postprocess.py
index 655a1b865e4..ee4d585ac0f 100644
--- a/model_zoo/official/cv/centerface/postprocess.py
+++ b/model_zoo/official/cv/centerface/postprocess.py
@@ -14,9 +14,9 @@
 # ============================================================================
 """post process for 310 inference"""
 import os
-import argparse
 import numpy as np
-from src.config import ConfigCenterface
+from src.model_utils.config import config
+
 from dependency.centernet.src.lib.detectors.base_detector import CenterFaceDetector
 from dependency.evaluate.eval import evaluation
 
@@ -36,16 +36,8 @@ dct_map = {'16': '16--Award_Ceremony', '26': '26--Soldier_Drilling', '29': '29--
            '14': '14--Traffic', '41': '41--Swimming', '46': '46--Jockey', '10': '10--People_Marching',
            '54': '54--Rescue', '57': '57--Angler', '31': '31--Waiter_Waitress', '27': '27--Spa', '21': '21--Festival'}
 
-parser = argparse.ArgumentParser(description='centerface calcul AP')
-parser.add_argument("--result_path", type=str, required=True, default='', help="result file path")
-parser.add_argument("--label_file", type=str, required=True, default='', help="label file")
-parser.add_argument("--meta_file", type=str, required=True, default='', help="label file")
-parser.add_argument("--save_path", type=str, required=True, default='', help="label file")
-args = parser.parse_args()
-
 
 def cal_acc(result_path, label_file, meta_file, save_path):
-    config = ConfigCenterface()
     detector = CenterFaceDetector(config, None)
     if not os.path.exists(save_path):
         for im_dir in dct_map.values():
@@ -87,4 +79,4 @@ def cal_acc(result_path, label_file, meta_file, save_path):
 
 
 if __name__ == '__main__':
-    cal_acc(args.result_path, args.label_file, args.meta_file, args.save_path)
+    cal_acc(config.result_path, config.label_file, config.meta_file, config.save_path)
diff --git a/model_zoo/official/cv/centerface/preprocess.py b/model_zoo/official/cv/centerface/preprocess.py
index f27e21f41f6..a985c6b6442 100644
--- a/model_zoo/official/cv/centerface/preprocess.py
+++ b/model_zoo/official/cv/centerface/preprocess.py
@@ -14,21 +14,14 @@
 # ============================================================================
 """pre process for 310 inference"""
 import os
-import argparse
 import shutil
 import cv2
 import numpy as np
-from src.config import ConfigCenterface
+from src.model_utils.config import config
 from dependency.centernet.src.lib.detectors.base_detector import CenterFaceDetector
 
-parser = argparse.ArgumentParser(description="centerface preprocess")
-parser.add_argument("--dataset_path", type=str, required=True, help="dataset path.")
-parser.add_argument("--preprocess_path", type=str, required=True, help="preprocess path.")
-args = parser.parse_args()
-
 
 def preprocess(dataset_path, preprocess_path):
-    config = ConfigCenterface()
     event_list = os.listdir(dataset_path)
     input_path = os.path.join(preprocess_path, "input")
     meta_path = os.path.join(preprocess_path, "meta/meta")
@@ -65,4 +58,4 @@ def preprocess(dataset_path, preprocess_path):
 
 
 if __name__ == '__main__':
-    preprocess(args.dataset_path, args.preprocess_path)
+    preprocess(config.dataset_path, config.preprocess_path)
diff --git a/model_zoo/official/cv/centerface/scripts/eval.sh b/model_zoo/official/cv/centerface/scripts/eval.sh
index 671b61883b8..8f2a65a5e17 100644
--- a/model_zoo/official/cv/centerface/scripts/eval.sh
+++ b/model_zoo/official/cv/centerface/scripts/eval.sh
@@ -16,7 +16,7 @@
 
 root=$PWD
 save_path=$root/output/centerface/
-ground_truth_path=$root/dataset/centerface/ground_truth
+ground_truth_path=$1
 echo "start eval"
 python ../dependency/evaluate/eval.py --pred=$save_path --gt=$ground_truth_path
 echo "end eval"
diff --git a/model_zoo/official/cv/centerface/scripts/eval_all.sh b/model_zoo/official/cv/centerface/scripts/eval_all.sh
index 993016621b6..a38c137cab7 100644
--- a/model_zoo/official/cv/centerface/scripts/eval_all.sh
+++ b/model_zoo/official/cv/centerface/scripts/eval_all.sh
@@ -16,11 +16,11 @@
 
 root=$PWD
 save_path=$root/output/centerface/
-ground_truth_path=$root/dataset/centerface/ground_truth
+ground_truth_path=$1
 #for i in $(seq start_epoch end_epoch+1)
 for i in $(seq 89 200)
 do
-    python ../dependency/evaluate/eval.py --pred=$save_path$i --gt=$ground_truth_path &
+    python ../dependency/evaluate/eval.py --pred=$save_path$i --gt=$ground_truth_path >> log_eval_all.txt 2>&1 &
     sleep 10
 done
 wait
diff --git a/model_zoo/official/cv/centerface/scripts/test_and_eval.sh b/model_zoo/official/cv/centerface/scripts/test_and_eval.sh
index 973415adc00..6a6e1ea4f34 100644
--- a/model_zoo/official/cv/centerface/scripts/test_and_eval.sh
+++ b/model_zoo/official/cv/centerface/scripts/test_and_eval.sh
@@ -55,27 +55,28 @@ device_id=0
 ckpt="0-125_24750.ckpt" # the model saved for epoch=125
 ground_truth_path=$root/dataset/centerface/ground_truth
 
-if [ $# == 1 ]
+if [ $# -ge 1 ]
 then
     model_path=$(get_real_path $1)
-    if [ ! -f $model_path ]
+#    if [ ! -f $model_path ]
+    if [ ! -d $model_path ]
     then
-        echo "error: model_path=$model_path is not a file"
+        echo "error: model_path=$model_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 2 ]
+if [ $# -ge 2 ]
 then
     dataset_path=$(get_real_path $2)
-    if [ ! -f $dataset_path ]
+    if [ ! -d $dataset_path ]
     then
-        echo "error: dataset_path=$dataset_path is not a file"
+        echo "error: dataset_path=$dataset_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 3 ]
+if [ $# -ge 3 ]
 then
     ground_truth_mat=$(get_real_path $3)
     if [ ! -f $ground_truth_mat ]
@@ -85,27 +86,27 @@ then
     fi
 fi
 
-if [ $# == 4 ]
+if [ $# -ge 4 ]
 then
     save_path=$(get_real_path $4)
-    if [ ! -f $save_path ]
+    if [ ! -d $save_path ]
     then
-        echo "error: save_path=$save_path is not a file"
+        echo "error: save_path=$save_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 5 ]
+if [ $# -ge 5 ]
 then
     device_id=$5
 fi
 
-if [ $# == 6 ]
+if [ $# -ge 6 ]
 then
     ckpt=$6
 fi
 
-if [ $# == 7 ]
+if [ $# -ge 7 ]
 then
     ground_truth_path=$(get_real_path $7)
     if [ ! -f $ground_truth_path ]
diff --git a/model_zoo/official/cv/centerface/scripts/test_distribute.sh b/model_zoo/official/cv/centerface/scripts/test_distribute.sh
index ef51c49f7a1..3cfc82934e4 100644
--- a/model_zoo/official/cv/centerface/scripts/test_distribute.sh
+++ b/model_zoo/official/cv/centerface/scripts/test_distribute.sh
@@ -63,27 +63,28 @@ steps_per_epoch=198 #198 for 8P; 1583 for 1p
 start=11 # start epoch number = start * device_num + min(device_phy_id) + 1
 end=18 # end epoch number = end * device_num + max(device_phy_id) + 1
 
-if [ $# == 1 ]
+if [ $# -ge 1 ]
 then
     model_path=$(get_real_path $1)
-    if [ ! -f $model_path ]
+#    if [ ! -f $model_path ]
+    if [ ! -d $model_path ]
     then
-        echo "error: model_path=$model_path is not a file"
+        echo "error: model_path=$model_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 2 ]
+if [ $# -ge 2 ]
 then
     dataset_path=$(get_real_path $2)
-    if [ ! -f $dataset_path ]
+    if [ ! -d $dataset_path ]
     then
-        echo "error: dataset_path=$dataset_path is not a file"
+        echo "error: dataset_path=$dataset_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 3 ]
+if [ $# -ge 3 ]
 then
     ground_truth_mat=$(get_real_path $3)
     if [ ! -f $ground_truth_mat ]
@@ -93,27 +94,27 @@ then
     fi
 fi
 
-if [ $# == 4 ]
+if [ $# -ge 4 ]
 then
     save_path=$(get_real_path $4)
-    if [ ! -f $save_path ]
+    if [ ! -d $save_path ]
     then
-        echo "error: save_path=$save_path is not a file"
+        echo "error: save_path=$save_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 5 ]
+if [ $# -ge 5 ]
 then
     device_num=$5
 fi
 
-if [ $# == 6 ]
+if [ $# -ge 6 ]
 then
     steps_per_epoch=$6
 fi
 
-if [ $# == 7 ]
+if [ $# -ge 7 ]
 then
     start=$7
 fi
diff --git a/model_zoo/official/cv/centerface/scripts/train_distribute.sh b/model_zoo/official/cv/centerface/scripts/train_distribute.sh
index a4668bed9e0..5030ca222c0 100644
--- a/model_zoo/official/cv/centerface/scripts/train_distribute.sh
+++ b/model_zoo/official/cv/centerface/scripts/train_distribute.sh
@@ -51,7 +51,7 @@ annot_path=$dataset_path/annotations/train.json
 img_dir=$dataset_path/images/train/images
 rank_table=$root/rank_table_8p.json
 
-if [ $# == 1 ]
+if [ $# -ge 1 ]
 then
     rank_table=$(get_real_path $1)
     if [ ! -f $rank_table ]
@@ -61,7 +61,7 @@ then
     fi
 fi
 
-if [ $# == 2 ]
+if [ $# -ge 2 ]
 then
     pretrained_backbone=$(get_real_path $2)
     if [ ! -f $pretrained_backbone ]
@@ -71,17 +71,17 @@ then
     fi
 fi
 
-if [ $# == 3 ]
+if [ $# -ge 3 ]
 then
     dataset_path=$(get_real_path $3)
-    if [ ! -f $dataset_path ]
+    if [ ! -d $dataset_path ]
     then
-        echo "error: dataset_path=$dataset_path is not a file"
+        echo "error: dataset_path=$dataset_path is not a dir"
     exit 1
     fi
 fi
 
-if [ $# == 4 ]
+if [ $# -ge 4 ]
 then
     annot_path=$(get_real_path $4)
     if [ ! -f $annot_path ]
@@ -91,12 +91,12 @@ then
     fi
 fi
 
-if [ $# == 5 ]
+if [ $# -ge 5 ]
 then
     img_dir=$(get_real_path $5)
-    if [ ! -f $img_dir ]
+    if [ ! -d $img_dir ]
     then
-        echo "error: img_dir=$img_dir is not a file"
+        echo "error: img_dir=$img_dir is not a dir"
     exit 1
     fi
 fi
diff --git a/model_zoo/official/cv/centerface/scripts/train_standalone.sh b/model_zoo/official/cv/centerface/scripts/train_standalone.sh
index 0438d74319f..ebf0a3999d1 100644
--- a/model_zoo/official/cv/centerface/scripts/train_standalone.sh
+++ b/model_zoo/official/cv/centerface/scripts/train_standalone.sh
@@ -50,38 +50,28 @@ annot_path=$dataset_path/annotations/train.json
 img_dir=$dataset_path/images/train/images
 use_device_id=0
 
-if [ $# == 1 ]
+if [ $# -ge 1 ]
 then
     use_device_id=$1
 fi
 
-if [ $# == 2 ]
+if [ $# -ge 2 ]
 then
-    use_device_id=$1
     pretrained_backbone=$(get_real_path $2)
 fi
 
-if [ $# == 3 ]
+if [ $# -ge 3 ]
 then
-    use_device_id=$1
-    pretrained_backbone=$(get_real_path $2)
     dataset_path=$(get_real_path $3)
 fi
 
-if [ $# == 4 ]
+if [ $# -ge 4 ]
 then
-    use_device_id=$1
-    pretrained_backbone=$(get_real_path $2)
-    dataset_path=$(get_real_path $3)
     annot_path=$(get_real_path $4)
 fi
 
-if [ $# == 5 ]
+if [ $# -ge 5 ]
 then
-    use_device_id=$1
-    pretrained_backbone=$(get_real_path $2)
-    dataset_path=$(get_real_path $3)
-    annot_path=$(get_real_path $4)
     img_dir=$(get_real_path $5)
 fi
 
diff --git a/model_zoo/official/cv/centerface/src/centerface.py b/model_zoo/official/cv/centerface/src/centerface.py
index 6ce93732f35..a5b482f0edb 100644
--- a/model_zoo/official/cv/centerface/src/centerface.py
+++ b/model_zoo/official/cv/centerface/src/centerface.py
@@ -14,7 +14,8 @@
 # ============================================================================
 """centerface networks"""
 
-from src.config import ConfigCenterface
+# from src.config import ConfigCenterface
+from src.model_utils.config import config
 from src.mobile_v2 import mobilenet_v2
 from src.losses import FocalLoss, SmoothL1LossNew, SmoothL1LossNewCMask
 
@@ -132,7 +133,7 @@ class CenterfaceMobilev2(nn.Cell):
 
     def __init__(self):
         super(CenterfaceMobilev2, self).__init__()
-        self.config = ConfigCenterface()
+        self.config = config
 
         self.base = mobilenet_v2()
         channels = self.base.feat_channel
@@ -199,7 +200,7 @@ class CenterFaceWithLossCell(nn.Cell):
     def __init__(self, network):
         super(CenterFaceWithLossCell, self).__init__()
         self.centerface_network = network
-        self.config = ConfigCenterface()
+        self.config = config
         self.loss = CenterFaceLoss(self.config.wh_weight, self.config.reg_offset, self.config.off_weight,
                                    self.config.hm_weight, self.config.lm_weight)
         self.reduce_sum = P.ReduceSum()
@@ -295,7 +296,7 @@ class CenterFaceWithNms(nn.Cell):
     def __init__(self, network):
         super(CenterFaceWithNms, self).__init__()
         self.centerface_network = network
-        self.config = ConfigCenterface()
+        self.config = config
         # two type of maxpool self.maxpool2d = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='same')
         self.maxpool2d = P.MaxPoolWithArgmax(kernel_size=3, strides=1, pad_mode='same')
         self.topk = P.TopK(sorted=True)
diff --git a/model_zoo/official/cv/centerface/src/config.py b/model_zoo/official/cv/centerface/src/config.py
deleted file mode 100644
index b0ccefdf369..00000000000
--- a/model_zoo/official/cv/centerface/src/config.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ============================================================================
-"""centerface unique configs"""
-
-class ConfigCenterface():
-    """
-    Config setup
-    """
-    flip_idx = [[0, 1], [3, 4]]
-    default_resolution = [512, 512]
-    heads = {'hm': 1, 'wh': 2, 'hm_offset': 2, 'landmarks': 5 * 2}
-    head_conv = 64
-    max_objs = 64
-
-    rand_crop = True
-    scale = 0.4
-    shift = 0.1
-    aug_rot = 0
-    color_aug = True
-    flip = 0.5
-    input_res = 512 #768 #800
-    output_res = 128 #192 #200
-    num_classes = 1
-    num_joints = 5
-    reg_offset = True
-    hm_hp = True
-    reg_hp_offset = True
-    dense_hp = False
-    hm_weight = 1.0
-    wh_weight = 0.1
-    off_weight = 1.0
-    lm_weight = 0.1
-    rotate = 0
-
-    # for test
-    mean = [0.408, 0.447, 0.470]
-    std = [0.289, 0.274, 0.278]
-    test_scales = [0.999,]
-    nms = 1
-    flip_test = 0
-    fix_res = True
-    input_h = 832 #800
-    input_w = 832 #800
-    K = 200
-    down_ratio = 4
-    test_batch_size = 1
-
-    master_batch_size = 8
-    num_workers = 8
-    not_rand_crop = False
-    no_color_aug = False
diff --git a/model_zoo/official/cv/centerface/src/convert_weight.py b/model_zoo/official/cv/centerface/src/convert_weight.py
index 66fe32c69c5..61b3aa236a5 100644
--- a/model_zoo/official/cv/centerface/src/convert_weight.py
+++ b/model_zoo/official/cv/centerface/src/convert_weight.py
@@ -16,20 +16,11 @@
 Centerface model transform
 """
 import os
-import argparse
 import torch
 from mindspore.train.serialization import load_checkpoint, save_checkpoint
 from mindspore import Tensor
+from src.model_utils.config import config
 
-parser = argparse.ArgumentParser(description='')
-parser.add_argument('--ckpt_fn', type=str, default='/model_path/centerface.ckpt',
-                    help='ckpt for user to get cell/module name')
-parser.add_argument('--pt_fn', type=str, default='/model_path/centerface.pth', help='checkpoint filename to convert')
-parser.add_argument('--out_fn', type=str, default='/model_path/centerface_out.ckpt',
-                    help='convert output ckpt/pth path')
-parser.add_argument('--pt2ckpt', type=int, default=1, help='1 : pt2ckpt; 0 : ckpt2pt')
-
-args = parser.parse_args()
 
 def load_model(model_path):
     """
@@ -160,10 +151,10 @@ def ckpt_to_pt(pt, ckpt, out_path):
     return state_dict
 
 if __name__ == "__main__":
-    if args.pt2ckpt == 1:
-        pt_to_ckpt(args.pt_fn, args.ckpt_fn, args.out_fn)
-    elif args.pt2ckpt == 0:
-        ckpt_to_pt(args.pt_fn, args.ckpt_fn, args.out_fn)
+    if config.pt2ckpt == 1:
+        pt_to_ckpt(config.pt_fn, config.ckpt_fn, config.out_fn)
+    elif config.pt2ckpt == 0:
+        ckpt_to_pt(config.pt_fn, config.ckpt_fn, config.out_fn)
     else:
         # user defined functions
         pass
diff --git a/model_zoo/official/cv/centerface/src/convert_weight_mobilenetv2.py b/model_zoo/official/cv/centerface/src/convert_weight_mobilenetv2.py
index 798da8b8f00..f6c27d93556 100644
--- a/model_zoo/official/cv/centerface/src/convert_weight_mobilenetv2.py
+++ b/model_zoo/official/cv/centerface/src/convert_weight_mobilenetv2.py
@@ -16,20 +16,14 @@
 Mobilenet model transform: torch => mindspore
 """
 import os
-import argparse
 import torch
 from mindspore.train.serialization import load_checkpoint, save_checkpoint
 from mindspore import Tensor
+from src.model_utils.config import config
 
-parser = argparse.ArgumentParser(description='')
-parser.add_argument('--ckpt_fn', type=str, default='/model_path/mobilenet_v2_key.ckpt',
-                    help='ckpt for user to get cell/module name')
-parser.add_argument('--pt_fn', type=str, default='/model_path/mobilenet_v2-b0353104.pth',
-                    help='checkpoint filename to convert')
-parser.add_argument('--out_ckpt_fn', type=str, default='/model_path/mobilenet_v2-b0353104.ckpt',
-                    help='convert output ckpt path')
 
-args = parser.parse_args()
+config.ckpt_fn = config.ckpt_fn_v2
+config.pt_fn = config.pt_fn_v2
 
 def load_model(model_path):
     """
@@ -129,4 +123,4 @@ def pt_to_ckpt(pt, ckpt, out_ckpt):
 
 if __name__ == "__main__":
     # beta <=> bias, gamma <=> weight
-    pt_to_ckpt(args.pt_fn, args.ckpt_fn, args.out_ckpt_fn)
+    pt_to_ckpt(config.pt_fn, config.ckpt_fn, config.out_ckpt_fn)
diff --git a/model_zoo/official/cv/centerface/src/model_utils/__init__.py b/model_zoo/official/cv/centerface/src/model_utils/__init__.py
new file mode 100644
index 00000000000..e69de29bb2d
diff --git a/model_zoo/official/cv/centerface/src/model_utils/config.py b/model_zoo/official/cv/centerface/src/model_utils/config.py
new file mode 100644
index 00000000000..7f1ff6e2b8d
--- /dev/null
+++ b/model_zoo/official/cv/centerface/src/model_utils/config.py
@@ -0,0 +1,127 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Parse arguments"""
+
+import os
+import ast
+import argparse
+from pprint import pprint, pformat
+import yaml
+
+class Config:
+    """
+    Configuration namespace. Convert dictionary to members.
+    """
+    def __init__(self, cfg_dict):
+        for k, v in cfg_dict.items():
+            if isinstance(v, (list, tuple)):
+                setattr(self, k, [Config(x) if isinstance(x, dict) else x for x in v])
+            else:
+                setattr(self, k, Config(v) if isinstance(v, dict) else v)
+
+    def __str__(self):
+        return pformat(self.__dict__)
+
+    def __repr__(self):
+        return self.__str__()
+
+
+def parse_cli_to_yaml(parser, cfg, helper=None, choices=None, cfg_path="default_config.yaml"):
+    """
+    Parse command line arguments to the configuration according to the default yaml.
+
+    Args:
+        parser: Parent parser.
+        cfg: Base configuration.
+        helper: Helper description.
+        cfg_path: Path to the default yaml config.
+    """
+    parser = argparse.ArgumentParser(description="[REPLACE THIS at config.py]",
+                                     parents=[parser])
+    helper = {} if helper is None else helper
+    choices = {} if choices is None else choices
+    for item in cfg:
+        if not isinstance(cfg[item], list) and not isinstance(cfg[item], dict):
+            help_description = helper[item] if item in helper else "Please reference to {}".format(cfg_path)
+            choice = choices[item] if item in choices else None
+            if isinstance(cfg[item], bool):
+                parser.add_argument("--" + item, type=ast.literal_eval, default=cfg[item], choices=choice,
+                                    help=help_description)
+            else:
+                parser.add_argument("--" + item, type=type(cfg[item]), default=cfg[item], choices=choice,
+                                    help=help_description)
+    args = parser.parse_args()
+    return args
+
+
+def parse_yaml(yaml_path):
+    """
+    Parse the yaml config file.
+
+    Args:
+        yaml_path: Path to the yaml config.
+    """
+    with open(yaml_path, 'r') as fin:
+        try:
+            cfgs = yaml.load_all(fin.read(), Loader=yaml.FullLoader)
+            cfgs = [x for x in cfgs]
+            if len(cfgs) == 1:
+                cfg_helper = {}
+                cfg = cfgs[0]
+                cfg_choices = {}
+            elif len(cfgs) == 2:
+                cfg, cfg_helper = cfgs
+                cfg_choices = {}
+            elif len(cfgs) == 3:
+                cfg, cfg_helper, cfg_choices = cfgs
+            else:
+                raise ValueError("At most 3 docs (config, description for help, choices) are supported in config yaml")
+            print(cfg_helper)
+        except:
+            raise ValueError("Failed to parse yaml")
+    return cfg, cfg_helper, cfg_choices
+
+
+def merge(args, cfg):
+    """
+    Merge the base config from yaml file and command line arguments.
+
+    Args:
+        args: Command line arguments.
+        cfg: Base configuration.
+    """
+    args_var = vars(args)
+    for item in args_var:
+        cfg[item] = args_var[item]
+    return cfg
+
+
+def get_config():
+    """
+    Get Config according to the yaml file and cli arguments.
+    """
+    parser = argparse.ArgumentParser(description="default name", add_help=False)
+    current_dir = os.path.dirname(os.path.abspath(__file__))
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
+                        help="Config file path")
+    path_args, _ = parser.parse_known_args()
+    default, helper, choices = parse_yaml(path_args.config_path)
+    pprint(default)
+    args = parse_cli_to_yaml(parser=parser, cfg=default, helper=helper, choices=choices, cfg_path=path_args.config_path)
+    final_config = merge(args, default)
+    return Config(final_config)
+
+config = get_config()
diff --git a/model_zoo/official/cv/centerface/src/model_utils/device_adapter.py b/model_zoo/official/cv/centerface/src/model_utils/device_adapter.py
new file mode 100644
index 00000000000..7c5d7f837dd
--- /dev/null
+++ b/model_zoo/official/cv/centerface/src/model_utils/device_adapter.py
@@ -0,0 +1,27 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Device adapter for ModelArts"""
+
+from .config import config
+
+if config.enable_modelarts:
+    from .moxing_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+else:
+    from .local_adapter import get_device_id, get_device_num, get_rank_id, get_job_id
+
+__all__ = [
+    "get_device_id", "get_device_num", "get_rank_id", "get_job_id"
+]
diff --git a/model_zoo/official/cv/centerface/src/model_utils/local_adapter.py b/model_zoo/official/cv/centerface/src/model_utils/local_adapter.py
new file mode 100644
index 00000000000..769fa6dc78e
--- /dev/null
+++ b/model_zoo/official/cv/centerface/src/model_utils/local_adapter.py
@@ -0,0 +1,36 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Local adapter"""
+
+import os
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    return "Local Job"
diff --git a/model_zoo/official/cv/centerface/src/model_utils/moxing_adapter.py b/model_zoo/official/cv/centerface/src/model_utils/moxing_adapter.py
new file mode 100644
index 00000000000..830d19a6fc9
--- /dev/null
+++ b/model_zoo/official/cv/centerface/src/model_utils/moxing_adapter.py
@@ -0,0 +1,122 @@
+# Copyright 2021 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+"""Moxing adapter for ModelArts"""
+
+import os
+import functools
+from mindspore import context
+from mindspore.profiler import Profiler
+from .config import config
+
+_global_sync_count = 0
+
+def get_device_id():
+    device_id = os.getenv('DEVICE_ID', '0')
+    return int(device_id)
+
+
+def get_device_num():
+    device_num = os.getenv('RANK_SIZE', '1')
+    return int(device_num)
+
+
+def get_rank_id():
+    global_rank_id = os.getenv('RANK_ID', '0')
+    return int(global_rank_id)
+
+
+def get_job_id():
+    job_id = os.getenv('JOB_ID')
+    job_id = job_id if job_id != "" else "default"
+    return job_id
+
+def sync_data(from_path, to_path):
+    """
+    Download data from remote obs to local directory if the first url is remote url and the second one is local path
+    Upload data from local directory to remote obs in contrast.
+    """
+    import moxing as mox
+    import time
+    global _global_sync_count
+    sync_lock = "/tmp/copy_sync.lock" + str(_global_sync_count)
+    _global_sync_count += 1
+
+    # Each server contains 8 devices as most.
+    if get_device_id() % min(get_device_num(), 8) == 0 and not os.path.exists(sync_lock):
+        print("from path: ", from_path)
+        print("to path: ", to_path)
+        mox.file.copy_parallel(from_path, to_path)
+        print("===finish data synchronization===")
+        try:
+            os.mknod(sync_lock)
+        except IOError:
+            pass
+        print("===save flag===")
+
+    while True:
+        if os.path.exists(sync_lock):
+            break
+        time.sleep(1)
+
+    print("Finish sync data from {} to {}.".format(from_path, to_path))
+
+
+def moxing_wrapper(pre_process=None, post_process=None):
+    """
+    Moxing wrapper to download dataset and upload outputs.
+    """
+    def wrapper(run_func):
+        @functools.wraps(run_func)
+        def wrapped_func(*args, **kwargs):
+            # Download data from data_url
+            if config.enable_modelarts:
+                if config.data_url:
+                    sync_data(config.data_url, config.data_path)
+                    print("Dataset downloaded: ", os.listdir(config.data_path))
+                if config.checkpoint_url:
+                    sync_data(config.checkpoint_url, config.load_path)
+                    print("Preload downloaded: ", os.listdir(config.load_path))
+                if config.train_url:
+                    sync_data(config.train_url, config.output_path)
+                    print("Workspace downloaded: ", os.listdir(config.output_path))
+
+                context.set_context(save_graphs_path=os.path.join(config.output_path, str(get_rank_id())))
+                config.device_num = get_device_num()
+                config.device_id = get_device_id()
+                if not os.path.exists(config.output_path):
+                    os.makedirs(config.output_path)
+
+                if pre_process:
+                    pre_process()
+
+            if config.enable_profiling:
+                profiler = Profiler()
+
+            run_func(*args, **kwargs)
+
+            if config.enable_profiling:
+                profiler.analyse()
+
+            # Upload data to train_url
+            if config.enable_modelarts:
+                if post_process:
+                    post_process()
+
+                if config.train_url:
+                    print("Start to copy output directory")
+                    sync_data(config.output_path, config.train_url)
+        return wrapped_func
+    return wrapper
diff --git a/model_zoo/official/cv/centerface/src/utils.py b/model_zoo/official/cv/centerface/src/utils.py
index e35ad0abc81..e205bb8884f 100644
--- a/model_zoo/official/cv/centerface/src/utils.py
+++ b/model_zoo/official/cv/centerface/src/utils.py
@@ -91,11 +91,11 @@ def load_backbone(net, ckpt_path, args):
                 else:
                     not_found_param.append(mobilev2_beta)
 
-    args.logger.info('================found_param {}========='.format(len(find_param)))
-    args.logger.info(find_param)
-    args.logger.info('================not_found_param {}========='.format(len(not_found_param)))
-    args.logger.info(not_found_param)
-    args.logger.info('=====load {} successfully ====='.format(ckpt_path))
+    print('================found_param {}========='.format(len(find_param)))
+    print(find_param)
+    print('================not_found_param {}========='.format(len(not_found_param)))
+    print(not_found_param)
+    print('=====load {} successfully ====='.format(ckpt_path))
 
     return net
 
diff --git a/model_zoo/official/cv/centerface/test.py b/model_zoo/official/cv/centerface/test.py
index 029e76a37dc..47654739e2d 100644
--- a/model_zoo/official/cv/centerface/test.py
+++ b/model_zoo/official/cv/centerface/test.py
@@ -17,67 +17,54 @@ Test centerface example
 """
 import os
 import time
-import argparse
 import datetime
 import scipy.io as sio
 
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 
-from src.utils import get_logger
 from src.var_init import default_recurisive_init
 from src.centerface import CenterfaceMobilev2, CenterFaceWithNms
-from src.config import ConfigCenterface
+
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id
 
 from dependency.centernet.src.lib.detectors.base_detector import CenterFaceDetector
 from dependency.evaluate.eval import evaluation
 
-dev_id = int(os.getenv('DEVICE_ID'))
+dev_id = get_device_id()
 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=False,
                     device_target="Ascend", save_graphs=False, device_id=dev_id)
 
-parser = argparse.ArgumentParser('mindspore coco training')
-parser.add_argument('--data_dir', type=str, default='', help='train data dir')
-parser.add_argument('--test_model', type=str, default='', help='test model dir')
-parser.add_argument('--ground_truth_mat', type=str, default='', help='ground_truth, mat type')
-parser.add_argument('--save_dir', type=str, default='', help='save_path for evaluate')
-parser.add_argument('--ground_truth_path', type=str, default='', help='ground_truth path, contain all mat file')
-parser.add_argument('--eval', type=int, default=0, help='if do eval after test')
-parser.add_argument('--eval_script_path', type=str, default='', help='evaluate script path')
-parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
-parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
-parser.add_argument('--ckpt_name', type=str, default="", help='input model name')
-parser.add_argument('--device_num', type=int, default=1, help='device num for testing')
-parser.add_argument('--steps_per_epoch', type=int, default=198, help='steps for each epoch')
-parser.add_argument('--start', type=int, default=0, help='start loop number, used to calculate first epoch number')
-parser.add_argument('--end', type=int, default=18, help='end loop number, used to calculate last epoch number')
+def modelarts_process():
+    config.data_dir = config.data_path
+    config.save_dir = config.output_path
+    config.ckpt_path = os.path.join(config.output_path, config.ckpt_path)
 
-args, _ = parser.parse_known_args()
 
-if __name__ == "__main__":
-    # logger
-    args.outputs_dir = os.path.join(args.ckpt_path,
-                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-    args.logger = get_logger(args.outputs_dir, args.rank)
-    args.logger.save_args(args)
+@moxing_wrapper(pre_process=modelarts_process)
+def test_centerface():
+    """" test_centerface """
+    config.outputs_dir = os.path.join(config.ckpt_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
 
-    if args.ckpt_name != "":
-        args.start = 0
-        args.end = 1
+    if config.ckpt_name != "":
+        config.start = 0
+        config.end = 1
 
-    for loop in range(args.start, args.end, 1):
+    for loop in range(config.start, config.end, 1):
         network = CenterfaceMobilev2()
         default_recurisive_init(network)
 
-        if args.ckpt_name == "":
-            ckpt_num = loop * args.device_num + args.rank + 1
-            ckpt_name = "0-" + str(ckpt_num) + "_" + str(args.steps_per_epoch * ckpt_num) + ".ckpt"
+        if config.ckpt_name == "":
+            ckpt_num = loop * config.device_num + config.rank + 1
+            ckpt_name = "0-" + str(ckpt_num) + "_" + str(config.steps_per_epoch * ckpt_num) + ".ckpt"
         else:
-            ckpt_name = args.ckpt_name
+            ckpt_name = config.ckpt_name
 
-        test_model = args.test_model + ckpt_name
+        test_model = config.test_model + ckpt_name
         if not test_model:
-            args.logger.info('load_model {} none'.format(test_model))
+            print('load_model {} none'.format(test_model))
             continue
 
         if os.path.isfile(test_model):
@@ -92,30 +79,28 @@ if __name__ == "__main__":
                     param_dict_new[key] = values
 
             load_param_into_net(network, param_dict_new)
-            args.logger.info('load_model {} success'.format(test_model))
+            print('load_model {} success'.format(test_model))
         else:
-            args.logger.info('{} not exists or not a pre-trained file'.format(test_model))
+            print('{} not exists or not a pre-trained file'.format(test_model))
             continue
 
         train_network_type_nms = 1 # default with num
         if train_network_type_nms:
             network = CenterFaceWithNms(network)
-            args.logger.info('train network type with nms')
+            print('train network type with nms')
         network.set_train(False)
-        args.logger.info('finish get network')
-
-        config = ConfigCenterface()
+        print('finish get network')
 
         # test network -----------
         start = time.time()
 
-        ground_truth_mat = sio.loadmat(args.ground_truth_mat)
+        ground_truth_mat = sio.loadmat(config.ground_truth_mat)
         event_list = ground_truth_mat['event_list']
         file_list = ground_truth_mat['file_list']
-        if args.ckpt_name == "":
-            save_path = args.save_dir + str(ckpt_num) + '/'
+        if config.ckpt_name == "":
+            save_path = config.save_dir + str(ckpt_num) + '/'
         else:
-            save_path = args.save_dir+ '/'
+            save_path = config.save_dir+ '/'
         detector = CenterFaceDetector(config, network)
 
         for index, event in enumerate(event_list):
@@ -123,12 +108,12 @@ if __name__ == "__main__":
             im_dir = event[0][0]
             if not os.path.exists(save_path + im_dir):
                 os.makedirs(save_path + im_dir)
-                args.logger.info('save_path + im_dir={}'.format(save_path + im_dir))
+                print('save_path + im_dir={}'.format(save_path + im_dir))
             for num, file in enumerate(file_list_item):
                 im_name = file[0][0]
                 zip_name = '%s/%s.jpg' % (im_dir, im_name)
-                img_path = os.path.join(args.data_dir, zip_name)
-                args.logger.info('img_path={}'.format(img_path))
+                img_path = os.path.join(config.data_dir, zip_name)
+                print('img_path={}'.format(img_path))
 
                 dets = detector.run(img_path)['results']
 
@@ -139,22 +124,25 @@ if __name__ == "__main__":
                     x1, y1, x2, y2, s = b[0], b[1], b[2], b[3], b[4]
                     f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(x1, y1, (x2 - x1 + 1), (y2 - y1 + 1), s))
                 f.close()
-                args.logger.info('event:{}, num:{}'.format(index + 1, num + 1))
+                print('event:{}, num:{}'.format(index + 1, num + 1))
 
                 end = time.time()
-                args.logger.info("============num {} time {}".format(num, (end-start)*1000))
+                print("============num {} time {}".format(num, (end-start)*1000))
                 start = end
 
-        if args.eval:
-            args.logger.info('==========start eval===============')
-            args.logger.info("test output path = {}".format(save_path))
+        if config.eval:
+            print('==========start eval===============')
+            print("test output path = {}".format(save_path))
             if os.path.isdir(save_path):
-                evaluation(save_path, args.ground_truth_path)
+                evaluation(save_path, config.ground_truth_path)
             else:
-                args.logger.info('no test output path')
-            args.logger.info('==========end eval===============')
+                print('no test output path')
+            print('==========end eval===============')
 
-        if args.ckpt_name != "":
+        if config.ckpt_name != "":
             break
 
-    args.logger.info('==========end testing===============')
+    print('==========end testing===============')
+
+if __name__ == "__main__":
+    test_centerface()
diff --git a/model_zoo/official/cv/centerface/train.py b/model_zoo/official/cv/centerface/train.py
index b04ffc5e598..74650ce842f 100644
--- a/model_zoo/official/cv/centerface/train.py
+++ b/model_zoo/official/cv/centerface/train.py
@@ -18,7 +18,6 @@ Train centerface and get network model files(.ckpt)
 
 import os
 import time
-import argparse
 import datetime
 import numpy as np
 
@@ -35,7 +34,6 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.profiler.profiling import Profiler
 from mindspore.common import set_seed
 
-from src.utils import get_logger
 from src.utils import AverageMeter
 from src.lr_scheduler import warmup_step_lr
 from src.lr_scheduler import warmup_cosine_annealing_lr, \
@@ -44,78 +42,23 @@ from src.lr_scheduler import MultiStepLR
 from src.var_init import default_recurisive_init
 from src.centerface import CenterfaceMobilev2
 from src.utils import load_backbone, get_param_groups
-from src.config import ConfigCenterface
+
 from src.centerface import CenterFaceWithLossCell, TrainingWrapper
 from src.dataset import GetDataLoader
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.model_utils.device_adapter import get_device_id
+
 
 set_seed(1)
-dev_id = int(os.getenv('DEVICE_ID'))
+dev_id = get_device_id()
 context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=False,
                     device_target="Ascend", save_graphs=False, device_id=dev_id, reserve_class_name_in_scope=False)
 
-parser = argparse.ArgumentParser('mindspore coco training')
-
-# dataset related
-parser.add_argument('--data_dir', type=str, default='', help='train data dir')
-parser.add_argument('--annot_path', type=str, default='', help='train data annotation path')
-parser.add_argument('--img_dir', type=str, default='', help='train data img dir')
-parser.add_argument('--per_batch_size', default=8, type=int, help='batch size for per gpu')
-
-# network related
-parser.add_argument('--pretrained_backbone', default='', type=str, help='model_path, local pretrained backbone'
-                                                                        ' model to load')
-parser.add_argument('--resume', default='', type=str, help='path of pretrained centerface_model')
-
-# optimizer and lr related
-parser.add_argument('--lr_scheduler', default='multistep', type=str,
-                    help='lr-scheduler, option type: exponential, cosine_annealing')
-parser.add_argument('--lr', default=4e-3, type=float, help='learning rate of the training')
-parser.add_argument('--lr_epochs', type=str, default='90,120', help='epoch of lr changing')
-parser.add_argument('--lr_gamma', type=float, default=0.1,
-                    help='decrease lr by a factor of exponential lr_scheduler')
-parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
-parser.add_argument('--t_max', type=int, default=140, help='T-max in cosine_annealing scheduler')
-parser.add_argument('--max_epoch', type=int, default=140, help='max epoch num to train the model')
-parser.add_argument('--warmup_epochs', default=0, type=float, help='warmup epoch')
-parser.add_argument('--weight_decay', type=float, default=0.0005, help='weight decay')
-parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
-parser.add_argument('--optimizer', default='adam', type=str,
-                    help='optimizer type, default: adam')
-
-# loss related
-parser.add_argument('--loss_scale', type=int, default=1024, help='static loss scale')
-parser.add_argument('--label_smooth', type=int, default=0, help='whether to use label smooth in CE')
-parser.add_argument('--label_smooth_factor', type=float, default=0.1, help='smooth strength of original one-hot')
-
- # logging related
-parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
-parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
-parser.add_argument('--ckpt_interval', type=int, default=None, help='ckpt_interval')
-
-parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')
-
-# distributed related
-parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
-parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
-parser.add_argument('--group_size', type=int, default=1, help='world size of distributed')
-
-# roma obs
-parser.add_argument('--train_url', type=str, default="", help='train url')
-
-# profiler init, can open when you debug. if train, donot open, since it cost memory and disk space
-parser.add_argument('--need_profiler', type=int, default=0, help='whether use profiler')
-
-# reset default config
-parser.add_argument('--training_shape', type=str, default="", help='fix training shape')
-parser.add_argument('--resize_rate', type=int, default=None, help='resize rate for multi-scale training')
-
-args, _ = parser.parse_known_args()
-
-if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.t_max:
-    args.t_max = args.max_epoch
-
-args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
+if config.lr_scheduler == 'cosine_annealing' and config.max_epoch > config.t_max:
+    config.t_max = config.max_epoch
 
+config.lr_epochs = list(map(int, config.lr_epochs.split(',')))
 
 def convert_training_shape(args_):
     """
@@ -135,34 +78,40 @@ class InternalCallbackParam(dict):
         self[para_name] = para_value
 
 
+def modelarts_pre_process():
+    config.ckpt_path = os.path.join(config.output_path, config.ckpt_path)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def train_centerface():
+    pass
+
 if __name__ == "__main__":
+    train_centerface()
+    print('\ntrain.py config:\n', config)
     # init distributed
-    if args.is_distributed:
+    if config.is_distributed:
         init()
-        args.rank = get_rank()
-        args.group_size = get_group_size()
+        config.rank = get_rank()
+        config.group_size = get_group_size()
 
     # select for master rank save ckpt or all rank save, compatible for model parallel
-    args.rank_save_ckpt_flag = 0
-    if args.is_save_on_master:
-        if args.rank == 0:
-            args.rank_save_ckpt_flag = 1
+    config.rank_save_ckpt_flag = 0
+    if config.is_save_on_master:
+        if config.rank == 0:
+            config.rank_save_ckpt_flag = 1
     else:
-        args.rank_save_ckpt_flag = 1
+        config.rank_save_ckpt_flag = 1
 
     # logger
-    args.outputs_dir = os.path.join(args.ckpt_path,
-                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
-    args.logger = get_logger(args.outputs_dir, args.rank)
-    args.logger.save_args(args)
+    config.outputs_dir = os.path.join(config.ckpt_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
 
-    if args.need_profiler:
-        profiler = Profiler(output_path=args.outputs_dir)
+    if config.need_profiler:
+        profiler = Profiler(output_path=config.outputs_dir)
 
     loss_meter = AverageMeter('loss')
 
     context.reset_auto_parallel_context()
-    if args.is_distributed:
+    if config.is_distributed:
         parallel_mode = ParallelMode.DATA_PARALLEL
         degree = get_group_size()
     else:
@@ -176,14 +125,14 @@ if __name__ == "__main__":
     # init, to avoid overflow, some std of weight should be enough small
     default_recurisive_init(network)
 
-    if args.pretrained_backbone:
-        network = load_backbone(network, args.pretrained_backbone, args)
-        args.logger.info('load pre-trained backbone {} into network'.format(args.pretrained_backbone))
+    if config.pretrained_backbone:
+        network = load_backbone(network, config.pretrained_backbone, config)
+        print('load pre-trained backbone {} into network'.format(config.pretrained_backbone))
     else:
-        args.logger.info('Not load pre-trained backbone, please be careful')
+        print('Not load pre-trained backbone, please be careful')
 
-    if os.path.isfile(args.resume):
-        param_dict = load_checkpoint(args.resume)
+    if os.path.isfile(config.resume):
+        param_dict = load_checkpoint(config.resume)
         param_dict_new = {}
         for key, values in param_dict.items():
             if key.startswith('moments.') or key.startswith('moment1.') or key.startswith('moment2.'):
@@ -194,107 +143,94 @@ if __name__ == "__main__":
                 param_dict_new[key] = values
 
         load_param_into_net(network, param_dict_new)
-        args.logger.info('load_model {} success'.format(args.resume))
+        print('load_model {} success'.format(config.resume))
     else:
-        args.logger.info('{} not set/exists or not a pre-trained file'.format(args.resume))
+        print('{} not set/exists or not a pre-trained file'.format(config.resume))
 
     network = CenterFaceWithLossCell(network)
-    args.logger.info('finish get network')
+    print('finish get network')
 
-    config = ConfigCenterface()
-    config.data_dir = args.data_dir
-    config.annot_path = args.annot_path
-    config.img_dir = args.img_dir
-
-    config.label_smooth = args.label_smooth
-    config.label_smooth_factor = args.label_smooth_factor
     # -------------reset config-----------------
-    if args.training_shape:
-        config.multi_scale = [convert_training_shape(args)]
-
-    if args.resize_rate:
-        config.resize_rate = args.resize_rate
+    if config.training_shape:
+        config.multi_scale = [convert_training_shape(config)]
 
     # data loader
-    data_loader, args.steps_per_epoch = GetDataLoader(per_batch_size=args.per_batch_size,
-                                                      max_epoch=args.max_epoch,
-                                                      rank=args.rank,
-                                                      group_size=args.group_size,
-                                                      config=config,
-                                                      split='train')
-    args.steps_per_epoch = args.steps_per_epoch // args.max_epoch
-    args.logger.info('Finish loading dataset')
+    data_loader, config.steps_per_epoch = GetDataLoader(per_batch_size=config.per_batch_size, \
+        max_epoch=config.max_epoch, rank=config.rank, group_size=config.group_size, \
+        config=config, split='train')
+    config.steps_per_epoch = config.steps_per_epoch // config.max_epoch
+    print('Finish loading dataset')
 
-    if not args.ckpt_interval:
-        args.ckpt_interval = args.steps_per_epoch
+    if not config.ckpt_interval:
+        config.ckpt_interval = config.steps_per_epoch
 
     # lr scheduler
-    if args.lr_scheduler == 'multistep':
-        lr_fun = MultiStepLR(args.lr, args.lr_epochs, args.lr_gamma, args.steps_per_epoch, args.max_epoch,
-                             args.warmup_epochs)
+    if config.lr_scheduler == 'multistep':
+        lr_fun = MultiStepLR(config.lr, config.lr_epochs, config.lr_gamma, config.steps_per_epoch, config.max_epoch,
+                             config.warmup_epochs)
         lr = lr_fun.get_lr()
-    elif args.lr_scheduler == 'exponential':
-        lr = warmup_step_lr(args.lr,
-                            args.lr_epochs,
-                            args.steps_per_epoch,
-                            args.warmup_epochs,
-                            args.max_epoch,
-                            gamma=args.lr_gamma
+    elif config.lr_scheduler == 'exponential':
+        lr = warmup_step_lr(config.lr,
+                            config.lr_epochs,
+                            config.steps_per_epoch,
+                            config.warmup_epochs,
+                            config.max_epoch,
+                            gamma=config.lr_gamma
                             )
-    elif args.lr_scheduler == 'cosine_annealing':
-        lr = warmup_cosine_annealing_lr(args.lr,
-                                        args.steps_per_epoch,
-                                        args.warmup_epochs,
-                                        args.max_epoch,
-                                        args.t_max,
-                                        args.eta_min)
-    elif args.lr_scheduler == 'cosine_annealing_V2':
-        lr = warmup_cosine_annealing_lr_v2(args.lr,
-                                           args.steps_per_epoch,
-                                           args.warmup_epochs,
-                                           args.max_epoch,
-                                           args.t_max,
-                                           args.eta_min)
-    elif args.lr_scheduler == 'cosine_annealing_sample':
-        lr = warmup_cosine_annealing_lr_sample(args.lr,
-                                               args.steps_per_epoch,
-                                               args.warmup_epochs,
-                                               args.max_epoch,
-                                               args.t_max,
-                                               args.eta_min)
+    elif config.lr_scheduler == 'cosine_annealing':
+        lr = warmup_cosine_annealing_lr(config.lr,
+                                        config.steps_per_epoch,
+                                        config.warmup_epochs,
+                                        config.max_epoch,
+                                        config.t_max,
+                                        config.eta_min)
+    elif config.lr_scheduler == 'cosine_annealing_V2':
+        lr = warmup_cosine_annealing_lr_v2(config.lr,
+                                           config.steps_per_epoch,
+                                           config.warmup_epochs,
+                                           config.max_epoch,
+                                           config.t_max,
+                                           config.eta_min)
+    elif config.lr_scheduler == 'cosine_annealing_sample':
+        lr = warmup_cosine_annealing_lr_sample(config.lr,
+                                               config.steps_per_epoch,
+                                               config.warmup_epochs,
+                                               config.max_epoch,
+                                               config.t_max,
+                                               config.eta_min)
     else:
-        raise NotImplementedError(args.lr_scheduler)
+        raise NotImplementedError(config.lr_scheduler)
 
-    if args.optimizer == "adam":
+    if config.optimizer == "adam":
         opt = Adam(params=get_param_groups(network),
                    learning_rate=Tensor(lr),
-                   weight_decay=args.weight_decay,
-                   loss_scale=args.loss_scale)
-        args.logger.info("use adam optimizer")
-    elif args.optimizer == "sgd":
+                   weight_decay=config.weight_decay,
+                   loss_scale=config.loss_scale)
+        print("use adam optimizer")
+    elif config.optimizer == "sgd":
         opt = SGD(params=get_param_groups(network),
                   learning_rate=Tensor(lr),
-                  momentum=args.momentum,
-                  weight_decay=args.weight_decay,
-                  loss_scale=args.loss_scale)
+                  momentum=config.momentum,
+                  weight_decay=config.weight_decay,
+                  loss_scale=config.loss_scale)
     else:
         opt = Momentum(params=get_param_groups(network),
                        learning_rate=Tensor(lr),
-                       momentum=args.momentum,
-                       weight_decay=args.weight_decay,
-                       loss_scale=args.loss_scale)
+                       momentum=config.momentum,
+                       weight_decay=config.weight_decay,
+                       loss_scale=config.loss_scale)
 
-    network = TrainingWrapper(network, opt, sens=args.loss_scale)
+    network = TrainingWrapper(network, opt, sens=config.loss_scale)
     network.set_train()
 
-    if args.rank_save_ckpt_flag:
+    if config.rank_save_ckpt_flag:
         # checkpoint save
-        ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval
-        ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
+        ckpt_max_num = config.max_epoch * config.steps_per_epoch // config.ckpt_interval
+        ckpt_config = CheckpointConfig(save_checkpoint_steps=config.ckpt_interval,
                                        keep_checkpoint_max=ckpt_max_num)
         ckpt_cb = ModelCheckpoint(config=ckpt_config,
-                                  directory=args.outputs_dir,
-                                  prefix='{}'.format(args.rank))
+                                  directory=config.outputs_dir,
+                                  prefix='{}'.format(config.rank))
         cb_params = InternalCallbackParam()
         cb_params.train_network = network
         cb_params.epoch_num = ckpt_max_num
@@ -302,14 +238,14 @@ if __name__ == "__main__":
         run_context = RunContext(cb_params)
         ckpt_cb.begin(run_context)
 
-        args.logger.info('args.steps_per_epoch = {} args.ckpt_interval ={}'.format(args.steps_per_epoch,
-                                                                                   args.ckpt_interval))
+        print('config.steps_per_epoch = {} config.ckpt_interval ={}'.format(config.steps_per_epoch, \
+                                                                            config.ckpt_interval))
 
     t_end = time.time()
 
     for i_all, batch_load in enumerate(data_loader):
-        i = i_all % args.steps_per_epoch
-        epoch = i_all // args.steps_per_epoch + 1
+        i = i_all % config.steps_per_epoch
+        epoch = i_all // config.steps_per_epoch + 1
         images, hm, reg_mask, ind, wh, wight_mask, hm_offset, hps_mask, landmarks = batch_load
 
         images = Tensor(images)
@@ -327,33 +263,28 @@ if __name__ == "__main__":
         overflow = np.all(overflow.asnumpy())
         loss = loss.asnumpy()
         loss_meter.update(loss)
-        args.logger.info('epoch:{}, iter:{}, avg_loss:{}, loss:{}, overflow:{}, loss_scale:{}'.format(epoch,
-                                                                                                      i,
-                                                                                                      loss_meter,
-                                                                                                      loss,
-                                                                                                      overflow,
-                                                                                                      scaling.asnumpy()
-                                                                                                      ))
+        print('epoch:{}, iter:{}, avg_loss:{}, loss:{}, overflow:{}, loss_scale:{}'.format( \
+            epoch, i, loss_meter, loss, overflow, scaling.asnumpy()))
 
-        if args.rank_save_ckpt_flag:
+        if config.rank_save_ckpt_flag:
             # ckpt progress
             cb_params.cur_epoch_num = epoch
-            cb_params.cur_step_num = i + 1 + (epoch-1)*args.steps_per_epoch
-            cb_params.batch_num = i + 2 + (epoch-1)*args.steps_per_epoch
+            cb_params.cur_step_num = i + 1 + (epoch-1)*config.steps_per_epoch
+            cb_params.batch_num = i + 2 + (epoch-1)*config.steps_per_epoch
             ckpt_cb.step_end(run_context)
 
-        if (i_all+1) % args.steps_per_epoch == 0:
+        if (i_all+1) % config.steps_per_epoch == 0:
             time_used = time.time() - t_end
-            fps = args.per_batch_size * args.steps_per_epoch * args.group_size / time_used
-            if args.rank == 0:
-                args.logger.info(
+            fps = config.per_batch_size * config.steps_per_epoch * config.group_size / time_used
+            if config.rank == 0:
+                print(
                     'epoch[{}], {}, {:.2f} imgs/sec, lr:{}'
-                    .format(epoch, loss_meter, fps, lr[i + (epoch-1)*args.steps_per_epoch])
+                    .format(epoch, loss_meter, fps, lr[i + (epoch-1)*config.steps_per_epoch])
                     )
             t_end = time.time()
             loss_meter.reset()
 
-    if args.need_profiler:
+    if config.need_profiler:
         profiler.analyse()
 
-    args.logger.info('==========end training===============')
+    print('==========end training===============')