diff --git a/official/cv/densenet/eval.py b/official/cv/densenet/eval.py
index cf3ea715b1ec4e36e5e46d394c133797964a44b7..2a80490483e49a05bcedcd899030e8da5f62ac5d 100644
--- a/official/cv/densenet/eval.py
+++ b/official/cv/densenet/eval.py
@@ -25,7 +25,7 @@ import numpy as np
 from mindspore import context
 import mindspore.nn as nn
 from mindspore import Tensor
-from mindspore.communication.management import init, get_group_size, release
+from mindspore.communication.management import init, get_group_size, get_rank, release
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.ops import operations as P
 from mindspore.ops import functional as F
@@ -33,7 +33,7 @@ from mindspore.common import dtype as mstype
 from src.utils.logging import get_logger
 from src.model_utils.moxing_adapter import moxing_wrapper
 from src.model_utils.config import config
-from src.model_utils.device_adapter import get_device_id, get_rank_id
+from src.model_utils.device_adapter import get_device_id
 
 
 class ParameterReduce(nn.Cell):
@@ -115,7 +115,7 @@ def test():
     # init distributed
     if config.is_distributed:
         init()
-        config.rank = get_rank_id()
+        config.rank = get_rank()
         config.group_size = get_group_size()
 
     config.outputs_dir = os.path.join(config.log_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
diff --git a/official/cv/densenet/modelarts/train_start.py b/official/cv/densenet/modelarts/train_start.py
index 51c93e7010cb7ef93594b0668fdab9b5d5a624bd..f3a8b7d7071061fd1781fd1cac20af1a7d866dfa 100644
--- a/official/cv/densenet/modelarts/train_start.py
+++ b/official/cv/densenet/modelarts/train_start.py
@@ -21,7 +21,7 @@ import moxing as mox
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.nn.optim import Momentum
-from mindspore.communication.management import init, get_group_size
+from mindspore.communication.management import init, get_group_size, get_rank
 from mindspore.train.callback import ModelCheckpoint
 from mindspore.train.callback import CheckpointConfig, Callback
 from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
@@ -36,7 +36,7 @@ from src.lr_scheduler import MultiStepLR, CosineAnnealingLR
 from src.utils.logging import get_logger
 from src.model_utils.moxing_adapter import moxing_wrapper
 from src.model_utils.config import config
-from src.model_utils.device_adapter import get_device_id, get_rank_id
+from src.model_utils.device_adapter import get_device_id
 
 set_seed(1)
 
@@ -174,7 +174,7 @@ def train():
     # init distributed
     if config.is_distributed:
         init()
-        config.rank = get_rank_id()
+        config.rank = get_rank()
         config.group_size = get_group_size()
 
     if config.is_dynamic_loss_scale == 1:
diff --git a/official/cv/densenet/train.py b/official/cv/densenet/train.py
index d17f5ff7f3431be5f94708cf639746bc3638a838..603c62bac9c7e474d0884f786b6bd61a01810fa4 100644
--- a/official/cv/densenet/train.py
+++ b/official/cv/densenet/train.py
@@ -19,7 +19,7 @@ import datetime
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.nn.optim import Momentum
-from mindspore.communication.management import init, get_group_size
+from mindspore.communication.management import init, get_group_size, get_rank
 from mindspore.train.callback import ModelCheckpoint
 from mindspore.train.callback import CheckpointConfig, Callback
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -34,7 +34,7 @@ from src.lr_scheduler import MultiStepLR, CosineAnnealingLR
 from src.utils.logging import get_logger
 from src.model_utils.moxing_adapter import moxing_wrapper
 from src.model_utils.config import config
-from src.model_utils.device_adapter import get_device_id, get_rank_id
+from src.model_utils.device_adapter import get_device_id
 
 set_seed(1)
 
@@ -142,7 +142,7 @@ def train():
     # init distributed
     if config.is_distributed:
         init()
-        config.rank = get_rank_id()
+        config.rank = get_rank()
         config.group_size = get_group_size()
 
     if config.is_dynamic_loss_scale == 1: