diff --git a/model_zoo/official/cv/posenet/README_CN.md b/model_zoo/official/cv/posenet/README_CN.md
index 93253f0a743..aef7e640550 100644
--- a/model_zoo/official/cv/posenet/README_CN.md
+++ b/model_zoo/official/cv/posenet/README_CN.md
@@ -79,7 +79,7 @@ PoseNet是剑桥大学提出的一种鲁棒、实时的6DOF（单目六自由度
   bash run_standalone_train.sh [DATASET_NAME] [DEVICE_ID]
 
   # 运行分布式训练示例
-  bash run_distribute_train.sh [DATASET_NAME] [RANK_SIZE]
+  bash run_distribute_train.sh [DATASET_NAME] [RANK_TABLE]
 
   # 运行评估示例
   bash run_eval.sh [DEVICE_ID] [DATASET_NAME] [CKPT_PATH]
@@ -100,7 +100,7 @@ PoseNet是剑桥大学提出的一种鲁棒、实时的6DOF（单目六自由度
   bash run_standalone_train_gpu.sh [DATASET_NAME] [DEVICE_ID]
 
   # 运行分布式训练示例
-  bash run_distribute_train_gpu.sh [DATASET_NAME] [RANK_SIZE]
+  bash run_distribute_train_gpu.sh [DATASET_NAME] [RANK_TABLE]
 
   # 运行评估示例
   bash run_eval_gpu.sh [DEVICE_ID] [DATASET_NAME] [CKPT_PATH]
@@ -211,7 +211,7 @@ PoseNet是剑桥大学提出的一种鲁棒、实时的6DOF（单目六自由度
 - Ascend处理器环境运行
 
   ```bash
-  bash run_distribute_train.sh [DATASET_NAME] [RANK_SIZE]
+  bash run_distribute_train.sh [DATASET_NAME] [RANK_TABLE]
   ```
 
   上述shell脚本将在后台运行分布训练。您可以通过device[X]/log文件查看结果。采用以下方式达到损失值：
diff --git a/model_zoo/official/cv/posenet/eval.py b/model_zoo/official/cv/posenet/eval.py
index c74a389acfe..dda86fc76f0 100644
--- a/model_zoo/official/cv/posenet/eval.py
+++ b/model_zoo/official/cv/posenet/eval.py
@@ -41,6 +41,9 @@ if __name__ == '__main__':
     parser.add_argument('--is_modelarts', type=ast.literal_eval, default=False, help='Train in Modelarts.')
     parser.add_argument('--data_url', default=None, help='Location of data.')
     parser.add_argument('--train_url', default=None, help='Location of training outputs.')
+    parser.add_argument('--device_target', type=str, default='Ascend',
+                        choices=['Ascend', 'GPU'],
+                        help='Name of device target.')
     args_opt = parser.parse_args()
 
     cfg = common_config
@@ -49,8 +52,8 @@ if __name__ == '__main__':
     elif args_opt.dataset == "StMarysChurch":
         dataset_cfg = StMarysChurch
 
-    device_target = cfg.device_target
-    context.set_context(mode=context.GRAPH_MODE, device_target=cfg.device_target)
+    device_target = args_opt.device_target
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
 
     if args_opt.device_id is not None:
         context.set_context(device_id=args_opt.device_id)
diff --git a/model_zoo/official/cv/posenet/scripts/run_distribute_train.sh b/model_zoo/official/cv/posenet/scripts/run_distribute_train.sh
index 55f7a800caa..df941706476 100644
--- a/model_zoo/official/cv/posenet/scripts/run_distribute_train.sh
+++ b/model_zoo/official/cv/posenet/scripts/run_distribute_train.sh
@@ -17,37 +17,30 @@
 echo "=============================================================================================================="
 echo "Please run the script as: "
 echo "bash run.sh DATASET_NAME RANK_SIZE"
-echo "For example: bash run_distribute.sh dataset_name 8"
+echo "For example: bash run_distribute.sh dataset_name rank_table"
 echo "It is better to use the absolute path."
 echo "=============================================================================================================="
 set -e
+get_real_path(){
+  if [ "${1:0:1}" == "/" ]; then
+    echo "$1"
+  else
+    echo "$(realpath -m $PWD/$1)"
+  fi
+}
 
 DATASET_NAME=$1
-RANK_SIZE=$2
 export DATASET_NAME
-export RANK_SIZE
+export RANK_TABLE_FILE=$(get_real_path $2)
+export RANK_SIZE=8
 
 EXEC_PATH=$(pwd)
 echo "$EXEC_PATH"
 
-test_dist_8pcs()
-{
-    export RANK_TABLE_FILE=${EXEC_PATH}/rank_table_8pcs.json
-    export RANK_SIZE=8
-}
-
-test_dist_2pcs()
-{
-    export RANK_TABLE_FILE=${EXEC_PATH}/rank_table_2pcs.json
-    export RANK_SIZE=2
-}
-
-test_dist_${RANK_SIZE}pcs
-
 export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
 
 cd ../
-for((i=0;i<${RANK_SIZE};i++))
+for((i=0;i<8;i++))
 do
     rm -rf device$i
     mkdir device$i
@@ -61,7 +54,10 @@ do
     export RANK_ID=$i
     echo "start training for device $i"
     env > env$i.log
-    python train.py --run_distribute True --dataset $1 --device_num $2 --is_modelarts False > train$i.log 2>&1 &
+    python train.py --run_distribute True \
+                    --dataset $1 --device_num $2 \
+                    --is_modelarts False \
+                    --device_target "Ascend" > train$i.log 2>&1 &
     echo "$i finish"
     cd ../
 done
diff --git a/model_zoo/official/cv/posenet/scripts/run_distribute_train_gpu.sh b/model_zoo/official/cv/posenet/scripts/run_distribute_train_gpu.sh
index 2906990d518..69877d2ebb3 100644
--- a/model_zoo/official/cv/posenet/scripts/run_distribute_train_gpu.sh
+++ b/model_zoo/official/cv/posenet/scripts/run_distribute_train_gpu.sh
@@ -39,5 +39,8 @@ cd ./train_parallel
 env > env.log
 echo "start training"
     mpirun -n $1 --allow-run-as-root \
-           python train.py --device_num $1 --dataset $2 --is_modelarts False --run_distribute True > train.log 2>&1 &
+           python train.py --device_num $1 \
+                           --dataset $2 --is_modelarts False \
+                           --run_distribute True \
+                           --device_target "GPU" > train.log 2>&1 &
 
diff --git a/model_zoo/official/cv/posenet/scripts/run_eval_gpu.sh b/model_zoo/official/cv/posenet/scripts/run_eval_gpu.sh
index b43fe278d56..fdf84e7226f 100644
--- a/model_zoo/official/cv/posenet/scripts/run_eval_gpu.sh
+++ b/model_zoo/official/cv/posenet/scripts/run_eval_gpu.sh
@@ -46,7 +46,7 @@ cd ./eval
 
 env > env0.log
 echo "Eval begin."
-python eval.py --device_id $1 --dataset $2 --ckpt_url $3 --is_modelarts False > ./eval.log 2>&1 &
+python eval.py --device_id $1 --dataset $2 --ckpt_url $3 --is_modelarts False --device_target "GPU" > ./eval.log 2>&1 &
 
 if [ $? -eq 0 ];then
     echo "evaling success"
diff --git a/model_zoo/official/cv/posenet/scripts/run_standalone_train.sh b/model_zoo/official/cv/posenet/scripts/run_standalone_train.sh
index 6eb6c28c0c3..99289269512 100644
--- a/model_zoo/official/cv/posenet/scripts/run_standalone_train.sh
+++ b/model_zoo/official/cv/posenet/scripts/run_standalone_train.sh
@@ -45,7 +45,10 @@ cd ./train
 env > env0.log
 
 echo "Standalone train begin."
-python train.py --run_distribute False --device_id $2 --dataset $1 --device_num 1 --is_modelarts False > ./train_alone.log 2>&1 &
+python train.py --run_distribute False \
+                --device_id $2 --dataset $1 \
+                --device_num 1 --is_modelarts False \
+                --device_target "Ascend" > ./train_alone.log 2>&1 &
 
 if [ $? -eq 0 ];then
     echo "training success"
diff --git a/model_zoo/official/cv/posenet/scripts/run_standalone_train_gpu.sh b/model_zoo/official/cv/posenet/scripts/run_standalone_train_gpu.sh
index 6eb6c28c0c3..20894964d36 100644
--- a/model_zoo/official/cv/posenet/scripts/run_standalone_train_gpu.sh
+++ b/model_zoo/official/cv/posenet/scripts/run_standalone_train_gpu.sh
@@ -45,7 +45,7 @@ cd ./train
 env > env0.log
 
 echo "Standalone train begin."
-python train.py --run_distribute False --device_id $2 --dataset $1 --device_num 1 --is_modelarts False > ./train_alone.log 2>&1 &
+python train.py --run_distribute False --device_id $2 --dataset $1 --device_num 1 --is_modelarts False --device_target "Ascend" > ./train_alone.log 2>&1 &
 
 if [ $? -eq 0 ];then
     echo "training success"
diff --git a/model_zoo/official/cv/posenet/src/config.py b/model_zoo/official/cv/posenet/src/config.py
index 0f749b92b5b..631c1a99aff 100644
--- a/model_zoo/official/cv/posenet/src/config.py
+++ b/model_zoo/official/cv/posenet/src/config.py
@@ -18,7 +18,6 @@ network config setting
 from easydict import EasyDict as edict
 
 common_config = edict({
-    'device_target': 'GPU',
     'device_id': 0,
     'pre_trained': True,
     'max_steps': 30000,
diff --git a/model_zoo/official/cv/posenet/src/loss.py b/model_zoo/official/cv/posenet/src/loss.py
index 31ec464234a..65ad97e77bb 100644
--- a/model_zoo/official/cv/posenet/src/loss.py
+++ b/model_zoo/official/cv/posenet/src/loss.py
@@ -14,7 +14,7 @@
 # ============================================================================
 """define evaluation loss function for network."""
 import mindspore.nn as nn
-from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import LossBase
 from mindspore.common import dtype as mstype
 from mindspore.ops import operations as P
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
@@ -42,7 +42,7 @@ class EuclideanDistance(nn.Cell):
 
         return res
 
-class PoseLoss(_Loss):
+class PoseLoss(LossBase):
     """define loss function"""
     def __init__(self, w1_x, w2_x, w3_x, w1_q, w2_q, w3_q):
         super(PoseLoss, self).__init__()
diff --git a/model_zoo/official/cv/posenet/src/posenet.py b/model_zoo/official/cv/posenet/src/posenet.py
index 642b75c09ab..bed571b5dc9 100644
--- a/model_zoo/official/cv/posenet/src/posenet.py
+++ b/model_zoo/official/cv/posenet/src/posenet.py
@@ -16,6 +16,12 @@
 import mindspore.nn as nn
 from mindspore.common.initializer import TruncatedNormal
 from mindspore.ops import operations as P
+from mindspore.ops import functional as F
+from mindspore.ops import composite as C
+from mindspore.context import ParallelMode
+from mindspore.parallel._utils import (_get_device_num, _get_gradients_mean,
+                                       _get_parallel_mode)
+from mindspore.nn.wrap.grad_reducer import DistributedGradReducer
 
 def weight_variable():
     """Weight variable."""
@@ -172,3 +178,80 @@ class PoseNet(nn.Cell):
         return cls1_fc_pose_xyz, cls1_fc_pose_wpqr, \
                cls2_fc_pose_xyz, cls2_fc_pose_wpqr, \
                cls3_fc_pose_xyz, cls3_fc_pose_wpqr
+
+
+GRADIENT_CLIP_TYPE = 1
+GRADIENT_CLIP_VALUE = 50.0
+
+clip_grad = C.MultitypeFuncGraph("clip_grad")
+
+@clip_grad.register("Number", "Number", "Tensor")
+def _clip_grad(clip_type, clip_value, grad):
+    """
+    Clip gradients
+    Inputs:
+        clip_type: The way to clip, 0 for 'value', 1 for 'norm'
+        clip_value: Specifies how much to clip
+        grad: Gradients
+    Outputs:
+        tuple[Tensor], clipped gradients
+    """
+    if clip_type not in (0, 1):
+        return grad
+    dt = F.dtype(grad)
+    if clip_type == 0:
+        new_grad = C.clip_by_value(grad, F.cast(F.tuple_to_array((-clip_value,)), dt),
+                                   F.cast(F.tuple_to_array((clip_value,)), dt))
+    else:
+        new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt))
+    return new_grad
+
+class PoseTrainOneStepCell(nn.Cell):
+    r"""
+    Network training package class.
+
+    Wraps the network with an optimizer. The resulting Cell is trained with input '\*inputs'.
+    The backward graph will be created in the construct function to update the parameter. Different
+    parallel modes are available for training.
+
+    Args:
+        network (Cell): The training network. The network only supports single output.
+        optimizer (Union[Cell]): Optimizer for updating the weights.
+        sens (numbers.Number): The scaling number to be filled as the input of backpropagation. Default value is 1.0.
+
+    Inputs:
+        - **(\*inputs)** (Tuple(Tensor)) - Tuple of input tensors with shape :math:`(N, \ldots)`.
+
+    Outputs:
+        Tensor, a tensor means the loss value, the shape of which is usually :math:`()`.
+
+    Raises:
+        TypeError: If `sens` is not a number.
+    """
+
+    def __init__(self, network, optimizer, sens=1.0):
+        super(PoseTrainOneStepCell, self).__init__(auto_prefix=False)
+        self.network = network
+        self.network.set_grad()
+        self.optimizer = optimizer
+        self.weights = self.optimizer.parameters
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
+        self.sens = sens
+        self.reducer_flag = False
+        self.grad_reducer = F.identity
+        self.hyper_map = C.HyperMap()
+        self.parallel_mode = _get_parallel_mode()
+        self.reducer_flag = self.parallel_mode in (ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL)
+        if self.reducer_flag:
+            self.mean = _get_gradients_mean()
+            self.degree = _get_device_num()
+            self.grad_reducer = DistributedGradReducer(self.weights, self.mean, self.degree)
+
+    def construct(self, *inputs):
+        loss = self.network(*inputs)
+        sens = F.fill(loss.dtype, loss.shape, self.sens)
+        grads = self.grad(self.network, self.weights)(*inputs, sens)
+        grads = self.hyper_map(F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads)
+        grads = self.grad_reducer(grads)
+        loss = F.depend(loss, self.optimizer(grads))
+        return loss
diff --git a/model_zoo/official/cv/posenet/train.py b/model_zoo/official/cv/posenet/train.py
index eb2f65f2317..570e20b61da 100644
--- a/model_zoo/official/cv/posenet/train.py
+++ b/model_zoo/official/cv/posenet/train.py
@@ -29,6 +29,7 @@ from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMoni
 from src.config import common_config, KingsCollege, StMarysChurch
 from src.dataset import data_to_mindrecord, create_posenet_dataset
 from src.loss import PosenetWithLoss
+from src.posenet import PoseTrainOneStepCell
 
 set_seed(1)
 
@@ -45,6 +46,9 @@ if __name__ == '__main__':
     parser.add_argument('--is_modelarts', type=ast.literal_eval, default=False, help='Train in Modelarts.')
     parser.add_argument('--data_url', default=None, help='Location of data.')
     parser.add_argument('--train_url', default=None, help='Location of training outputs.')
+    parser.add_argument('--device_target', type=str, default='Ascend',
+                        choices=['Ascend', 'GPU'],
+                        help='Name of device target.')
     args_opt = parser.parse_args()
 
     cfg = common_config
@@ -53,7 +57,7 @@ if __name__ == '__main__':
     elif args_opt.dataset == "StMarysChurch":
         dataset_cfg = StMarysChurch
 
-    device_target = cfg.device_target
+    device_target = args_opt.device_target
     context.set_context(mode=context.GRAPH_MODE, device_target=device_target)
     if args_opt.run_distribute:
         if device_target == "Ascend":
@@ -104,7 +108,8 @@ if __name__ == '__main__':
     opt = Adagrad(params=net_with_loss.trainable_params(),
                   learning_rate=dataset_cfg.lr_init,
                   weight_decay=dataset_cfg.weight_decay)
-    model = Model(net_with_loss, optimizer=opt)
+    net_with_grad = PoseTrainOneStepCell(net_with_loss, opt)
+    model = Model(net_with_grad)
 
     time_cb = TimeMonitor(data_size=step_per_epoch)
     loss_cb = LossMonitor()