diff --git a/model_zoo/official/cv/resnet/src/dataset.py b/model_zoo/official/cv/resnet/src/dataset.py
index c62a5b8c21..5c2b438b5d 100755
--- a/model_zoo/official/cv/resnet/src/dataset.py
+++ b/model_zoo/official/cv/resnet/src/dataset.py
@@ -170,6 +170,84 @@ def create_dataset2(dataset_path, do_train, repeat_num=1, batch_size=32, target=
 
     return data_set
 
+def create_dataset_pynative(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
+                            enable_cache=False, cache_session_id=None):
+    """
+    create a train or eval imagenet2012 dataset for resnet50 benchmark
+
+    Args:
+        dataset_path(string): the path of dataset.
+        do_train(bool): whether dataset is used for train or eval.
+        repeat_num(int): the repeat times of dataset. Default: 1
+        batch_size(int): the batch size of dataset. Default: 32
+        target(str): the device target. Default: Ascend
+        distribute(bool): data for distribute or not. Default: False
+        enable_cache(bool): whether tensor caching service is used for eval. Default: False
+        cache_session_id(int): If enable_cache, cache session_id need to be provided. Default: None
+
+    Returns:
+        dataset
+    """
+    ds.config.set_numa_enable(True)
+    if target == "Ascend":
+        device_num, rank_id = _get_rank_info()
+    else:
+        if distribute:
+            init()
+            rank_id = get_rank()
+            device_num = get_group_size()
+        else:
+            device_num = 1
+
+    if device_num == 1:
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+    else:
+        data_set = ds.ImageFolderDataset(dataset_path, num_parallel_workers=2, shuffle=True,
+                                         num_shards=device_num, shard_id=rank_id)
+
+    image_size = 224
+    mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
+    std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
+
+    # define map operations
+    if do_train:
+        trans = [
+            C.RandomCropDecodeResize(image_size, scale=(0.08, 1.0), ratio=(0.75, 1.333)),
+            C.RandomHorizontalFlip(prob=0.5),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+    else:
+        trans = [
+            C.Decode(),
+            C.Resize(256),
+            C.CenterCrop(image_size),
+            C.Normalize(mean=mean, std=std),
+            C.HWC2CHW()
+        ]
+
+    type_cast_op = C2.TypeCast(mstype.int32)
+
+    data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=4)
+    # only enable cache for eval
+    if do_train:
+        enable_cache = False
+    if enable_cache:
+        if not cache_session_id:
+            raise ValueError("A cache session_id must be provided to use cache.")
+        eval_cache = ds.DatasetCache(session_id=int(cache_session_id), size=0)
+        data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=2,
+                                cache=eval_cache)
+    else:
+        data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=2)
+
+    # apply batch operations
+    data_set = data_set.batch(batch_size, drop_remainder=True)
+
+    # apply dataset repeat operation
+    data_set = data_set.repeat(repeat_num)
+
+    return data_set
 
 def create_dataset3(dataset_path, do_train, repeat_num=1, batch_size=32, target="Ascend", distribute=False,
                     enable_cache=False, cache_session_id=None):
diff --git a/model_zoo/official/cv/resnet/train.py b/model_zoo/official/cv/resnet/train.py
index fcf67e43d1..f03a7e2a9b 100755
--- a/model_zoo/official/cv/resnet/train.py
+++ b/model_zoo/official/cv/resnet/train.py
@@ -63,6 +63,8 @@ parser.add_argument("--eval_interval", type=int, default=1,
 parser.add_argument('--enable_cache', type=ast.literal_eval, default=False,
                     help='Caching the eval dataset in memory to speedup evaluation, default is False.')
 parser.add_argument('--cache_session_id', type=str, default="", help='The session id for cache service.')
+parser.add_argument('--mode', type=str, default='GRAPH', choices=('GRAPH', 'PYNATIVE'),
+                    help="Graph mode or PyNative mode, default is Graph mode")
 args_opt = parser.parse_args()
 
 set_seed(1)
@@ -77,7 +79,10 @@ if args_opt.net in ("resnet18", "resnet50"):
         from src.dataset import create_dataset1 as create_dataset
     else:
         from src.config import config2 as config
-        from src.dataset import create_dataset2 as create_dataset
+        if args_opt.mode == "GRAPH":
+            from src.dataset import create_dataset2 as create_dataset
+        else:
+            from src.dataset import create_dataset_pynative as create_dataset
 
 elif args_opt.net == "resnet101":
     from src.resnet import resnet101 as resnet
@@ -119,7 +124,10 @@ if __name__ == '__main__':
     ckpt_save_dir = config.save_checkpoint_path
 
     # init context
-    context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
+    if args_opt.mode == 'GRAPH':
+        context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
+    else:
+        context.set_context(mode=context.PYNATIVE_MODE, device_target=target, save_graphs=False)
     if args_opt.parameter_server:
         context.set_ps_context(enable_ps=True)
     if args_opt.run_distribute: