fix dpn&xception spelling error

model_zoo/official/cv/dpn/README.md

@@ -183,7 +183,7 @@ For example, you can run the shell command below to launch the training procedur
 sh scripts/train_standalone.sh 0 /data/dataset/imagenet/ scripts/pretrian/ 0
 ```
 
-If eval_each_epoch is 1, it will evaluate after each epoch and save the parameters with the max accurracy. But in this case, the time of one epoch will be longer.
+If eval_each_epoch is 1, it will evaluate after each epoch and save the parameters with the max accuracy. But in this case, the time of one epoch will be longer.
 
 If eval_each_epoch is 0, it will save parameters every some epochs instead of evaluating in the training process.
 

model_zoo/official/cv/dpn/train.py

@@ -91,7 +91,7 @@ def dpn_train(args):
         context.set_auto_parallel_context(device_num=args.group_size, parallel_mode=ParallelMode.DATA_PARALLEL,
                                           gradients_mean=True)
 
-    # select for master rank save ckpt or all rank save, compatiable for model parallel
+    # 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:

model_zoo/official/cv/xception/README.md

@@ -148,7 +148,7 @@ sh scripts/run_standalone_train.sh DEVICE_ID DATA_PATH
 
 ### Result
 
-Training result will be stored in the example path. Checkpoints will be stored at `. /ckpt_0` by default, and training log will be redirected to `log.txt` like followings.
+Training result will be stored in the example path. Checkpoints will be stored at `. /ckpt_0` by default, and training log will be redirected to `log.txt` like following.
 
 ``` shell
 epoch: 1 step: 1251, loss is 4.8427444
@@ -182,7 +182,7 @@ sh scripts/run_eval.sh DEVICE_ID DATA_DIR PATH_CHECKPOINT
 
 ### Result
 
-Evaluation result will be stored in the example path, you can find result like the followings in `eval.log`.
+Evaluation result will be stored in the example path, you can find result like the following in `eval.log`.
 
 ```shell
 result: {'Loss': 1.7797744848789312, 'Top_1_Acc': 0.7985777243589743, 'Top_5_Acc': 0.9485777243589744}