From 467c230de9b9bf04fcc5124674a352f6794d7e24 Mon Sep 17 00:00:00 2001
From: zhouneng <zhouneng2@huawei.com>
Date: Fri, 12 Mar 2021 18:16:13 +0800
Subject: [PATCH] fix word spell mistakes

---
 model_zoo/official/cv/resnext50/README.md   | 12 ++++++------
 model_zoo/official/cv/resnext50/src/head.py |  2 +-
 model_zoo/official/cv/resnext50/train.py    |  2 +-
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/model_zoo/official/cv/resnext50/README.md b/model_zoo/official/cv/resnext50/README.md
index d3bc18e9af..0fcd8e632c 100644
--- a/model_zoo/official/cv/resnext50/README.md
+++ b/model_zoo/official/cv/resnext50/README.md
@@ -76,20 +76,20 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
     └─run_eval.sh                     # launch evaluating
   ├─src
     ├─backbone
-      ├─_init_.py                     # initalize
+      ├─_init_.py                     # initialize
       ├─resnet.py                     # resnext50 backbone
     ├─utils
-      ├─_init_.py                     # initalize
+      ├─_init_.py                     # initialize
       ├─cunstom_op.py                 # network operation
       ├─logging.py                    # print log
       ├─optimizers_init_.py           # get parameters
       ├─sampler.py                    # distributed sampler
       ├─var_init_.py                  # calculate gain value
-    ├─_init_.py                       # initalize
+    ├─_init_.py                       # initialize
     ├─config.py                       # parameter configuration
     ├─crossentropy.py                 # CrossEntropy loss function
     ├─dataset.py                      # data preprocessing
-    ├─head.py                         # commom head
+    ├─head.py                         # common head
     ├─image_classification.py         # get resnet
     ├─linear_warmup.py                # linear warmup learning rate
     ├─warmup_cosine_annealing.py      # learning rate each step
@@ -140,7 +140,7 @@ You can start training by python script:
 python train.py --data_dir ~/imagenet/train/ --platform Ascend --is_distributed 0
 ```
 
-or shell stript:
+or shell script:
 
 ```script
 Ascend:
@@ -181,7 +181,7 @@ You can start training by python script:
 python eval.py --data_dir ~/imagenet/val/ --platform Ascend --pretrained resnext.ckpt
 ```
 
-or shell stript:
+or shell script:
 
 ```script
 # Evaluation
diff --git a/model_zoo/official/cv/resnext50/src/head.py b/model_zoo/official/cv/resnext50/src/head.py
index a7bd85c906..6dac84d23a 100644
--- a/model_zoo/official/cv/resnext50/src/head.py
+++ b/model_zoo/official/cv/resnext50/src/head.py
@@ -22,7 +22,7 @@ __all__ = ['CommonHead']
 
 class CommonHead(nn.Cell):
     """
-    commom architecture definition.
+    common architecture definition.
 
     Args:
         num_classes (int): Number of classes.
diff --git a/model_zoo/official/cv/resnext50/train.py b/model_zoo/official/cv/resnext50/train.py
index 7a7aaa6157..972309fd28 100644
--- a/model_zoo/official/cv/resnext50/train.py
+++ b/model_zoo/official/cv/resnext50/train.py
@@ -161,7 +161,7 @@ def parse_args(cloud_args=None):
     if args.is_dynamic_loss_scale == 1:
         args.loss_scale = 1  # for dynamic loss scale can not set loss scale in momentum opt
 
-    # 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: