add newtwork run demo

official/cv/FCN8s/README.md

@@ -225,7 +225,7 @@ ckpt_file: /home/FCN8s/ckpt/FCN8s_1-133_300.ckpt
 
   #Ascend八卡并行训练
   bash scripts/run_train.sh [DEVICE_NUM] rank_table.json
-  # example: bash scripts/run_train.sh 8 /home/hccl_8p_01234567_10.155.170.71.json
+  # example: bash scripts/run_train.sh 8 ~/hccl_8p.json
   ```
 
   分布式训练需要提前创建JSON格式的HCCL配置文件,请遵循[链接说明](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools)

official/cv/MCNN/README.md

@@ -71,9 +71,9 @@ After installing MindSpore via the official website, you can start training and
 
 ```bash
 # enter script dir, train MCNN example
-sh run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
+bash run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
 # enter script dir, evaluate MCNN example
-sh run_standalone_eval_ascend.sh 0 ./original/shanghaitech/part_A_final/test_data/images ./original/shanghaitech/part_A_final/test_data/ground_truth_csv ./train/ckpt/best.ckpt
+bash run_standalone_eval_ascend.sh 0 ./original/shanghaitech/part_A_final/test_data/images ./original/shanghaitech/part_A_final/test_data/ground_truth_csv ./train/ckpt/best.ckpt
 ```
 
 # [Script Description](#contents)
@@ -126,14 +126,14 @@ Major parameters in train.py and config.py as follows:
 
   ```bash
   # enter script dir, and run the distribute script
-  sh run_distribute_train.sh ./hccl_table.json ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
+  bash run_distribute_train.sh ~/hccl_8p.json ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
   # enter script dir, and run the standalone script
-  sh run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
+  bash run_standalone_train_ascend.sh 0 ./formatted_trainval/shanghaitech_part_A_patches_9/train ./formatted_trainval/shanghaitech_part_A_patches_9/train_den ./formatted_trainval/shanghaitech_part_A_patches_9/val ./formatted_trainval/shanghaitech_part_A_patches_9/val_den ./ckpt
   ```
 
   After training, the loss value will be achieved as follows:
 
-  ```text
+  ```log
   # grep "loss is " log
   epoch: 1 step: 305, loss is 0.00041025918
   epoch: 2 step: 305, loss is 3.7117527e-05
@@ -161,7 +161,7 @@ Before running the command below, please check the checkpoint path used for eval
 
   You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:
 
-  ```text
+  ```log
   # grep "MAE: " eval_log
   MAE: 105.87984801910736 MSE: 161.6687899899305
   ```

official/cv/MCNN/requirements.txt

+numpy
+pandas
+opencv
\ No newline at end of file

official/cv/alexnet/README.md

@@ -46,7 +46,7 @@ Dataset used: [CIFAR-10](<http://www.cs.toronto.edu/~kriz/cifar.html>)
     - Note：Data will be processed in dataset.py
 - Download the dataset, the directory structure is as follows:
 
-```bash
+```cifar10
 ├─cifar-10-batches-bin
 │
 └─cifar-10-verify-bin

official/cv/alexnet/README_CN.md

@@ -48,7 +48,7 @@ AlexNet由5个卷积层和3个全连接层组成。多个卷积核用于提取
     - 注意：数据在dataset.py中处理。
 - 下载数据集。目录结构如下：
 
-```bash
+```cifar10
 ├─cifar-10-batches-bin
 │
 └─cifar-10-verify-bin

official/cv/cnn_direction_model/README.md

@@ -179,7 +179,7 @@ bash scripts/run_distribute_train_ascend.sh [rank_table] [train_dataset_path] [P
 For example, you can run the shell command below to launch the training procedure.
 
 ```shell
-bash run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json /home/DataSet/FSNS/train/
+bash run_distribute_train_ascend.sh ~/hccl_8p.json /home/DataSet/FSNS/train/
 ```
 
 - running on ModelArts

official/cv/cnnctc/README.md

@@ -147,7 +147,7 @@ bash scripts/run_standalone_train_gpu.sh $PRETRAINED_CKPT(options)
 
 ```bash
 bash scripts/run_distribute_train_ascend.sh $RANK_TABLE_FILE $PRETRAINED_CKPT(options)
-# example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json
+# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json
 ```
 
 - Distributed GPU Training:
@@ -254,7 +254,7 @@ Results and checkpoints are written to `./train` folder. Log can be found in `./
 
 ```bash
 bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT(options)]
-# example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json
+# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json
 ```
 
   For distributed training, a hccl configuration file with JSON format needs to be created in advance.

official/cv/cnnctc/README_CN.md

@@ -144,7 +144,7 @@ bash scripts/run_standalone_train_ascend.sh [DEVICE_ID] [PRETRAINED_CKPT(options
 
 ```shell
 bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT(options)]
-# example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json
+# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json
 ```
 
 - 评估：
@@ -239,7 +239,7 @@ bash scripts/run_standalone_train_ascend.sh [DEVICE_ID] [PRETRAINED_CKPT(options
 
 ```shell
 bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [PRETRAINED_CKPT(options)]
-# example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json
+# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json
 ```
 
 结果和检查点分别写入设备`i`的`./train_parallel_{i}`文件夹。  

official/cv/ctpn/README.md

@@ -171,8 +171,7 @@ Modify the parameters according to the actual path
 ```bash
 # distribute training
 bash scripts/run_distribute_train_ascend.sh [RANK_TABLE_FILE] [TASK_TYPE] [PRETRAINED_PATH]
-# example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json Pretraining(or Finetune) \
-# /home/DataSet/ctpn_dataset/backbone/0-150_5004.ckpt
+# example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json Pretraining(or Finetune) /home/DataSet/ctpn_dataset/backbone/0-150_5004.ckpt
 
 # standalone training
 bash scrpits/run_standalone_train_ascend.sh [TASK_TYPE] [PRETRAINED_PATH] [DEVICE_ID]
@@ -226,7 +225,7 @@ ICDAR2013, SCUT-FORU to improve precision and recall, and when doing Finetune, w
     Ascend:
       # distribute training example(8p)
       bash run_distribute_train_ascend.sh [RANK_TABLE_FILE] [TASK_TYPE] [PRETRAINED_PATH]
-      # example: bash scripts/run_distribute_train_ascend.sh /home/hccl_8p_01234567_10.155.170.71.json Pretraining(or Finetune) /home/DataSet/ctpn_dataset/backbone/0-150_5004.ckpt
+      # example: bash scripts/run_distribute_train_ascend.sh ~/hccl_8p.json Pretraining(or Finetune) /home/DataSet/ctpn_dataset/backbone/0-150_5004.ckpt
 
       # standalone training
       bash run_standalone_train_ascend.sh [TASK_TYPE] [PRETRAINED_PATH]

official/cv/ctpn/scripts/run_infer_310.sh

@@ -104,7 +104,7 @@ function cal_acc()
     fi
     mkdir output
     mkdir output_img
-    python ../postprocess.py --dataset_path=$data_path --result_path=result_Files --label_path=$label_path &> acc.log
+    python ../postprocess.py --export_dataset_path=$data_path --result_path=result_Files --label_path=$label_path &> acc.log
     if [ $? -ne 0 ]; then
         echo "calculate accuracy failed"
         exit 1

official/cv/deeplabv3/README.md

@@ -113,7 +113,7 @@ After installing MindSpore via the official website, you can start training and
 
 - Prepare backbone
 
-Download resnet101 for here(https://download.mindspore.cn/model_zoo/r1.2/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt).
+Download resnet101 for [here](https://download.mindspore.cn/model_zoo/r1.2/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt).
 
 - Running on Ascend
 
@@ -140,7 +140,7 @@ Enter the shell script to modify the data_file and ckpt_pre_trained parameters
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s16_r1.sh
+bash run_distribute_train_s16_r1.sh ~/hccl_8p.json
 ```
 
 2. Train s8 with vocaug dataset, finetuning from model in previous step, training script is:
@@ -151,7 +151,7 @@ Enter the shell script to modify the data_file and ckpt_pre_trained parameters
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s8_r1.sh
+bash run_distribute_train_s8_r1.sh ~/hccl_8p.json
 ```
 
 3. Train s8 with voctrain dataset, finetuning from model in previous step, training script is:
@@ -164,17 +164,19 @@ data_file=/home/DataSet/VOC2012/voctrain_mindrecords/votrain.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 
 
-bash run_distribute_train_s8_r2.sh
+bash run_distribute_train_s8_r2.sh ~/hccl_8p.json
 ```
 
 - For evaluation, evaluating steps are as follows:
 
 1. Enter the shell script to modify the data_file and ckpt_pre_trained parameters
 
-```default_config.yaml
+```shell
+modify the parameter according local path
 # example:
 data_root=/home/DataSet/VOC2012
 data_lst=/home/DataSet/VOC2012/voc_val_lst.txt
+ckpt_path=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 ```
 
 2. Eval s16 with voc val dataset, eval script is:

official/cv/deeplabv3/README_CN.md

@@ -144,7 +144,7 @@ bash run_standalone_train.sh
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s16_r1.sh
+bash run_distribute_train_s16_r1.sh ~/hccl_8p.json
 ```
 
 2. 使用VOCaug数据集训练s8，微调上一步的模型。脚本如下：
@@ -155,7 +155,7 @@ bash run_distribute_train_s16_r1.sh
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s8_r1.sh
+bash run_distribute_train_s8_r1.sh ~/hccl_8p.json
 ```
 
 3. 使用VOCtrain数据集训练s8，微调上一步的模型。脚本如下：
@@ -167,17 +167,18 @@ bash run_distribute_train_s8_r1.sh
 data_file=/home/DataSet/VOC2012/voctrain_mindrecords/votrain.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 
-bash run_distribute_train_s8_r2.sh
+bash run_distribute_train_s8_r2.sh ~/hccl_8p.json
 ```
 
 - 评估步骤如下：
 
 1. 进入对应的shell脚本修改参数
 
-```default_config.yaml
+```shell
 # example:
 data_root=/home/DataSet/VOC2012
 data_lst=/home/DataSet/VOC2012/voc_val_lst.txt
+ckpt_path=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 ```
 
 2. 使用voc val数据集评估s16。评估脚本如下：

official/cv/deeplabv3plus/README_CN.md

@@ -134,7 +134,7 @@ bash run_alone_train.sh
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s16_r1.sh
+bash run_distribute_train_s16_r1.sh ~/hccl_8p.json
 ```
 
 2.使用VOCaug数据集训练s8，微调上一步的模型。脚本如下：
@@ -145,7 +145,7 @@ bash run_distribute_train_s16_r1.sh
 data_file=/home/DataSet/VOC2012/vocaug_mindrecords/vocaug.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/predtrained/resnet101_ascend_v120_imagenet2012_official_cv_bs32_acc78.ckpt
 
-bash run_distribute_train_s8_r1.sh
+bash run_distribute_train_s8_r1.sh ~/hccl_8p.json
 ```
 
 3.使用VOCtrain数据集训练s8，微调上一步的模型。脚本如下：
@@ -157,17 +157,18 @@ bash run_distribute_train_s8_r1.sh
 data_file=/home/DataSet/VOC2012/voctrain_mindrecords/votrain.mindrecord0
 ckpt_pre_trained=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 
-run_distribute_train_s8_r2.sh
+bash run_distribute_train_s8_r2.sh ~/hccl_8p.json
 ```
 
 评估步骤如下：
 
 1. 进入对应的shell脚本修改参数
 
-```default_cofig.yaml
+```shell
 # example:
 data_root=/home/DataSet/VOC2012
 data_lst=/home/DataSet/VOC2012/voc_val_lst.txt
+ckpt_path=/home/model/deeplabv3/ckpt/deeplabv3-800_330.ckpt
 ```
 
 2.使用voc val数据集评估s16。评估脚本如下：

official/cv/densenet/README.md

@@ -102,13 +102,13 @@ After installing MindSpore via the official website, you can start training and
 
   # run distributed training example
   bash scripts/run_distribute_train.sh [DEVICE_NUM] [RANK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [TRAIN_DATA_DIR]
-  # example bash scripts/run_distribute_train.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
+  # example bash scripts/run_distribute_train.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
 
   # run evaluation example
   python eval.py --net [NET_NAME] --dataset [DATASET_NAME] --eval_data_dir /PATH/TO/DATASET --ckpt_files /PATH/TO/CHECKPOINT > eval.log 2>&1 &
   OR
   bash scripts/run_distribute_eval.sh [DEVICE_NUM] [RANDK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [EVAL_DATA_DIR][CKPT_PATH]
-  # example: bash script/run_distribute_eval.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
+  # example: bash script/run_distribute_eval.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
   ```
 
   For distributed training, a hccl configuration file with JSON format needs to be created in advance.
@@ -293,7 +293,7 @@ You can modify the training behaviour through the various flags in the `densenet
   2020-08-22 17:02:19,921:INFO:local passed
   2020-08-22 17:05:43,112:INFO:epoch[2], iter[15011], loss:3.096, mean_fps:6304.53 imgs/sec
   2020-08-22 17:05:43,113:INFO:local passed
-
+  ...
   ```
 
 - running on GPU
@@ -326,7 +326,7 @@ You can modify the training behaviour through the various flags in the `densenet
   ```bash
 
   bash scripts/run_distribute_train.sh [DEVICE_NUM] [RANK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [TRAIN_DATA_DIR]
-  # example bash scripts/run_distribute_train.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
+  # example bash scripts/run_distribute_train.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
 
   ```
 
@@ -340,7 +340,8 @@ You can modify the training behaviour through the various flags in the `densenet
   2020-08-22 17:09:05,686:INFO:epoch[3], iter[20015], loss:3.113, mean_fps:6304.37 imgs/sec
   2020-08-22 17:12:28,925:INFO:epoch[4], iter[25019], loss:3.29, mean_fps:6303.07 imgs/sec
   2020-08-22 17:15:52,167:INFO:epoch[5], iter[30023], loss:2.865, mean_fps:6302.98 imgs/sec
-
+  ...
+  ...
   ```
 
 - running on GPU
@@ -367,7 +368,7 @@ You can modify the training behaviour through the various flags in the `densenet
   python eval.py --net [NET_NAME] --dataset [DATASET_NAME] --eval_data_dir /PATH/TO/DATASET --ckpt_files /PATH/TO/CHECKPOINT > eval.log 2>&1 &
   OR
   bash scripts/run_distribute_eval.sh [DEVICE_NUM] [RANDK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [EVAL_DATA_DIR][CKPT_PATH]
-  # example: bash script/run_distribute_eval.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
+  # example: bash script/run_distribute_eval.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
 
   ```
 

official/cv/densenet/README_CN.md

@@ -107,13 +107,13 @@ DenseNet-100使用的数据集： Cifar-10
 
    # 分布式训练示例
    bash scripts/run_distribute_train.sh [DEVICE_NUM] [RANK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [TRAIN_DATA_DIR]
-   # example bash scripts/run_distribute_train.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
+   # example bash scripts/run_distribute_train.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
 
    # 单卡评估示例
    python eval.py --net [NET_NAME] --dataset [DATASET_NAME] --eval_data_dir /PATH/TO/DATASET --ckpt_files /PATH/TO/CHECKPOINT > eval.log 2>&1 &
 
    bash scripts/run_distribute_eval.sh [DEVICE_NUM] [RANDK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [EVAL_DATA_DIR][CKPT_PATH]
-   # example: bash script/run_distribute_eval.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
+   # example: bash script/run_distribute_eval.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/validation_preprocess/ /home/model/densenet/ckpt/0-120_500.ckpt
    ```
 
   分布式训练需要提前创建JSON格式的HCCL配置文件。
@@ -310,7 +310,7 @@ python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATA
 
 ```shell
 bash scripts/run_distribute_train.sh [DEVICE_NUM] [RANK_TABLE_FILE] [NET_NAME] [DATASET_NAME] [TRAIN_DATA_DIR]
-# example bash scripts/run_distribute_train.sh 8 /root/hccl_8p_01234567_10.155.170.71.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
+# example bash scripts/run_distribute_train.sh 8 ~/hccl_8p.json densenet121 imagenet /home/DataSet/ImageNet_Original/train/
 ```
 
   上述shell脚本将在后台进行分布式训练。可以通过文件`train[X]/output/202x-xx-xx_time_xx_xx_xx/`查看结果日志和模型检查点。在ImageNet数据集上训练DenseNet-121的损失值的实现如下：

official/cv/dpn/README.md

@@ -228,7 +228,7 @@ Run `scripts/train_distributed.sh` to train the model distributed. The usage of
 
 ```text
 bash scripts/train_distributed.sh [rank_table] [train_data_dir] [ckpt_path_to_save]  [rank_size] [eval_each_epoch] [pretrained_ckpt(optional)]
-# example: bash scripts/train_distributed.sh /root/hccl_8p_01234567_10.155.170.71.json /home/DataSet/ImageNet_Original/train/ ./ckpt/ 8 0
+# example: bash scripts/train_distributed.sh ~/hccl_8p.json /home/DataSet/ImageNet_Original/train/ ./ckpt/ 8 0
 ```
 
 The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log.txt` as follows:

official/cv/east/README.md

@@ -81,7 +81,7 @@ Dataset used [ICDAR 2015](https://rrc.cvc.uab.es/?ch=4&com=downloads)
 ```bash
 # distribute training example(8p)
 bash run_distribute_train.sh [DATASET_PATH] [PRETRAINED_BACKBONE] [RANK_TABLE_FILE]
-# example: bash run_distribute_train.sh /home/DataSet/ICDAR2015/ic15/ home/model/east/pretrained/0-150_5004.ckpt /root/hccl_8p_01234567_10.155.170.71.json
+# example: bash run_distribute_train.sh /home/DataSet/ICDAR2015/ic15/ home/model/east/pretrained/0-150_5004.ckpt ~/hccl_8p.json
 
 # standalone training
 bash run_standalone_train_ascend.sh [DATASET_PATH] [PRETRAINED_BACKBONE] [DEVICE_ID]
@@ -106,7 +106,7 @@ bash run_eval_ascend.sh [DATASET_PATH] [CKPT_PATH] [DEVICE_ID]
     Ascend:
       # distribute training example(8p)
       bash run_distribute_train.sh [DATASET_PATH] [PRETRAINED_BACKBONE] [RANK_TABLE_FILE]
-      # example: bash run_distribute_train.sh /home/DataSet/ICDAR2015/ic15/ home/model/east/pretrained/0-150_5004.ckpt /root/hccl_8p_01234567_10.155.170.71.json
+      # example: bash run_distribute_train.sh /home/DataSet/ICDAR2015/ic15/ home/model/east/pretrained/0-150_5004.ckpt ~/hccl_8p.json
 
       # standalone training
       bash run_standalone_train_ascend.sh [DATASET_PATH] [PRETRAINED_BACKBONE] [DEVICE_ID]

official/cv/googlenet/README.md

@@ -105,7 +105,7 @@ After installing MindSpore via the official website, you can start training and
 
   # run distributed training example
   bash scripts/run_train.sh [RANK_TABLE_FILE] [DATASET_NAME]
-  # example: bash scripts/run_train.sh /root/hccl_8p_01234567_10.155.170.71.json cifar10
+  # example: bash scripts/run_train.sh ~/hccl_8p.json cifar10
 
   # run evaluation example
   python eval.py > eval.log 2>&1 &
@@ -401,7 +401,7 @@ For more configuration details, please refer the script `config.py`.
 - running on Ascend
 
   ```bash
-  bash scripts/run_train.sh /root/hccl_8p_01234567_10.155.170.71.json cifar10
+  bash scripts/run_train.sh ~/hccl_8p.json cifar10
   ```
 
   The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log`. The loss value will be achieved as follows:

official/cv/googlenet/README_CN.md

@@ -107,7 +107,7 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
 
   # 运行分布式训练示例
   bash scripts/run_train.sh [RANK_TABLE_FILE] [DATASET_NAME]
-  # example: bash scripts/run_train.sh /root/hccl_8p_01234567_10.155.170.71.json cifar10
+  # example: bash scripts/run_train.sh ~/hccl_8p.json cifar10
 
   # 运行评估示例
   python eval.py > eval.log 2>&1 &
@@ -371,7 +371,7 @@ GoogleNet由多个inception模块串联起来，可以更加深入。  降维的
 - Ascend处理器环境运行
 
   ```bash
-  bash scripts/run_train.sh /root/hccl_8p_01234567_10.155.170.71.json cifar10
+  bash scripts/run_train.sh ~/hccl_8p.json cifar10
   ```
 
   上述shell脚本将在后台运行分布训练。您可以通过train_parallel[X]/log文件查看结果。采用以下方式达到损失值：

official/cv/inceptionv3/README.md

@@ -284,7 +284,7 @@ Take training cifar10 as an example, the ds_type parameter is set to cifar10
 ```shell
 # distribute training(8p)
 bash run_distribute_train.sh [RANK_TABLE_FILE] [DATA_PATH] [CKPT_PATH]
-# example: bash run_distribute_train.sh /root/hccl_8p_012345467_10.155.170.71.json /home/DataSet/cifar10/ ./ckpt/
+# example: bash run_distribute_train.sh ~/hccl_8p.json /home/DataSet/cifar10/ ./ckpt/
 
 # standalone training
 bash scripts/run_standalone_train.sh [DEVICE_ID] [DATA_PATH] [CKPT_PATH]