modify alexnet&resnet readme file.

official/cv/alexnet/README.md

@@ -68,12 +68,12 @@ After installing MindSpore via the official website, you can start training and
 
 ```python
 # enter script dir, train AlexNet
-bash run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
-# example: bash run_standalone_train_ascend.sh /home/DataSet/Cifar10/cifar-10-batches-bin/ /home/model/alexnet/ckpt/
+bash run_standalone_train_ascend.sh [cifar10|imagenet] [DATA_PATH] [DEVICE_ID] [CKPT_PATH]
+# example: bash run_standalone_train_ascend.sh cifar10 /home/DataSet/Cifar10/cifar-10-batches-bin/ 0 /home/model/alexnet/ckpt/
 
 # enter script dir, evaluate AlexNet
-bash run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
-# example: bash run_standalone_eval_ascend.sh /home/DataSet/cifar10/cifar-10-verify-bin /home/model/cv/alxnet/ckpt/checkpoint_alexnet-1_1562.ckpt
+bash run_standalone_eval_ascend.sh [cifar10|imagenet] [DATA_PATH] [CKPT_NAME] [DEVICE_ID]
+# example: bash run_standalone_eval_ascend.sh cifar10 /home/DataSet/cifar10/cifar-10-verify-bin /home/model/cv/alxnet/ckpt/checkpoint_alexnet-1_1562.ckpt 0
 ```
 
 - Running on [ModelArts](https://support.huaweicloud.com/modelarts/)
@@ -231,7 +231,7 @@ Major parameters in train.py and config.py as follows:
   ```bash
   python train.py --config_path default_config.yaml --data_path cifar-10-batches-bin --ckpt_path ckpt > log 2>&1 &
   # or enter script dir, and run the script
-  bash run_standalone_train_ascend.sh /home/DataSet/Cifar10/cifar-10-batches-bin/ /home/model/alexnet/ckpt/
+  bash run_standalone_train_ascend.sh cifar10 /home/DataSet/Cifar10/cifar-10-batches-bin/ 0 /home/model/alexnet/ckpt/
   ```
 
   After training, the loss value will be achieved as follows:
@@ -253,7 +253,7 @@ Major parameters in train.py and config.py as follows:
   ```bash
   python train.py --config_path default_config.yaml --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log 2>&1 &
   # or enter script dir, and run the script
-  bash run_standalone_train_for_gpu.sh cifar-10-batches-bin ckpt
+  bash run_standalone_train_gpu.sh cifar10 cifar-10-batches-bin ckpt
   ```
 
   After training, the loss value will be achieved as follows:
@@ -278,7 +278,7 @@ Before running the command below, please check the checkpoint path used for eval
   ```bash
   python eval.py --config_path default_config.yaml --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > eval_log.txt 2>&1 &
   # or enter script dir, and run the script
-  bash run_standalone_eval_ascend.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt
+  bash run_standalone_eval_ascend.sh cifar10 cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt 0
   ```
 
   You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:
@@ -293,7 +293,7 @@ Before running the command below, please check the checkpoint path used for eval
   ```bash
   python eval.py --config_path default_config.yaml --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > eval_log 2>&1 &
   # or enter script dir, and run the script
-  bash run_standalone_eval_for_gpu.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt
+  bash run_standalone_eval_gpu.sh cifar10 cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt 0
   ```
 
   You can view the results through the file "eval_log". The accuracy of the test dataset will be as follows:

official/cv/alexnet/README_CN.md

@@ -70,14 +70,14 @@ 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
 
 ```python
 # 进入脚本目录，训练AlexNet
-bash run_standalone_train_ascend.sh [DATA_PATH] [CKPT_SAVE_PATH]
-# example: bash run_standalone_train_ascend.sh /home/DataSet/Cifar10/cifar-10-batches-bin/ /home/model/alexnet/ckpt/
+bash run_standalone_train_ascend.sh [cifar10|imagenet] [DATA_PATH] [DEVICE_ID] [CKPT_PATH]
+# example: bash run_standalone_train_ascend.sh cifar10 /home/DataSet/Cifar10/cifar-10-batches-bin/ 0 /home/model/alexnet/ckpt/
 
 # 分布式训练AlexNet
 
 # 进入脚本目录，评估AlexNet
-bash run_standalone_eval_ascend.sh [DATA_PATH] [CKPT_NAME]
-# example: bash run_standalone_eval_ascend.sh /home/DataSet/cifar10/cifar-10-verify-bin /home/model/cv/alxnet/ckpt/checkpoint_alexnet-1_1562.ckpt
+bash run_standalone_eval_ascend.sh [cifar10|imagenet] [DATA_PATH] [CKPT_NAME] [DEVICE_ID]
+# example: bash run_standalone_eval_ascend.sh cifar10 /home/DataSet/cifar10/cifar-10-verify-bin /home/model/cv/alxnet/ckpt/checkpoint_alexnet-1_1562.ckpt 0
 ```
 
 - 在 ModelArts 进行训练 (如果你想在modelarts上运行，可以参考以下文档 [modelarts](https://support.huaweicloud.com/modelarts/))
@@ -221,7 +221,7 @@ train.py和config.py中主要参数如下：
   ```bash
   python train.py --config_path default_config.yaml --data_path cifar-10-batches-bin --ckpt_path ckpt > log 2>&1 &
   # 或进入脚本目录，执行脚本
-  bash run_standalone_train_ascend.sh /home/DataSet/Cifar10/cifar-10-batches-bin/ /home/model/alexnet/ckpt/
+  bash run_standalone_train_ascend.sh cifar10 /home/DataSet/Cifar10/cifar-10-batches-bin/ 0 /home/model/alexnet/ckpt/
   ```
 
   经过训练后，损失值如下：
@@ -243,7 +243,7 @@ train.py和config.py中主要参数如下：
   ```bash
   python train.py --config_path default_config.yaml --device_target "GPU" --data_path cifar-10-batches-bin --ckpt_path ckpt > log 2>&1 &
   # 或进入脚本目录，执行脚本
-  bash run_standalone_train_for_gpu.sh cifar-10-batches-bin ckpt
+  bash run_standalone_train_gpu.sh cifar10 cifar-10-batches-bin ckpt
   ```
 
   经过训练后，损失值如下：
@@ -268,7 +268,7 @@ train.py和config.py中主要参数如下：
   ```bash
   python eval.py --config_path default_config.yaml --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-1_1562.ckpt > eval_log.txt 2>&1 &
   #或进入脚本目录，执行脚本
-  bash run_standalone_eval_ascend.sh /home/DataSet/cifar10/cifar-10-verify-bin /home/model/cv/alxnet/ckpt/checkpoint_alexnet-1_1562.ckpt
+  bash run_standalone_eval_ascend.sh cifar10 cifar-10-verify-bin ckpt/checkpoint_alexnet-1_1562.ckpt 0
   ```
 
   可通过"eval_log”文件查看结果。测试数据集的准确率如下：
@@ -283,7 +283,7 @@ train.py和config.py中主要参数如下：
   ```bash
   python eval.py --config_path default_config.yaml --device_target "GPU" --data_path cifar-10-verify-bin --ckpt_path ckpt/checkpoint_alexnet-30_1562.ckpt > eval_log 2>&1 &
   #或进入脚本目录，执行脚本
-  bash run_standalone_eval_for_gpu.sh cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt
+  bash run_standalone_eval_gpu.sh cifar10 cifar-10-verify-bin ckpt/checkpoint_alexnet-30_1562.ckpt 0
   ```
 
   可通过"eval_log”文件查看结果。测试数据集的准确率如下：

official/cv/resnet/README.md

@@ -167,27 +167,27 @@ If you want to run in modelarts, please check the official documentation of [mod
 
 ```python
 # run distributed training on modelarts example
-# (1) First, Perform a or b.
+# (1) Add "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'" on the website UI interface.
+# (2) First, Perform a or b.
 #       a. Set "enable_modelarts=True" on yaml file.
 #          Set other parameters on yaml file you need.
 #       b. Add "enable_modelarts=True" on the website UI interface.
 #          Add other parameters on the website UI interface.
-# (2) Set the config directory to "config_path=/The path of config in S3/"
 # (3) Set the code directory to "/path/resnet" on the website UI interface.
 # (4) Set the startup file to "train.py" on the website UI interface.
 # (5) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
 # (6) Create your job.
 
 # run evaluation on modelarts example
-# (1) Copy or upload your trained model to S3 bucket.
-# (2) Perform a or b.
+# (1) Add "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'" on the website UI interface.
+# (2) Copy or upload your trained model to S3 bucket.
+# (3) Perform a or b.
 #       a. Set "enable_modelarts=True" on yaml file.
 #          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on yaml file.
 #          Set "checkpoint_url=/The path of checkpoint in S3/" on yaml file.
 #       b. Add "enable_modelarts=True" on the website UI interface.
 #          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
 #          Add "checkpoint_url=/The path of checkpoint in S3/" on the website UI interface.
-# (3) Set the config directory to "config_path=/The path of config in S3/"
 # (4) Set the code directory to "/path/resnet" on the website UI interface.
 # (5) Set the startup file to "eval.py" on the website UI interface.
 # (6) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
@@ -702,7 +702,9 @@ Export on ModelArts (If you want to run in modelarts, please check the official
 
 ```python
 # Export on ModelArts
-# (1) Perform a or b.
+# (1) Add "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'" on the website UI interface.
+# (2) Upload or copy your trained model to S3 bucket.
+# (3) Perform a or b.
 #       a. Set "enable_modelarts=True" on default_config.yaml file.
 #          Set "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
 #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on default_config.yaml file.
@@ -715,11 +717,10 @@ Export on ModelArts (If you want to run in modelarts, please check the official
 #          Add "file_name='./resnet'" on the website UI interface.
 #          Add "file_format='AIR'" on the website UI interface.
 #          Add other parameters on the website UI interface.
-# (2) Set the config_path="/path/yaml file" on the website UI interface.
-# (3) Set the code directory to "/path/resnet" on the website UI interface.
-# (4) Set the startup file to "export.py" on the website UI interface.
-# (5) Set the "Output file path" and "Job log path" to your path on the website UI interface.
-# (6) Create your job.
+# (4) Set the code directory to "/path/resnet" on the website UI interface.
+# (5) Set the startup file to "export.py" on the website UI interface.
+# (6) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+# (7) Create your job.
 ```
 
 ### Infer on Ascend310

official/cv/resnet/README_CN.md

@@ -151,12 +151,12 @@ bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]  [CONFIG_PATH]
 
 ```python
 # 在modelarts上使用分布式训练的示例：
-# (1) 选址a或者b其中一种方式。
+# (1) 在网页上设置 "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'"
+# (2) 选址a或者b其中一种方式。
 #       a. 设置 "enable_modelarts=True" 。
 #          在yaml文件上设置网络所需的参数。
 #       b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
 #          在modelarts的界面上设置网络所需的参数。
-# (2) 在modelarts的界面上设置配置文件的路径"config_path=/The path of config in S3/"
 # (3) 在modelarts的界面上设置代码的路径 "/path/resnet"。
 # (4) 在modelarts的界面上设置模型的启动文件 "train.py" 。
 # (5) 在modelarts的界面上设置模型的数据路径 "Dataset path" ,
@@ -164,20 +164,20 @@ bash run_eval_gpu.sh [DATASET_PATH] [CHECKPOINT_PATH]  [CONFIG_PATH]
 # (6) 开始模型的训练。
 
 # 在modelarts上使用模型推理的示例
-# (1) 把训练好的模型地方到桶的对应位置。
-# (2) 选址a或者b其中一种方式。
+# (1) 在网页上设置 "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'"
+# (2) 把训练好的模型地方到桶的对应位置。
+# (3) 选址a或者b其中一种方式。
 #       a. 设置 "enable_modelarts=True"
 #          设置 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 在 yaml 文件.
 #          设置 "checkpoint_url=/The path of checkpoint in S3/" 在 yaml 文件.
 #       b. 增加 "enable_modelarts=True" 参数在modearts的界面上。
 #          增加 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" 参数在modearts的界面上。
 #          增加 "checkpoint_url=/The path of checkpoint in S3/" 参数在modearts的界面上。
-# (2) 在modelarts的界面上设置配置文件的路径"config_path=/The path of config in S3/"
-# (3) 在modelarts的界面上设置代码的路径 "/path/resnet"。
-# (4) 在modelarts的界面上设置模型的启动文件 "eval.py" 。
-# (5) 在modelarts的界面上设置模型的数据路径 "Dataset path" ,
+# (4) 在modelarts的界面上设置代码的路径 "/path/resnet"。
+# (5) 在modelarts的界面上设置模型的启动文件 "eval.py" 。
+# (6) 在modelarts的界面上设置模型的数据路径 "Dataset path" ,
 # 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。
-# (6) 开始模型的推理。
+# (7) 开始模型的推理。
 ```
 
 # 脚本说明
@@ -667,8 +667,9 @@ python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --fi
 ModelArts导出mindir
 
 ```python
-# (1) 把训练好的模型地方到桶的对应位置。
-# (2) 选址a或者b其中一种方式。
+# (1) 在网页上设置 "config_path='/path_to_code/config/resnet50_imagenet2021_config.yaml'"
+# (2) 把训练好的模型地方到桶的对应位置。
+# (3) 选址a或者b其中一种方式。
 #       a. 设置 "enable_modelarts=True"
 #          设置 "checkpoint_file_path='/cache/checkpoint_path/model.ckpt" 在 yaml 文件。
 #          设置 "checkpoint_url=/The path of checkpoint in S3/" 在 yaml 文件。
@@ -679,7 +680,6 @@ ModelArts导出mindir
 #          增加 "checkpoint_url=/The path of checkpoint in S3/" 参数在modearts的界面上。
 #          设置 "file_name='./resnet'"参数在modearts的界面上。
 #          设置 "file_format='AIR'" 参数在modearts的界面上。
-# (3) 设置网络配置文件的路径 "config_path=/The path of config in S3/"
 # (4) 在modelarts的界面上设置代码的路径 "/path/resnet"。
 # (5) 在modelarts的界面上设置模型的启动文件 "export.py" 。
 # 模型的输出路径"Output file path" 和模型的日志路径 "Job log path" 。