!517 Change format of example of `device_target` in readme.

Merge pull request !517 from chenhaozhe/change-readme

official/cv/deeptext/README.md

@@ -232,13 +232,13 @@ sh run_eval_gpu.sh [IMGS_PATH] [ANNOS_PATH] [CHECKPOINT_PATH] [COCO_TEXT_PARSER_
 #       a. Set "enable_modelarts=True" on default_config.yaml file.
 #          Set "checkpoint_url='s3://dir_to_trained_model/'" on default_config.yaml file.
 #          Set "ckpt_file='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
-#          Set "device_target='Ascend'" on default_config.yaml file.
+#          Set "device_target=Ascend" on default_config.yaml file.
 #          Set "file_format='MINDIR'" on default_config.yaml file.
 #          Set other parameters on default_config.yaml file you need.
 #       b. Add "enable_modelarts=True" on the website UI interface.
 #          Add "checkpoint_url='s3://dir_to_trained_model/'" on the website UI interface.
 #          Add "ckpt_file='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
-#          Add "device_target='Ascend'" on the website UI interface.
+#          Add "device_target=Ascend" on the website UI interface.
 #          Add "file_format='MINDIR'" on the website UI interface.
 #          Add other parameters on the website UI interface.
 # (2) Set the code directory to "/path/deeptext" on the website UI interface.

official/cv/densenet/README.md

 # Contents
 
+- [Contents](#contents)
 - [DenseNet Description](#densenet-description)
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
@@ -12,17 +13,20 @@
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
         - [Training](#training)
-        - [Distributed Training](#distributed-training)  
+        - [Distributed Training](#distributed-training)
     - [Evaluation Process](#evaluation-process)
         - [Evaluation](#evaluation)
     - [Export Process](#export-process)
-        - [Export](#Export)
-    - [Inferenct Process](#Inferenct-process)
-        - [Inferenct](#Inferenct)
+        - [export](#export)
+    - [Inference Process](#inference-process)
+        - [Inference](#inference)
 - [Model Description](#model-description)
     - [Performance](#performance)
+        - [DenseNet121](#densenet121)
         - [Training accuracy results](#training-accuracy-results)
         - [Training performance results](#training-performance-results)
+        - [DenseNet100](#densenet100)
+        - [Training performance](#training-performance)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 
@@ -179,13 +183,13 @@ After installing MindSpore via the official website, you can start training and
 
   # run training example
   export CUDA_VISIBLE_DEVICES=0
-  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='GPU' > train.log 2>&1 &
+  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=GPU > train.log 2>&1 &
 
   # run distributed training example
   bash run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7  [NET_NAME] [DATASET_NAME] [DATASET_PATH]
 
   # run evaluation example
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='GPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=GPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
   OR
   bash run_distribute_eval_gpu.sh 1 0  [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
 
@@ -301,7 +305,7 @@ You can modify the training behaviour through the various flags in the `densenet
   ```python
 
   export CUDA_VISIBLE_DEVICES=0
-  python train.py --net [NET_NAME] --dataset [DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='GPU' > train.log 2>&1 &
+  python train.py --net [NET_NAME] --dataset [DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=GPU > train.log 2>&1 &
 
   ```
 
@@ -313,7 +317,7 @@ You can modify the training behaviour through the various flags in the `densenet
 
   ```python
 
-  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='CPU' > train.log 2>&1 &
+  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=CPU > train.log 2>&1 &
 
   ```
 
@@ -387,7 +391,7 @@ You can modify the training behaviour through the various flags in the `densenet
 
   ```python
 
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='GPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=GPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
   OR
   bash run_distribute_eval_gpu.sh 1 0 [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
 
@@ -416,7 +420,7 @@ You can modify the training behaviour through the various flags in the `densenet
 
   ```python
 
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='CPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=CPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
 
   ```
 

official/cv/densenet/README_CN.md

@@ -24,8 +24,11 @@
         - [推理](#推理)
 - [模型描述](#模型描述)
     - [性能](#性能)
+        - [DenseNet121](#densenet121)
         - [训练准确率结果](#训练准确率结果)
         - [训练性能结果](#训练性能结果)
+        - [DenseNet100](#densenet100)
+        - [训练结果](#训练结果)
 - [随机情况说明](#随机情况说明)
 - [ModelZoo主页](#modelzoo主页)
 
@@ -178,13 +181,13 @@ DenseNet-100使用的数据集： Cifar-10
   ```python
   # 训练示例
   export CUDA_VISIBLE_DEVICES=0
-  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='GPU' > train.log 2>&1 &
+  python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=GPU > train.log 2>&1 &
 
   # 分布式训练示例
   bash run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 [NET_NAME] [DATASET_NAME] [DATASET_PATH]
 
   # 评估示例
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='GPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=GPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
   OR
   bash run_distribute_eval_gpu.sh 1 0 [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
   ```
@@ -291,7 +294,7 @@ DenseNet-100使用的数据集： Cifar-10
 
 ```python
 export CUDA_VISIBLE_DEVICES=0
-python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='GPU' > train.log 2>&1 &
+python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=GPU > train.log 2>&1 &
 ```
 
 以上python命令在后台运行，在`output/202x-xx-xx_time_xx_xx/`目录下生成日志和模型检查点。
@@ -299,7 +302,7 @@ python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATA
 - CPU处理器环境运行
 
 ```python
-python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target='CPU' > train.log 2>&1 &
+python train.py --net=[NET_NAME] --dataset=[DATASET_NAME] --train_data_dir=[DATASET_PATH] --is_distributed=0 --device_target=CPU > train.log 2>&1 &
 ```
 
 以上python命令在后台运行，在`output/202x-xx-xx_time_xx_xx/`目录下生成日志和模型检查点。
@@ -362,7 +365,7 @@ bash run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 [NET_NAME] [DATASET_NAME] [DA
   运行以下命令进行评估。
 
   ```eval
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='GPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=GPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
   OR
   bash run_distribute_eval_gpu.sh 1 0 [NET_NAME] [DATASET_NAME] [DATASET_PATH] [CHECKPOINT_PATH]
   ```
@@ -385,7 +388,7 @@ bash run_distribute_train_gpu.sh 8 0,1,2,3,4,5,6,7 [NET_NAME] [DATASET_NAME] [DA
   运行以下命令进行评估。
 
   ```eval
-  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target='CPU' --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
+  python eval.py --net=[NET_NAME] --dataset=[DATASET_NAME] --eval_data_dir=[DATASET_PATH] --device_target=CPU --ckpt_files=[CHECKPOINT_PATH] > eval.log 2>&1 &
   ```
 
   上述python命令在后台运行。可以通过“eval/eval.log”文件查看结果。DenseNet-100在Cifar-10的测试数据集的准确率如下：

official/cv/shufflenetv2/README.md

@@ -83,7 +83,7 @@ You can start training using python or shell scripts. The usage of shell scripts
 ```bash
 # training example
   python:
-      GPU: mpirun --allow-run-as-root -n 8 --output-filename log_output --merge-stderr-to-stdout python train.py --is_distributed=True --platform='GPU' --dataset_path='~/imagenet/train/' > train.log 2>&1 &
+      GPU: mpirun --allow-run-as-root -n 8 --output-filename log_output --merge-stderr-to-stdout python train.py --is_distributed=True --platform=GPU --dataset_path='~/imagenet/train/' > train.log 2>&1 &
 
   shell:
       GPU: cd scripts & bash run_distribute_train_for_gpu.sh 8 0,1,2,3,4,5,6,7 ~/imagenet/train/
@@ -106,7 +106,7 @@ You can start evaluation using python or shell scripts. The usage of shell scrip
 ```bash
 # infer example
   python:
-      GPU: CUDA_VISIBLE_DEVICES=0 python eval.py --platform='GPU' --dataset_path='~/imagenet/val/' > eval.log 2>&1 &
+      GPU: CUDA_VISIBLE_DEVICES=0 python eval.py --platform=GPU --dataset_path='~/imagenet/val/' > eval.log 2>&1 &
 
   shell:
       GPU: cd scripts & bash run_eval_for_gpu.sh '~/imagenet/val/' 'checkpoint_file'

official/cv/squeezenet/README.md

@@ -695,14 +695,14 @@ If you need to use the trained model to perform inference on multiple hardware p
   # Set context
   device_id = int(os.getenv('DEVICE_ID'))
   context.set_context(mode=context.GRAPH_MODE,
-                      device_target='Ascend',
+                      device_target=Ascend,
                       device_id=device_id)
 
   # Load unseen dataset for inference
   dataset = create_dataset(dataset_path=config.data_path,
                            do_train=False,
                            batch_size=config.batch_size,
-                           target='Ascend')
+                           target=Ascend)
 
   # Define model
   net = squeezenet(num_classes=config.class_num)
@@ -731,7 +731,7 @@ If you need to use the trained model to perform inference on multiple hardware p
                            do_train=True,
                            repeat_num=1,
                            batch_size=config.batch_size,
-                           target='Ascend')
+                           target=Ascend)
   step_size = dataset.get_dataset_size()
 
   # define net

official/gnn/bgcf/README.md

@@ -123,7 +123,7 @@ After installing MindSpore via the official website and Dataset is correctly gen
       #       a. Set "enable_modelarts=True" on default_config.yaml file.
       #          Set "datapath='/cache/data/amazon_beauty/data_mr'" on default_config.yaml file.
       #          Set "ckptpath='./ckpts'" on default_config.yaml file.
-      #          (options)Set "device_target='GPU'" on default_config.yaml file if run on GPU.
+      #          (options)Set "device_target=GPU" on default_config.yaml file if run on GPU.
       #          (options)Set "num_epoch=680" on default_config.yaml file if run on GPU.
       #          (options)Set "dist_reg=0" on default_config.yaml file if run on GPU.
       #          Set other parameters on default_config.yaml file you need.
@@ -150,7 +150,7 @@ After installing MindSpore via the official website and Dataset is correctly gen
       #          Set "datapath='/cache/data/amazon_beauty/data_mr'" on default_config.yaml file.
       #          Set "ckptpath='/cache/checkpoint_path'" on default_config.yaml file.
       #          Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on default_config.yaml file.
-      #          (options)Set "device_target='GPU'" on default_config.yaml file if run on GPU.
+      #          (options)Set "device_target=GPU" on default_config.yaml file if run on GPU.
       #          (options)Set "num_epoch=680" on default_config.yaml file if run on GPU.
       #          (options)Set "dist_reg=0" on default_config.yaml file if run on GPU.
       #          Set other parameters on default_config.yaml file you need.
@@ -179,7 +179,7 @@ After installing MindSpore via the official website and Dataset is correctly gen
       #          Set "checkpoint_url='s3://dir_to_your_trained_ckpt/'" on default_config.yaml file.
       #          Set "file_name='bgcf'" on default_config.yaml file.
       #          Set "file_format='AIR'" on default_config.yaml file.
-      #          (options)Set "device_target='GPU'" on default_config.yaml file if run on GPU.
+      #          (options)Set "device_target=GPU" on default_config.yaml file if run on GPU.
       #          Set other parameters on default_config.yaml file you need.
       #       b. Add "enable_modelarts=True" on the website UI interface.
       #          Add "ckpt_file=/cache/checkpoint_path/model.ckpt" on the website UI interface.

official/gnn/bgcf/README_CN.md

@@ -136,7 +136,7 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
       #       a. 在 default_config.yaml 文件中设置 "enable_modelarts=True"
       #          在 default_config.yaml 文件中设置 "datapath='/cache/data/amazon_beauty/data_mr'"
       #          在 default_config.yaml 文件中设置 "ckptpath='./ckpts'"
-      #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "device_target='GPU'"
+      #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "device_target=GPU"
       #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "num_epoch=680"
       #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "dist_reg=0"
       #          在 default_config.yaml 文件中设置 其他参数
@@ -162,7 +162,7 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
       #       a. 在 default_config.yaml 文件中设置 "enable_modelarts=True"
       #          在 default_config.yaml 文件中设置 "datapath='/cache/data/amazon_beauty/data_mr'"
       #          在 default_config.yaml 文件中设置 "ckptpath='./ckpts'"
-      #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "device_target='GPU'"
+      #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "device_target=GPU"
       #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "num_epoch=680"
       #          (可选)如果选择GPU运行，在 default_config.yaml 文件中设置 "dist_reg=0"
       #          在 default_config.yaml 文件中设置 其他参数
@@ -190,7 +190,7 @@ BGCF包含两个主要模块。首先是抽样，它生成基于节点复制的
       #          在 default_config.yaml 文件中设置 "checkpoint_url='s3://dir_to_your_trained_ckpt/'"
       #          在 default_config.yaml 文件中设置 "file_name='bgcf'"
       #          在 default_config.yaml 文件中设置 "file_format='AIR'"
-      #          (可选)在 default_config.yaml 文件中设置 "device_target='GPU'"
+      #          (可选)在 default_config.yaml 文件中设置 "device_target=GPU"
       #          在 default_config.yaml 文件中设置 其他参数
       #       b. 在网页上设置 "enable_modelarts=True"
       #          在网页上设置 "ckpt_file=/cache/checkpoint_path/model.ckpt"

official/nlp/fasttext/README.md

@@ -84,12 +84,12 @@ After dataset preparation, you can start training and evaluation as follows:
     #       a. Set "enable_modelarts=True" on [DATASET_NAME]_config.yaml file.
     #          Set "dataset_path='/cache/data/[DATASET_NAME]'" on [DATASET_NAME]_config.yaml file.
     #          Set "data_name='[DATASET_NAME]'" on [DATASET_NAME]_config.yaml file.
-    #          (option)Set "device_target='GPU'" on [DATASET_NAME]_config.yaml file if run with GPU.
+    #          (option)Set "device_target=GPU" on [DATASET_NAME]_config.yaml file if run with GPU.
     #          (option)Set other parameters on [DATASET_NAME]_config.yaml file you need.
     #       b. Add "enable_modelarts=True" on the website UI interface.
     #          Add "dataset_path='/cache/data/[DATASET_NAME]'" on the website UI interface.
     #          Add "data_name='[DATASET_NAME]'" on the website UI interface.
-    #          (option)Set "device_target='GPU'" on the website UI interface if run with GPU.
+    #          (option)Set "device_target=GPU" on the website UI interface if run with GPU.
     #          (option)Set other parameters on the website UI interface.
     # (3) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)
     # (4) Set the code directory to "/path/fasttext" on the website UI interface.
@@ -105,14 +105,14 @@ After dataset preparation, you can start training and evaluation as follows:
     #          Set "data_name='[DATASET_NAME]'" on [DATASET_NAME]_config.yaml file.
     #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on [DATASET_NAME]_config.yaml file.
     #          Set "model_ckpt='/cache/checkpoint_path/model.ckpt'" on [DATASET_NAME]_config.yaml file.
-    #          (option)Set "device_target='GPU'" on [DATASET_NAME]_config.yaml file if run with GPU.
+    #          (option)Set "device_target=GPU" on [DATASET_NAME]_config.yaml file if run with GPU.
     #          (option)Set other parameters on [DATASET_NAME]_config.yaml file you need.
     #       b. Add "enable_modelarts=True" on the website UI interface.
     #          Add "dataset_path='/cache/data/[DATASET_NAME]'" on the website UI interface.
     #          Add "data_name='[DATASET_NAME]'" on the website UI interface.
     #          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
     #          Add "model_ckpt='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
-    #          (option)Set "device_target='GPU'" on the website UI interface if run with GPU.
+    #          (option)Set "device_target=GPU" on the website UI interface if run with GPU.
     #          (option)Set other parameters on the website UI interface.
     # (3) Upload or copy your trained model to S3 bucket.
     # (4) Upload a zip dataset to S3 bucket. (you could also upload the origin dataset, but it can be so slow.)

official/recommend/deepfm/README.md

@@ -76,7 +76,7 @@ After installing MindSpore via the official website, you can start training and
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # run distributed training example
@@ -86,7 +86,7 @@ After installing MindSpore via the official website, you can start training and
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
   ```
@@ -108,7 +108,7 @@ After installing MindSpore via the official website, you can start training and
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='GPU' \
+    --device_target=GPU \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # run distributed training example
@@ -118,7 +118,7 @@ After installing MindSpore via the official website, you can start training and
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='GPU' > ms_log/eval_output.log 2>&1 &
+    --device_target=GPU > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 GPU /dataset_path /checkpoint_path/deepfm.ckpt
   ```
@@ -132,14 +132,14 @@ After installing MindSpore via the official website, you can start training and
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='CPU' \
+    --device_target=CPU \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # run evaluation example
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='CPU' > ms_log/eval_output.log 2>&1 &
+    --device_target=CPU > ms_log/eval_output.log 2>&1 &
   ```
 
 - Running on [ModelArts](https://support.huaweicloud.com/modelarts/)
@@ -316,7 +316,7 @@ Parameters for both training and evaluation can be set in config.py
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
   ```
 
@@ -362,7 +362,7 @@ Parameters for both training and evaluation can be set in config.py
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
   ```

official/recommend/deepfm/README_CN.md

@@ -79,7 +79,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # 运行分布式训练示例
@@ -89,7 +89,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
   ```
@@ -111,7 +111,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='GPU' \
+    --device_target=GPU \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # 运行分布式训练示例
@@ -121,7 +121,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='GPU' > ms_log/eval_output.log 2>&1 &
+    --device_target=GPU > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 GPU /dataset_path /checkpoint_path/deepfm.ckpt
   ```
@@ -300,7 +300,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
   ```
 
@@ -344,7 +344,7 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
   python eval.py \
     --dataset_path='dataset/test' \
     --checkpoint_path='./checkpoint/deepfm.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /dataset_path /checkpoint_path/deepfm.ckpt
   ```

official/recommend/naml/README.md

@@ -117,7 +117,7 @@ You can start training using python or shell scripts. The usage of shell scripts
       # (1) Add "config_path='/path_to_code/MINDlarge_config.yaml'" on the website UI interface.
       # (2) Perform a or b.
       #       a. Set "enable_modelarts=True" on MINDlarge_config.yaml file.
-      #          Set "platform='Ascend'" on MINDlarge_config.yaml file.
+      #          Set "platform=Ascend" on MINDlarge_config.yaml file.
       #          Set "dataset='large'" on MINDlarge_config.yaml file.
       #          Set "dataset_path='/cache/data/MINDlarge'" on MINDlarge_config.yaml file.
       #          Set "save_checkpoint_path='./checkpoint'" on MINDlarge_config.yaml file.
@@ -145,7 +145,7 @@ You can start training using python or shell scripts. The usage of shell scripts
       # (1) Add "config_path='/path_to_code/MINDlarge_config.yaml'" on the website UI interface.
       # (2) Perform a or b.
       #       a. Set "enable_modelarts=True" on MINDlarge_config.yaml file.
-      #          Set "platform='Ascend'" on MINDlarge_config.yaml file.
+      #          Set "platform=Ascend" on MINDlarge_config.yaml file.
       #          Set "dataset='large'" on MINDlarge_config.yaml file.
       #          Set "dataset_path='/cache/data/MINDlarge'" on MINDlarge_config.yaml file.
       #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on MINDlarge_config.yaml file.
@@ -172,7 +172,7 @@ You can start training using python or shell scripts. The usage of shell scripts
       # (1) Add "config_path='/path_to_code/MINDlarge_config.yaml'" on the website UI interface.
       # (2) Perform a or b.
       #       a. Set "enable_modelarts=True" on MINDlarge_config.yaml file.
-      #          Set "platform='Ascend'" on MINDlarge_config.yaml file.
+      #          Set "platform=Ascend" on MINDlarge_config.yaml file.
       #          Set "file_format='AIR'" on MINDlarge_config.yaml file.
       #          Set "batch_size=1" on MINDlarge_config.yaml file.
       #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on MINDlarge_config.yaml file.

research/cv/FaceDetection/README.md

@@ -238,13 +238,13 @@ The entire code structure is as following:
     # Train 1p with Ascend
     # (1) Perform a or b.
     #       a. Set "enable_modelarts=True" on base_config.yaml file.
-    #          Set "run_platform='Ascend'" on default_config.yaml file.
+    #          Set "run_platform=Ascend" on default_config.yaml file.
     #          Set "mindrecord_path='/cache/data/face_detect_dataset/mindrecord_train/data.mindrecord'" on default_config.yaml file.
     #          (optional)Set "checkpoint_url='s3://dir_to_your_pretrain/'" on default_config.yaml file.
     #          (optional)Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
     #          Set other parameters on default_config.yaml file you need.
     #       b. Add "enable_modelarts=True" on the website UI interface.
-    #          Add "run_platform='Ascend'" on the website UI interface.
+    #          Add "run_platform=Ascend" on the website UI interface.
     #          Add "mindrecord_path='/cache/data/face_detect_dataset/mindrecord_train/data.mindrecord'" on the website UI interface.
     #          (optional)Add "checkpoint_url='s3://dir_to_your_pretrain/'" on the website UI interface.
     #          (optional)Add "pretrained='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
@@ -259,13 +259,13 @@ The entire code structure is as following:
     # Eval 1p with Ascend
     # (1) Perform a or b.
     #       a. Set "enable_modelarts=True" on base_config.yaml file.
-    #          Set "run_platform='Ascend'" on default_config.yaml file.
+    #          Set "run_platform=Ascend" on default_config.yaml file.
     #          Set "mindrecord_path='/cache/data/face_detect_dataset/mindrecord_train/data.mindrecord'" on default_config.yaml file.
     #          Set "checkpoint_url='s3://dir_to_your_pretrain/'" on default_config.yaml file.
     #          Set "pretrained='/cache/checkpoint_path/model.ckpt'" on default_config.yaml file.
     #          Set other parameters on default_config.yaml file you need.
     #       b. Add "enable_modelarts=True" on the website UI interface.
-    #          Add "run_platform='Ascend'" on the website UI interface.
+    #          Add "run_platform=Ascend" on the website UI interface.
     #          Add "mindrecord_path='/cache/data/face_detect_dataset/mindrecord_test/data.mindrecord'" on the website UI interface.
     #          Add "checkpoint_url='s3://dir_to_your_pretrain/'" on the website UI interface.
     #          Add "pretrained='/cache/checkpoint_path/model.ckpt'" on the website UI interface.

research/cv/FaceRecognitionForTracking/README.md

@@ -298,7 +298,7 @@ epoch[179], iter[14930], loss:1.694281, 13417.38 imgs/sec, lr=0.0250000003725290
     #          Set "batch_size=1" on reid_1p_config.yaml file.
     #          Set "file_format='AIR'" on reid_1p_config.yaml file.
     #          Set "file_name='FaceRecognitionForTracking'" on reid_1p_config.yaml file.
-    #          Set "device_target='Ascend'" on reid_1p_config.yaml file.
+    #          Set "device_target=Ascend" on reid_1p_config.yaml file.
     #          Set "checkpoint_url='s3://dir_to_trained_ckpt/'" on reid_1p_config.yaml file.
     #          Set "pretrained='/cache/checkpoint_path/model.ckpt'" on reid_1p_config.yaml file.
     #          Set other parameters on reid_1p_config.yaml file you need.
@@ -306,7 +306,7 @@ epoch[179], iter[14930], loss:1.694281, 13417.38 imgs/sec, lr=0.0250000003725290
     #          Add "batch_size=1" on the website UI interface.
     #          Add "file_format='AIR'" on the website UI interface.
     #          Add "file_name='FaceRecognitionForTracking'" on the website UI interface.
-    #          Add "device_target='Ascend'" on the website UI interface.
+    #          Add "device_target=Ascend" on the website UI interface.
     #          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
     #          Add "pretrained='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
     #          Add other parameters on the website UI interface.

research/cv/fishnet99/README_CN.md

@@ -83,13 +83,13 @@
 
   ```bash
   # 运行训练示例
-  python train.py --device_id=0 --device_type='Ascend' > train.log 2>&1 &
+  python train.py --device_id=0 --device_type=Ascend > train.log 2>&1 &
 
   # 运行分布式训练示例
   bash ./scripts/run_train_ascend.sh [RANK_TABLE_FILE]
 
   # 运行评估示例
-  python eval.py --checkpoint_path ./ckpt_0 --device_type='Ascend' > ./eval.log 2>&1 &
+  python eval.py --checkpoint_path ./ckpt_0 --device_type=Ascend > ./eval.log 2>&1 &
 
   # 运行推理示例
   bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DEVICE_ID]
@@ -107,13 +107,13 @@
 
   ```bash
   # 运行训练示例
-  python train.py --device_id=0 --device_type='GPU' > train_gpu.log 2>&1 &
+  python train.py --device_id=0 --device_type=GPU > train_gpu.log 2>&1 &
 
   # 运行分布式训练示例
   bash ./scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
 
   # 运行评估示例
-  python eval.py --checkpoint_path ./ckpt_0 --device_type='GPU' > ./eval_gpu.log 2>&1 &
+  python eval.py --checkpoint_path ./ckpt_0 --device_type=GPU > ./eval_gpu.log 2>&1 &
   ```
 
 # 脚本说明
@@ -176,7 +176,7 @@
 - Ascend处理器环境运行
 
   ```bash
-  python train.py --device_id=0 --device_type='Ascend' > train.log 2>&1 &
+  python train.py --device_id=0 --device_type=Ascend > train.log 2>&1 &
   ```
 
   上述python命令将在后台运行，可以通过生成的train.log文件查看结果。
@@ -196,7 +196,7 @@
   为了在GPU处理器环境运行，请将配置文件src/config.py中的device_target从Ascend改为GPU。
 
   ```bash
-  python train.py --device_id=0 --device_type='GPU' > train_gpu.log 2>&1 &
+  python train.py --device_id=0 --device_type=GPU > train_gpu.log 2>&1 &
   ```
 
   上述python命令将在后台运行，可以通过生成的train_gpu.log文件查看结果。
@@ -232,7 +232,7 @@
   “./ckpt_0”是保存了训练好的.ckpt模型文件的目录。
 
   ```bash
-  python eval.py --checkpoint_path ./ckpt_0 --device_type='Ascend' > ./eval.log 2>&1 &
+  python eval.py --checkpoint_path ./ckpt_0 --device_type=Ascend > ./eval.log 2>&1 &
   ```
 
 - 在GPU处理器环境运行时评估ImageNet-1k数据集
@@ -240,7 +240,7 @@
   “./ckpt_0”是保存了训练好的.ckpt模型文件的目录。
 
   ```bash
-  python eval.py --checkpoint_path ./ckpt_0 --device_type='GPU' > ./eval_gpu.log 2>&1 &
+  python eval.py --checkpoint_path ./ckpt_0 --device_type=GPU > ./eval_gpu.log 2>&1 &
   OR
   bash ./scripts/run_eval.sh
   ```

research/cv/squeezenet/README.md

 # Contents
 
+- [Contents](#contents)
 - [SqueezeNet Description](#squeezenet-description)
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
@@ -11,15 +12,31 @@
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
+        - [Usage](#usage)
+            - [Running on Ascend](#running-on-ascend)
+            - [Running on GPU](#running-on-gpu)
+        - [Result](#result)
     - [Evaluation Process](#evaluation-process)
+        - [Usage](#usage-1)
+            - [Running on Ascend](#running-on-ascend-1)
+            - [Running on GPU](#running-on-gpu-1)
+        - [Result](#result-1)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
+            - [SqueezeNet on CIFAR-10](#squeezenet-on-cifar-10)
+            - [SqueezeNet on ImageNet](#squeezenet-on-imagenet)
+            - [SqueezeNet_Residual on CIFAR-10](#squeezenet_residual-on-cifar-10)
+            - [SqueezeNet_Residual on ImageNet](#squeezenet_residual-on-imagenet)
         - [Inference Performance](#inference-performance)
+            - [SqueezeNet on CIFAR-10](#squeezenet-on-cifar-10-1)
+            - [SqueezeNet on ImageNet](#squeezenet-on-imagenet-1)
+            - [SqueezeNet_Residual on CIFAR-10](#squeezenet_residual-on-cifar-10-1)
+            - [SqueezeNet_Residual on ImageNet](#squeezenet_residual-on-imagenet-1)
     - [How to use](#how-to-use)
         - [Inference](#inference)
         - [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
-       - [Transfer Learning](#transfer-learning)
+        - [Transfer Learning](#transfer-learning)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 

research/cv/squeezenet1_1/README.md

 # Contents
 
+- [Contents](#contents)
 - [SqueezeNet1_1 Description](#squeezenet1_1-description)
 - [Model Architecture](#model-architecture)
 - [Dataset](#dataset)
@@ -9,19 +10,27 @@
     - [Script and Sample Code](#script-and-sample-code)
     - [Script Parameters](#script-parameters)
     - [Training Process](#training-process)
+        - [Usage](#usage)
+            - [Running on Ascend](#running-on-ascend)
+        - [Result](#result)
     - [Evaluation Process](#evaluation-process)
-    - [Inference Process](#inference-process)
-            - [Export MindIR](#export-mindir)
-            - [Infer on Ascend310](#infer-on-ascend310)
-            - [result](#result)
+        - [Usage](#usage-1)
+            - [Running on Ascend](#running-on-ascend-1)
+        - [Result](#result-1)
+    - [Inference process](#inference-process)
+        - [Export MindIR](#export-mindir)
+        - [Infer on Ascend310](#infer-on-ascend310)
+        - [result](#result-2)
 - [Model Description](#model-description)
     - [Performance](#performance)
         - [Evaluation Performance](#evaluation-performance)
+            - [SqueezeNet on ImageNet](#squeezenet-on-imagenet)
         - [Inference Performance](#inference-performance)
+            - [SqueezeNet on ImageNet](#squeezenet-on-imagenet-1)
         - [310 Inference Performance](#310-inference-performance)
+            - [SqueezeNet on ImageNet](#squeezenet-on-imagenet-2)
     - [How to use](#how-to-use)
         - [Inference](#inference)
-        - [Continue Training on the Pretrained Model](#continue-training-on-the-pretrained-model)
 - [Description of Random Situation](#description-of-random-situation)
 - [ModelZoo Homepage](#modelzoo-homepage)
 

research/nlp/dscnn/README.md

@@ -102,7 +102,7 @@ First set the config for data, train, eval in src/config.py
 
   ```python
   # run training example
-  python train.py --amp_level 'O3' --device_target='GPU' --train_feat_dir your train dataset dir
+  python train.py --amp_level 'O3' --device_target=GPU --train_feat_dir your train dataset dir
 
   # run evaluation example
   # if you want to eval a specific model, you should specify model_dir to the ckpt path:

research/recommend/Fat-DeepFFM/README.md

@@ -75,7 +75,7 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
     --ckpt_path='./checkpoint/Fat-DeepFFM' \
     --eval_file_name='./auc.log' \
     --loss_file_name='./loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > output.log 2>&1 &
 
   # run distributed training example
@@ -106,7 +106,7 @@ Fat - DeepFFM consists of three parts. The FFM component is a factorization mach
     --ckpt_path='./checkpoint/Fat-DeepFFM' \
     --eval_file_name='./auc.log' \
     --loss_file_name='./loss.log' \
-    --device_target='GPU' \
+    --device_target=GPU \
     --do_eval=True > output.log 2>&1 &
 
   # run distributed training example
@@ -196,7 +196,7 @@ Parameters for both training and evaluation can be set in config.py
     --ckpt_path='./checkpoint' \
     --eval_file_name='./auc.log' \
     --loss_file_name='./loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > output.log 2>&1 &
   ```
 
@@ -237,7 +237,7 @@ Parameters for both training and evaluation can be set in config.py
     --dataset_path=' /dataset_path' \
     --checkpoint_path='/ckpt_path' \
     --device_id=0 \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
    bash scripts/run_eval.sh  0 Ascend  /dataset_path  /ckpt_path
   ```

research/recommend/autodis/README.md

@@ -62,14 +62,14 @@ After installing MindSpore via the official website, you can start training and
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
 
   # run evaluation example
   python eval.py \
     --test_data_dir='dataset/test' \
     --checkpoint_path='./checkpoint/autodis.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /test_data_dir /checkpoint_path/autodis.ckpt
   ```
@@ -191,7 +191,7 @@ Parameters for both training and evaluation can be set in `default_config.yaml`
     --ckpt_path='./checkpoint' \
     --eval_file_name='auc.log' \
     --loss_file_name='loss.log' \
-    --device_target='Ascend' \
+    --device_target=Ascend \
     --do_eval=True > ms_log/output.log 2>&1 &
   ```
 
@@ -219,7 +219,7 @@ Parameters for both training and evaluation can be set in `default_config.yaml`
   python eval.py \
     --test_data_dir='dataset/test' \
     --checkpoint_path='./checkpoint/autodis.ckpt' \
-    --device_target='Ascend' > ms_log/eval_output.log 2>&1 &
+    --device_target=Ascend > ms_log/eval_output.log 2>&1 &
   OR
   bash scripts/run_eval.sh 0 Ascend /test_data_dir /checkpoint_path/autodis.ckpt
   ```