From 1aeb26321ddbab3fe92e69d9956b6feed6ea7998 Mon Sep 17 00:00:00 2001
From: lilei <lilei120@huawei.com>
Date: Mon, 28 Jun 2021 16:38:50 +0800
Subject: [PATCH] modify model_zoo net export

---
 model_zoo/official/cv/resnext/README.md       | 28 +++++++++-
 model_zoo/official/cv/resnext/README_CN.md    | 26 +++++++++-
 model_zoo/official/cv/resnext/export.py       | 13 ++++-
 model_zoo/official/cv/squeezenet/README.md    | 30 ++++++++++-
 model_zoo/official/cv/squeezenet/export.py    | 14 ++++-
 model_zoo/official/cv/ssd/README.md           | 26 ++++++++++
 model_zoo/official/cv/ssd/README_CN.md        | 24 +++++++++
 model_zoo/official/cv/ssd/export.py           | 13 ++++-
 .../official/cv/ssd/scripts/run_infer_310.sh  | 11 ++--
 model_zoo/official/cv/unet/README.md          | 26 +++++++++-
 model_zoo/official/cv/unet/README_CN.md       | 24 +++++++++
 model_zoo/official/cv/unet/export.py          | 13 ++++-
 model_zoo/official/cv/unet3d/README.md        |  8 +--
 model_zoo/official/nlp/mass/README.md         | 25 +++++++++
 model_zoo/official/nlp/mass/README_CN.md      | 23 ++++++++
 model_zoo/official/nlp/mass/export.py         | 16 ++++--
 model_zoo/official/nlp/textcnn/README.md      | 52 +++++++++++++++----
 model_zoo/official/nlp/textcnn/export.py      | 13 ++++-
 model_zoo/research/cv/ssd_ghostnet/README.md  | 31 +++++++++--
 model_zoo/research/cv/ssd_ghostnet/export.py  | 13 ++++-
 20 files changed, 389 insertions(+), 40 deletions(-)

diff --git a/model_zoo/official/cv/resnext/README.md b/model_zoo/official/cv/resnext/README.md
index c338ae8893d..86a7e50efd5 100644
--- a/model_zoo/official/cv/resnext/README.md
+++ b/model_zoo/official/cv/resnext/README.md
@@ -244,13 +244,39 @@ acc=93.88%(TOP5)
 
 ## [Model Export](#contents)
 
+Export MindIR on local
+
 ```shell
 python export.py --device_target [PLATFORM] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]
 ```
 
-The `ckpt_file` parameter is required.
+The `checkpoint_file_path` parameter is required.
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"].
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./resnext50'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./resnext50'" 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/resnext50" 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.
+```
+
 ## [Inference Process](#contents)
 
 ### Usage
diff --git a/model_zoo/official/cv/resnext/README_CN.md b/model_zoo/official/cv/resnext/README_CN.md
index 96a50f9fa8c..6fb4b7ea614 100644
--- a/model_zoo/official/cv/resnext/README_CN.md
+++ b/model_zoo/official/cv/resnext/README_CN.md
@@ -257,13 +257,37 @@ acc=94.72%(TOP5)
 
 ## 模型导出
 
+本地导出mindir
+
 ```shell
 python export.py --device_target [PLATFORM] --checkpoint_file_path [CKPT_PATH] --file_format [EXPORT_FORMAT]
 ```
 
-`ckpt_file` 参数为必填项。
+`checkpoint_file_path` 参数为必填项。
 `EXPORT_FORMAT` 可选 ["AIR", "MINDIR"]。
 
+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。
+#       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。
+#          设置 "file_name='./resnext50'"参数在yaml文件。
+#          设置 "file_format='AIR'" 参数在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。
+#          设置 "file_name='./resnext50'"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/resnext50"。
+# (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" 。
+# (6) 开始导出mindir。
+```
+
 ## [推理过程](#contents)
 
 ### 用法
diff --git a/model_zoo/official/cv/resnext/export.py b/model_zoo/official/cv/resnext/export.py
index cecdb488a0f..e26f4842ffb 100644
--- a/model_zoo/official/cv/resnext/export.py
+++ b/model_zoo/official/cv/resnext/export.py
@@ -15,10 +15,12 @@
 """
 resnext export mindir.
 """
+import os
 import numpy as np
 from mindspore.common import dtype as mstype
 from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
 from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 from src.image_classification import get_network
 from src.utils.auto_mixed_precision import auto_mixed_precision
 
@@ -27,7 +29,13 @@ context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=config.device_id)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     network = get_network(network=config.network, num_classes=config.num_classes, platform=config.device_target)
 
     param_dict = load_checkpoint(config.checkpoint_file_path)
@@ -40,3 +48,6 @@ if __name__ == '__main__':
     input_shp = [config.batch_size, 3, config.height, config.width]
     input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp).astype(np.float32))
     export(network, input_array, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/official/cv/squeezenet/README.md b/model_zoo/official/cv/squeezenet/README.md
index 018a379cb0c..830365badb8 100644
--- a/model_zoo/official/cv/squeezenet/README.md
+++ b/model_zoo/official/cv/squeezenet/README.md
@@ -384,16 +384,42 @@ result: {'top_1_accuracy': 0.6094950384122919, 'top_5_accuracy': 0.8263244238156
 
 ### Export MindIR
 
+Export MindIR on local
+
 ```shell
-python export.py --checkpoint_file_path [CKPT_PATH] --batch_size [BATCH_SIZE] --net_name [NET] --dataset [DATASET] --file_format [EXPORT_FORMAT]
+python export.py --checkpoint_file_path [CKPT_PATH] --batch_size [BATCH_SIZE] --net_name [NET] --dataset [DATASET] --file_format [EXPORT_FORMAT] --config_path [CONFIG_PATH]
 ```
 
-The ckpt_file parameter is required,
+The checkpoint_file_path parameter is required,
 `BATCH_SIZE` can only be set to 1
 `NET` should be in ["squeezenet", "squeezenet_residual"]
 `DATASET` should be in ["cifar10", "imagenet"]
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./squeezenet'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./squeezenet'" 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/squeezenet" 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.
+```
+
 ### Infer on Ascend310
 
 Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
diff --git a/model_zoo/official/cv/squeezenet/export.py b/model_zoo/official/cv/squeezenet/export.py
index fd9a36c5e16..06b22bcf7ad 100755
--- a/model_zoo/official/cv/squeezenet/export.py
+++ b/model_zoo/official/cv/squeezenet/export.py
@@ -16,9 +16,10 @@
 ##############export checkpoint file into air , mindir and onnx models#################
 python export.py --net squeezenet --dataset cifar10 --checkpoint_path squeezenet_cifar10-120_1562.ckpt
 """
-
+import os
 import numpy as np
 from model_utils.config import config
+from model_utils.moxing_adapter import moxing_wrapper
 from mindspore import context, Tensor, load_checkpoint, load_param_into_net, export
 
 if config.net_name == "squeezenet":
@@ -34,7 +35,13 @@ context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=config.device_id)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     net = squeezenet(num_classes=num_classes)
 
     param_dict = load_checkpoint(config.checkpoint_file_path)
@@ -42,3 +49,6 @@ if __name__ == '__main__':
 
     input_data = Tensor(np.zeros([config.batch_size, 3, config.height, config.width], np.float32))
     export(net, input_data, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/official/cv/ssd/README.md b/model_zoo/official/cv/ssd/README.md
index 0d7eeee3b93..f6b934fe595 100644
--- a/model_zoo/official/cv/ssd/README.md
+++ b/model_zoo/official/cv/ssd/README.md
@@ -470,6 +470,8 @@ mAP: 0.2244936111705981
 
 ### [Export MindIR](#contents)
 
+Export MindIR on local
+
 ```shell
 python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_PATH]
 ```
@@ -477,6 +479,30 @@ python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --fi
 The ckpt_file parameter is required,
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./ssd'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./ssd'" 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/ssd" 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.
+```
+
 ### Infer on Ascend310
 
 Before performing inference, the mindir file must bu exported by `export.py` script. We only provide an example of inference using MINDIR model.
diff --git a/model_zoo/official/cv/ssd/README_CN.md b/model_zoo/official/cv/ssd/README_CN.md
index 7e0d05b45ef..a2578e60b72 100644
--- a/model_zoo/official/cv/ssd/README_CN.md
+++ b/model_zoo/official/cv/ssd/README_CN.md
@@ -395,6 +395,8 @@ mAP: 0.2244936111705981
 
 ### [导出MindIR](#contents)
 
+本地导出mindir
+
 ```shell
 python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_PATH]
 ```
@@ -402,6 +404,28 @@ python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --fi
 参数ckpt_file为必填项，
 `EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
 
+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。
+#       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。
+#          设置 "file_name='./ssd'"参数在yaml文件。
+#          设置 "file_format='AIR'" 参数在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。
+#          设置 "file_name='./ssd'"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/ssd"。
+# (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" 。
+# (6) 开始导出mindir。
+```
+
 ### 在Ascend310执行推理
 
 在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用minir模型执行推理的示例。
diff --git a/model_zoo/official/cv/ssd/export.py b/model_zoo/official/cv/ssd/export.py
index 656cc2afc24..b3434e6b6b3 100644
--- a/model_zoo/official/cv/ssd/export.py
+++ b/model_zoo/official/cv/ssd/export.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
+import os
 import numpy as np
 
 import mindspore
@@ -20,13 +21,20 @@ from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
 from src.ssd import SSD300, SsdInferWithDecoder, ssd_mobilenet_v2, ssd_mobilenet_v1_fpn, ssd_resnet50_fpn, ssd_vgg16
 from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 from src.box_utils import default_boxes
 
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=config.device_id)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     if hasattr(config, 'num_ssd_boxes') and config.num_ssd_boxes == -1:
         num = 0
         h, w = config.img_shape
@@ -55,3 +63,6 @@ if __name__ == '__main__':
     input_shp = [config.batch_size, 3] + config.img_shape
     input_array = Tensor(np.random.uniform(-1.0, 1.0, size=input_shp), mindspore.float32)
     export(net, input_array, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/official/cv/ssd/scripts/run_infer_310.sh b/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
index 0daa12a2a26..55bf7681643 100644
--- a/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
+++ b/model_zoo/official/cv/ssd/scripts/run_infer_310.sh
@@ -14,8 +14,8 @@
 # limitations under the License.
 # ============================================================================
 
-if [[ $# -lt 3 || $# -gt 4 ]]; then
-    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [DEVICE_ID]
+if [[ $# -lt 4 || $# -gt 5 ]]; then
+    echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [DVPP] [CONFIG_PATH] [DEVICE_ID]
     DVPP is mandatory, and must choose from [DVPP|CPU], it's case-insensitive
     DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
 exit 1
@@ -31,10 +31,11 @@ get_real_path(){
 model=$(get_real_path $1)
 data_path=$(get_real_path $2)
 DVPP=${3^^}
+cfg_path=$4
 
 device_id=0
-if [ $# == 4 ]; then
-    device_id=$4
+if [ $# == 5 ]; then
+    device_id=$5
 fi
 
 echo "mindir name: "$model
@@ -85,7 +86,7 @@ function infer()
 
 function cal_acc()
 {
-    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --drop=True &> acc.log &
+    python3.7 ../postprocess.py --result_path=./result_Files --img_path=$data_path --config_path=${cfg_path} --drop=True &> acc.log &
 }
 
 compile_app
diff --git a/model_zoo/official/cv/unet/README.md b/model_zoo/official/cv/unet/README.md
index 10280b7a944..0d2e727f92d 100644
--- a/model_zoo/official/cv/unet/README.md
+++ b/model_zoo/official/cv/unet/README.md
@@ -472,7 +472,7 @@ the steps below, this is a simple example:
 
 #### Running on Ascend 310
 
-Export MindIR
+Export MindIR on local
 
 Before exporting, you need to modify the parameter in the configuration — checkpoint_file_path and batch_ Size . checkpoint_ file_ Path is the CKPT file path, batch_ Size is set to 1.
 
@@ -483,6 +483,30 @@ python export.py --config_path=[CONFIG_PATH]
 The checkpoint_file_path parameter is required,
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./unet'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./unet'" 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/unet" 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.
+```
+
 Before performing inference, the MINDIR file must be exported by export script on the 910 environment.
 
 ```shell
diff --git a/model_zoo/official/cv/unet/README_CN.md b/model_zoo/official/cv/unet/README_CN.md
index 32c507be44f..5a3b01f5ee8 100644
--- a/model_zoo/official/cv/unet/README_CN.md
+++ b/model_zoo/official/cv/unet/README_CN.md
@@ -473,10 +473,34 @@ python eval.py --data_path=/path/to/data/ --checkpoint_file_path=/path/to/checkp
 
 在执行导出前需要修改配置文件中的checkpoint_file_path和batch_size参数。checkpoint_file_path为ckpt文件路径，batch_size设置为1。
 
+本地导出mindir
+
 ```shell
 python export.py --config_path=[CONFIG_PATH]
 ```
 
+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。
+#       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。
+#          设置 "file_name='./unet'"参数在yaml文件。
+#          设置 "file_format='AIR'" 参数在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。
+#          设置 "file_name='./unet'"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/unet"。
+# (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" 。
+# (6) 开始导出mindir。
+```
+
 在执行推理前，MINDIR文件必须在910上通过export.py文件导出。
 
 ```shell
diff --git a/model_zoo/official/cv/unet/export.py b/model_zoo/official/cv/unet/export.py
index aa6908de9d0..4797de2663e 100644
--- a/model_zoo/official/cv/unet/export.py
+++ b/model_zoo/official/cv/unet/export.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 # ============================================================================
 
+import os
 import numpy as np
 
 from mindspore import Tensor, export, load_checkpoint, load_param_into_net, context
@@ -22,13 +23,20 @@ from src.unet_nested import NestedUNet, UNet
 from src.utils import UnetEval
 from src.model_utils.config import config
 from src.model_utils.device_adapter import get_device_id
+from src.model_utils.moxing_adapter import moxing_wrapper
 
 
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=get_device_id())
 
-if __name__ == "__main__":
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     if config.model_name == 'unet_medical':
         net = UNetMedical(n_channels=config.num_channels, n_classes=config.num_classes)
     elif config.model_name == 'unet_nested':
@@ -46,3 +54,6 @@ if __name__ == "__main__":
     input_data = Tensor(np.ones([config.batch_size, config.num_channels, config.height, \
         config.width]).astype(np.float32))
     export(net, input_data, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/official/cv/unet3d/README.md b/model_zoo/official/cv/unet3d/README.md
index 8470791c8ca..76b52f9d012 100644
--- a/model_zoo/official/cv/unet3d/README.md
+++ b/model_zoo/official/cv/unet3d/README.md
@@ -144,10 +144,10 @@ If you want to run in modelarts, please check the official documentation of [mod
 #          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) Download nibabel and set pip-requirements.txt to code directory
-# (5) Set the code directory to "/path/unet3d" on the website UI interface.
-# (6) Set the startup file to "eval.py" on the website UI interface.
-# (7) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
-# (8) Create your job.
+# (4) Set the code directory to "/path/unet3d" 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.
+# (7) Create your job.
 ```
 
 ## [Script Description](#contents)
diff --git a/model_zoo/official/nlp/mass/README.md b/model_zoo/official/nlp/mass/README.md
index bdbc56d90e0..84c4edf1a14 100644
--- a/model_zoo/official/nlp/mass/README.md
+++ b/model_zoo/official/nlp/mass/README.md
@@ -619,10 +619,35 @@ sh run_gpu.sh -t i -n 1 -i 1 -o {outputfile}
 
 ### [Export MindIR](#contents)
 
+Export MindIR on local
+
 ```shell
 python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
 ```
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./mass'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./mass'" 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 code directory to "/path/mass" on the website UI interface.
+# (3) Set the startup file to "export.py" on the website UI interface.
+# (4) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+# (5) Create your job.
+```
+
 The ckpt_file parameter is required,
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
diff --git a/model_zoo/official/nlp/mass/README_CN.md b/model_zoo/official/nlp/mass/README_CN.md
index 822109a5cc7..cd083401fdc 100644
--- a/model_zoo/official/nlp/mass/README_CN.md
+++ b/model_zoo/official/nlp/mass/README_CN.md
@@ -623,6 +623,8 @@ sh run_gpu.sh -t i -n 1 -i 1 -o {outputfile}
 
 ### [导出模型](#contents)
 
+本地导出mindir
+
 ```shell
 python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
 ```
@@ -630,6 +632,27 @@ python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --fi
 参数checkpoint_file_path为必填项，
 `EXPORT_FORMAT` 必须在 ["AIR", "MINDIR"]中选择。
 
+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。
+#       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。
+#          设置 "file_name='./mass'"参数在yaml文件。
+#          设置 "file_format='AIR'" 参数在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。
+#          设置 "file_name='./mass'"参数在modearts的界面上。
+#          设置 "file_format='AIR'" 参数在modearts的界面上。
+# (3) 在modelarts的界面上设置代码的路径 "/path/mass"。
+# (4) 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" 。
+# (5) 开始导出mindir。
+```
+
 ### 在Ascend310执行推理
 
 在执行推理前，mindir文件必须通过`export.py`脚本导出。以下展示了使用minir模型执行推理的示例。
diff --git a/model_zoo/official/nlp/mass/export.py b/model_zoo/official/nlp/mass/export.py
index ccc172c9b99..0e921c8424a 100644
--- a/model_zoo/official/nlp/mass/export.py
+++ b/model_zoo/official/nlp/mass/export.py
@@ -14,15 +14,16 @@
 # ============================================================================
 """export checkpoint file into air models"""
 
+import os
 import numpy as np
 
 from mindspore import Tensor, context
 from mindspore.common import dtype as mstype
 from mindspore.train.serialization import export
 
-from src.utils import Dictionary
 from src.utils.load_weights import load_infer_weights
 from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 from src.transformer.transformer_for_infer import TransformerInferModel
 
 
@@ -34,10 +35,14 @@ context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=config.device_id)
 
-if __name__ == '__main__':
-    vocab = Dictionary.load_from_persisted_dict(config.vocab_file)
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     get_config()
-    dec_len = config.max_decode_length
 
     tfm_model = TransformerInferModel(config=config, use_one_hot_embeddings=False)
     tfm_model.init_parameters_data()
@@ -72,3 +77,6 @@ if __name__ == '__main__':
     source_mask = Tensor(np.ones((1, config.seq_length)).astype(np.int32))
 
     export(tfm_model, source_ids, source_mask, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/official/nlp/textcnn/README.md b/model_zoo/official/nlp/textcnn/README.md
index 180f91f888f..04f74b884ae 100644
--- a/model_zoo/official/nlp/textcnn/README.md
+++ b/model_zoo/official/nlp/textcnn/README.md
@@ -78,10 +78,11 @@ If you want to run in modelarts, please check the official documentation of [mod
 #          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 code directory to "/path/textcnn" on the website UI interface.
-# (3) Set the startup file to "train.py" on the website UI interface.
-# (4) Set the "Dataset path" and "Output file path" and "Job log path" to your path on the website UI interface.
-# (5) Create your job.
+# (2) Set the config_path="/path/yaml" on the website UI interface.
+# (3) Set the code directory to "/path/textcnn" 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.
@@ -92,10 +93,11 @@ If you want to run in modelarts, please check the official documentation of [mod
 #       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 code directory to "/path/textcnn" on the website UI interface.
-# (4) Set the startup file to "eval.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.
+# (3) Set the config_path="/path/yaml" on the website UI interface
+# (4) Set the code directory to "/path/textcnn" 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.
+# (7) Create your job.
 ```
 
 # [Script Description](#contents)
@@ -184,7 +186,7 @@ For more configuration details, please refer the script `*.yaml`.
   Before running the command below, please check the checkpoint path used for evaluation. Please set the checkpoint path to be the absolute full path, e.g., "username/textcnn/ckpt/train_textcnn.ckpt".
 
   ```python
-  # need set config_path in config.py file and set data_path, checkpoint_file_path in yaml file
+  # need set config_path and set data_path in yaml file, checkpoint_file_path in yaml file
   python eval.py > eval.log 2>&1 &
   OR
   sh scripts/run_eval.sh checkpoint_file_path dataset
@@ -199,13 +201,41 @@ For more configuration details, please refer the script `*.yaml`.
 
 ## [Export MindIR](#contents)
 
+Export on local
+
 ```shell
-python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT] --config_path [CONFIG_FILE]
 ```
 
-The ckpt_file parameter is required,
+The checkpoint_file_path parameter is required,
 `EXPORT_FORMAT` should be in ["AIR", "MINDIR"]
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) Perform a or b.
+#       a. Set "enable_modelarts=True" on default_config.yaml file.
+#          Set "data_path='/cache/data/' " 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.
+#          Set "file_name='./textcnn'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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 "data_path='/cache/data/' " on default_config.yaml file.
+#          Add "checkpoint_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./textcnn'" 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/textcnn" 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.
+```
+
 ## [Inference Process](#contents)
 
 ### Usage
diff --git a/model_zoo/official/nlp/textcnn/export.py b/model_zoo/official/nlp/textcnn/export.py
index 0f762069ce5..8ab52cae88a 100644
--- a/model_zoo/official/nlp/textcnn/export.py
+++ b/model_zoo/official/nlp/textcnn/export.py
@@ -16,11 +16,12 @@
 ##############export checkpoint file into air, onnx, mindir models#################
 python export.py
 """
+import os
 import numpy as np
-
 from mindspore import Tensor, load_checkpoint, load_param_into_net, export, context
 
 from model_utils.config import config
+from model_utils.moxing_adapter import moxing_wrapper
 from src.textcnn import TextCNN
 from src.dataset import MovieReview, SST2, Subjectivity
 
@@ -28,8 +29,13 @@ context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=config.device_id)
 
-if __name__ == '__main__':
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
 
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     if config.dataset == 'MR':
         instance = MovieReview(root_dir=config.data_path, maxlen=config.word_len, split=0.9)
     elif config.dataset == 'SUBJ':
@@ -47,3 +53,6 @@ if __name__ == '__main__':
 
     input_arr = Tensor(np.ones([config.batch_size, config.word_len], np.int32))
     export(net, input_arr, file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()
diff --git a/model_zoo/research/cv/ssd_ghostnet/README.md b/model_zoo/research/cv/ssd_ghostnet/README.md
index 4d48667af62..3e6e1d173a0 100644
--- a/model_zoo/research/cv/ssd_ghostnet/README.md
+++ b/model_zoo/research/cv/ssd_ghostnet/README.md
@@ -70,7 +70,7 @@ Dataset used: [COCO2017](<http://images.cocodataset.org/>)
 
         ```
 
-        And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
+        And change the COCO_ROOT and other settings you need in `default_config.yaml`. The directory structure is as follows:
 
         ```python
         .
@@ -238,13 +238,38 @@ python eval.py --device_id 0 --dataset coco --checkpoint_path LOG4/ssd-500_458.c
 
 ### Export MindIR
 
+Export MindIR on local
+
 ```shell
-python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
+python export.py --checkpoint_file_path [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]
 ```
 
-The ckpt_file parameter is required,
+The checkpoint_file_path parameter is required,
 `FILE_FORMAT` should be in ["AIR", "MINDIR"]
 
+Export on ModelArts (If you want to run in modelarts, please check the official documentation of [modelarts](https://support.huaweicloud.com/modelarts/), and you can start as follows)
+
+```python
+# Export on ModelArts
+# (1) 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.
+#          Set "file_name='./ssd_ghostnet'" on default_config.yaml file.
+#          Set "file_format='AIR'" 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_file_path='/cache/checkpoint_path/model.ckpt'" on the website UI interface.
+#          Add "checkpoint_url='s3://dir_to_trained_ckpt/'" on the website UI interface.
+#          Add "file_name='./ssd_ghostnet'" 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 code directory to "/path/ssd_ghostnet" on the website UI interface.
+# (3) Set the startup file to "export.py" on the website UI interface.
+# (4) Set the "Output file path" and "Job log path" to your path on the website UI interface.
+# (5) Create your job.
+```
+
 ### Infer on Ascend310
 
 Before performing inference, the mindir file must be exported by `export.py` script. We only provide an example of inference using MINDIR model.
diff --git a/model_zoo/research/cv/ssd_ghostnet/export.py b/model_zoo/research/cv/ssd_ghostnet/export.py
index dd3ef0f3ad0..3c001ca950e 100644
--- a/model_zoo/research/cv/ssd_ghostnet/export.py
+++ b/model_zoo/research/cv/ssd_ghostnet/export.py
@@ -14,6 +14,7 @@
 # ============================================================================
 """export"""
 
+import os
 import numpy as np
 from mindspore import Tensor
 from mindspore import context
@@ -21,10 +22,17 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net,
 
 from src.ssd_ghostnet import SSD300, ssd_ghostnet
 from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target, device_id=config.device_id)
 
-if __name__ == "__main__":
+def modelarts_pre_process():
+    '''modelarts pre process function.'''
+    config.file_name = os.path.join(config.output_path, config.file_name)
+
+@moxing_wrapper(pre_process=modelarts_pre_process)
+def run_export():
+    """run export."""
     context.set_context(mode=context.GRAPH_MODE, save_graphs=False)
     # define net
     net = SSD300(ssd_ghostnet(), is_training=False)
@@ -35,3 +43,6 @@ if __name__ == "__main__":
     input_shape = config.img_shape
     inputs = np.ones([config.batch_size, 3, input_shape[0], input_shape[1]]).astype(np.float32)
     export(net, Tensor(inputs), file_name=config.file_name, file_format=config.file_format)
+
+if __name__ == '__main__':
+    run_export()