!526 fix on_device inference md

Merge pull request !526 from yankai10/merge

tutorials/source_en/advanced_use/on_device_inference.md

@@ -98,22 +98,21 @@ To perform on-device model inference using MindSpore, perform the following step
    param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
    load_param_into_net(net, param_dict)
    ```
-2. Call the `export` API to export the `.ms` model file on the device.
+2. Call the `export` API to export the `.pb` model file on the device.
    ```python
-   export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
+   export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
    ```
-
-Take the LeNet network as an example. The generated on-device model file is `lenet.ms`. The complete sample code `lenet.py` is as follows:
-```python
-import os
-import numpy as np
-import mindspore.nn as nn
-import mindspore.ops.operations as P
-import mindspore.context as context
-from mindspore.common.tensor import Tensor
-from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
-
-class LeNet(nn.Cell):
+    Take the LeNet network as an example. The generated on-device model file is `lenet.pb`. The complete sample code `lenet.py` is as follows:
+    ```python
+    import os
+    import numpy as np
+    import mindspore.nn as nn
+    import mindspore.ops.operations as P
+    import mindspore.context as context
+    from mindspore.common.tensor import Tensor
+    from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+    
+    class LeNet(nn.Cell):
         def __init__(self):
             super(LeNet, self).__init__()
             self.relu = P.ReLU()
@@ -141,7 +140,7 @@ class LeNet(nn.Cell):
             output = self.fc3(output)
             return output
             
-if __name__ == '__main__':
+    if __name__ == '__main__':
         context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
         seed = 0
         np.random.seed(seed)
@@ -155,11 +154,20 @@ if __name__ == '__main__':
         if is_ckpt_exist:
             param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
             load_param_into_net(net, param_dict)
-        export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
+            export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
             print("export model success.")
         else:
             print("checkpoint file does not exist.")
-```
+    ```
+3. Calling MindSpore convert tool named `converter_lite`, convert model file (`.pb`) to on_device inference model file (`.ms`).
+    ```
+    ./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
+    ```
+    Result:
+    ```
+    INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
+    ```
+    This means that the model has been successfully converted to the mindspore on_device inference model.
 
 ### Implementing On-Device Inference
 

tutorials/source_zh_cn/advanced_use/on_device_inference.md

@@ -97,22 +97,22 @@ MindSpore进行端侧模型推理的步骤如下。
    param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
    load_param_into_net(net, param_dict)
    ```
-2. 调用`export`接口，导出端侧模型文件(`.ms`)。
+2. 调用`export`接口，导出模型文件(`.pb`)。
    ```python
-   export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
+   export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
    ```
 
-以LeNet网络为例，生成的端侧模型文件为`lenet.ms`，完整示例代码`lenet.py`如下。
-```python
-import os
-import numpy as np
-import mindspore.nn as nn
-import mindspore.ops.operations as P
-import mindspore.context as context
-from mindspore.common.tensor import Tensor
-from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
-
-class LeNet(nn.Cell):
+    以LeNet网络为例，生成的端侧模型文件为`lenet.pb`，完整示例代码`lenet.py`如下。
+    ```python
+    import os
+    import numpy as np
+    import mindspore.nn as nn
+    import mindspore.ops.operations as P
+    import mindspore.context as context
+    from mindspore.common.tensor import Tensor
+    from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+    
+    class LeNet(nn.Cell):
         def __init__(self):
             super(LeNet, self).__init__()
             self.relu = P.ReLU()
@@ -140,7 +140,7 @@ class LeNet(nn.Cell):
             output = self.fc3(output)
             return output
             
-if __name__ == '__main__':
+    if __name__ == '__main__':
         context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
         seed = 0
         np.random.seed(seed)
@@ -154,11 +154,20 @@ if __name__ == '__main__':
         if is_ckpt_exist:
             param_dict = load_checkpoint(ckpt_file_name=ckpt_file_path)
             load_param_into_net(net, param_dict)
-        export(net, input_data, file_name="./lenet.ms", file_format='BINARY')
+            export(net, input_data, file_name="./lenet.pb", file_format='BINARY')
             print("export model success.")
         else:
             print("checkpoint file does not exist.")
-```
+    ```
+3. 调用MindSpore端侧转化工具`converter_lite`工具，将模型文件(`.pb`)转换为端侧模型文件(`.ms`)。
+    ```
+    ./converter_lite --fmk=MS --modelFile=./lenet.pb --outputFile=lenet
+    ```
+   结果显示为：
+   ```
+   INFO [converter/converter.cc:146] Runconverter] CONVERTER RESULT: SUCCESS!
+   ```
+   这表示已经成功将模型转化为MindSpore端侧模型。
    
 ### 在端侧实现推理