提交 7c171bbc 编写于 作者: J Jolin Zhang46

updata quick start cn and add quick start en

上级 efef0964
...@@ -36,7 +36,7 @@ In addition, you can use the preset model to perform migration learning to imple ...@@ -36,7 +36,7 @@ In addition, you can use the preset model to perform migration learning to imple
## Converting a Model ## Converting a Model
After you retrain a model provided by MindSpore, export the model in the [.mindir format](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#mindir). Use the MindSpore Lite [model conversion tool](https://www.mindspore.cn/lite/tutorial/zh-CN/master/use/converter.html) to convert the .mindir model to a .ms model. After you retrain a model provided by MindSpore, export the model in the [.mindir format](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html#mindir). Use the MindSpore Lite [model conversion tool](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/use/converter_tool.md) to convert the .mindir model to a .ms model.
Take the MindSpore MobileNetV2 model as an example. Execute the following script to convert a model into a MindSpore Lite model for on-device inference. Take the MindSpore MobileNetV2 model as an example. Execute the following script to convert a model into a MindSpore Lite model for on-device inference.
```bash ```bash
...@@ -86,7 +86,7 @@ The following section describes how to build and execute an on-device image clas ...@@ -86,7 +86,7 @@ The following section describes how to build and execute an on-device image clas
## Detailed Description of the Sample Program ## Detailed Description of the Sample Program
This image classification sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://www.mindspore.cn/tutorial/zh-CN/master/use/lite_runtime.html). This image classification sample program on the Android device includes a Java layer and a JNI layer. At the Java layer, the Android Camera 2 API is used to enable a camera to obtain image frames and process images. At the JNI layer, the model inference process is completed in [Runtime](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/use/runtime.md).
> This following describes the JNI layer implementation of the sample program. At the Java layer, the Android Camera 2 API is used to enable a device camera and process image frames. Readers are expected to have the basic Android development knowledge. > This following describes the JNI layer implementation of the sample program. At the Java layer, the Android Camera 2 API is used to enable a device camera and process image frames. Readers are expected to have the basic Android development knowledge.
...@@ -132,7 +132,7 @@ app ...@@ -132,7 +132,7 @@ app
### Configuring MindSpore Lite Dependencies ### Configuring MindSpore Lite Dependencies
When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://www.mindspore.cn/lite/docs/zh-CN/master/deploy.html) to generate the `libmindspore-lite.so` library file. When MindSpore C++ APIs are called at the Android JNI layer, related library files are required. You can use MindSpore Lite [source code compilation](https://gitee.com/mindspore/docs/blob/master/lite/tutorials/source_en/compile.md) to generate the `libmindspore-lite.so` library file.
In Android Studio, place the compiled `libmindspore-lite.so` library file (which can contain multiple compatible architectures) in the `app/libs/ARM64-V8a` (Arm64) or `app/libs/armeabi-v7a` (Arm32) directory of the application project. In the `build.gradle` file of the application, configure the compilation support of CMake, `arm64-v8a`, and `armeabi-v7a`.   In Android Studio, place the compiled `libmindspore-lite.so` library file (which can contain multiple compatible architectures) in the `app/libs/ARM64-V8a` (Arm64) or `app/libs/armeabi-v7a` (Arm32) directory of the application project. In the `build.gradle` file of the application, configure the compilation support of CMake, `arm64-v8a`, and `armeabi-v7a`.  
...@@ -311,7 +311,7 @@ The inference code process is as follows. For details about the complete code, s ...@@ -311,7 +311,7 @@ The inference code process is as follows. For details about the complete code, s
float scores[RET_CATEGORY_SUM]; float scores[RET_CATEGORY_SUM];
for (int i = 0; i < RET_CATEGORY_SUM; ++i) { for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
scores[i] = temp_scores[i]; scores[i] = temp_scores[i];
} }
// Converted to text information that needs to be displayed in the APP. // Converted to text information that needs to be displayed in the APP.
std::string retStr = ""; std::string retStr = "";
...@@ -325,13 +325,12 @@ The inference code process is as follows. For details about the complete code, s ...@@ -325,13 +325,12 @@ The inference code process is as follows. For details about the complete code, s
retStr += ";"; retStr += ";";
} }
} }
} else { else {
MS_PRINT("MindSpore run net failed!"); MS_PRINT("MindSpore run net failed!");
for (int i = 0; i < RET_CATEGORY_SUM; ++i) { for (int i = 0; i < RET_CATEGORY_SUM; ++i) {
retStr += " :0.0;"; retStr += " :0.0;";
} }
} }
return retStr; return retStr;
} }
``` ```
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