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Deployment by PaddleLite

1. Introduction

PaddlePaddle provides several inference libraries to deploy models on different devices as the following image. Please refer to the document for more detail information about PaddlePaddle.

inference_ecosystem

In this tutorial, we introduce the usage of Paddle Lite by the demo of deploying the human segmentation model on Android mobile phone.

2. Usage of the Andoird App

2.1 Requirements

  • Android Studio 3.4
  • Android mobile phone

2.2 Install the Android App

  • run git clone https://github.com/PaddlePaddle/PaddleSeg.git.
  • Open Android Studio, click "Open an existing Android Studio project" in the "Welcome to Android Studio" window, select the "PaddleSeg/deploy/lite/humanseg_android_demo/" as the path, click "Open" button to import the project. It will automatically download the inference model and the inference libray of Paddle Lite, and then build the project.
  • Use USB to connect the Android mobile phone to our computer.
  • Once the project is built, click the 'Run->Run APP', select the connected mobile phone in 'Select Deployment Target' window, click 'OK' button to install the Android App.
  • Please refer to Paddle-Lite-Demo for more detail information about Paddle Lite.

2.3 Run the Android APP

  • Open the App, it will load a portrait image and show the segmentation result and time-consuming.
  • The App also support loading other images from gallery or taking photoes, and then outputting the segmentation results.

2.4 Results

img

3. Development of the Android App

Based on the Android App, we can easily update the inference library and use other models.

3.1 Update the Inference Libray

The inference libray of Paddle Lite contains three files:

  • PaddlePredictor.jar
  • arm64-v8a/libpaddle_lite_jni.so
  • armeabi-v7a/libpaddle_lite_jni.so

There are two ways to obtain the inference libray of Paddle Lite as follows.

Firstly, download the release library from github. For instance, the Android App use the v2.8.0 inference library in default. Besides, the dirname of PaddlePredictor.jar is jave/, the dirname of arm64-v8a and armeabi-v7a is jave/libs.

Secondly, prepare the compiling environment and compile the inference libray of Paddle Lite. Please refer to the preparement document and compiling document. There are several demos about the usege of Paddle Lite.

3.2 Export the Inference Model

We use the HRNet_w18_small_v1 as the backbone of humanseg model. We have exported the inference model and you can download it from link. If you need to export other models, please refer to the document.

3.3 Convert the Inference Model

3.3.1 OPT Tool

After preparing the inference model, we use the opt tool in Paddle Lite to convert it.

Download the opt tool from github. The version of opt tool unsed in the Android App is v2.8.

The usage of opt tool is as follows and the full usage can be found in PaddleLite.

./opt \
    --model_dir=<model_param_dir> \
    --model_file=<model_path> \
    --param_file=<param_path> \
    --optimize_out_type=(protobuf|naive_buffer) \
    --optimize_out=<output_optimize_model_dir> \
    --valid_targets=(arm|opencl|x86|x86_opencl|npu) \
    --record_tailoring_info =(true|false) \
    --quant_model=(true|false) \
    --quant_type=(QUANT_INT8|QUANT_INT16)

3.3.2 Update the Inference Model

Use the converted model to replace the old model in app/src/main/assets/image_segmentation/models/hrnet_small_for_cpu.

FAQ

Q: No permission to build Android project, /Users/xxx/human_segmentation_demo/app/cache/71j4bd3k08cpahbhs9f81a9gj9/cxx/libs/arm64-v8a/libhiai_ir_build.so (Permission denied).

A: Run chmod -R 777 /Users/xxx/human_segmentation_demo/app/cache/.