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Sample projects for TensorFlow Lite in C++ with delegates such as GPU, EdgeTPU, XNNPACK, NNAPI

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iwatake2222/play_with_tflite

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Play with tflite

  • Sample projects to use TensorFlow Lite in C++ for multi-platform
  • Typical project structure is like the following diagram
    • 00_doc/design.jpg

Target

  • Platform
    • Linux (x64)
    • Linux (armv7)
    • Linux (aarch64)
    • Android (aarch64)
    • Windows (x64). Visual Studio 2019
  • Delegate
    • Edge TPU
    • XNNPACK
    • GPU
    • NNAPI(CPU, GPU, DSP)

Usage

./main [input]

 - input = blank
    - use the default image file set in source code (main.cpp)
    - e.g. ./main
 - input = *.mp4, *.avi, *.webm
    - use video file
    - e.g. ./main test.mp4
 - input = *.jpg, *.png, *.bmp
    - use image file
    - e.g. ./main test.jpg
 - input = number (e.g. 0, 1, 2, ...)
    - use camera
    - e.g. ./main 0

How to build a project

0. Requirements

  • OpenCV 4.x

1. Download

  • Download source code and pre-built libraries
    git clone https://github.com/iwatake2222/play_with_tflite.git
    cd play_with_tflite
    git submodule update --init
    sh InferenceHelper/third_party/download_prebuilt_libraries.sh
  • Download models
    sh ./download_resource.sh

2-a. Build in Linux

cd pj_tflite_cls_mobilenet_v2   # for example
mkdir -p build && cd build
cmake ..
make
./main

2-b. Build in Windows (Visual Studio)

  • Configure and Generate a new project using cmake-gui for Visual Studio 2019 64-bit
    • Where is the source code : path-to-play_with_tflite/pj_tflite_cls_mobilenet_v2 (for example)
    • Where to build the binaries : path-to-build (any)
  • Open main.sln
  • Set main project as a startup project, then build and run!

2-c. Build in Android Studio

  • Please refer to
  • Copy resource directory to /storage/emulated/0/Android/data/com.iwatake.viewandroidtflite/files/Documents/resource
    • the directory will be created after running the app (so the first run should fail because model files cannot be read)
  • Modify ViewAndroid\app\src\main\cpp\CMakeLists.txt to select a image processor you want to use
    • set(ImageProcessor_DIR "${CMAKE_CURRENT_LIST_DIR}/../../../../../pj_tflite_cls_mobilenet_v2/image_processor")
    • replace pj_tflite_cls_mobilenet_v2 to another
  • By default, InferenceHelper::TENSORFLOW_LITE_DELEGATE_XNNPACK is used. You can modify ViewAndroid\app\src\main\cpp\CMakeLists.txt to select which delegate to use. It's better to use InferenceHelper::TENSORFLOW_LITE_GPU to get high performance.
    • You also need to select framework when calling InferenceHelper::create .

Note

Options (Delegate)

# Edge TPU
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU=on  -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK=off
cp libedgetpu.so.1.0 libedgetpu.so.1
#export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`pwd`
sudo LD_LIBRARY_PATH=./ ./main
# you may get "Segmentation fault (core dumped)" without sudo

# GPU
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU=on  -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK=off
# you may need `sudo apt install ocl-icd-opencl-dev` or `sudo apt install libgles2-mesa-dev`

# XNNPACK
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK=on

# NNAPI (Note: You use Android for NNAPI. Therefore, you will modify CMakeLists.txt in Android Studio rather than the following command)
cmake .. -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_EDGETPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_GPU=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_XNNPACK=off -DINFERENCE_HELPER_ENABLE_TFLITE_DELEGATE_NNAPI=on

You also need to select framework when calling InferenceHelper::create .

EdgeTPU

NNAPI

By default, NNAPI will select the most appropreate accelerator for the model. You can specify which accelerator to use by yourself. Modify the following code in InferenceHelperTensorflowLite.cpp

// options.accelerator_name = "qti-default";
// options.accelerator_name = "qti-dsp";
// options.accelerator_name = "qti-gpu";

License

Acknowledgements

  • This project utilizes OSS (Open Source Software)
  • This project utilizes models from other projects:
    • Please find model_information.md in resource.zip