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GAN Compression Interactive Demo on Jetson Nano

Overview

We show how to deploy our compressed Pix2pix model on NVIDIA Jetson Nano. Our model is compiled with TVM Auto Scheduler [1,2] for acceleration. The final model achieves about 8 FPS on Jetson Nano GPU.

[1] Tianqi Chen et al., TVM: An automated end-to-end optimizing compiler for deep learning, in OSDI 2018

[2] Lianmin Zheng et al., Ansor: Generating High-Performance Tensor Programs for Deep Learning., in OSDI 2020

Getting Started

  1. Get an NVIDIA Jeston Nano board (it is only $99!).

  2. Get a micro SD card and burn the Nano system image into it following here. Insert the card and boot the Nano. Note: you may want to get a power adaptor for a stable power supply.

  3. Follow here to install PyTorch and torchvision.

  4. Install TVM 0.8.

    # upgrade cmake
    cd ~
    sudo apt install openssl libssl-dev
    sudo apt remove cmake
    wget https://github.com/Kitware/CMake/releases/download/v3.21.0-rc2/cmake-3.21.0-rc2-linux-aarch64.sh
    sudo bash cmake-3.21.0-rc2-linux-aarch64.sh --prefix=/usr --exclude-subdir --skip-license
    
    cd ~
    sudo apt install llvm # install llvm which is required by tvm
    git clone --recursive https://github.com/apache/tvm tvm
    cd tvm
    mkdir build
    cp cmake/config.cmake build/
    cd build
    vim config.cmake
    # edit config.cmake to change
    # USE_CUDA OFF -> USE_CUDA ON
    # USE_LLVM OFF -> USE_LLVM ON
    # USE_THRUST OFF -> USE_THRUST ON
    # USE_GRAPH_EXECUTOR_CUDA_GRAPH OFF -> USE_GRAPH_EXECUTOR_CUDA_GRAPH ON
    
    cmake ..
    make -j4
    cd ..
    cd python; python3 setup.py install --user; cd ..
  5. Install PyQt5 (Jetson Nano may have pre-installed it).

  6. You now can try our demo with

    python3 paint.py

Acknowledgement

This demo code is developed based on Piecasso.