Skip to content

Latest commit

 

History

History
68 lines (64 loc) · 3.95 KB

INSTALL.md

File metadata and controls

68 lines (64 loc) · 3.95 KB
Raw

Installation

  • Set up a Python3 environment.

  • Install Pytorch 1.6.0 and TorchVision.

  • Install TensorRT 7.1.3.4 and torch2trt 0.1.0 (optional for evaluating models without TensorRT, currently TensorRT optimization only supports devices with Tensor Cores, and already included in JetPack SDK if using Jetson devices):

    1. Install CUDA 10.2/11.0 and cuDNN 8.0.0.
    2. Download TensorRT 7.1.3.4 tar file here and install TensorRT (refer to official documentation for more details).
    tar xzvf TensorRT-${version}.${os}.${arch}-gnu.${cuda}.${cudnn}.tar.gz
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<TensorRT-${version}/lib>

cd TensorRT-${version}/python
pip3 install tensorrt-*-cp3x-none-linux_x86_64.whl

cd TensorRT-${version}/uff
pip3 install uff-0.6.9-py2.py3-none-any.whl

cd TensorRT-${version}/graphsurgeon
pip3 install graphsurgeon-0.4.5-py2.py3-none-any.whl
    1. Install torch2trt.
    git clone https://github.com/NVIDIA-AI-IOT/torch2trt
cd torch2trt
sudo python setup.py install --plugins

  • Install some other packages:

    # Cython needs to be installed before pycocotools
pip install cython
pip install opencv-python pillow matplotlib
pip install git+https://github.com/haotian-liu/cocoapi.git#"egg=pycocotools&subdirectory=PythonAPI"
pip install GitPython termcolor tensorboard

  • Clone this repository and enter it:

    git clone https://github.com/haotian-liu/yolact_edge.git
cd yolact_edge

  • If you'd like to train YolactEdge on COCO, download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into ./data/coco.

    sh data/scripts/COCO.sh

  • If you'd like to evaluate YolactEdge on COCO test-dev, download test-dev with this script.

    sh data/scripts/COCO_test.sh

  • To evaluate YolactEdge with TensorRT INT8 calibration you need to download the calibration dataset (this avoids having to download the entire COCO/YouTube-VIS dataset and their annotations) for COCO and YouTube VIS. Store the calib_images folder under its corresponding dataset folder as shown in the example below. Note that our best models use INT8 calibration so this step is highly advised.

  • If you'd like to train YolactEdge on YouTube VIS, download the YouTube VIS dataset (you need to register to download) and our training/validation split annotations into ./data/YoutubeVIS.

    • If you'd like to train jointly with COCO, download the COCO dataset and the 2014/2017 annotations using the script above.
    • If you'd like to train on all video frames, download the FlyingChairs dataset into ./data/FlyingChairs.
    • Your dataset folder should be organized like this:
    ./data
├── coco
│   ├── annotations
│   ├── calib_images
│   └── images
├── FlyingChairs
│   ├── data
│   └── train_val.txt
└── YoutubeVIS
    ├── annotations
    ├── calib_images
    ├── JPEGImages
    └── train_all_frames
        └── JPEGImages