This repository contains the source code for our paper:

RAFT: Recurrent All Pairs Field Transforms for Optical Flow
ECCV 2020
Zachary Teed and Jia Deng

The code has been tested with PyTorch 1.5.1 and PyTorch Nightly. If you want to train with mixed precision, you will have to install the nightly build.

conda create --name raft
conda activate raft
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch-nightly
conda install matplotlib
conda install tensorboard
conda install scipy
conda install opencv

Pretrained models can be downloaded by running

./download_models.sh

or downloaded from google drive

You can demo a trained model on a sequence of frames

python demo.py --model=models/raft-things.pth --path=demo-frames

To evaluate/train RAFT, you will need to download the required datasets.

• FlyingChairs
• FlyingThings3D
• Sintel
• KITTI
• HD1K (optional)

By default datasets.py will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the datasets folder

├── datasets
    ├── Sintel
        ├── test
        ├── training
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow

You can evaluate a trained model using evaluate.py

python evaluate.py --model=models/raft-things.pth --dataset=sintel

Training code will be made available in the next few days