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 1.6 and Cuda 10.1.
conda create --name raft
conda activate raft
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 -c pytorch
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
python setup.py install
Use RAFT as a torch module
import raft
# construct raft_model
raft_model = raft.RAFT()
# construct raft model and load pretrained weight
from types import SimpleNamespace
args = SimpleNamespace()
args.model = 'raft-sintel'
raft_model = raft.RAFT(args)
## Required Data
To evaluate/train RAFT, you will need to download the required datasets.
* [FlyingChairs](https://lmb.informatik.uni-freiburg.de/resources/datasets/FlyingChairs.en.html#flyingchairs)
* [FlyingThings3D](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
* [Sintel](http://sintel.is.tue.mpg.de/)
* [KITTI](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow)
* [HD1K](http://hci-benchmark.iwr.uni-heidelberg.de/) (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
```Shell
├── 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 --mixed_precision
We used the following training schedule in our paper (2 GPUs). Training logs will be written to the runs
which can be visualized using tensorboard
./train_standard.sh
If you have a RTX GPU, training can be accelerated using mixed precision. You can expect similiar results in this setting (1 GPU)
./train_mixed.sh
You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
cd alt_cuda_corr && python setup.py install && cd ..
and running demo.py
and evaluate.py
with the --alternate_corr
flag Note, this implementation is somewhat slower than all-pairs, but uses significantly less GPU memory during the forward pass.