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# RAFT
This repository contains the source code for our paper:
[RAFT: Recurrent All Pairs Field Transforms for Optical Flow](https://arxiv.org/pdf/2003.12039.pdf)<br/>
ECCV 2020 <br/>
Zachary Teed and Jia Deng<br/>
<img src="RAFT.png">
## Requirements
The code has been tested with PyTorch 1.6 and Cuda 10.1.
```Shell
conda create --name raft
conda activate raft
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
```
## Demos
Pretrained models can be downloaded by running
```Shell
./download_models.sh
```
or downloaded from [google drive](https://drive.google.com/drive/folders/1sWDsfuZ3Up38EUQt7-JDTT1HcGHuJgvT?usp=sharing)
You can demo a trained model on a sequence of frames
```Shell
python demo.py --model=models/raft-things.pth --path=demo-frames
```
## 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
```
## Evaluation
You can evaluate a trained model using `evaluate.py`
```Shell
python evaluate.py --model=models/raft-things.pth --dataset=sintel --mixed_precision
```
## Training
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
```Shell
./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)
```Shell
./train_mixed.sh
```
## (Optional) Efficent Implementation
You can optionally use our alternate (efficent) implementation by compiling the provided cuda extension
```Shell
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.
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