iicd/RAFT

Changes the conda install instructions to a one-liner instead of multiple command lines.
This is an important change, because with the current version of conda, using the multiline version results the environment not being able to be solved in the later added packages due to clashes with the former, and thus not being able to correctly install all of the requirements.
However, the one line version solves this and allows the install to proceed much more easily and quickly.

2020-12-28 13:45:27 +01:00

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RAFT

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

Requirements

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 matplotlib tensorboard scipy opencv -c pytorch

Demos

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

Required Data

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

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

Evaluation

You can evaluate a trained model using evaluate.py

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

./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

(Optional) Efficent Implementation

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.

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