RAFT meets DICL

This repository contains the code for

RAFT meets DICL: A Recurrent All-Pair Transform for Optical Flow Estimation based on Displacement Invariant Cost Volume Learning
Master Thesis, University of Stuttgart, 2022
Maximilian Luz

If you find this work useful please cite via BibTeX.

Setup

This project uses pipenv, i.e. in terms of code, setup is as easy installing pipenv (e.g. via python -m pip install --user pipenv) and running pipenv install (or pipenv install -d if you want to also install development dependencies). Commands and scripts of this project can then be run either in a shell opened with pipenv shell or directly via pipenv run <command>. The pipenv command can also be used to generate a requirements.txt file for integration into other python environments.

Datasets

Datasets are expected to be placed in ../datasets, i.e. a datasets folder next to the folder containing this code. The dataset folder should follow the specification below: ../datasets ├── <some-dataset-id> │ └── data # the dataset root directory For all supported datasets see cfg/data/dataset/. The path attribute of these config files specifies the path local to the config file and may be adapted if needed.

A subset of supported datasets would look like this: ../datasets ├── hci-hd1k # HCI HD1K │ └── data │ ├── hd1k_challenge │ ├── hd1k_flow_gt │ ├── hd1k_flow_uncertainty │ └── hd1k_input ├── kitti-flow-2012 # KITTI 2012 │ └── data │ ├── testing │ └── training ├── mpi-sintel-flow # MPI Sintel │ └── data │ ├── bundler │ ├── flow_code │ ├── test │ ├── training │ └── README.txt ├── ufreiburg-flyingchairs # Uni Freiburg FlyingChairs │ ├── data │ │ ├── data │ │ └── README.txt │ └── train_val.txt └── ufreiburg-flyingthings3d # Uni Freiburg FlyingThings3D └── data ├── frames_cleanpass ├── frames_finalpass └── optical_flow

Training

Training is done via ./main.py train -d <strategy> -m <model> where <strategy> is a strategy config specifying training stages, i.e. data and optimizer parameters, and <model> is a model specification file, describing the model parameters. For strategy config files see cfg/strategy/, for model config files see cfg/model/. Config files are documented in cfg/_doc/.

By default, training will store tensorboard and other log files in the runs/<timestamp>/ directory. If specified in the training strategy and stages, checkpoints will be stored in runs/<timestamp>/checkpoints/. Metrics etc. can be customized via an inspection config

See ./main.py train -h for more information.

Evaluation

Saved checkpoints can be evaluated via ./main.py evaluate -d <data> -m <model> -c <checkpoint> where <data> is a data source specification (cfg/data/), <model> is a model specification (cfg/model/), and <checkpoint> is the checkpoint to evaluate. Note that the model specification should match the one used for training the model (i.e. generating the checkpoint).

By default, this prints a set of metrics per sample and a summary collecting means. This can e.g. be adapted via a custom evaluation config file. The eval sub-command can also be used to visualize flow, visualize error metrics, and generate flow files for submission.

See ./main.py eval -h for more information.

Using original RAFT/DICL checkpoints

Original RAFT and DICL checkpoints can be used for evaluation with this framework. To do this, the checkpoints have to be converted via ./scripts/chkpt_convert.py -i <original> -o <output> -f <format> where <original> is the original checkpoint file, <output> is the output file (to be generated) and <format> is the format of the original checkpoint, i.e. one of dicl or raft.