HEAL-SWIN: A Vision Transformer On The Sphere

This repository contains the reference implementation of the HEAL-SWIN model¹ for semantic segmentation of high-dimensional spherical images. HEAL-SWIN extends a UNet-variant² of the Hierarchical Shifted-Window (SWIN) transformer³ to the spherical Hierarchical Equal Area iso-Latitude Pixelation (HEALPix) grid⁴. For handling the HEALPix grid efficiently in Python, we use the healpy package⁵.

We provide a PyTorch model for HEAL-SWIN and the baseline SWIN-UNet as well as a training and evaluation environment based on PyTorch Lightning. The dataloaders, models and training environments provided are suitable for semantic segmentation and depth estimation on the WoodScape⁶ and SynWoodScape⁷ datasets. For logging, we use MLflow⁸ with either a filesystem backend or a database backend.

Compute Environment

The code assumes that the root directory HEAL-SWIN is in the Python path. This can be achieved e.g. by running Python from the root directory instead of a subdirectory.

A number of directories outside the heal_swin directory can be moved to other places. These are

• datasets: The directory for the training data
• mlruns: The directory which is used to save logging information during training and evaluation results with MLflow
• containers: The directory in which the singularity container is saved
• slurm: The directory where MLflow looks for the slurm output file to be logged with the run
• matplotlib_cache: A cache directory for matplotlib

The paths to these directories are read from the file compute_environment/current_environment.py. Here, the name of the singularity container file can also be set. In the absence of this file the defaults from compute_environment/local_environment.py will be used which uses subdirectories of HEAL-SWIN. It is convenient to make current_environment.py a symlink to the existing environments.

Build container

The Python packages required to run the code are specified in containers/requirements.txt. To build the singularity/apptainer container with which the code was tested, run python3 run.py build-singularity. The container will be saved according to the specifications in the compute environment. To build the docker container heal_swin from the requirements file, run python3 run.py build-docker.

Install dependencies using pip

Although it is recommended to run the code inside the singularity/apptainer container which was used for development as described above, we also provide a setup.py to install the necessary dependencies using pip. To do this, execute

pip3 install --upgrade pip
pip3 install -e .[test,formatting,dev]

in the root directory. This will require Python 3.8 which can e.g. be installed using Conda. By installing in editable mode (-e), this will also add the root directory to the Python path as required. In order to compute the Chamfer distance for the depth estimation task, PyTorch3D in version 0.7.2 is required and has to be installed separately via

FORCE_CUDA=1 pip3 install git+https://github.com/facebookresearch/pytorch3d.git@3145dd4d16edaceb394838364b8e87a440f83c10

Further details on the installation of PyTorch3D can be found here.

After installation, you can then use the local environment of the run.py script as detailed below. Note that in order to use the database backend of MLflow for logging, you additionally need to install SQLite.

Data

The training and evaluation scripts in this repository require the WoodScapes dataset⁶ for semantic segmentation and/or the SynWoodScapes dataset⁷ for semantic segmentation and depth estimation. As of 2023-06-20, the WoodScapes dataset is available here, while the SynWoodScapes dataset is available here. Both datasets should be saved in subdirectories woodscape and synwoodscape, respectively, of the datasets directory specified in the compute environment.

Using the synwoodscape_merge_classes.py script in heal_swin/data/segmentation, it is possible to generate copies of the semantic segmentation part of the SynWoodScapes dataset with merged classes. The calibration data and ground truth images are added to the new dataset via symlinks to the original synwoodscape directory. Together, all subdirectories of the datasets directory form "woodscape versions" and the data to be used can be selected by setting the corresponding option to the name of the desired subdirectory.

For the flat evaluation masked according to the utilized base pixels of the HEALPix grid, lists with samples corresponding to different camera calibrations are needed. This metadata as well as class color legends can be generated using the script generate_metadata.py in heal_swin/data/segmentation.py. The script data_stats.py in heal_swin/data/segmentation can be used to compute class prevalences useful for determining class weights in the cross-entropy loss.

To train a HEAL-SWIN model, the flat data needs to be projected onto the HEALPix grid. If the projected data is not available, the projection is initiated automatically at the beginning of training.

Run code

The central entry point of the code is the run.py Python script. It is called as

run.py [--env {local, singularity, docker}] <task> [task-specific arguments]

The optional --env argument specifies the environment in which the task is executed:

• local (default): no container, useful e.g. if you work inside a virtual Python environment
• singularity: the singularity container specified in the compute environment
• docker: the docker container heal_swin

If a task is run inside a container, then the necessary directories specified in the compute environment are bound and the root directory is added to the Python path.

Available tasks are:

• build-singularity to build the singularity/apptainer container (see above)
• build-docker to build the docker container (see above)
• bash to run an interactive bash session inside a container
• train to train a network
• resume to resume a training run
• evaluate to evaluate a trained network
• start-mlflow-server to start an HTTP MLflow server for logging
• test-repo to run integration tests (trains multiple small networks and evaluates them)
• format-code to run the code formatter

The train, resume and evaluate tasks just run the respective scripts in heal_swin inside the specified environment, they are documented there. The task-specific arguments are in these cases the arguments of the respective scripts.

The start-mlflow-server task accepts the following arguments:

• --backend, default sqlite: can be either sqlite or filesystem and specifies whether an SQLite server should be used for logging or the filesystem (much slower)
• --port, default 5000: port at which the web interface is accessible
• --workers, default 1: number of workers which access the database
• --timeout, default 30: database access timeout in seconds before the server crashes

The test-repo task starts pytest and runs the tests inside heal_swin/testing. It accepts all pytest arguments.

Reference

HEAL-SWIN was introduced in

O. Carlsson, J. E. Gerken, H. Linander, H. Spieß, F. Ohlsson, C. Petersson, and D. Persson, “HEAL-SWIN: A Vision Transformer On The Sphere,” 2023. arXiv:2307.07313

If you use this code, please cite

@inproceedings{carlsson2023,
  title = {{{HEAL-SWIN}}: {{A Vision Transformer On The Sphere}}},
  shorttitle = {{{HEAL-SWIN}}},
  booktitle = {Proceedings of the {{IEEE}}/{{CVF Conference}} on {{Computer Vision}} and {{Pattern Recognition}}},
  author = {Carlsson, Oscar and Gerken, Jan E. and Linander, Hampus and Spie{\ss}, Heiner and Ohlsson, Fredrik and Petersson, Christoffer and Persson, Daniel},
  year = {2023},
  month = jul,
  eprint = {2307.07313},
  primaryclass = {cs},
  pages = {6067--6077},
  urldate = {2024-07-12},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Computer Vision and Pattern Recognition,Computer Science - Machine Learning}
}

Footnotes

1. O. Carlsson, J. E. Gerken, H. Linander, H. Spieß, F. Ohlsson, C. Petersson, and D. Persson, “HEAL-SWIN: A Vision Transformer On The Sphere,” 2023. arXiv:2307.07313. ↩

2. H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian, and M. Wang, “Swin-Unet: Unet-like pure transformer for medical image segmentation,” in Computer Vision – ECCV 2022 Workshops. ECCV 2022, L. Karlinsky, T. Michaeli, and K. Nishino, eds., vol. 13803 of Lecture Notes in Computer Science, pp. 205–218. Springer International Publishing, 2022. arXiv:2105.05537. ↩

3. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9992–10002. IEEE, 2021. arXiv:2103.14030. ↩

4. K. M. Gorski, E. Hivon, and B. D. Wandelt, “Analysis issues for large CMB data sets,” arXiv:astro-ph/9812350. ↩

5. A. Zonca, L. Singer, D. Lenz, M. Reinecke, C. Rosset, E. Hivon, and K. Gorski, “healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python,” Journal of Open Source Software 4 (2019) 1298. github:healpy/healpy ↩

6. S. Yogamani, C. Hughes, J. Horgan, G. Sistu, S. Chennupati, M. Uricar, S. Milz, M. Simon, K. Amende, C. Witt, H. Rashed, S. Nayak, S. Mansoor, P. Varley, X. Perrotton, D. Odea, and P. Perez, “Woodscape: A multi-task, multi-camera fisheye dataset for autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9307–9317. IEEE, 2019. arXiv:1905.01489. ↩ ↩²

7. A. R. Sekkat, Y. Dupuis, V. R. Kumar, H. Rashed, S. Yogamani, P. Vasseur, and P. Honeine, “SynWoodScape: Synthetic surround-view fisheye camera dataset for autonomous driving,” IEEE Robotics and Automation Letters 7 (2022) 8502–8509, arXiv:2203.05056 ↩ ↩²

8. MLflow ↩