Pit30M Development Kit

Overview

This is the Python software development kit for the Pit30M benchmark for large-scale global localization. The devkit is currently in a pre-release state and many features are coming soon!

Consider checking out the original paper. If you would like to, you could also follow some of the authors' social media, e.g., Andrei's in order to be among the first to hear of any updates!

Getting Started

The recommended way to interact with the dataset is with the pip package, which you can install with:

pip install pit30m

The devkit lets you efficiently access data on the fly. Here is a "hello world" command which renders a demo video from a random log segment. Note that it assumes ffmpeg is installed:

python -m pit30m.cli multicam_demo --out-dir .

To preview data more interactively, check out the tutorial notebook.

More tutorials coming soon.

Torch Data Loading

We provide basic log-based PyTorch dataloaders. Visual-localization-specific ones are coming soon. To see an example on how to use one of these dataloaders, have a look at demo_dataloader in torch/dataset.py.

An example command:

python -m pit30m.torch.dataset --root-uri s3://pit30m/ --logs 00682fa6-2183-4a0d-dcfe-bc38c448090f,021286dc-5fe5-445f-e5fa-f875f2eb3c57,1c915eda-c18a-46d5-e1ec-e4f624605ff0 --num-workers 16 --batch-size 64

Features

• Framework-agnostic multiprocessing-safe log reader objects
• PyTorch dataloaders

In-progress

• More lightweight package with fewer dependencies.
• Very efficient native S3 support through AWS-authored PyTorch-optimized S3 DataPipes.
• Support for non-S3 data sources, for users who wish to copy the dataset, or parts of it, to their own storage.
• Tons of examples and tutorials. See examples/ for more information.

Development

Package development, testing, and releasing is performed with poetry. If you just want to use the pit30m package, you don't need to care about this section; just have a look at "Getting Started" above!

1. Install poetry
2. Setup/update your dev virtual environments with poetry install --with=dev in the project root
   • If you encounter strange keyring/credential errors, you may need PYTHON_KEYRING_BACKEND=keyring.backends.null.Keyring poetry install --with=dev
3. Develop away
   • run commands like poetry run python -m pit30m.cli
4. Test with poetry run pytest
5. Remember to run poetry install after pulling and/or updating dependencies.

Note that in the pre-release time, torch will be a "dev" dependency, since it's necessary for all tests to pass.

Publishing

1. Configure poetry with a PyPI account which has access to edit the package. You need to make sure poetry is configured with your API key.
2. poetry publish --build

Citation

@inproceedings{martinez2020pit30m,
  title={Pit30m: A benchmark for global localization in the age of self-driving cars},
  author={Martinez, Julieta and Doubov, Sasha and Fan, Jack and B{\^a}rsan, Ioan Andrei and Wang, Shenlong and M{\'a}ttyus, Gell{\'e}rt and Urtasun, Raquel},
  booktitle={{IROS}},
  pages={4477--4484},
  year={2020},
  organization={IEEE}
}