torchdrive 0.0.1

PyTorch library for BEV style perception models

# 

A PyTorch library for training BEV style perception models for self driving tasks. This is unaffiliated with the PyTorch project. This currently includes helpful primitives needed to put together a model.

## Install from Source

` $ pip install git+https://github.com/d4l3k/torchdrive.git `

or

` $ git clone --recursive https://github.com/d4l3k/torchdrive.git $ cd torchdrive $ pip install -e . `

## Background

I’ve been documenting the process for this code. Please see my blog at https://fn.lc/post/3d-detr/ for more details.

### 3D Object Detection

3D bounding boxes and velocities for dynamic objects such as cars.


### Voxel Occupancy

Grids of occupancy around the vehicle trained with differential rendering.


### BEV Lane Lines and Drivable Space

Lane line and drivable space trained purely from image space labels.


### Semantic Voxel

Per voxel semantic labels for static objects.


## Data Access

The training dataset for this repo has been collected from my car and thus has lots of personally identifying information so I’m not willing to make it public at this time. If you’re interested in contributing or collaborating feel free to reach out. I’m happy to test changes on my own hardware and there may be other options too.

## Contact

If you have any questions or concerns, please reach out to me either by filing an issue or emailing me at [[e-mail]](mailto:[e-mail]).

## License

This project is a hobby project and done in my free time. This is non-commercial and no profit has been made from this work.

See the [LICENSE](LICENSE) file for more information. Some files and functions have different licenses and are marked accordingly.

BSD 3-Clause License

Copyright (c) 2023, Tristan Rice

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.