WIMP - What If Motion Predictor
Reference PyTorch Implementation for What If Motion Prediction [PDF] [Dynamic Visualizations]
Setup
Requirements
The WIMP reference implementation and setup procedure has been tested to work with Ubuntu 16.04+ and has the following requirements:
- python >= 3.7
- pytorch >= 1.5.0
Installing Dependencies
-
Install remaining required Python dependencies using pip.
pip install -r requirements.txt
-
Install the Argoverse API module into the local Python environment by following steps 1, 2, and 4 in the README.
Argoverse Data
In order to set up the Argoverse dataset for training and evaluation, follow the steps below:
-
Download the the Argoverse Motion Forecasting v1.1 dataset and extract the compressed data subsets such that the raw CSV files are stored in the following directory structure:
├── WIMP │ ├── src │ ├── scripts │ ├── data │ │ ├── argoverse_raw │ │ │ ├── train │ │ │ │ ├── *.csv │ │ │ ├── val │ │ │ │ ├── *.csv │ │ │ ├── test │ │ │ │ ├── *.csv
-
Pre-process the raw Argoverse data into a WIMP-compatible format by running the following script. It should be noted that the Argoverse dataset is quite large and this script may take a few hours to run on a multi-threaded machine.
python scripts/run_preprocess.py --dataroot ./data/argoverse_raw/ \ --mode val --save-dir ./data/argoverse_processed --social-features \ --map-features --xy-features --normalize --extra-map-features \ --compute-all --generate-candidate-centerlines 6
Usage
For a detailed description of all possible configuration arguments, please run scripts with the -h flag.
Training
To train WIMP from scratch using a configuration similar to that reported in the paper, run a variant of the following command:
python src/main.py --mode train --dataroot ./data/argoverse_processed --IFC \
--lr 0.0001 --weight-decay 0.0 --non-linearity relu --use-centerline-features \
--segment-CL-Encoder-Prob --num-mixtures 6 --output-conv --output-prediction \
--gradient-clipping --hidden-key-generator --k-value-threshold 10 \
--scheduler-step-size 60 90 120 150 180 --distributed-backend ddp \
--experiment-name example --gpus 4 --batch-size 25
Citing
If you've found this code to be useful, please consider citing our paper!
@article{khandelwal2020if,
title={What-If Motion Prediction for Autonomous Driving},
author={Khandelwal, Siddhesh and Qi, William and Singh, Jagjeet and Hartnett, Andrew and Ramanan, Deva},
journal={arXiv preprint arXiv:2008.10587},
year={2020}
}
Questions
This repo is maintained by William Qi and Siddhesh Khandelwal - please feel free to reach out or open an issue if you have additional questions/concerns.
We plan to clean up the codebase and add some additional utilities (possibly NuScenes data loaders and inference/visualization tools) in the near future, but don't expect to make significant breaking changes.
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