sancarlim/pgp

Code for "Towards Explainable Multi-modal Motion Prediction using Graph Representations"

PGP Repository

This repository contains code for "Towards Explainable Multi-modal Motion Prediction using Graph Representations" by Sandra Carrasco, Sylwia Majchrowska, ..., presented at .. 2022.

citation

Note: This repository is based on PGP repository

Installation

1. Clone this repository

2. Set up a new conda environment

conda create --name xscout python=3.7.10

3. Install dependencies

conda activate xscout

# nuScenes devkit
pip install nuscenes-devkit

# Pytorch: The code has been tested with Pytorch 1.7.1, CUDA 10.1, but should work with newer versions
conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.1 -c pytorch

# Additional utilities
pip install ray
pip install psutil
pip install scipy
pip install positional-encodings
pip install imageio
pip install tensorboard
pip install dgl-cu101

Dataset

1. Download the nuScenes dataset. For this project we just need the following.

   • Metadata for the Trainval split (v1.0)
   • Map expansion pack (v1.3)

2. Organize the nuScenes root directory as follows

└── nuScenes/
    ├── maps/
    |   ├── basemaps/
    |   ├── expansion/
    |   ├── prediction/
    |   ├── 36092f0b03a857c6a3403e25b4b7aab3.png
    |   ├── 37819e65e09e5547b8a3ceaefba56bb2.png
    |   ├── 53992ee3023e5494b90c316c183be829.png
    |   └── 93406b464a165eaba6d9de76ca09f5da.png
    └── v1.0-trainval
        ├── attribute.json
        ├── calibrated_sensor.json
        ...
        └── visibility.json         

3. Run the following script to extract pre-processed data. This speeds up training significantly.

python preprocess.py -c configs/preprocess_nuscenes.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data

You can download the preprocessed data in this link.

Inference

You can download the trained model weights using this link.

To evaluate on the nuScenes val set run the following script. This will generate a text file with evaluation metrics at the specified output directory. The results should match the benchmark entry on Eval.ai.

python evaluate.py -c configs/xscout_pgp.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -w path/to/trained/weights

To visualize predictions run the following script. This will generate gifs for a set of instance tokens (track ids) from nuScenes val at the specified output directory.

python visualize.py -c configs/xscout_pgp.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -w path/to/trained/weights

Training

To train the model from scratch, run

python train.py -c configs/xscout_pgp.yml -r path/to/nuScenes/root/directory -d path/to/directory/with/preprocessed/data -o path/to/output/directory -n 100

The training script will save training checkpoints and tensorboard logs in the output directory. Wandb logger is also supported. You need to specify the entity and project in wandb.init in train.py.

To launch tensorboard, run

tensorboard --logdir=path/to/output/directory/tensorboard_logs