jchengai/forecast-mae

[ICCV'2023] Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

Forecast-MAE: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders

Jie Cheng¹    Xiaodong Mei¹    Ming Liu^1,2   
HKUST¹    HKUST(GZ)^1,2   

Highlight

• A neat yet effective MAE-based pre-training scheme for motion forecasting.
• A pretty simple forecasting model (basically pure transformer encoders) with relative good performance.
• This repo also provides a exemplary multi-agent motion forecasting baseline on Argoverse 2.0 dataset.

Getting Started

• Getting Started
• Setup Environment
• Preprocess
• Training
• Evaluation
• Results and checkpoints
• Acknowledgements
• Citation

Setup Environment

1. Clone this repository:

git clone https://github.com/jchengai/forecast-mae.git
cd forecast-mae

2. Setup conda environment:

conda create -n forecast_mae python=3.8
conda activate forecast_mae
sh ./scripts/setup.sh

3. Setup Argoverse 2 Motion Forecasting Dataset, the expected data structure should be:

data_root
    ├── train
    │   ├── 0000b0f9-99f9-4a1f-a231-5be9e4c523f7
    │   ├── 0000b6ab-e100-4f6b-aee8-b520b57c0530
    │   ├── ...
    ├── val
    │   ├── 00010486-9a07-48ae-b493-cf4545855937
    │   ├── 00062a32-8d6d-4449-9948-6fedac67bfcd
    │   ├── ...
    ├── test
    │   ├── 0000b329-f890-4c2b-93f2-7e2413d4ca5b
    │   ├── 0008c251-e9b0-4708-b762-b15cb6effc27
    │   ├── ...

Preprocess

(recommend) By default, we use ray and 16 cpu cores for preprocessing. It will take about 30 minutes to finish.

Single-agent

python3 preprocess.py --data_root=/path/to/data_root -p

Multi-agent

python3 preprocess.py --data_root=/path/to/data_root -m -p

or you can disable parallel preprocessing by removing -p.

Training

• For single-card training, remove gpus=4 in the following commands. batch_size refers to the batch size of each GPU.
• If you use WandB, you can enable wandb logging by adding option wandb=online.

1. Pre-training + fine-tuning (single-agent)

phase 1 - pre-training:

python3 train.py data_root=/path/to/data_root model=model_mae gpus=4 batch_size=32

phase 2 - fine-tuning:

(Note that quotes in 'pretrained_weights="/path/to/pretrain_ckpt"' are necessary)

python3 train.py data_root=/path/to/data_root model=model_forecast gpus=4 batch_size=32 monitor=val_minFDE 'pretrained_weights="/path/to/pretrain_ckpt"'

2. Training from scratch (single-agent)

python3 train.py data_root=/path/to/data_root model=model_forecast gpus=4 batch_size=32 monitor=val_minFDE

3. Multi-agent motion forecasting

We also provide a simple multi-agent motion forecasting baseline using Forecast-MAE's backbone model.

python train.py data_root=/path/to/data_root model=model_forecast_mutliagent gpus=4 batch_size=32 monitor=val_AvgMinFDE

Evaluation

Single-agent

Evaluate on the validation set

python3 eval.py model=model_forecast data_root=/path/to/data_root batch_size=64 'checkpoint="/path/to/checkpoint"'

Generate submission file for the AV2 multi-agent motion forecasting benchmark

python3 eval.py model=model_forecast data_root=/path/to/data_root batch_size=64 'checkpoint="/path/to/checkpoint"' test=true

Multi-agent

Evaluate on the validation set

python3 eval.py model=model_forecast_multiagent data_root=/path/to/data_root batch_size=64 'checkpoint="/path/to/checkpoint"'

Generate submission file for the AV2 multi-agent motion forecasting benchmark

python3 eval.py model=model_forecast_multiagent data_root=/path/to/data_root batch_size=64 'checkpoint="/path/to/checkpoint"' test=true

Results and checkpoints

MAE-pretrained_weights: download.

A visualization notebook of the mae reconstruction result can be found here.

For this repository, the expected performance on Argoverse 2 validation set is:

Single-agent

Models	minADE1	minFDE1	minADE6	minFDE6	MR6
Forecast-MAE (scratch)	1.802	4.529	0.7214	1.430	0.187
Forecast-MAE (fine-tune)	1.744	4.376	0.7117	1.408	0.178

Multi-agent

Models	AvgMinADE6	AvgMinFDE6	ActorMR6
Multiagent-Baseline	0.717	1.64	0.194

You can download the checkpoints with the corresponding link.

Qualitative Results

Acknowledgements

This repo benefits from MAE, Point-BERT, Point-MAE, NATTEN and HiVT. Thanks for their great works.

Citation

If you found this repository useful, please consider citing our work:

@article{cheng2023forecast,
  title={{Forecast-MAE}: Self-supervised Pre-training for Motion Forecasting with Masked Autoencoders},
  author={Cheng, Jie and Mei, Xiaodong and Liu, Ming},
  journal={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}