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최종 버전 다운로드 (.zip)

- - __init__.py
  - create_h5_argo.py
  - dataset.py
  - README.md
- - __init__.py
  - create_h5_indst.py
  - dataset.py
  - README.md
  - utils.py
- - __init__.py
  - create_h5_nusc.py
  - dataset.py
  - raw_dataset.py
  - README.md
- - __init__.py
  - create_data_npys.py
  - dataset.py
  - README.md
- __init__.py
- __init__.py
- autobot_ego.py
- autobot_joint.py
- context_encoders.py
- generate_argoverse_test.py
- generate_indst_test.py
- generate_nuscene_results.py
- __init__.py
- metric_helpers.py
- train_helpers.py
evaluate.py
LICENSE
process_args.py
README.md
requirements.txt
train.py

// repository documentation

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# AutoBots Official Repository

This repository is the official implementation of the [AutoBots](https://arxiv.org/abs/2104.00563) architectures.
We include support for the following datasets:
- [nuScenes](https://www.nuscenes.org/nuscenes) (ego-agent, multi-agent)
- [Argoverse](https://www.argoverse.org/av1.html) (ego-agent)
- [TrajNet++](https://www.aicrowd.com/challenges/trajnet-a-trajectory-forecasting-challenge) Synthetic dataset (multi-agent)
- [Interaction-Dataset](https://interaction-dataset.com/) (multi-agent)

Visit our [webpage](https://fgolemo.github.io/autobots/) for more information.

### Getting Started

1. Create a python 3.7 environment. I use Miniconda3 and create with 
`conda create --name AutoBots python=3.7`
2. Run `pip install -r requirements.txt`

That should be it!

### Setup the datasets

Follow the instructions in the READMEs of each dataset (found in `datasets`).

### Training an AutoBot model

All experiments were performed locally on a single GTX 1080Ti.
The trained models will be saved in `results/{Dataset}/{exp_name}`.

#### nuScenes
Training AutoBot-Ego on nuScenes while using the raw road segments in the map:
```
python train.py --exp-id test --seed 1 --dataset Nuscenes --model-type Autobot-Ego --num-modes 10 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --use-map-lanes True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/nuscenes_h5_files
```

Training AutoBot-Ego on nuScenes while using the Birds-eye-view image of the road network:
```
python train.py --exp-id test --seed 1 --dataset Nuscenes --model-type Autobot-Ego --num-modes 10 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --use-map-image True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/nuscenes_h5_files
```

Training AutoBot-Joint on nuScenes while using the raw road segments in the map:
```
python train.py --exp-id test --seed 1 --dataset Nuscenes --model-type Autobot-Joint --num-modes 10 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --use-map-lanes True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/nuscenes_h5_files
```

#### Argoverse
Training AutoBot-Ego on Argoverse while using the raw road segments in the map:
```
python train.py --exp-id test --seed 1 --dataset Argoverse --model-type Autobot-Ego --num-modes 6 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --use-map-lanes True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/argoverse_h5_files
```

#### TrajNet++
Training AutoBot-Joint on TrajNet++:
```
python train.py --exp-id test --seed 1 --dataset trajnet++ --model-type Autobot-Joint --num-modes 6 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/npy_files
```

#### Interaction-Dataset
Training AutoBot-Joint on the Interaction-Dataset while using the raw road segments in the map:
```
python train.py --exp-id test --seed 1 --dataset interaction-dataset --model-type Autobot-Joint --num-modes 6 --hidden-size 128 --num-encoder-layers 2 --num-decoder-layers 2 --dropout 0.1 --entropy-weight 40.0 --kl-weight 20.0 --use-FDEADE-aux-loss True --tx-hidden-size 384 --batch-size 64 --learning-rate 0.00075 --learning-rate-sched 10 20 30 40 50 --dataset-path /path/to/root/of/interaction_dataset_h5_files
```

### Evaluating an AutoBot model

For all experiments, you can evaluate the trained model on the validation dataset by running:
```
python evaluate.py --dataset-path /path/to/root/of/interaction_dataset_h5_files --models-path results/{Dataset}/{exp_name}/{model_epoch}.pth --batch-size 64
```
Note that the batch-size may need to be reduced for the Interaction-dataset since evaluation is performed on all agent scenes.

### Extra scripts

We also provide extra scripts that can be used for submitting to the nuScenes, Argoverse and Interaction-Dataset 
Evaluation server.

For nuScenes:

```
python useful_scripts/generate_nuscene_results.py --dataset-path /path/to/root/of/nuscenes_h5_files --models-path results/Nuscenes/{exp_name}/{model_epoch}.pth 
```

For Argoverse:

```
python useful_scripts/generate_argoverse_test.py --dataset-path /path/to/root/of/argoverse_h5_files --models-path results/Argoverse/{exp_name}/{model_epoch}.pth 
```

For the Interaction-Dataset:

```
python useful_scripts/generate_indst_test.py --dataset-path /path/to/root/of/interaction_dataset_h5_files --models-path results/interaction-dataset/{exp_name}/{model_epoch}.pth 
```

## Reference

If you use this repository, please cite our work:

```
@inproceedings{
  girgis2022latent,
  title={Latent Variable Sequential Set Transformers for Joint Multi-Agent Motion Prediction},
  author={Roger Girgis and Florian Golemo and Felipe Codevilla and Martin Weiss and Jim Aldon D'Souza and Samira Ebrahimi Kahou and Felix Heide and Christopher Pal},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=Dup_dDqkZC5}
}
```

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