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Adaptive Y-Net from a Causal Representation Perspective

Paper | Video | Spurious | Style

This is an addition to the official implementation for the paper

Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective <br> IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. <br> <a href="https://sites.google.com/view/yuejiangliu">Yuejiang Liu</a>, <a href="https://www.riccardocadei.com">Riccardo Cadei</a>, <a href="https://people.epfl.ch/jonas.schweizer/?lang=en">Jonas Schweizer</a>, <a href="https://www.linkedin.com/in/sherwin-bahmani-a2b5691a9">Sherwin Bahmani</a>, <a href="https://people.epfl.ch/alexandre.alahi/?lang=en/">Alexandre Alahi</a> <br> École Polytechnique Fédérale de Lausanne (EPFL)

TL;DR: incorporate causal invariance and structure into the design and training of motion forecasting models to improve the robustness and reusability of the learned representations under common distribution shifts

• causal formalism of motion forecasting with three groups of latent variables
• causal (invariant) representations to suppress spurious features and promote robust generalization
• causal (modular) structure to approximate a sparse causal graph and facilitate efficient adaptation

If you find this code useful for your research, please cite our paper:

@InProceedings{Liu2022CausalMotionRepresentations,
    title     = {Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective},
    author    = {Liu, Yuejiang and Cadei, Riccardo and Schweizer, Jonas and Bahmani, Sherwin and Alahi, Alexandre},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {17081-17092}
}

Setup

Install PyTorch, for example using pip

pip install --upgrade pip
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html

Install dependencies

pip install -r requirements.txt

Build ddf dependency

cd ddf
python setup.py install
mv build/lib*/* .

Dataset

Get the raw dataset, our filtered custom dataset and segmentation masks for SDD from the original Y-net authors

pip install gdown && gdown https://drive.google.com/uc?id=14Jn8HsI-MjNIwepksgW4b5QoRcQe97Lg
unzip sdd_ynet.zip

After unzipping the file the directory should have following structure:

sdd_ynet
├── dataset_raw
├── dataset_filter
│   ├── dataset_ped
│   ├── dataset_biker
│   │   ├── gap
│   │   └── no_gap
│   └── ...
└── ynet_additional_files

In addition to our custom datasets in sdd_ynet/dataset_filter, you can create custom datasets:

bash create_custom_dataset.sh

Scripts

1. Train Baseline

bash run_train.sh

    Our pretrained models can be downloaded from google drive.

cd ckpts
gdown https://drive.google.com/uc?id=180sMpRiGhZOyCaGMMakZPXsTS7Affhuf

2. Low-shot Adaptation

bash run_vanilla.sh

bash run_encoder.sh

python utils/visualize.py 

Basic Results

Results of different methods for low-shot transfer across agent types and speed limits.

Acknowledgement

Out code is developed upon the public code of Y-net and Decoupled Dynamic Filter.