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FlowNAS: Neural Architecture Search for Optical Flow Estimation

This is the official implementation of FlowNAS. The training and searching code is coming soon.

Introduction

Existing optical flow estimators usually employ the network architectures typically designed for image classification as the encoder to extract per-pixel features. However, due to the natural difference between the tasks, the architectures designed for image classification may be sub-optimal for flow estimation. To address this issue, we propose a neural architecture search method named FlowNAS to automatically find the better encoder architecture for flow estimation task. We first design a suitable search space including various convolutional operators and construct a weight-sharing super-network for efficiently evaluating the candidate architectures. Then, for better training the super-network, we propose Feature Alignment Distillation, which utilizes a well-trained flow estimator to guide the training of super-network. Finally, a resource-constrained evolutionary algorithm is exploited to find an optimal architecture (i.e., sub-network). Experimental results show that the discovered architecture with the weights inherited from the super-network achieves 4.67% F1-all error on KITTI, an 8.4% reduction of RAFT baseline, surpassing state-of-the-art handcrafted models GMA and AGFlow, while reducing the model complexity and latency.

Environments

Please follow the official to install torch and torchvision

Other requirements:

matplotlib tensorboard scipy opencv-python tqdm imageio einops

Data Preparation

Please follow RAFT to download and prepare the dataset.

The final dataset folder will be like:

├── datasets
    ├── Sintel
        ├── test
        ├── training
    ├── KITTI
        ├── testing
        ├── training
        ├── devkit
    ├── FlyingChairs_release
        ├── data
    ├── FlyingThings3D
        ├── frames_cleanpass
        ├── frames_finalpass
        ├── optical_flow

Evaluation

You can evaluate a trained model using evaluate_supernet.py

python evaluate_supernet.py --model <model_path> --dataset <sintel or kitti>

Main Results:

Architecture Sintel clean Sintel final KITTI Weights
RAFT 1.94 3.18 5.10 ——
FlowNAS-RAFT-S 1.93 3.38 —— Google_drive
FlowNAS-RAFT-K —— —— 4.67 Google_drive

TODO

  • SuperNet training code
  • Searching code

Acknowledgements

The overall code is based on RAFT and AttentiveNAS. We sincerely thank the authors for open sourcing their methods.

Citation

If FlowNAS is useful or relevant to your research, please cite our paper:

@article{lin2022flownas,
  title={FlowNAS: Neural Architecture Search for Optical Flow Estimation},
  author={Zhiwei Lin, Tingting Liang, Taihong Xiao, Yongtao Wang, Zhi Tang, Ming-Hsuan Yang},
  journal={arXiv preprint arXiv:2207.01271},
  year={2022}
}