Awesome

FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation

FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow Estimation
Xiaoyu Shi^*, Zhaoyang Huang^*, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, Hongsheng Li
CVPR 2023

Data Preparation

We first pretrain FlowFormer++ on the YoutubeVOS dataset. For finetuning, similar to RAFT, you will need to download the required datasets.

• YoutubeVOS
• FlyingChairs
• FlyingThings3D
• Sintel
• KITTI
• HD1K (optional)

By default datasets.py will search for the datasets in these locations. You can create symbolic links to wherever the datasets were downloaded in the datasets folder

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

Requirements

conda create --name flowformerpp
conda activate flowformerpp
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.1 matplotlib tensorboard scipy opencv -c pytorch
pip install yacs loguru einops timm==0.4.12 imageio

Training

The script will load the config according to the training stage. The trained model will be saved in a directory in logs. For example, the following script will load the config configs/pretrain_config.py. The trained model will be saved as logs/xxxx/final.

python -u pretrain_FlowFormer_maemask.py --stage youtube

To finish the entire finetuning schedule, you can run:

./run_train.sh

Models

We provide models trained in the four stages. The default path of the models for evaluation is:

├── checkpoints
    ├── chairs.pth
    ├── things.pth
    ├── sintel.pth
    ├── kitti.pth
    ├── things_288960.pth

Evaluation

The model to be evaluated is assigned by the _CN.model in the config file.

Evaluating the model on the Sintel training set and the KITTI training set. The corresponding config file is configs/submissions.py.

# with tiling technique
python evaluate_FlowFormer_tile.py --eval sintel_validation
python evaluate_FlowFormer_tile.py --eval kitti_validation --model checkpoints/things_kitti.pth

Generating the submission for the Sintel and KITTI benchmarks. The corresponding config file is configs/submissions.py.

python evaluate_FlowFormer_tile.py --eval sintel_submission
python evaluate_FlowFormer_tile.py --eval kitti_submission

Visualizing the sintel dataset:

python visualize_flow.py --eval_type sintel --keep_size

Visualizing an image sequence extracted from a video:

python visualize_flow.py --eval_type seq

The default image sequence format is:

├── demo_data
    ├── mihoyo
        ├── 000001.png
        ├── 000002.png
        ├── 000003.png
            .
            .
            .
        ├── 001000.png

License

FlowFormer++ is released under the Apache License

Citation

@inproceedings{shi2023flowformer++,
  title={Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation},
  author={Shi, Xiaoyu and Huang, Zhaoyang and Li, Dasong and Zhang, Manyuan and Cheung, Ka Chun and See, Simon and Qin, Hongwei and Dai, Jifeng and Li, Hongsheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={1599--1610},
  year={2023}
}

Acknowledgement

In this project, we use parts of codes in:

• RAFT
• GMA
• timm
• FlowFormer