Pytorch implementation of PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume. We made it as a off-the-shelf package:
- After installation, just copy the whole folder
PWC_srcto your codebase to use. See demo.py for details.
This code has been test with Python3.6 and PyTorch1.0.1, with a Tesla K80 GPU. The system is Ubuntu 14.04, and the CUDA version is 10.0. All the required python packages can be found in requirements.txt.
# install custom layers
cd PWC_src/correlation_package
python setup.py install
Note: you might need to add gencode here, according to the GPU you use. You can find more information about gencode here and here.
You can find them in models folder.
Modify the path to your input, then
python demo.py
If installation is sucessful, you should see the following:
If you find this implementation useful in your work, please acknowledge it appropriately and cite the paper using:
@inproceedings{sun2018pwc,
title={PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume},
author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={8934--8943},
year={2018}
}
- sniklaus/pytorch-pwc: Network defintion and converted PyTorch model weights.
- NVIDIA/flownet2-pytorch: Correlation module.