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README.md

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Instructions:

Testing (Inference)

  1. Please download the code from https://github.com/xmfbit/flownet2 or https://github.com/lmb-freiburg/flownet2 and follow the instructions there to compile the code. (If you encounter problems in compling the code, Please contact deqings@nvidia.com for a docker image)
  2. Put warp_layer.cu and warp_layer.cpp (in ./warping_code) to src/caffe/layers and warp_layer.hpp (in ./warping_code) to include/caffe/layers, recompile.
  3. Run python ./proc_images.py [img1.txt img2.txt out.txt]. Please compare your results with ./tmp/reference_frame_0010_forward.flo and ./tmp/reference_frame_0011_backward.flo.
  4. [TOO ADD] Modify the code and data directory in run_rob_test.py (lines 17-21); make sure that the ROB test images are in your disk.

The program assumes that images to process are of the same size.

Benchmarking time

Please modify the caffe directory below

KITTI resolution YOUR_DIRECTORY/flownet2/build/tools/caffe.bin time -model ./benchmark_time/pwc_net_1280_384_batch1.tpl.prototxt -weights ./model/pwc_net.caffemodel -iterations 100 -gpu 0;

Middlebury "Urban" resolution (accounting for the x2 image resizing) YOUR_DIRECTORY/flownet2/build/tools/caffe.bin time -model ./benchmark_time/pwc_net_1280_960_batch1.tpl.prototxt -weights ./model/pwc_net.caffemodel -iterations 100 -gpu 0;

Sintel resolution YOUR_DIRECTORY/flownet2/build/tools/caffe.bin time -model ./benchmark_time/pwc_net_1024_448_batch1.tpl.prototxt -weights ./model/pwc_net.caffemodel -iterations 100 -gpu 0;

HD1K resolution (requires a GPU with 16G+ memory, such as NVIDIA Tesla Volta 100) YOUR_DIRECTORY/flownet2/build/tools/caffe.bin time -model ./benchmark_time/pwc_net_2560_1088_batch1.tpl.prototxt -weights ./model/pwc_net.caffemodel -iterations 100 -gpu 0;

Training

1.Please download the FlyingChairs dataset from https://lmb.informatik.uni-freiburg.de/resources/datasets, make the LMDB file, modify the local directory in ./model/train.prototxt. 2.Modify the local directory in ./train.py and Run ./train.py

Method description

The model here is PWC-Net with a larger feature pyramid extractor (PWC-Net-feature-uparrow, second row in Table5(a) of Our CVPR 2018 paper below).

Paper & Citation

Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." CVPR 2018. Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume." arXiv preprint arXiv:1709.02371(https://arxiv.org/abs/1709.02371), 2017. Project webpage: http://research.nvidia.com/publication/2018-02_PWC-Net:-CNNs-for https://github.com/NVlabs/PWC-Net

If you use PWC-Net, please cite the following paper:

@InProceedings{Sun2018PWC-Net,
  author    = {Deqing Sun and Xiaodong Yang and Ming-Yu Liu and Jan Kautz},
  title     = {{PWC-Net}: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume},
  booktitle = CVPR,
  year      = {2018},
}

or the arXiv paper

@article{sun2017pwc,
  author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan},
  title={{PWC-Net}: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume},
  journal={arXiv preprint arXiv:1709.02371},
  year={2017}
}

Contact

Deqing Sun (deqings@nvidia.com)

License

Copyright (C) 2018 NVIDIA Corporation. All rights reserved.