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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 [email protected] 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. 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.
  4. 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.

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

TBA

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 ([email protected])

License

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