{"id":20434219,"url":"https://github.com/vt-vl-lab/pwc-net.pytorch","last_synced_at":"2025-04-12T21:11:45.625Z","repository":{"id":47223898,"uuid":"191706713","full_name":"vt-vl-lab/pwc-net.pytorch","owner":"vt-vl-lab","description":"Off-the-shelf PWC-Net module in PyTorch-1.0＋","archived":false,"fork":false,"pushed_at":"2022-12-08T05:15:21.000Z","size":67123,"stargazers_count":32,"open_issues_count":8,"forks_count":6,"subscribers_count":4,"default_branch":"master","last_synced_at":"2025-03-26T15:21:19.084Z","etag":null,"topics":["optical-flow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vt-vl-lab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-06-13T06:46:35.000Z","updated_at":"2024-12-08T07:44:08.000Z","dependencies_parsed_at":"2023-01-24T12:15:43.474Z","dependency_job_id":null,"html_url":"https://github.com/vt-vl-lab/pwc-net.pytorch","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vt-vl-lab%2Fpwc-net.pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vt-vl-lab%2Fpwc-net.pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vt-vl-lab%2Fpwc-net.pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vt-vl-lab%2Fpwc-net.pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vt-vl-lab","download_url":"https://codeload.github.com/vt-vl-lab/pwc-net.pytorch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248631681,"owners_count":21136562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["optical-flow"],"created_at":"2024-11-15T08:25:14.173Z","updated_at":"2025-04-12T21:11:45.600Z","avatar_url":"https://github.com/vt-vl-lab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## PWC-Net (PyTorch v1.0.1)\n\nPytorch implementation of [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://arxiv.org/pdf/1709.02371.pdf). We made it as a off-the-shelf package:\n- After installation, just copy the whole folder `PWC_src` to your codebase to use. See demo.py for details.\n\n### Environment\n\nThis 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`.\n\n### Installation \n\n    # install custom layers\n    cd PWC_src/correlation_package\n    python setup.py install\n\nNote: you might need to add `gencode` [here](https://github.com/vt-vl-lab/pwc-net.pytorch/blob/master/PWC_src/correlation_package/setup.py#L9), according to the GPU you use. You can find more information about `gencode` [here](https://developer.nvidia.com/cuda-gpus) and [here](http://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/).\n\n### Converted Caffe Pre-trained Models\nYou can find them in `models` folder.\n\n### Inference mode\nModify the path to your input, then\n\n```\npython demo.py\n```    \n\nIf installation is sucessful, you should see the following:\n![PWC-Net Sample Prediction](https://github.com/vt-vl-lab/pwc-net.pytorch/blob/master/misc/demo.png)\n   \n### Reference \nIf you find this implementation useful in your work, please acknowledge it appropriately and cite the paper using:\n````\n@inproceedings{sun2018pwc,\n  title={PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume},\n  author={Sun, Deqing and Yang, Xiaodong and Liu, Ming-Yu and Kautz, Jan},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n  pages={8934--8943},\n  year={2018}\n}\n````\n\n### Acknowledgments\n* [sniklaus/pytorch-pwc](https://github.com/sniklaus/pytorch-pwc): Network defintion and converted PyTorch model weights.\n* [NVIDIA/flownet2-pytorch](https://github.com/NVIDIA/flownet2-pytorch): Correlation module.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvt-vl-lab%2Fpwc-net.pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvt-vl-lab%2Fpwc-net.pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvt-vl-lab%2Fpwc-net.pytorch/lists"}