https://github.com/vt-vl-lab/flownet2.pytorch
Off-the-shelf FlowNet module in PyTorch-0.3.0
https://github.com/vt-vl-lab/flownet2.pytorch
optical-flow
Last synced: 10 months ago
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Off-the-shelf FlowNet module in PyTorch-0.3.0
- Host: GitHub
- URL: https://github.com/vt-vl-lab/flownet2.pytorch
- Owner: vt-vl-lab
- License: apache-2.0
- Created: 2018-02-08T01:58:00.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-04-03T19:10:55.000Z (about 8 years ago)
- Last Synced: 2025-04-12T21:11:47.547Z (about 1 year ago)
- Topics: optical-flow
- Language: Python
- Homepage:
- Size: 1.68 MB
- Stars: 117
- Watchers: 10
- Forks: 35
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## FlowNet2 (PyTorch v0.3.0)
Pytorch implementation of [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://arxiv.org/abs/1612.01925). Most part are from this [repo](https://github.com/NVIDIA/flownet2-pytorch), we made it as a off-the-shelf package:
- After installation, just copy the whole folder FlowNet2_src to your codebase to use. See demo.py for details.
### Environment
This code has been test with Python3.6 and PyTorch0.3.0, with a Tesla K80 GPU. The system is Ubuntu 14.04.
### Installation
# install custom layers
cd FlowNet2_src
bash install.sh
Note: you might need to modify [here](https://github.com/vt-vl-lab/pytorch_flownet2/blob/master/FlowNet2_src/models/components/ops/channelnorm/make.sh#L10), [here](https://github.com/vt-vl-lab/pytorch_flownet2/blob/master/FlowNet2_src/models/components/ops/correlation/make.sh#L12), and [here](https://github.com/vt-vl-lab/pytorch_flownet2/blob/master/FlowNet2_src/models/components/ops/resample2d/make.sh#L10), according to the GPU you use.
### Converted Caffe Pre-trained Models
* [FlowNet2](https://drive.google.com/file/d/1hF8vS6YeHkx3j2pfCeQqqZGwA_PJq_Da/view?usp=sharing)[620MB]
* [FlowNet2-C](https://drive.google.com/file/d/1BFT6b7KgKJC8rA59RmOVAXRM_S7aSfKE/view?usp=sharing)[149MB]
* [FlowNet2-CS](https://drive.google.com/file/d/1iBJ1_o7PloaINpa8m7u_7TsLCX0Dt_jS/view?usp=sharing)[297MB]
* [FlowNet2-CSS](https://drive.google.com/file/d/157zuzVf4YMN6ABAQgZc8rRmR5cgWzSu8/view?usp=sharing)[445MB]
* [FlowNet2-CSS-ft-sd](https://drive.google.com/file/d/1R5xafCIzJCXc8ia4TGfC65irmTNiMg6u/view?usp=sharing)[445MB]
* [FlowNet2-S](https://drive.google.com/file/d/1V61dZjFomwlynwlYklJHC-TLfdFom3Lg/view?usp=sharing)[148MB]
* [FlowNet2-SD](https://drive.google.com/file/d/1QW03eyYG_vD-dT-Mx4wopYvtPu_msTKn/view?usp=sharing)[173MB]
### Inference mode
First download pre-trained models of FlowNet2 and modify the path, then
```
python demo.py
```
If installation is sucessful, you should see the following:
### Reference
If you find this implementation useful in your work, please acknowledge it appropriately and cite the paper using:
````
@InProceedings{IMKDB17,
author = "E. Ilg and N. Mayer and T. Saikia and M. Keuper and A. Dosovitskiy and T. Brox",
title = "FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
month = "Jul",
year = "2017",
url = "http://lmb.informatik.uni-freiburg.de//Publications/2017/IMKDB17"
}
````
### Acknowledgments
* [NVIDIA/flownet2-pytorch](https://github.com/NVIDIA/flownet2-pytorch): Most part
* [hellock/flownet2-pytorch](https://github.com/hellock/flownet2-pytorch): Python3.x and PyTorch0.3.0 support