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ptlflow

(★ 549)

PyTorch Lightning Optical Flow models, scripts, and pretrained weights.

파일 탐색기

최종 버전 다운로드 (.zip)

- - build.yml
  - lightning.yml
  - publish_pypi.yml
  - python.yml
  - pytorch.yml
- - model_benchmark_all.yaml
  - validate_all.yaml
  - validate_all_things.yaml
- - conf.py
  - index.rst
- make.bat
- Makefile
- requirements.txt
- create_compare_paper.sh
- create_metric_summaries.sh
- create_metrics.sh
- create_plots.sh
- - __init__.py
  - AutoFlow_val.txt
  - datasets.py
  - flow_datamodule.py
  - flow_transforms.py
  - FlyingChairs_val.txt
  - Hd1k_val.txt
  - Kitti2012_val.txt
  - Kitti2015_val.txt
  - Sintel_val.txt
  - split_autoflow.py
- - __init__.py
  - README.md
- - __init__.py
  - correlation.py
  - dummy_datasets.py
  - flow_metrics.py
  - flow_utils.py
  - flowpy_torch.py
  - io_adapter.py
  - registry.py
  - timer.py
  - utils.py
- __init__.py
- - test_lightning.py
- test_infer.py
- test_model_benchmark.py
- test_summary_metrics.py
- test_test.py
- test_validate.py
.gitignore
.readthedocs.yaml
compare_paper_results.py
CONTRIBUTING.md
datasets.yaml
infer.py
LICENSE
MANIFEST.in
model_benchmark.py
plot_results.py
pyproject.toml
README.md
requirements.txt
summary_metrics.py
test.py
train.py
validate.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/hmorimitsu/ptlflow

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd ptlflow

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r ptlflow/models/dpflow/requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# PyTorch Lightning Optical Flow

![GitHub CI python status](https://github.com/hmorimitsu/ptlflow/actions/workflows/python.yml/badge.svg)
![GitHub CI pytorch status](https://github.com/hmorimitsu/ptlflow/actions/workflows/pytorch.yml/badge.svg)
![GitHub CI lightning status](https://github.com/hmorimitsu/ptlflow/actions/workflows/lightning.yml/badge.svg)
![GitHub CI build status](https://github.com/hmorimitsu/ptlflow/actions/workflows/build.yml/badge.svg)

> [!IMPORTANT]
> 🏆 **RoCo-Spring: The Robust Correspondence Challenge @ NeurIPS 2026**
>
> We are organizing **RoCo-Spring**, a NeurIPS 2026 Challenge on robust dense correspondence under realistic distribution shifts!
> 
> * **Tracks:** Optical Flow | Scene Flow | Stereo Matching | Exploration Track
> * **Benchmarks:** Evaluated on high-detail [Spring](https://spring-benchmark.org/) & RobustSpring (blur, color, noise, quality, and weather shifts)
> * **Key Dates:** Workshop Paper Submission: Sept 15, 2026 | Final Quantitative Submission: Sept 30, 2026
>
> 📌 **Learn more & register:** [Challenge Website](https://roco-spring.github.io/index.html) | [Starter Kit Devkit](https://github.com/hmorimitsu/roco-spring-devkit)

## Introduction

This is a collection of state-of-the-art deep model for estimating optical flow. The main goal is to provide a unified framework where multiple models can be trained and tested more easily.

The work and code from many others are present here. I tried to make sure everything is properly referenced, but please let me know if I missed something.

This is still under development, so some things may not work as intended. I plan to add more models in the future, as well keep improving the platform.

- [What's new](#whats-new)
- [Available models](#available-models)
- [Results](#results)
- [Getting started](#getting-started)
- [Licenses](#licenses)
- [Contributing](#contributing)
- [Citing](#citing)
- [Acknowledgements](#acknowledgements)

## What's new

###  - v0.4.2

- Added new models:
  - Flow-Anything [https://arxiv.org/abs/2506.07740](https://arxiv.org/abs/2506.07740)
  - FlowSeek [https://arxiv.org/abs/2509.05297](https://arxiv.org/abs/2509.05297)
  - MEMFOF [https://arxiv.org/abs/2506.23151](https://arxiv.org/abs/2506.23151)
  - ReCoVEr [https://arxiv.org/abs/2510.13317](https://arxiv.org/abs/2510.13317)
  - WAFT [https://arxiv.org/abs/2506.21526](https://arxiv.org/abs/2506.21526)
- Additions / fixes:
  - Fix compatibility with Lightning < 2.7
  - Add support to log to SwanLab

###  - v0.4.1

- Added new models:
  - DPFlow [https://arxiv.org/abs/2503.14880](https://arxiv.org/abs/2503.14880)
  - StreamFlow [https://arxiv.org/abs/2311.17099](https://arxiv.org/abs/2311.17099)
- Additions / fixes:
  - Add 4K read mode for Spring dataset
  - Add missing arguments for Middlebury-ST and VIPER datasets
  - Fix RAFT config files
  - Fix RAPIDFlow config files

###  - v0.4.0

Major update to support Lightning 2 (finally!). However, it also introduces breaking changes from the previous v0.3 code. See the details below.

- Transitioning from v0.3 to v0.4: check the [v0.4 upgrade guide](https://ptlflow.readthedocs.io/en/latest/starting/v04_upgrade_guide.html)
- Added features:
  - Support for YAML config files. See the [config file documentation](https://ptlflow.readthedocs.io/en/latest/starting/config_files.html)
  - Table [comparing PTLFlow results with the original papers](https://ptlflow.readthedocs.io/en/latest/results/paper_ptlflow.html) to check the stability of the included models.
- Added new models:
  - NeuFlow v2 [https://arxiv.org/abs/2408.10161](https://arxiv.org/abs/2408.10161)
- Add support for more datasets:
  - Middlebury-ST [https://vision.middlebury.edu/stereo/data/scenes2014/]{https://vision.middlebury.edu/stereo/data/scenes2014/}
  - VIPER [https://playing-for-benchmarks.org/](https://playing-for-benchmarks.org/)

###  - v0.3.2

- Added new models:
  - MemFlow [https://arxiv.org/abs/2404.04808](https://arxiv.org/abs/2404.04808)
  - NeuFlow [https://arxiv.org/abs/2403.10425](https://arxiv.org/abs/2403.10425)
  - SEA-RAFT [https://arxiv.org/abs/2405.14793](https://arxiv.org/abs/2405.14793)
  - SplatFlow [https://arxiv.org/abs/2306.08887](https://arxiv.org/abs/2306.08887)
- Add support for more datasets:
  - TartanAir [https://theairlab.org/tartanair-dataset/](https://theairlab.org/tartanair-dataset/)
  - Kubric [https://github.com/google-research/kubric](https://github.com/google-research/kubric)
- Add ONNX and TensorRT conversion to RAPIDFlow
- Fix LR scheduler when accumulating gradients

###  - v0.3.1

- Added new models:
  - CCMR [https://arxiv.org/abs/2311.02661](https://arxiv.org/abs/2311.02661)
  - LLA-Flow [https://arxiv.org/abs/2304.08101](https://arxiv.org/abs/2304.08101)
  - RAPIDFlow [https://hmorimitsu.com/publication/2024-icra-rapidflow/](https://hmorimitsu.com/publication/2024-icra-rapidflow/)
- Enable FP16 in most models.
  - Except the following models, since they have operations that cannot run in FP16: lcv_raft, matchflow, and separableflow
- Add FP16 mode in infer, model_benchmark, and validate scripts
- Create [plot_results.py](plot_results.py) script
- Move resize operations to CUDA (thanks to coca-huang)

###  - v0.3.0

- Added new models:
  - DIP [https://arxiv.org/abs/2204.00330](https://arxiv.org/abs/2204.00330)
  - Flow1D [https://arxiv.org/abs/2103.04524](https://arxiv.org/abs/2103.04524)
  - FlowFormer++ [https://arxiv.org/abs/2303.01237](https://arxiv.org/abs/2303.01237)
  - GMFlow+, UniMatch [https://arxiv.org/abs/2211.05783](https://arxiv.org/abs/2211.05783)
  - MatchFlow [https://arxiv.org/abs/2303.08384](https://arxiv.org/abs/2303.08384)
  - MS-RAFT+ [https://arxiv.org/abs/2210.16900](https://arxiv.org/abs/2210.16900)
  - RPKNet [https://hmorimitsu.com/publication/2024-aaai-rpknet](https://hmorimitsu.com/publication/2024-aaai-rpknet)
  - SeparableFlow [https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Separable_Flow_Learning_Motion_Cost_Volumes_for_Optical_Flow_Estimation_ICCV_2021_paper.pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Separable_Flow_Learning_Motion_Cost_Volumes_for_Optical_Flow_Estimation_ICCV_2021_paper.pdf)
  - SKFlow [https://arxiv.org/abs/2205.14623](https://arxiv.org/abs/2205.14623)
  - VideoFlow [https://arxiv.org/abs/2303.08340](https://arxiv.org/abs/2303.08340)
- `speed_benchmark.py` becomes `model_benchmark.py` and records more metrics
- Fix compatibility with PyTorch 2.0
- Fix compatibility with PyTorch Lightning 1.9
- Fix resizing augmentation when the valid mask is sparse
- Add support for more datasets:
  - Middlebury [https://vision.middlebury.edu/flow/](https://vision.middlebury.edu/flow/)
  - Monkaa [https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html)
  - Spring [https://spring-benchmark.org/](https://spring-benchmark.org/)

## Available models

- CCMR [https://arxiv.org/abs/2311.02661](https://arxiv.org/abs/2311.02661)
- CRAFT [https://arxiv.org/abs/2203.16896](https://arxiv.org/abs/2203.16896)
- CSFlow [https://arxiv.org/abs/2202.00909](https://arxiv.org/abs/2202.00909)
- DICL-Flow [https://arxiv.org/abs/2010.14851](https://arxiv.org/abs/2010.14851)
- DIP [https://arxiv.org/abs/2204.00330](https://arxiv.org/abs/2204.00330)
- DPFlow [https://arxiv.org/abs/2503.14880](https://arxiv.org/abs/2503.14880)
- FastFlowNet [https://arxiv.org/abs/2103.04524](https://arxiv.org/abs/2103.04524)
- Flow1D [https://arxiv.org/abs/2103.04524](https://arxiv.org/abs/2103.04524)
- Flow-Anything [https://arxiv.org/abs/2506.07740](https://arxiv.org/abs/2506.07740)
- FlowFormer [https://arxiv.org/abs/2203.16194](https://arxiv.org/abs/2203.16194)
- FlowFormer++ [https://arxiv.org/abs/2303.01237](https://arxiv.org/abs/2303.01237)
- FlowNet [https://arxiv.org/abs/1504.06852](https://arxiv.org/abs/1504.06852)
- FlowNet2 [https://arxiv.org/abs/1612.01925](https://arxiv.org/abs/1612.01925)
- FlowSeek [https://arxiv.org/abs/2509.05297](https://arxiv.org/abs/2509.05297)
- GMA [https://arxiv.org/abs/2104.02409](https://arxiv.org/abs/2104.02409)
- GMFlow [https://arxiv.org/abs/2111.13680](https://arxiv.org/abs/2111.13680)
- GMFlow+, UniMatch [https://arxiv.org/abs/2211.05783](https://arxiv.org/abs/2211.05783)
- GMFlowNet [https://arxiv.org/abs/2203.11335](https://arxiv.org/abs/2203.11335)
- HD3 [https://arxiv.org/abs/1812.06264](https://arxiv.org/abs/1812.06264)
- IRR [https://arxiv.org/abs/1904.05290](https://arxiv.org/abs/1904.05290)
- LCV [https://arxiv.org/abs/2007.11431](https://arxiv.org/abs/2007.11431)
- LiteFlowNet [https://arxiv.org/abs/1805.07036](https://arxiv.org/abs/1805.07036)
- LiteFlowNet2 [https://arxiv.org/abs/1903.07414](https://arxiv.org/abs/1903.07414)
- LiteFlowNet3 [https://arxiv.org/abs/2007.09319](https://arxiv.org/abs/2007.09319)
- LLA-Flow [https://arxiv.org/abs/2304.08101](https://arxiv.org/abs/2304.08101)
- MaskFlownet [https://arxiv.org/abs/2003.10955](https://arxiv.org/abs/2003.10955)
- MatchFlow [https://arxiv.org/abs/2303.08384](https://arxiv.org/abs/2303.08384)
- MemFlow [https://arxiv.org/abs/2404.04808](https://arxiv.org/abs/2404.04808)
- MEMFOF [https://arxiv.org/abs/2506.23151](https://arxiv.org/abs/2506.23151)
- MS-RAFT+ [https://arxiv.org/abs/2210.16900](https://arxiv.org/abs/2210.16900)
- NeuFlow v1 [https://arxiv.org/abs/2403.10425](https://arxiv.org/abs/2403.10425)
- NeuFlow v2 [https://arxiv.org/abs/2408.10161](https://arxiv.org/abs/2408.10161)
- PWCNet [https://arxiv.org/abs/1709.02371](https://arxiv.org/abs/1709.02371)
- RAFT [https://arxiv.org/abs/2003.12039](https://arxiv.org/abs/2003.12039)
- RAPIDFlow [https://hmorimitsu.com/publication/2024-icra-rapidflow/](https://hmorimitsu.com/publication/2024-icra-rapidflow/)
- ReCoVEr [https://arxiv.org/abs/2510.13317](https://arxiv.org/abs/2510.13317)
- RPKNet [https://hmorimitsu.com/publication/2024-aaai-rpknet](https://hmorimitsu.com/publication/2024-aaai-rpknet)
- ScopeFlow [https://arxiv.org/abs/2002.10770](https://arxiv.org/abs/2002.10770)
- SCV [https://arxiv.org/abs/2104.02166](https://arxiv.org/abs/2104.02166)
- SEA-RAFT [https://arxiv.org/abs/2405.14793](https://arxiv.org/abs/2405.14793)
- SeparableFlow [https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Separable_Flow_Learning_Motion_Cost_Volumes_for_Optical_Flow_Estimation_ICCV_2021_paper.pdf](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhang_Separable_Flow_Learning_Motion_Cost_Volumes_for_Optical_Flow_Estimation_ICCV_2021_paper.pdf)
- SKFlow [https://arxiv.org/abs/2205.14623](https://arxiv.org/abs/2205.14623)
- SplatFlow [https://arxiv.org/abs/2306.08887](https://arxiv.org/abs/2306.08887)
- STaRFlow [https://arxiv.org/abs/2007.05481](https://arxiv.org/abs/2007.05481)
- StreamFlow [https://arxiv.org/abs/2311.17099](https://arxiv.org/abs/2311.17099)
- VCN [https://papers.nips.cc/paper/2019/file/bbf94b34eb32268ada57a3be5062fe7d-Paper.pdf](https://papers.nips.cc/paper/2019/file/bbf94b34eb32268ada57a3be5062fe7d-Paper.pdf)
- VideoFlow [https://arxiv.org/abs/2303.08340](https://arxiv.org/abs/2303.08340)
- WAFT [https://arxiv.org/abs/2506.21526](https://arxiv.org/abs/2506.21526)

Read more details about the models on [https://ptlflow.readthedocs.io/en/latest/models/models_list.html](https://ptlflow.readthedocs.io/en/latest/models/models_list.html).

# Results

You can see a table with main evaluation results of the available models [here](https://ptlflow.readthedocs.io/en/latest/results/accuracy_epe.html). More results are also available in the folder [docs/source/results](docs/source/results).

**Disclaimer**: These results are the ones obtained by evaluating the available models in this framework in my machine. Your results may be different due to differences in hardware and software. I also do not guarantee that the results of each model will be similar to the ones presented in the respective papers or other original sources. If you need to replicate the original results from a paper, you should use the original implementations.

## Getting started

Please take a look at the [documentation](https://ptlflow.readthedocs.io/) to learn how to install and use PTLFlow.

You can also check the notebooks below running on Google Colab for some practical examples:

- [Inference with a pretrained model](https://colab.research.google.com/drive/1_WXvIRweQJgex0X-HS0LFXBb0IWZIvR4?usp=sharing).
- [Training and using the learned weights for inference](https://colab.research.google.com/drive/1b_SMGSXh9F9TkinqZt0c64EH-GE87HVi?usp=sharing).

If you are using the previous v0.3.X code, then check the [v0.3.2 documentation](https://ptlflow.readthedocs.io/en/v0.3.2/) and the following example notebooks:

- [Inference with a pretrained model (PTLFlow v0.3)](https://colab.research.google.com/drive/1YARBRUGplqTRnRuY9sKIs6LY_2kWAWZJ?usp=sharing).
- [Training and using the learned weights for inference (PTLFlow v0.3)](https://colab.research.google.com/drive/1mbuAEF728_jZpFEsQHXDxjIGAcB1-nVs?usp=sharing).

## Licenses

The original code of this repository is licensed under the [Apache 2.0 license](LICENSE).

Each model may be subjected to different licenses. The license of each model is included in their respective folders. It is your responsibility to make sure that your project is in compliance with all the licenses and conditions involved.

The external pretrained weights all have different licenses, which are listed in their respective folders.

The pretrained weights that were trained within this project are available under the [CC BY-NC-SA 4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/), which I believe that covers the licenses of the datasets used in the training. That being said, I am not a legal expert so if you plan to use them to any purpose other than research, you should check all the involved licenses by yourself. Additionally, the datasets used for the training usually require the user to cite the original papers, so be sure to include their respective references in your work.

## Contributing

Contribution are welcome! Please check [CONTRIBUTING.md](CONTRIBUTING.md) to see how to contribute.

## Citing

### BibTeX

```
@misc{morimitsu2021ptlflow,
  author = {Henrique Morimitsu},
  title = {PyTorch Lightning Optical Flow},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/hmorimitsu/ptlflow}}
}
```

## Acknowledgements

- This README file is heavily inspired by the one from the [timm](https://github.com/rwightman/pytorch-image-models) repository.
- Some parts of the code were inspired by or taken from [FlowNetPytorch](https://github.com/ClementPinard/FlowNetPytorch).
- [flownet2-pytorch](https://github.com/NVIDIA/flownet2-pytorch) was also another important source.
- The current main training routine is based on [RAFT](https://github.com/princeton-vl/RAFT).