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-[](https://mmpose.readthedocs.io/en/latest/?badge=latest)
-[](https://github.com/open-mmlab/mmpose/actions)
-[](https://codecov.io/gh/open-mmlab/mmpose)
-[](https://pypi.org/project/mmpose/)
-[](https://github.com/open-mmlab/mmpose/blob/master/LICENSE)
-[](https://github.com/open-mmlab/mmpose/issues)
-[](https://github.com/open-mmlab/mmpose/issues)
-
-[📘Documentation](https://mmpose.readthedocs.io/en/v0.28.0/) |
-[🛠️Installation](https://mmpose.readthedocs.io/en/v0.28.0/install.html) |
-[👀Model Zoo](https://mmpose.readthedocs.io/en/v0.28.0/modelzoo.html) |
-[📜Papers](https://mmpose.readthedocs.io/en/v0.28.0/papers/algorithms.html) |
-[🆕Update News](https://mmpose.readthedocs.io/en/v0.28.0/changelog.html) |
-[🤔Reporting Issues](https://github.com/open-mmlab/mmpose/issues/new/choose)
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- OpenMMLab website
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- HOT
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-
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- OpenMMLab platform
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- TRY IT OUT
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-
-English | [简体中文](README_CN.md)
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-
-
-## Introduction
-
-MMPose is an open-source toolbox for pose estimation based on PyTorch.
-It is a part of the [OpenMMLab project](https://github.com/open-mmlab).
-
-The master branch works with **PyTorch 1.5+**.
-
-https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
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-
-
-
-## What's New
-
-- 2022-07-06: MMPose [v0.28.0](https://github.com/open-mmlab/mmpose/releases/tag/v0.28.0) is released. Major updates include:
- - Support [TCFormer](https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html) (CVPR'2022). See the [model page](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/tcformer_coco-wholebody.md)
- - Add [RLE](https://arxiv.org/abs/2107.11291) pre-trained model on COCO dataset. See the [model page](/configs/body/2d_kpt_sview_rgb_img/deeppose/coco/resnet_rle_coco.md)
- - Update [Swin](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/swin_coco.md) models with better performance
-- 2022-02-28: MMPose model deployment is supported by [MMDeploy](https://github.com/open-mmlab/mmdeploy) v0.3.0
- MMPose Webcam API is a simple yet powerful tool to develop interactive webcam applications with MMPose features.
-- 2021-12-29: OpenMMLab Open Platform is online! Try our [pose estimation demo](https://platform.openmmlab.com/web-demo/demo/poseestimation)
-
-## Installation
-
-MMPose depends on [PyTorch](https://pytorch.org/) and [MMCV](https://github.com/open-mmlab/mmcv).
-Below are quick steps for installation.
-Please refer to [install.md](docs/en/install.md) for detailed installation guide.
-
-```shell
-conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
-conda activate open-mmlab
-pip3 install openmim
-mim install mmcv-full
-git clone https://github.com/open-mmlab/mmpose.git
-cd mmpose
-pip3 install -e .
-```
-
-## Getting Started
-
-Please see [get_started.md](docs/en/get_started.md) for the basic usage of MMPose.
-There are also tutorials:
-
-- [learn about configs](docs/en/tutorials/0_config.md)
-- [finetune model](docs/en/tutorials/1_finetune.md)
-- [add new dataset](docs/en/tutorials/2_new_dataset.md)
-- [customize data pipelines](docs/en/tutorials/3_data_pipeline.md)
-- [add new modules](docs/en/tutorials/4_new_modules.md)
-- [export a model to ONNX](docs/en/tutorials/5_export_model.md)
-- [customize runtime settings](docs/en/tutorials/6_customize_runtime.md)
-
-## Model Zoo
-
-Results and models are available in the *README.md* of each method's config directory.
-A summary can be found in the [Model Zoo](https://mmpose.readthedocs.io/en/latest/modelzoo.html) page.
-
-Major Features
- -- **Support diverse tasks** - - We support a wide spectrum of mainstream pose analysis tasks in current research community, including 2d multi-person human pose estimation, 2d hand pose estimation, 2d face landmark detection, 133 keypoint whole-body human pose estimation, 3d human mesh recovery, fashion landmark detection and animal pose estimation. - See [demo.md](demo/README.md) for more information. - -- **Higher efficiency and higher accuracy** - - MMPose implements multiple state-of-the-art (SOTA) deep learning models, including both top-down & bottom-up approaches. We achieve faster training speed and higher accuracy than other popular codebases, such as [HRNet](https://github.com/leoxiaobin/deep-high-resolution-net.pytorch). - See [benchmark.md](docs/en/benchmark.md) for more information. - -- **Support for various datasets** - - The toolbox directly supports multiple popular and representative datasets, COCO, AIC, MPII, MPII-TRB, OCHuman etc. - See [data_preparation.md](docs/en/data_preparation.md) for more information. - -- **Well designed, tested and documented** - - We decompose MMPose into different components and one can easily construct a customized - pose estimation framework by combining different modules. - We provide detailed documentation and API reference, as well as unittests. - -
-
-
-Supported algorithms:
- -- [x] [DeepPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014) -- [x] [CPM](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#cpm-cvpr-2016) (CVPR'2016) -- [x] [Hourglass](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hourglass-eccv-2016) (ECCV'2016) -- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017) -- [x] [Associative Embedding](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017) -- [x] [HMR](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#hmr-cvpr-2018) (CVPR'2018) -- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018) -- [x] [HRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019) -- [x] [VideoPose3D](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019) -- [x] [HRNetv2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019) -- [x] [MSPN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019) -- [x] [SCNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#scnet-cvpr-2020) (CVPR'2020) -- [x] [HigherHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020) -- [x] [RSN](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#rsn-eccv-2020) (ECCV'2020) -- [x] [InterNet](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#internet-eccv-2020) (ECCV'2020) -- [x] [VoxelPose](https://mmpose.readthedocs.io/en/latest/papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020) -- [x] [LiteHRNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021) -- [x] [ViPNAS](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021) - -
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-Supported techniques:
- -- [x] [FPN](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fpn-cvpr-2017) (CVPR'2017) -- [x] [FP16](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017) -- [x] [Wingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018) -- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019) -- [x] [DarkPose](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020) -- [x] [UDP](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020) -- [x] [Albumentations](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#albumentations-information-2020) (Information'2020) -- [x] [SoftWingloss](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021) -- [x] [SmoothNet](/configs/_base_/filters/smoothnet_h36m.md) (arXiv'2021) -- [x] [RLE](https://mmpose.readthedocs.io/en/latest/papers/techniques.html#rle-iccv-2021) (ICCV'2021) - -
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-Supported datasets:
- -- [x] [AFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011) -- [x] [sub-JHMDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013) -- [x] [COFW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013) -- [x] [MPII](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014) -- [x] [Human3.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014) -- [x] [COCO](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014) -- [x] [CMU Panoptic](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015) -- [x] [DeepFashion](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016) -- [x] [300W](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016) -- [x] [RHD](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017) -- [x] [CMU Panoptic HandDB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#cmu-panoptic-handdb-cvpr-2017) \[[homepage](http://domedb.perception.cs.cmu.edu/handdb.html)\] (CVPR'2017) -- [x] [AI Challenger](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017) -- [x] [MHP](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018) -- [x] [WFLW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018) -- [x] [PoseTrack18](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018) -- [x] [OCHuman](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019) -- [x] [CrowdPose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019) -- [x] [MPII-TRB](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019) -- [x] [FreiHand](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019) -- [x] [Animal-Pose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019) -- [x] [OneHand10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019) -- [x] [Vinegar Fly](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019) -- [x] [Desert Locust](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) -- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) -- [x] [ATRW](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020) -- [x] [Halpe](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020) -- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020) -- [x] [MacaquePose](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020) -- [x] [InterHand2.6M](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020) -- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021) -- [x] [Horse-10](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021) - -
-
-
-### Model Request
-
-We will keep up with the latest progress of the community, and support more popular algorithms and frameworks. If you have any feature requests, please feel free to leave a comment in [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/9).
-
-### Benchmark
-
-#### Accuracy and Training Speed
-
-MMPose achieves superior of training speed and accuracy on the standard keypoint detection benchmarks like COCO. See more details at [benchmark.md](docs/en/benchmark.md).
-
-#### Inference Speed
-
-We summarize the model complexity and inference speed of major models in MMPose, including FLOPs, parameter counts and inference speeds on both CPU and GPU devices with different batch sizes. Please refer to [inference_speed_summary.md](docs/en/inference_speed_summary.md) for more details.
-
-## Data Preparation
-
-Please refer to [data_preparation.md](docs/en/data_preparation.md) for a general knowledge of data preparation.
-
-## FAQ
-
-Please refer to [FAQ](docs/en/faq.md) for frequently asked questions.
-
-## Contributing
-
-We appreciate all contributions to improve MMPose. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.
-
-## Acknowledgement
-
-MMPose is an open source project that is contributed by researchers and engineers from various colleges and companies.
-We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
-We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new models.
-
-## Citation
-
-If you find this project useful in your research, please consider cite:
-
-```bibtex
-@misc{mmpose2020,
- title={OpenMMLab Pose Estimation Toolbox and Benchmark},
- author={MMPose Contributors},
- howpublished = {\url{https://github.com/open-mmlab/mmpose}},
- year={2020}
-}
-```
-
-## License
-
-This project is released under the [Apache 2.0 license](LICENSE).
-
-## Projects in OpenMMLab
-
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
-- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
-- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
-- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
-- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
-- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
-- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.
diff --git a/SMPLer-X/main/transformer_utils/mmpose_README_CN.md b/SMPLer-X/main/transformer_utils/mmpose_README_CN.md
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@@ -1,311 +0,0 @@
-Supported backbones:
- -- [x] [AlexNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012) -- [x] [VGG](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#vgg-iclr-2015) (ICLR'2015) -- [x] [ResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnet-cvpr-2016) (CVPR'2016) -- [x] [ResNext](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnext-cvpr-2017) (CVPR'2017) -- [x] [SEResNet](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018) -- [x] [ShufflenetV1](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018) -- [x] [ShufflenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018) -- [x] [MobilenetV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018) -- [x] [ResNetV1D](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019) -- [x] [ResNeSt](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020) -- [x] [Swin](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#swin-cvpr-2021) (CVPR'2021) -- [x] [HRFormer](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#hrformer-nips-2021) (NIPS'2021) -- [x] [PVT](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvt-iccv-2021) (ICCV'2021) -- [x] [PVTV2](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022) - -
-
-
-
-
-[](https://mmpose.readthedocs.io/en/latest/?badge=latest)
-[](https://github.com/open-mmlab/mmpose/actions)
-[](https://codecov.io/gh/open-mmlab/mmpose)
-[](https://pypi.org/project/mmpose/)
-[](https://github.com/open-mmlab/mmpose/blob/master/LICENSE)
-[](https://github.com/open-mmlab/mmpose/issues)
-[](https://github.com/open-mmlab/mmpose/issues)
-
-[📘文档](https://mmpose.readthedocs.io/zh_CN/v0.28.0/) |
-[🛠️安装](https://mmpose.readthedocs.io/zh_CN/v0.28.0/install.html) |
-[👀模型库](https://mmpose.readthedocs.io/zh_CN/v0.28.0/modelzoo.html) |
-[📜论文库](https://mmpose.readthedocs.io/zh_CN/v0.28.0/papers/algorithms.html) |
-[🆕更新日志](https://mmpose.readthedocs.io/en/v0.28.0/changelog.html) |
-[🤔报告问题](https://github.com/open-mmlab/mmpose/issues/new/choose)
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-
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-
-
- OpenMMLab 官网
-
-
- HOT
-
-
-
- OpenMMLab 开放平台
-
-
- TRY IT OUT
-
-
-
-
-
-[English](README.md) | 简体中文
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-
-
-## 简介
-
-MMPose 是一款基于 PyTorch 的姿态分析的开源工具箱，是 [OpenMMLab](http://openmmlab.org/) 项目的成员之一。
-
-主分支代码目前支持 **PyTorch 1.5 以上**的版本。
-
-https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-84f6-24eeddbf4d91.mp4
-
-
-
-
-## 最新进展
-
-- 2022-07-06: MMPose [v0.28.0](https://github.com/open-mmlab/mmpose/releases/tag/v0.28.0) 已经发布. 主要更新包括:
- - 支持了新的主干网络 [TCFormer](https://openaccess.thecvf.com/content/CVPR2022/html/Zeng_Not_All_Tokens_Are_Equal_Human-Centric_Visual_Analysis_via_Token_CVPR_2022_paper.html) (CVPR'2022)，详见 [模型信息](/configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/tcformer_coco-wholebody.md)
- - 增加了 [RLE](https://arxiv.org/abs/2107.11291) 在 COCO 数据集上的模型，详见 [模型信息](/configs/body/2d_kpt_sview_rgb_img/deeppose/coco/resnet_rle_coco.md)
- - 优化了 [Swin](/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/swin_coco.md) 模型精度
-- 2022-04: MMPose 代码可以通过 [Gitee](https://gitee.com/open-mmlab/mmpose) 访问
-- 2022-02-28: [MMDeploy](https://github.com/open-mmlab/mmdeploy) v0.3.0 支持 MMPose 模型部署
-- 2021-12-29: OpenMMLab 开放平台已经正式上线! 欢迎试用基于 MMPose 的[姿态估计 Demo](https://platform.openmmlab.com/web-demo/demo/poseestimation)
-
-## 安装
-
-MMPose 依赖 [PyTorch](https://pytorch.org/) 和 [MMCV](https://github.com/open-mmlab/mmcv)，以下是安装的简要步骤。
-更详细的安装指南请参考 [install.md](docs/zh_cn/install.md)。
-
-```shell
-conda create -n open-mmlab python=3.8 pytorch=1.10 cudatoolkit=11.3 torchvision -c pytorch -y
-conda activate open-mmlab
-pip3 install openmim
-mim install mmcv-full
-git clone https://github.com/open-mmlab/mmpose.git
-cd mmpose
-pip3 install -e .
-```
-
-## 教程
-
-请参考 [get_started.md](docs/zh_cn/get_started.md) 了解 MMPose 的基本使用。
-MMPose 也提供了其他更详细的教程:
-
-- [如何编写配置文件](docs/zh_cn/tutorials/0_config.md)
-- [如何微调模型](docs/zh_cn/tutorials/1_finetune.md)
-- [如何增加新数据集](docs/zh_cn/tutorials/2_new_dataset.md)
-- [如何设计数据处理流程](docs/zh_cn/tutorials/3_data_pipeline.md)
-- [如何增加新模块](docs/zh_cn/tutorials/4_new_modules.md)
-- [如何导出模型为 onnx 格式](docs/zh_cn/tutorials/5_export_model.md)
-- [如何自定义运行配置](docs/zh_cn/tutorials/6_customize_runtime.md)
-- [如何使用摄像头应用接口（Webcam API）](docs/zh_cn/tutorials/7_webcam_api.md)
-
-## 模型库
-
-各个模型的结果和设置都可以在对应的 config（配置）目录下的 *README.md* 中查看。
-整体的概况也可也在 [模型库](https://mmpose.readthedocs.io/zh_CN/latest/recognition_models.html) 页面中查看。
-
-主要特性
- -- **支持多种人体姿态分析相关任务** - - MMPose 支持当前学界广泛关注的主流姿态分析任务：主要包括 2D多人姿态估计、2D手部姿态估计、2D人脸关键点检测、133关键点的全身人体姿态估计、3D人体形状恢复、服饰关键点检测、动物关键点检测等。 - 具体请参考 [功能演示](demo/README.md)。 - -- **更高的精度和更快的速度** - - MMPose 复现了多种学界最先进的人体姿态分析模型，包括“自顶向下”和“自底向上”两大类算法。MMPose 相比于其他主流的代码库，具有更高的模型精度和训练速度。 - 具体请参考 [基准测试](docs/en/benchmark.md)（英文）。 - -- **支持多样的数据集** - - MMPose 支持了很多主流数据集的准备和构建，如 COCO、 MPII 等。 具体请参考 [数据集准备](docs/en/data_preparation.md)。 - -- **模块化设计** - - MMPose 将统一的人体姿态分析框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的人体姿态分析模型。 - -- **详尽的单元测试和文档** - - MMPose 提供了详尽的说明文档，API 接口说明，全面的单元测试，以供社区参考。 - -
-
-
-支持的算法
- -- [x] [DeepPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#deeppose-cvpr-2014) (CVPR'2014) -- [x] [CPM](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#cpm-cvpr-2016) (CVPR'2016) -- [x] [Hourglass](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hourglass-eccv-2016) (ECCV'2016) -- [x] [SimpleBaseline3D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#simplebaseline3d-iccv-2017) (ICCV'2017) -- [x] [Associative Embedding](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#associative-embedding-nips-2017) (NeurIPS'2017) -- [x] [HMR](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#hmr-cvpr-2018) (CVPR'2018) -- [x] [SimpleBaseline2D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#simplebaseline2d-eccv-2018) (ECCV'2018) -- [x] [HRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrnet-cvpr-2019) (CVPR'2019) -- [x] [VideoPose3D](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#videopose3d-cvpr-2019) (CVPR'2019) -- [x] [HRNetv2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrnetv2-tpami-2019) (TPAMI'2019) -- [x] [MSPN](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#mspn-arxiv-2019) (ArXiv'2019) -- [x] [SCNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#scnet-cvpr-2020) (CVPR'2020) -- [x] [HigherHRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#higherhrnet-cvpr-2020) (CVPR'2020) -- [x] [RSN](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#rsn-eccv-2020) (ECCV'2020) -- [x] [InterNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#internet-eccv-2020) (ECCV'2020) -- [x] [VoxelPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/algorithms.html#voxelpose-eccv-2020) (ECCV'2020 -- [x] [LiteHRNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#litehrnet-cvpr-2021) (CVPR'2021) -- [x] [ViPNAS](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#vipnas-cvpr-2021) (CVPR'2021) - -
-
-
-支持的技术
- -- [x] [FPN](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#fpn-cvpr-2017) (CVPR'2017) -- [x] [FP16](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#fp16-arxiv-2017) (ArXiv'2017) -- [x] [Wingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#wingloss-cvpr-2018) (CVPR'2018) -- [x] [AdaptiveWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#adaptivewingloss-iccv-2019) (ICCV'2019) -- [x] [DarkPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#darkpose-cvpr-2020) (CVPR'2020) -- [x] [UDP](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#udp-cvpr-2020) (CVPR'2020) -- [x] [Albumentations](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#albumentations-information-2020) (Information'2020) -- [x] [SoftWingloss](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#softwingloss-tip-2021) (TIP'2021) -- [x] [SmoothNet](/configs/_base_/filters/smoothnet_h36m.md) (arXiv'2021) -- [x] [RLE](https://mmpose.readthedocs.io/zh_CN/latest/papers/techniques.html#rle-iccv-2021) (ICCV'2021) - -
-
-
-支持的数据集
- -- [x] [AFLW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#aflw-iccvw-2011) \[[homepage](https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/aflw/)\] (ICCVW'2011) -- [x] [sub-JHMDB](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#jhmdb-iccv-2013) \[[homepage](http://jhmdb.is.tue.mpg.de/dataset)\] (ICCV'2013) -- [x] [COFW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cofw-iccv-2013) \[[homepage](http://www.vision.caltech.edu/xpburgos/ICCV13/)\] (ICCV'2013) -- [x] [MPII](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mpii-cvpr-2014) \[[homepage](http://human-pose.mpi-inf.mpg.de/)\] (CVPR'2014) -- [x] [Human3.6M](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#human3-6m-tpami-2014) \[[homepage](http://vision.imar.ro/human3.6m/description.php)\] (TPAMI'2014) -- [x] [COCO](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#coco-eccv-2014) \[[homepage](http://cocodataset.org/)\] (ECCV'2014) -- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cmu-panoptic-iccv-2015) (ICCV'2015) -- [x] [DeepFashion](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#deepfashion-cvpr-2016) \[[homepage](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion/LandmarkDetection.html)\] (CVPR'2016) -- [x] [300W](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#300w-imavis-2016) \[[homepage](https://ibug.doc.ic.ac.uk/resources/300-W/)\] (IMAVIS'2016) -- [x] [RHD](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#rhd-iccv-2017) \[[homepage](https://lmb.informatik.uni-freiburg.de/resources/datasets/RenderedHandposeDataset.en.html)\] (ICCV'2017) -- [x] [CMU Panoptic](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#cmu-panoptic-iccv-2015) \[[homepage](http://domedb.perception.cs.cmu.edu/)\] (ICCV'2015) -- [x] [AI Challenger](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#ai-challenger-arxiv-2017) \[[homepage](https://github.com/AIChallenger/AI_Challenger_2017)\] (ArXiv'2017) -- [x] [MHP](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mhp-acm-mm-2018) \[[homepage](https://lv-mhp.github.io/dataset)\] (ACM MM'2018) -- [x] [WFLW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#wflw-cvpr-2018) \[[homepage](https://wywu.github.io/projects/LAB/WFLW.html)\] (CVPR'2018) -- [x] [PoseTrack18](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#posetrack18-cvpr-2018) \[[homepage](https://posetrack.net/users/download.php)\] (CVPR'2018) -- [x] [OCHuman](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#ochuman-cvpr-2019) \[[homepage](https://github.com/liruilong940607/OCHumanApi)\] (CVPR'2019) -- [x] [CrowdPose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#crowdpose-cvpr-2019) \[[homepage](https://github.com/Jeff-sjtu/CrowdPose)\] (CVPR'2019) -- [x] [MPII-TRB](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#mpii-trb-iccv-2019) \[[homepage](https://github.com/kennymckormick/Triplet-Representation-of-human-Body)\] (ICCV'2019) -- [x] [FreiHand](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#freihand-iccv-2019) \[[homepage](https://lmb.informatik.uni-freiburg.de/projects/freihand/)\] (ICCV'2019) -- [x] [Animal-Pose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#animal-pose-iccv-2019) \[[homepage](https://sites.google.com/view/animal-pose/)\] (ICCV'2019) -- [x] [OneHand10K](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#onehand10k-tcsvt-2019) \[[homepage](https://www.yangangwang.com/papers/WANG-MCC-2018-10.html)\] (TCSVT'2019) -- [x] [Vinegar Fly](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#vinegar-fly-nature-methods-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Nature Methods'2019) -- [x] [Desert Locust](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#desert-locust-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) -- [x] [Grévy’s Zebra](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#grevys-zebra-elife-2019) \[[homepage](https://github.com/jgraving/DeepPoseKit-Data)\] (Elife'2019) -- [x] [ATRW](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#atrw-acm-mm-2020) \[[homepage](https://cvwc2019.github.io/challenge.html)\] (ACM MM'2020) -- [x] [Halpe](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#halpe-cvpr-2020) \[[homepage](https://github.com/Fang-Haoshu/Halpe-FullBody/)\] (CVPR'2020) -- [x] [COCO-WholeBody](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#coco-wholebody-eccv-2020) \[[homepage](https://github.com/jin-s13/COCO-WholeBody/)\] (ECCV'2020) -- [x] [MacaquePose](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#macaquepose-biorxiv-2020) \[[homepage](http://www.pri.kyoto-u.ac.jp/datasets/macaquepose/index.html)\] (bioRxiv'2020) -- [x] [InterHand2.6M](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#interhand2-6m-eccv-2020) \[[homepage](https://mks0601.github.io/InterHand2.6M/)\] (ECCV'2020) -- [x] [AP-10K](https://mmpose.readthedocs.io/en/latest/papers/datasets.html#ap-10k-neurips-2021) \[[homepage](https://github.com/AlexTheBad/AP-10K)\] (NeurIPS'2021) -- [x] [Horse-10](https://mmpose.readthedocs.io/zh_CN/latest/papers/datasets.html#horse-10-wacv-2021) \[[homepage](http://www.mackenziemathislab.org/horse10)\] (WACV'2021) - -
-
-
-### 模型需求
-
-我们将跟进学界的最新进展，并支持更多算法和框架。如果您对 MMPose 有任何功能需求，请随时在 [MMPose Roadmap](https://github.com/open-mmlab/mmpose/issues/9) 中留言。
-
-### 基准测试
-
-#### 训练精度和速度
-
-MMPose 在主流关键点检测基准 COCO 上达到了优越的模型精度和训练速度。
-
-详细信息可见 [基准测试](docs/en/benchmark.md)(英文)。
-
-#### 推理速度
-
-我们总结了 MMPose 中主要模型的复杂度信息和推理速度，包括模型的计算复杂度、参数数量，以及以不同的批处理大小在 CPU 和 GPU 上的推理速度。
-
-详细信息可见 [模型推理速度](docs/zh_cn/inference_speed_summary.md)。
-
-## 数据准备
-
-请参考 [data_preparation.md](docs/en/data_preparation.md)（英文） 进行数据集准备。
-
-## 常见问题
-
-请参考 [FAQ](docs/en/faq.md) 了解其他用户的常见问题。
-
-## 参与贡献
-
-我们非常欢迎用户对于 MMPose 做出的任何贡献，可以参考 [CONTRIBUTION.md](.github/CONTRIBUTING.md) 文件了解更多细节。
-
-## 致谢
-
-MMPose 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
-我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
-
-## 引用
-
-如果您觉得 MMPose 对您的研究有所帮助，请考虑引用它：
-
-```bibtex
-@misc{mmpose2020,
- title={OpenMMLab Pose Estimation Toolbox and Benchmark},
- author={MMPose Contributors},
- howpublished = {\url{https://github.com/open-mmlab/mmpose}},
- year={2020}
-}
-```
-
-## 许可证
-
-该项目采用 [Apache 2.0 license](LICENSE) 开源协议。
-
-## OpenMMLab的其他项目
-
-- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
-- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
-- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
-- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
-- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
-- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
-- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
-- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
-- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
-- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
-- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
-- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
-- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
-- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
-- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
-- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
-- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱
-- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
-- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
-
-## 欢迎加入 OpenMMLab 社区
-
-扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，联络 OpenMMLab [官方微信小助手](/docs/en/imgs/wechat_assistant_qrcode.png)或加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=GJP18SjI)
-
-支持的骨干网络
- -- [x] [AlexNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#alexnet-neurips-2012) (NeurIPS'2012) -- [x] [VGG](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#vgg-iclr-2015) (ICLR'2015) -- [x] [ResNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnet-cvpr-2016) (CVPR'2016) -- [x] [ResNext](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnext-cvpr-2017) (CVPR'2017) -- [x] [SEResNet](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#seresnet-cvpr-2018) (CVPR'2018) -- [x] [ShufflenetV1](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#shufflenetv1-cvpr-2018) (CVPR'2018) -- [x] [ShufflenetV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#shufflenetv2-eccv-2018) (ECCV'2018) -- [x] [MobilenetV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#mobilenetv2-cvpr-2018) (CVPR'2018) -- [x] [ResNetV1D](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnetv1d-cvpr-2019) (CVPR'2019) -- [x] [ResNeSt](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#resnest-arxiv-2020) (ArXiv'2020) -- [x] [Swin](https://mmpose.readthedocs.io/en/latest/papers/backbones.html#swin-cvpr-2021) (CVPR'2021) -- [x] [HRFormer](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#hrformer-nips-2021) (NIPS'2021) -- [x] [PVT](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#pvt-iccv-2021) (ICCV'2021) -- [x] [PVTV2](https://mmpose.readthedocs.io/zh_CN/latest/papers/backbones.html#pvtv2-cvmj-2022) (CVMJ'2022) - -
-
-
-
-我们会在 OpenMMLab 社区为大家
-
-- 📢 分享 AI 框架的前沿核心技术
-- 💻 解读 PyTorch 常用模块源码
-- 📰 发布 OpenMMLab 的相关新闻
-- 🚀 介绍 OpenMMLab 开发的前沿算法
-- 🏃 获取更高效的问题答疑和意见反馈
-- 🔥 提供与各行各业开发者充分交流的平台
-
-干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
diff --git a/SMPLer-X/main/transformer_utils/model-index.yml b/SMPLer-X/main/transformer_utils/model-index.yml
deleted file mode 100644
index aa7691be16b169e2c08c6d9a482a32f23d17d80c..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/model-index.yml
+++ /dev/null
@@ -1,147 +0,0 @@
-Import:
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/hrnet_animalpose.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/animalpose/resnet_animalpose.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/hrnet_ap10k.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/ap10k/resnet_ap10k.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/hrnet_atrw.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/atrw/resnet_atrw.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/fly/resnet_fly.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/hrnet_horse10.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/horse10/resnet_horse10.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/locust/resnet_locust.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/hrnet_macaque.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/macaque/resnet_macaque.yml
-- configs/animal/2d_kpt_sview_rgb_img/topdown_heatmap/zebra/resnet_zebra.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/aic/higherhrnet_aic.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/aic/hrnet_aic.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/higherhrnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/higherhrnet_udp_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hourglass_ae_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/hrnet_udp_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/mobilenetv2_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/coco/resnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/crowdpose/higherhrnet_crowdpose.yml
-- configs/body/2d_kpt_sview_rgb_img/associative_embedding/mhp/hrnet_mhp.yml
-- configs/body/2d_kpt_sview_rgb_img/deeppose/coco/resnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/deeppose/coco/resnet_rle_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/deeppose/mpii/resnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/deeppose/mpii/resnet_rle_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/hrnet_aic.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/aic/resnet_aic.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/alexnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/cpm_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hourglass_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrformer_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_augmentation_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_dark_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_fp16_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/hrnet_udp_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/litehrnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/mobilenetv2_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/mspn_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/pvt_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnest_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnet_dark_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnet_fp16_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnetv1d_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/resnext_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/rsn_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/scnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/seresnet_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/shufflenetv1_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/shufflenetv2_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/swin_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vgg_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/vipnas_coco.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/crowdpose/hrnet_crowdpose.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/crowdpose/resnet_crowdpose.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/h36m/hrnet_h36m.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/jhmdb/cpm_jhmdb.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/jhmdb/resnet_jhmdb.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mhp/resnet_mhp.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/cpm_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/hourglass_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/hrnet_dark_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/hrnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/litehrnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/mobilenetv2_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/resnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/resnetv1d_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/resnext_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/scnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/seresnet_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/shufflenetv1_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii/shufflenetv2_mpii.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/mpii_trb/resnet_mpii_trb.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/hrnet_ochuman.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/ochuman/resnet_ochuman.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/posetrack18/hrnet_posetrack18.yml
-- configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/posetrack18/resnet_posetrack18.yml
-- configs/body/2d_kpt_sview_rgb_vid/posewarper/posetrack18/hrnet_posetrack18_posewarper.yml
-- configs/body/3d_kpt_mview_rgb_img/voxelpose/campus/voxelpose_campus.yml
-- configs/body/3d_kpt_mview_rgb_img/voxelpose/panoptic/voxelpose_prn64x64x64_cpn80x80x20_panoptic_cam5.yml
-- configs/body/3d_kpt_mview_rgb_img/voxelpose/shelf/voxelpose_shelf.yml
-- configs/body/3d_kpt_sview_rgb_img/pose_lift/h36m/simplebaseline3d_h36m.yml
-- configs/body/3d_kpt_sview_rgb_img/pose_lift/mpi_inf_3dhp/simplebaseline3d_mpi-inf-3dhp.yml
-- configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/h36m/videopose3d_h36m.yml
-- configs/body/3d_kpt_sview_rgb_vid/video_pose_lift/mpi_inf_3dhp/videopose3d_mpi-inf-3dhp.yml
-- configs/body/3d_mesh_sview_rgb_img/hmr/mixed/resnet_mixed.yml
-- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_softwingloss_wflw.yml
-- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_wflw.yml
-- configs/face/2d_kpt_sview_rgb_img/deeppose/wflw/resnet_wingloss_wflw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/300w/hrnetv2_300w.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_aflw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/aflw/hrnetv2_dark_aflw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/hourglass_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/hrnetv2_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/hrnetv2_dark_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/mobilenetv2_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/resnet_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_face/scnet_coco_wholebody_face.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/cofw/hrnetv2_cofw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_awing_wflw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_dark_wflw.yml
-- configs/face/2d_kpt_sview_rgb_img/topdown_heatmap/wflw/hrnetv2_wflw.yml
-- configs/fashion/2d_kpt_sview_rgb_img/deeppose/deepfashion/resnet_deepfashion.yml
-- configs/fashion/2d_kpt_sview_rgb_img/topdown_heatmap/deepfashion/resnet_deepfashion.yml
-- configs/hand/2d_kpt_sview_rgb_img/deeppose/onehand10k/resnet_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/deeppose/panoptic2d/resnet_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/deeppose/rhd2d/resnet_rhd2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/hourglass_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/hrnetv2_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/hrnetv2_dark_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/litehrnet_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/mobilenetv2_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/resnet_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/coco_wholebody_hand/scnet_coco_wholebody_hand.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/freihand2d/resnet_freihand2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/interhand2d/resnet_interhand2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/hrnetv2_dark_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/hrnetv2_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/hrnetv2_udp_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/mobilenetv2_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/onehand10k/resnet_onehand10k.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/hrnetv2_dark_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/hrnetv2_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/hrnetv2_udp_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/mobilenetv2_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/panoptic2d/resnet_panoptic2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/hrnetv2_dark_rhd2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/hrnetv2_rhd2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/hrnetv2_udp_rhd2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/mobilenetv2_rhd2d.yml
-- configs/hand/2d_kpt_sview_rgb_img/topdown_heatmap/rhd2d/resnet_rhd2d.yml
-- configs/hand/3d_kpt_sview_rgb_img/internet/interhand3d/internet_interhand3d.yml
-- configs/hand/gesture_sview_rgbd_vid/mtut/nvgesture/i3d_nvgesture.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/associative_embedding/coco-wholebody/higherhrnet_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/associative_embedding/coco-wholebody/hrnet_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/hrnet_dark_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/resnet_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/tcformer_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/vipnas_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/coco-wholebody/vipnas_dark_coco-wholebody.yml
-- configs/wholebody/2d_kpt_sview_rgb_img/topdown_heatmap/halpe/hrnet_dark_halpe.yml
diff --git a/SMPLer-X/main/transformer_utils/pytest.ini b/SMPLer-X/main/transformer_utils/pytest.ini
deleted file mode 100644
index 9796e871e70c7c67345b1d6bcf708c0c82377a98..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/pytest.ini
+++ /dev/null
@@ -1,7 +0,0 @@
-[pytest]
-addopts = --xdoctest --xdoctest-style=auto
-norecursedirs = .git ignore build __pycache__ data docker docs .eggs
-
-filterwarnings= default
- ignore:.*No cfgstr given in Cacher constructor or call.*:Warning
- ignore:.*Define the __nice__ method for.*:Warning
diff --git a/SMPLer-X/main/transformer_utils/requirements.txt b/SMPLer-X/main/transformer_utils/requirements.txt
deleted file mode 100644
index b5b5d97a6ea7837890ff0247bac8c5f24f6eabab..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
--r requirements/build.txt
--r requirements/runtime.txt
--r requirements/tests.txt
--r requirements/optional.txt
diff --git a/SMPLer-X/main/transformer_utils/requirements/build.txt b/SMPLer-X/main/transformer_utils/requirements/build.txt
deleted file mode 100644
index a9566943cef029e5c8dab0b52ba564a7f9c7ad30..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/build.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-# These must be installed before building mmpose
-numpy
-torch>=1.3
diff --git a/SMPLer-X/main/transformer_utils/requirements/docs.txt b/SMPLer-X/main/transformer_utils/requirements/docs.txt
deleted file mode 100644
index 20170845c44eefcb139ee2baa1a3d375b71c34ec..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/docs.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-docutils==0.16.0
-myst-parser
--e git+https://github.com/gaotongxiao/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-sphinx==4.0.2
-sphinx_copybutton
-sphinx_markdown_tables
diff --git a/SMPLer-X/main/transformer_utils/requirements/mminstall.txt b/SMPLer-X/main/transformer_utils/requirements/mminstall.txt
deleted file mode 100644
index 89199e36061dcd5361d029606fa25cb791af110a..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/mminstall.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-mmcv-full>=1.3.8
-mmdet>=2.14.0
-mmtrack>=0.6.0
diff --git a/SMPLer-X/main/transformer_utils/requirements/optional.txt b/SMPLer-X/main/transformer_utils/requirements/optional.txt
deleted file mode 100644
index bfb1e75f86aba2fd074b0b1723e9b07a2037e9c3..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/optional.txt
+++ /dev/null
@@ -1,8 +0,0 @@
-albumentations>=0.3.2 --no-binary qudida,albumentations
-onnx
-onnxruntime
-poseval@git+https://github.com/svenkreiss/poseval.git
-pyrender
-requests
-smplx>=0.1.28
-trimesh
diff --git a/SMPLer-X/main/transformer_utils/requirements/readthedocs.txt b/SMPLer-X/main/transformer_utils/requirements/readthedocs.txt
deleted file mode 100644
index b8b69d3ca2f051dcb6d6a96a25e7cb9054483c76..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/readthedocs.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-mmcv-full
-munkres
-poseval@git+https://github.com/svenkreiss/poseval.git
-regex
-scipy
-titlecase
-torch
-torchvision
-xtcocotools>=1.8
diff --git a/SMPLer-X/main/transformer_utils/requirements/runtime.txt b/SMPLer-X/main/transformer_utils/requirements/runtime.txt
deleted file mode 100644
index 30f20b6f8b678dac9d0043454e9b6d6f980de21e..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/runtime.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-chumpy
-dataclasses; python_version == '3.6'
-json_tricks
-matplotlib
-munkres
-numpy
-opencv-python
-pillow
-scipy
-torchvision
-xtcocotools>=1.12
-# easydict
-# einops
diff --git a/SMPLer-X/main/transformer_utils/requirements/tests.txt b/SMPLer-X/main/transformer_utils/requirements/tests.txt
deleted file mode 100644
index aa23e69da611f7dec62cf84541b7b508f4437a26..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/requirements/tests.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-coverage
-flake8
-interrogate
-isort==4.3.21
-pytest
-pytest-runner
-smplx>=0.1.28
-xdoctest>=0.10.0
-yapf
diff --git a/SMPLer-X/main/transformer_utils/resources/mmpose-logo.png b/SMPLer-X/main/transformer_utils/resources/mmpose-logo.png
deleted file mode 100644
index 1704dc9abdc743f774d5e04782077d0be8bccd73..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/resources/mmpose-logo.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6342cc3ea98ab9910db6d28edaadd2c6b04713a685e486128615cf532cd5e615
-size 28553
diff --git a/SMPLer-X/main/transformer_utils/setup.cfg b/SMPLer-X/main/transformer_utils/setup.cfg
deleted file mode 100644
index c4d8643bc91a06cc48f0d88b23288e892121249c..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/setup.cfg
+++ /dev/null
@@ -1,24 +0,0 @@
-[bdist_wheel]
-universal=1
-
-[aliases]
-test=pytest
-
-[tool:pytest]
-addopts=tests/
-
-[yapf]
-based_on_style = pep8
-blank_line_before_nested_class_or_def = true
-split_before_expression_after_opening_paren = true
-split_penalty_import_names=0
-SPLIT_PENALTY_AFTER_OPENING_BRACKET=800
-
-[isort]
-line_length = 79
-multi_line_output = 0
-extra_standard_library = pkg_resources,setuptools
-known_first_party = mmpose
-known_third_party = PIL,cv2,h5py,json_tricks,matplotlib,mmcv,munkres,numpy,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,spacepy,titlecase,torch,torchvision,webcam_apis,xmltodict,xtcocotools
-no_lines_before = STDLIB,LOCALFOLDER
-default_section = THIRDPARTY
diff --git a/SMPLer-X/main/transformer_utils/setup.py b/SMPLer-X/main/transformer_utils/setup.py
deleted file mode 100644
index 13b4b1fba1c3da1e561d6480b11debb4e39b796a..0000000000000000000000000000000000000000
--- a/SMPLer-X/main/transformer_utils/setup.py
+++ /dev/null
@@ -1,194 +0,0 @@
-import os
-import os.path as osp
-import platform
-import shutil
-import sys
-import warnings
-from setuptools import find_packages, setup
-
-
-def readme():
- with open('README.md', encoding='utf-8') as f:
- content = f.read()
- return content
-
-
-version_file = 'mmpose/version.py'
-
-
-def get_version():
- with open(version_file, 'r') as f:
- exec(compile(f.read(), version_file, 'exec'))
- import sys
-
- # return short version for sdist
- if 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
- return locals()['short_version']
- else:
- return locals()['__version__']
-
-
-def parse_requirements(fname='requirements.txt', with_version=True):
- """Parse the package dependencies listed in a requirements file but strips
- specific versioning information.
-
- Args:
- fname (str): path to requirements file
- with_version (bool, default=False): if True include version specs
-
- Returns:
- List[str]: list of requirements items
-
- CommandLine:
- python -c "import setup; print(setup.parse_requirements())"
- """
- import re
- import sys
- from os.path import exists
- require_fpath = fname
-
- def parse_line(line):
- """Parse information from a line in a requirements text file."""
- if line.startswith('-r '):
- # Allow specifying requirements in other files
- target = line.split(' ')[1]
- for info in parse_require_file(target):
- yield info
- else:
- info = {'line': line}
- if line.startswith('-e '):
- info['package'] = line.split('#egg=')[1]
- elif '@git+' in line:
- info['package'] = line
- else:
- # Remove versioning from the package
- pat = '(' + '|'.join(['>=', '==', '>']) + ')'
- parts = re.split(pat, line, maxsplit=1)
- parts = [p.strip() for p in parts]
-
- info['package'] = parts[0]
- if len(parts) > 1:
- op, rest = parts[1:]
- if ';' in rest:
- # Handle platform specific dependencies
- # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
- version, platform_deps = map(str.strip,
- rest.split(';'))
- info['platform_deps'] = platform_deps
- else:
- version = rest # NOQA
- info['version'] = (op, version)
- yield info
-
- def parse_require_file(fpath):
- with open(fpath, 'r') as f:
- for line in f.readlines():
- line = line.strip()
- if line and not line.startswith('#'):
- for info in parse_line(line):
- yield info
-
- def gen_packages_items():
- if exists(require_fpath):
- for info in parse_require_file(require_fpath):
- parts = [info['package']]
- if with_version and 'version' in info:
- parts.extend(info['version'])
- if not sys.version.startswith('3.4'):
- # apparently package_deps are broken in 3.4
- platform_deps = info.get('platform_deps')
- if platform_deps is not None:
- parts.append(';' + platform_deps)
- item = ''.join(parts)
- yield item
-
- packages = list(gen_packages_items())
- return packages
-
-
-def add_mim_extension():
- """Add extra files that are required to support MIM into the package.
-
- These files will be added by creating a symlink to the originals if the
- package is installed in `editable` mode (e.g. pip install -e .), or by
- copying from the originals otherwise.
- """
-
- # parse installment mode
- if 'develop' in sys.argv:
- # installed by `pip install -e .`
- if platform.system() == 'Windows':
- mode = 'copy'
- else:
- mode = 'symlink'
- elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
- # installed by `pip install .`
- # or create source distribution by `python setup.py sdist`
- mode = 'copy'
- else:
- return
-
- filenames = ['tools', 'configs', 'demo', 'model-index.yml']
- repo_path = osp.dirname(__file__)
- mim_path = osp.join(repo_path, 'mmpose', '.mim')
- os.makedirs(mim_path, exist_ok=True)
-
- for filename in filenames:
- if osp.exists(filename):
- src_path = osp.join(repo_path, filename)
- tar_path = osp.join(mim_path, filename)
-
- if osp.isfile(tar_path) or osp.islink(tar_path):
- os.remove(tar_path)
- elif osp.isdir(tar_path):
- shutil.rmtree(tar_path)
-
- if mode == 'symlink':
- src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
- os.symlink(src_relpath, tar_path)
- elif mode == 'copy':
- if osp.isfile(src_path):
- shutil.copyfile(src_path, tar_path)
- elif osp.isdir(src_path):
- shutil.copytree(src_path, tar_path)
- else:
- warnings.warn(f'Cannot copy file {src_path}.')
- else:
- raise ValueError(f'Invalid mode {mode}')
-
-
-if __name__ == '__main__':
- add_mim_extension()
- setup(
- name='mmpose',
- version=get_version(),
- description='OpenMMLab Pose Estimation Toolbox and Benchmark.',
- author='MMPose Contributors',
- author_email='openmmlab@gmail.com',
- keywords='computer vision, pose estimation',
- long_description=readme(),
- long_description_content_type='text/markdown',
- packages=find_packages(exclude=('configs', 'tools', 'demo')),
- include_package_data=True,
- package_data={'mmpose.ops': ['*/*.so']},
- classifiers=[
- 'Development Status :: 4 - Beta',
- 'License :: OSI Approved :: Apache Software License',
- 'Operating System :: OS Independent',
- 'Programming Language :: Python :: 3',
- 'Programming Language :: Python :: 3.5',
- 'Programming Language :: Python :: 3.6',
- 'Programming Language :: Python :: 3.7',
- 'Programming Language :: Python :: 3.8',
- 'Programming Language :: Python :: 3.9',
- ],
- url='https://github.com/open-mmlab/mmpose',
- license='Apache License 2.0',
- install_requires=parse_requirements('requirements/runtime.txt'),
- extras_require={
- 'tests': parse_requirements('requirements/tests.txt'),
- 'build': parse_requirements('requirements/build.txt'),
- 'runtime': parse_requirements('requirements/runtime.txt'),
- 'mim': parse_requirements('requirements/mminstall.txt'),
- },
- zip_safe=False)
diff --git a/SMPLer-X/packages.txt b/SMPLer-X/packages.txt
deleted file mode 100644
index 89c879114f99513be813c03b5eb5d5330fd1dd85..0000000000000000000000000000000000000000
--- a/SMPLer-X/packages.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-libglfw3-dev
-libgles2-mesa-dev
-libgl1
-freeglut3-dev
-zip
-unzip
-ffmpeg
-libsm6
-libxext6
-libgl1-mesa-dri
-libegl1-mesa
-libgbm1
-build-essential
diff --git a/SMPLer-X/pre-requirements.txt b/SMPLer-X/pre-requirements.txt
deleted file mode 100644
index ba1cf2dec43cf1bd737527402e561cfb15db9b8c..0000000000000000000000000000000000000000
--- a/SMPLer-X/pre-requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-numpy==1.23
-torch==2.0.0
-torchvision
-torchaudio
\ No newline at end of file
diff --git a/SMPLer-X/pretrained_models/mmdet/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth b/SMPLer-X/pretrained_models/mmdet/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
deleted file mode 100644
index fbdd2e26cc16db3c55149dad743e001aba4198d8..0000000000000000000000000000000000000000
--- a/SMPLer-X/pretrained_models/mmdet/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:047c8118fc5ca88ba5ae1fab72f2cd6b070501fe3af2f3cba5cfa9a89b44b03e
-size 167287506
diff --git a/SMPLer-X/pretrained_models/mmdet/mmdet_faster_rcnn_r50_fpn_coco.py b/SMPLer-X/pretrained_models/mmdet/mmdet_faster_rcnn_r50_fpn_coco.py
deleted file mode 100644
index 912265dea5221988cf4ae9da29359ad56af7c5f7..0000000000000000000000000000000000000000
--- a/SMPLer-X/pretrained_models/mmdet/mmdet_faster_rcnn_r50_fpn_coco.py
+++ /dev/null
@@ -1,198 +0,0 @@
-checkpoint_config = dict(interval=1)
-# yapf: disable
-log_config = dict(
- interval=50,
- hooks=[
- dict(type='TextLoggerHook'),
- # dict(type='TensorboardLoggerHook')
- ])
-# yapf: enable
-dist_params = dict(backend='nccl')
-log_level = 'INFO'
-load_from = None
-resume_from = None
-workflow = [('train', 1)]
-# optimizer
-optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
-optimizer_config = dict(grad_clip=None)
-# learning policy
-lr_config = dict(
- policy='step',
- warmup='linear',
- warmup_iters=500,
- warmup_ratio=0.001,
- step=[8, 11])
-total_epochs = 12
-
-model = dict(
- type='FasterRCNN',
- data_preprocessor=dict(
- type='DetDataPreprocessor',
- mean=[123.675, 116.28, 103.53],
- std=[58.395, 57.12, 57.375],
- bgr_to_rgb=True,
- pad_size_divisor=32),
- backbone=dict(
- type='ResNet',
- depth=50,
- num_stages=4,
- out_indices=(0, 1, 2, 3),
- frozen_stages=1,
- norm_cfg=dict(type='BN', requires_grad=True),
- norm_eval=True,
- style='pytorch',
- init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
- neck=dict(
- type='FPN',
- in_channels=[256, 512, 1024, 2048],
- out_channels=256,
- num_outs=5),
- rpn_head=dict(
- type='RPNHead',
- in_channels=256,
- feat_channels=256,
- anchor_generator=dict(
- type='AnchorGenerator',
- scales=[8],
- ratios=[0.5, 1.0, 2.0],
- strides=[4, 8, 16, 32, 64]),
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[.0, .0, .0, .0],
- target_stds=[1.0, 1.0, 1.0, 1.0]),
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
- roi_head=dict(
- type='StandardRoIHead',
- bbox_roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- bbox_head=dict(
- type='Shared2FCBBoxHead',
- in_channels=256,
- fc_out_channels=1024,
- roi_feat_size=7,
- num_classes=80,
- bbox_coder=dict(
- type='DeltaXYWHBBoxCoder',
- target_means=[0., 0., 0., 0.],
- target_stds=[0.1, 0.1, 0.2, 0.2]),
- reg_class_agnostic=False,
- loss_cls=dict(
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
- # model training and testing settings
- train_cfg=dict(
- rpn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.3,
- min_pos_iou=0.3,
- match_low_quality=True,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=-1,
- add_gt_as_proposals=False),
- allowed_border=-1,
- pos_weight=-1,
- debug=False),
- rpn_proposal=dict(
- nms_pre=2000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.5,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='RandomSampler',
- num=512,
- pos_fraction=0.25,
- neg_pos_ub=-1,
- add_gt_as_proposals=True),
- pos_weight=-1,
- debug=False)),
- test_cfg=dict(
- rpn=dict(
- nms_pre=1000,
- max_per_img=1000,
- nms=dict(type='nms', iou_threshold=0.7),
- min_bbox_size=0),
- rcnn=dict(
- score_thr=0.05,
- nms=dict(type='nms', iou_threshold=0.5),
- max_per_img=100)
- # soft-nms is also supported for rcnn testing
- # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
- ))
-
-dataset_type = 'CocoDataset'
-data_root = 'data/coco/'
-backend_args = None
-
-train_pipeline = [
- dict(type='LoadImageFromFile', backend_args=backend_args),
- dict(type='LoadAnnotations', with_bbox=True),
- dict(type='Resize', scale=(1333, 800), keep_ratio=True),
- dict(type='RandomFlip', prob=0.5),
- dict(type='PackDetInputs')
-]
-test_pipeline = [
- dict(type='LoadImageFromFile', backend_args=backend_args),
- dict(type='Resize', scale=(1333, 800), keep_ratio=True),
- # If you don't have a gt annotation, delete the pipeline
- dict(type='LoadAnnotations', with_bbox=True),
- dict(
- type='PackDetInputs',
- meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
- 'scale_factor'))
-]
-train_dataloader = dict(
- batch_size=2,
- num_workers=2,
- persistent_workers=True,
- sampler=dict(type='DefaultSampler', shuffle=True),
- batch_sampler=dict(type='AspectRatioBatchSampler'),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file='annotations/instances_train2017.json',
- data_prefix=dict(img='train2017/'),
- filter_cfg=dict(filter_empty_gt=True, min_size=32),
- pipeline=train_pipeline,
- backend_args=backend_args))
-val_dataloader = dict(
- batch_size=1,
- num_workers=2,
- persistent_workers=True,
- drop_last=False,
- sampler=dict(type='DefaultSampler', shuffle=False),
- dataset=dict(
- type=dataset_type,
- data_root=data_root,
- ann_file='annotations/instances_val2017.json',
- data_prefix=dict(img='val2017/'),
- test_mode=True,
- pipeline=test_pipeline,
- backend_args=backend_args))
-test_dataloader = val_dataloader
-
-val_evaluator = dict(
- type='CocoMetric',
- ann_file=data_root + 'annotations/instances_val2017.json',
- metric='bbox',
- format_only=False,
- backend_args=backend_args)
-test_evaluator = val_evaluator
\ No newline at end of file
diff --git a/SMPLer-X/pretrained_models/smpler_x_s32.pth.tar b/SMPLer-X/pretrained_models/smpler_x_s32.pth.tar
deleted file mode 100644
index 597d217e10ecf51d977121f1c8c1bf6bebcb7959..0000000000000000000000000000000000000000
--- a/SMPLer-X/pretrained_models/smpler_x_s32.pth.tar
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1c78569f4e7b6e22e7166b55f322e5d5e019073b8d1a430a6926f93b4c86aa62
-size 384315279
diff --git a/SMPLer-X/requirements.txt b/SMPLer-X/requirements.txt
deleted file mode 100644
index b3d83d9fce533279363fb64e9d533d9c2af543f7..0000000000000000000000000000000000000000
--- a/SMPLer-X/requirements.txt
+++ /dev/null
@@ -1,33 +0,0 @@
-# --extra-index-url https://download.openmmlab.com/mmcv/dist/cu122/torch2.1.0/index.html
-# https://download.openmmlab.com/mmcv/dist/cu122/torch2.1.0/mmcv-2.2.0-cp310-cp310-manylinux1_x86_64.whl
-
-scikit-image
-scipy
-scikit-learn
-smplx==0.1.28
-tqdm
-yacs
-numba
-opencv-python
-tensorboardx
-filterpy
-cython
-chumpy
-Pillow
-trimesh
-pyrender
-matplotlib
-json_tricks
-torchgeometry
-einops
-joblib
-boto3
-requests
-easydict
-pycocotools
-plyfile
-timm
-pyglet
-mmcv==2.1.0
-mmdet==3.2.0
-eval_type_backport
\ No newline at end of file
diff --git a/Wav2Lip/Dockerfile b/Wav2Lip/Dockerfile
deleted file mode 100644
index d625a97d1018f8eef44c46dd41829446ce2d237a..0000000000000000000000000000000000000000
--- a/Wav2Lip/Dockerfile
+++ /dev/null
@@ -1,57 +0,0 @@
-FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
-
-ARG DEBIAN_FRONTEND=noninteractive
-
-# install python via pyenv
-RUN apt-get update && apt-get install -y --no-install-recommends \
- make \
- build-essential \
- libssl-dev \
- zlib1g-dev \
- libbz2-dev \
- libreadline-dev \
- libsqlite3-dev \
- wget \
- curl \
- llvm \
- libncurses5-dev \
- libncursesw5-dev \
- xz-utils \
- tk-dev \
- libffi-dev \
- liblzma-dev \
- git \
- ca-certificates \
- libgl1 \
- && rm -rf /var/lib/apt/lists/*
-ENV PATH="/root/.pyenv/shims:/root/.pyenv/bin:$PATH"
-ARG PYTHON_VERSION=3.8
-RUN curl -s -S -L https://raw.githubusercontent.com/pyenv/pyenv-installer/master/bin/pyenv-installer | bash && \
- pyenv install $PYTHON_VERSION && \
- pyenv global $PYTHON_VERSION
-
-# install cog
-RUN pip install cog
-
-# install deps
-RUN apt-get update && apt-get install -y --no-install-recommends \
- ffmpeg libsndfile1 \
- && rm -rf /var/lib/apt/lists/*
-
-# copy to /src
-ENV WORKDIR /src
-RUN mkdir -p $WORKDIR
-WORKDIR $WORKDIR
-
-# install requirements
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-RUN pip install git+https://github.com/elliottzheng/batch-face.git@master
-
-# copy sources
-COPY . .
-
-ENV PYTHONUNBUFFERED=1
-
-# run cog
-CMD python3 -m cog.server.http
diff --git a/Wav2Lip/README.md b/Wav2Lip/README.md
deleted file mode 100644
index 35f69daff0ee31e44c7b251f360365b2710585fb..0000000000000000000000000000000000000000
--- a/Wav2Lip/README.md
+++ /dev/null
@@ -1,152 +0,0 @@
-# **Wav2Lip**: *Accurately Lip-syncing Videos In The Wild*
-
-For commercial requests, please contact us at radrabha.m@research.iiit.ac.in or prajwal.k@research.iiit.ac.in. We have an HD model ready that can be used commercially.
-
-This code is part of the paper: _A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild_ published at ACM Multimedia 2020.
-
-[](https://paperswithcode.com/sota/lip-sync-on-lrs2?p=a-lip-sync-expert-is-all-you-need-for-speech)
-[](https://paperswithcode.com/sota/lip-sync-on-lrs3?p=a-lip-sync-expert-is-all-you-need-for-speech)
-[](https://paperswithcode.com/sota/lip-sync-on-lrw?p=a-lip-sync-expert-is-all-you-need-for-speech)
-
-|📑 Original Paper|📰 Project Page|🌀 Demo|⚡ Live Testing|📔 Colab Notebook
-|:-:|:-:|:-:|:-:|:-:|
-[Paper](http://arxiv.org/abs/2008.10010) | [Project Page](http://cvit.iiit.ac.in/research/projects/cvit-projects/a-lip-sync-expert-is-all-you-need-for-speech-to-lip-generation-in-the-wild/) | [Demo Video](https://youtu.be/0fXaDCZNOJc) | [Interactive Demo](https://bhaasha.iiit.ac.in/lipsync) | [Colab Notebook](https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing) /[Updated Collab Notebook](https://colab.research.google.com/drive/1IjFW1cLevs6Ouyu4Yht4mnR4yeuMqO7Y#scrollTo=MH1m608OymLH)
-
-
-Step1: Setup Wav2Lip
\n", - "#@markdown * Install dependency\n", - "#@markdown * Download pretrained model\n", - "!rm -rf /content/sample_data\n", - "!mkdir /content/sample_data\n", - "\n", - "!git clone https://github.com/zabique/Wav2Lip\n", - "\n", - "#download the pretrained model\n", - "!wget 'https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?share=EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA' -O '/content/Wav2Lip/checkpoints/wav2lip_gan.pth'\n", - "a = !pip install https://raw.githubusercontent.com/AwaleSajil/ghc/master/ghc-1.0-py3-none-any.whl\n", - "\n", - "# !pip uninstall tensorflow tensorflow-gpu\n", - "!cd Wav2Lip && pip install -r requirements.txt\n", - "\n", - "#download pretrained model for face detection\n", - "!wget \"https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth\" -O \"/content/Wav2Lip/face_detection/detection/sfd/s3fd.pth\"\n", - "\n", - "!pip install -q youtube-dl\n", - "!pip install ffmpeg-python\n", - "!pip install librosa==0.9.1\n", - "\n", - "#this code for recording audio\n", - "\"\"\"\n", - "To write this piece of code I took inspiration/code from a lot of places.\n", - "It was late night, so I'm not sure how much I created or just copied o.O\n", - "Here are some of the possible references:\n", - "https://blog.addpipe.com/recording-audio-in-the-browser-using-pure-html5-and-minimal-javascript/\n", - "https://stackoverflow.com/a/18650249\n", - "https://hacks.mozilla.org/2014/06/easy-audio-capture-with-the-mediarecorder-api/\n", - "https://air.ghost.io/recording-to-an-audio-file-using-html5-and-js/\n", - "https://stackoverflow.com/a/49019356\n", - "\"\"\"\n", - "from IPython.display import HTML, Audio\n", - "from google.colab.output import eval_js\n", - "from base64 import b64decode\n", - "import numpy as np\n", - "from scipy.io.wavfile import read as wav_read\n", - "import io\n", - "import ffmpeg\n", - "\n", - "AUDIO_HTML = \"\"\"\n", - "\n", - "\"\"\"\n", - "\n", - "%cd /\n", - "from ghc.l_ghc_cf import l_ghc_cf\n", - "%cd content\n", - "\n", - "def get_audio():\n", - " display(HTML(AUDIO_HTML))\n", - " data = eval_js(\"data\")\n", - " binary = b64decode(data.split(',')[1])\n", - " \n", - " process = (ffmpeg\n", - " .input('pipe:0')\n", - " .output('pipe:1', format='wav')\n", - " .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True, quiet=True, overwrite_output=True)\n", - " )\n", - " output, err = process.communicate(input=binary)\n", - " \n", - " riff_chunk_size = len(output) - 8\n", - " # Break up the chunk size into four bytes, held in b.\n", - " q = riff_chunk_size\n", - " b = []\n", - " for i in range(4):\n", - " q, r = divmod(q, 256)\n", - " b.append(r)\n", - "\n", - " # Replace bytes 4:8 in proc.stdout with the actual size of the RIFF chunk.\n", - " riff = output[:4] + bytes(b) + output[8:]\n", - "\n", - " sr, audio = wav_read(io.BytesIO(riff))\n", - "\n", - " return audio, sr\n", - "\n", - "\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "def showVideo(path):\n", - " mp4 = open(str(path),'rb').read()\n", - " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - " return HTML(\"\"\"\n", - " \n", - " \"\"\" % data_url)\n", - "\n", - "from IPython.display import clear_output" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SEdy6PWDXMRL" - }, - "source": [ - "# LipSync Youtube Video" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "QI4kcm8QEeGZ", - "cellView": "form" - }, - "source": [ - "#@title STEP2: Select a Youtube Video\n", - "# Install yt-dlp\n", - "!pip install yt-dlp\n", - "\n", - "#@markdown ### Find YouTube video ID from URL\n", - "from urllib import parse as urlparse\n", - "YOUTUBE_URL = 'https://www.youtube.com/watch?v=vAnWYLTdvfY' #@param {type:\"string\"}\n", - "url_data = urlparse.urlparse(YOUTUBE_URL)\n", - "query = urlparse.parse_qs(url_data.query)\n", - "YOUTUBE_ID = query[\"v\"][0]\n", - "\n", - "#@markdown ### Trim the video (start, end) seconds\n", - "start = 35 #@param {type:\"integer\"}\n", - "end = 62 #@param {type:\"integer\"}\n", - "interval = end - start\n", - "\n", - "# Download the YouTube video using yt-dlp\n", - "!yt-dlp -f 'bestvideo[ext=mp4]' --output \"youtube.%(ext)s\" https://www.youtube.com/watch?v=$YOUTUBE_ID\n", - "\n", - "# Cut the video using FFmpeg\n", - "!ffmpeg -y -i youtube.mp4 -ss {start} -t {interval} -async 1 /content/sample_data/input_vid.mp4\n", - "\n", - "# Preview the trimmed video\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "mp4 = open('/content/sample_data/input_vid.mp4','rb').read()\n", - "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - "HTML(f\"\"\"\"\"\")\n", - "\n" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "zS_RAeh-IfZy", - "cellView": "form" - }, - "source": [ - "#@title STEP3: Select Audio (Record or Upload)\n", - "from IPython.display import Audio \n", - "from IPython.core.display import display\n", - "\n", - "record_or_upload = 'Upload' #@param ['Record', 'Upload']\n", - "\n", - "def displayAudio():\n", - " display(Audio('/content/sample_data/input_audio.wav'))\n", - "if record_or_upload == 'Record':\n", - " audio, sr = get_audio()\n", - " import scipy\n", - " scipy.io.wavfile.write('/content/sample_data/input_audio.wav', sr, audio)\n", - "elif record_or_upload == 'Upload':\n", - " from google.colab import files\n", - " uploaded = files.upload()\n", - " for fn in uploaded.keys():\n", - " print('User uploaded file \"{name}\" with length {length} bytes'.format(\n", - " name=fn, length=len(uploaded[fn])))\n", - " \n", - " #concider only the first file\n", - " audio_file = str(list(uploaded.keys())[0])\n", - " \n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(audio_file, sr=None)\n", - " \n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - " \n", - " clear_output()\n", - " displayAudio()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "BQPLXJ8L0gms", - "cellView": "form" - }, - "source": [ - "#@title STEP4: Start Crunching and Preview Output\n", - "#@markdown Note: Only change these, if you have to\n", - "pad_top = 0#@param {type:\"integer\"}\n", - "pad_bottom = 10#@param {type:\"integer\"}\n", - "pad_left = 0#@param {type:\"integer\"}\n", - "pad_right = 0#@param {type:\"integer\"}\n", - "rescaleFactor = 1#@param {type:\"integer\"}\n", - "nosmooth = False #@param {type:\"boolean\"}\n", - "\n", - "\n", - "if nosmooth == False:\n", - " !cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor\n", - "else:\n", - " !cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor --nosmooth\n", - "#Preview output video\n", - "clear_output()\n", - "print(\"Final Video Preview\")\n", - "print(\"Download this video from\", '/content/Wav2Lip/results/result_voice.mp4')\n", - "showVideo('/content/Wav2Lip/results/result_voice.mp4')\n" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vYxpPeie1CYL" - }, - "source": [ - "# LipSync on Your Video File" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "nDuM7tfZ1F0t", - "cellView": "form" - }, - "source": [ - "import os\n", - "from google.colab import files\n", - "from IPython.display import HTML\n", - "\n", - "def showVideo(file_path):\n", - " \"\"\"Function to display video in Colab\"\"\"\n", - " mp4 = open(file_path,'rb').read()\n", - " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - " display(HTML(\"\"\"\n", - " \n", - " \"\"\" % data_url))\n", - "\n", - "#@markdown ### Select an uploading method\n", - "upload_or_path = \"Upload\" #@param [\"Upload\", \"Custom Path\"]\n", - "\n", - "if upload_or_path == \"Upload\":\n", - " uploaded = files.upload()\n", - " for filename in uploaded.keys():\n", - " os.rename(filename, '/content/sample_data/input_vid.mp4')\n", - " PATH_TO_YOUR_VIDEO = '/content/sample_data/input_vid.mp4'\n", - "else:\n", - " PATH_TO_YOUR_VIDEO = '/content/test.mp4' #@param {type:\"string\"}\n", - " if not os.path.isfile(PATH_TO_YOUR_VIDEO):\n", - " print(\"ERROR: File not found!\")\n", - " raise SystemExit(0)\n", - "\n", - "#@markdown ### Trim the video (start, end) seconds\n", - "start_time = 0 #@param {type:\"integer\"}\n", - "end_time = 0 #@param {type:\"integer\"}\n", - "\n", - "if start_time == 0 and end_time == 0:\n", - " print(\"No trimming applied\")\n", - "else:\n", - " duration = end_time - start_time\n", - " os.system(f\"ffmpeg -i {PATH_TO_YOUR_VIDEO} -ss {start_time} -t {duration} -async 1 /content/sample_data/trimmed_vid.mp4\")\n", - " PATH_TO_YOUR_VIDEO = \"/content/sample_data/input_vid.mp4\"\n", - " print(f\"Video trimmed from {start_time} to {end_time} seconds\")\n", - "\n", - "print(f\"PATH_TO_YOUR_VIDEO: {PATH_TO_YOUR_VIDEO}\")\n", - "\n", - "if upload_or_path == \"Upload\":\n", - " clear_output()\n", - " print(\"Input Video\")\n", - " showVideo(PATH_TO_YOUR_VIDEO)\n", - "else:\n", - " if os.path.isfile(PATH_TO_YOUR_VIDEO):\n", - " print(\"Input Video\")\n", - " showVideo(PATH_TO_YOUR_VIDEO)\n" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "XgF4794r7sWK", - "cellView": "form" - }, - "source": [ - "#@title STEP3: Select Audio (Record or Upload)\n", - "from IPython.display import Audio \n", - "from IPython.core.display import display\n", - "\n", - "record_or_upload = 'Upload' #@param ['Record', 'Upload']\n", - "\n", - "def displayAudio():\n", - " display(Audio('/content/sample_data/input_audio.wav'))\n", - "if record_or_upload == 'Record':\n", - " audio, sr = get_audio()\n", - " import scipy\n", - " scipy.io.wavfile.write('/content/sample_data/input_audio.wav', sr, audio)\n", - "elif record_or_upload == 'Upload':\n", - " from google.colab import files\n", - " uploaded = files.upload()\n", - " for fn in uploaded.keys():\n", - " print('User uploaded file \"{name}\" with length {length} bytes'.format(\n", - " name=fn, length=len(uploaded[fn])))\n", - " \n", - " #concider only the first file\n", - " audio_file = str(list(uploaded.keys())[0])\n", - " \n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(audio_file, sr=None)\n", - " \n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - " \n", - " clear_output()\n", - " displayAudio()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "ZgtO08V28ANf", - "cellView": "form" - }, - "source": [ - "#@title STEP4: Start Crunching and Preview Output\n", - "#@markdown Note: Only change these, if you have to\n", - "pad_top = 0#@param {type:\"integer\"}\n", - "pad_bottom = 10#@param {type:\"integer\"}\n", - "pad_left = 0#@param {type:\"integer\"}\n", - "pad_right = 0#@param {type:\"integer\"}\n", - "rescaleFactor = 1#@param {type:\"integer\"}\n", - "nosmooth = False #@param {type:\"boolean\"}\n", - "\n", - "if nosmooth == False:\n", - " !cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor\n", - "else:\n", - " !cd Wav2Lip && python inference.py --checkpoint_path checkpoints/wav2lip_gan.pth --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor --nosmooth\n", - "\n", - "#Preview output video\n", - "clear_output()\n", - "print(\"Final Video Preview\")\n", - "print(\"Dowload this video from\", '/content/Wav2Lip/results/result_voice.mp4')\n", - "showVideo('/content/Wav2Lip/results/result_voice.mp4')\n" - ], - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file diff --git a/Wav2Lip/Wav2Lip_simplified_v5.ipynb b/Wav2Lip/Wav2Lip_simplified_v5.ipynb deleted file mode 100644 index 5f0255d2d5e4a6c51b48f1014a2bab994d9f512e..0000000000000000000000000000000000000000 --- a/Wav2Lip/Wav2Lip_simplified_v5.ipynb +++ /dev/null @@ -1,645 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "U1xFNFU58_2j" - }, - "source": [ - "## Goal: Make anyone speak anything (LipSync)\n", - "\n", - "* Github: https://github.com/Rudrabha/Wav2Lip\n", - "* Paper: https://arxiv.org/abs/2008.10010\n", - "*Original notebook: https://colab.research.google.com/drive/1tZpDWXz49W6wDcTprANRGLo2D_EbD5J8?usp=sharing\n", - "\n", - "\n", - "\n", - "\n", - "**Modded by: [justinjohn-03](https://github.com/justinjohn0306)**\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "Qgo-oaI3JU2u" - }, - "outputs": [], - "source": [ - "#@titleStep1: Setup Wav2Lip
\n", - "#@markdown * Install dependency\n", - "#@markdown * Download pretrained model\n", - "from IPython.display import HTML, clear_output\n", - "!rm -rf /content/sample_data\n", - "!mkdir /content/sample_data\n", - "\n", - "!git clone https://github.com/justinjohn0306/Wav2Lip\n", - "\n", - "%cd /content/Wav2Lip\n", - "\n", - "#download the pretrained model\n", - "!wget 'https://github.com/justinjohn0306/Wav2Lip/releases/download/models/wav2lip.pth' -O 'checkpoints/wav2lip.pth'\n", - "!wget 'https://github.com/justinjohn0306/Wav2Lip/releases/download/models/wav2lip_gan.pth' -O 'checkpoints/wav2lip_gan.pth'\n", - "!wget 'https://github.com/justinjohn0306/Wav2Lip/releases/download/models/resnet50.pth' -O 'checkpoints/resnet50.pth'\n", - "!wget 'https://github.com/justinjohn0306/Wav2Lip/releases/download/models/mobilenet.pth' -O 'checkpoints/mobilenet.pth'\n", - "a = !pip install https://raw.githubusercontent.com/AwaleSajil/ghc/master/ghc-1.0-py3-none-any.whl\n", - "!pip install git+https://github.com/elliottzheng/batch-face.git@master\n", - "\n", - "!pip install ffmpeg-python mediapipe==0.8.11\n", - "\n", - "#this code for recording audio\n", - "\"\"\"\n", - "To write this piece of code I took inspiration/code from a lot of places.\n", - "It was late night, so I'm not sure how much I created or just copied o.O\n", - "Here are some of the possible references:\n", - "https://blog.addpipe.com/recording-audio-in-the-browser-using-pure-html5-and-minimal-javascript/\n", - "https://stackoverflow.com/a/18650249\n", - "https://hacks.mozilla.org/2014/06/easy-audio-capture-with-the-mediarecorder-api/\n", - "https://air.ghost.io/recording-to-an-audio-file-using-html5-and-js/\n", - "https://stackoverflow.com/a/49019356\n", - "\"\"\"\n", - "from IPython.display import HTML, Audio\n", - "from google.colab.output import eval_js\n", - "from base64 import b64decode\n", - "import numpy as np\n", - "from scipy.io.wavfile import read as wav_read\n", - "import io\n", - "import ffmpeg\n", - "\n", - "AUDIO_HTML = \"\"\"\n", - "\n", - "\"\"\"\n", - "\n", - "%cd /\n", - "from ghc.l_ghc_cf import l_ghc_cf\n", - "%cd content\n", - "\n", - "def get_audio():\n", - " display(HTML(AUDIO_HTML))\n", - " data = eval_js(\"data\")\n", - " binary = b64decode(data.split(',')[1])\n", - "\n", - " process = (ffmpeg\n", - " .input('pipe:0')\n", - " .output('pipe:1', format='wav')\n", - " .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True, quiet=True, overwrite_output=True)\n", - " )\n", - " output, err = process.communicate(input=binary)\n", - "\n", - " riff_chunk_size = len(output) - 8\n", - " # Break up the chunk size into four bytes, held in b.\n", - " q = riff_chunk_size\n", - " b = []\n", - " for i in range(4):\n", - " q, r = divmod(q, 256)\n", - " b.append(r)\n", - "\n", - " # Replace bytes 4:8 in proc.stdout with the actual size of the RIFF chunk.\n", - " riff = output[:4] + bytes(b) + output[8:]\n", - "\n", - " sr, audio = wav_read(io.BytesIO(riff))\n", - "\n", - " return audio, sr\n", - "\n", - "\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "def showVideo(path):\n", - " mp4 = open(str(path),'rb').read()\n", - " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - " return HTML(\"\"\"\n", - " \n", - " \"\"\" % data_url)\n", - "\n", - "from IPython.display import clear_output\n", - "\n", - "clear_output()\n", - "print(\"All set and ready!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SEdy6PWDXMRL" - }, - "source": [ - "# LipSync Youtube Video" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "QI4kcm8QEeGZ" - }, - "outputs": [], - "source": [ - "#@title STEP2: Select a Youtube Video\n", - "# Install yt-dlp\n", - "\n", - "import os\n", - "!pip install yt-dlp\n", - "\n", - "#@markdown ## Find YouTube video ID from URL\n", - "\n", - "#@markdown ___\n", - "\n", - "#@markdown Link format:\n", - "\n", - "#@markdown ``https://youtu.be/vAnWYLTdvfY`` ❌\n", - "\n", - "#@markdown ``https://www.youtube.com/watch?v=vAnWYLTdvfY`` ✔️\n", - "\n", - "!rm -df youtube.mp4\n", - "\n", - "#@markdown ___\n", - "from urllib import parse as urlparse\n", - "YOUTUBE_URL = 'https://www.youtube.com/watch?v=vAnWYLTdvfY' #@param {type:\"string\"}\n", - "url_data = urlparse.urlparse(YOUTUBE_URL)\n", - "query = urlparse.parse_qs(url_data.query)\n", - "YOUTUBE_ID = query[\"v\"][0]\n", - "\n", - "\n", - "# remove previous input video\n", - "!rm -f /content/sample_data/input_vid.mp4\n", - "\n", - "\n", - "#@markdown ___\n", - "\n", - "#@markdown ### Trim the video (start, end) seconds\n", - "start = 35 #@param {type:\"integer\"}\n", - "end = 62 #@param {type:\"integer\"}\n", - "interval = end - start\n", - "\n", - "#@markdown Note: ``the trimmed video must have face on all frames``\n", - "\n", - "# Download the YouTube video using yt-dlp\n", - "!yt-dlp -f 'bestvideo[ext=mp4]' --output \"youtube.%(ext)s\" https://www.youtube.com/watch?v=$YOUTUBE_ID\n", - "\n", - "# Cut the video using FFmpeg\n", - "!ffmpeg -y -i youtube.mp4 -ss {start} -t {interval} -async 1 /content/sample_data/input_vid.mp4\n", - "\n", - "# Preview the trimmed video\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "mp4 = open('/content/sample_data/input_vid.mp4','rb').read()\n", - "data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - "HTML(f\"\"\"\"\"\")\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "zS_RAeh-IfZy" - }, - "outputs": [], - "source": [ - "#@title STEP3: Select Audio (Record, Upload from local drive or Gdrive)\n", - "import os\n", - "from IPython.display import Audio\n", - "from IPython.core.display import display\n", - "\n", - "upload_method = 'Upload' #@param ['Record', 'Upload', 'Custom Path']\n", - "\n", - "#remove previous input audio\n", - "if os.path.isfile('/content/sample_data/input_audio.wav'):\n", - " os.remove('/content/sample_data/input_audio.wav')\n", - "\n", - "def displayAudio():\n", - " display(Audio('/content/sample_data/input_audio.wav'))\n", - "\n", - "if upload_method == 'Record':\n", - " audio, sr = get_audio()\n", - " import scipy\n", - " scipy.io.wavfile.write('/content/sample_data/input_audio.wav', sr, audio)\n", - "\n", - "elif upload_method == 'Upload':\n", - " from google.colab import files\n", - " uploaded = files.upload()\n", - " for fn in uploaded.keys():\n", - " print('User uploaded file \"{name}\" with length {length} bytes'.format(\n", - " name=fn, length=len(uploaded[fn])))\n", - "\n", - " # Consider only the first file\n", - " PATH_TO_YOUR_AUDIO = str(list(uploaded.keys())[0])\n", - "\n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(PATH_TO_YOUR_AUDIO, sr=None)\n", - "\n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - "\n", - " clear_output()\n", - " displayAudio()\n", - "\n", - "elif upload_method == 'Custom Path':\n", - " from google.colab import drive\n", - " drive.mount('/content/drive')\n", - " #@markdown ``Add the full path to your audio on your Gdrive`` 👇\n", - " PATH_TO_YOUR_AUDIO = '/content/drive/MyDrive/test.wav' #@param {type:\"string\"}\n", - "\n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(PATH_TO_YOUR_AUDIO, sr=None)\n", - "\n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - "\n", - " clear_output()\n", - " displayAudio()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "BQPLXJ8L0gms" - }, - "outputs": [], - "source": [ - "#@title STEP4: Start Crunching and Preview Output\n", - "#@markdown Note: Only change these, if you have to\n", - "\n", - "%cd /content/Wav2Lip\n", - "\n", - "# Set up paths and variables for the output file\n", - "output_file_path = '/content/Wav2Lip/results/result_voice.mp4'\n", - "\n", - "# Delete existing output file before processing, if any\n", - "if os.path.exists(output_file_path):\n", - " os.remove(output_file_path)\n", - "\n", - "pad_top = 0#@param {type:\"integer\"}\n", - "pad_bottom = 10#@param {type:\"integer\"}\n", - "pad_left = 0#@param {type:\"integer\"}\n", - "pad_right = 0#@param {type:\"integer\"}\n", - "rescaleFactor = 1#@param {type:\"integer\"}\n", - "nosmooth = True #@param {type:\"boolean\"}\n", - "#@markdown ___\n", - "#@markdown Model selection:\n", - "use_hd_model = False #@param {type:\"boolean\"}\n", - "checkpoint_path = 'checkpoints/wav2lip.pth' if not use_hd_model else 'checkpoints/wav2lip_gan.pth'\n", - "\n", - "\n", - "if nosmooth == False:\n", - " !python inference.py --checkpoint_path $checkpoint_path --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor\n", - "else:\n", - " !python inference.py --checkpoint_path $checkpoint_path --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor --nosmooth\n", - "\n", - "#Preview output video\n", - "if os.path.exists(output_file_path):\n", - " clear_output()\n", - " print(\"Final Video Preview\")\n", - " print(\"Download this video from\", output_file_path)\n", - " showVideo(output_file_path)\n", - "else:\n", - " print(\"Processing failed. Output video not found.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vYxpPeie1CYL" - }, - "source": [ - "# LipSync on Your Video File" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "nDuM7tfZ1F0t" - }, - "outputs": [], - "source": [ - "import os\n", - "import shutil\n", - "from google.colab import drive\n", - "from google.colab import files\n", - "from IPython.display import HTML, clear_output\n", - "from base64 import b64encode\n", - "import moviepy.editor as mp\n", - "\n", - "\n", - "def showVideo(file_path):\n", - " \"\"\"Function to display video in Colab\"\"\"\n", - " mp4 = open(file_path,'rb').read()\n", - " data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n", - " display(HTML(\"\"\"\n", - " \n", - " \"\"\" % data_url))\n", - "\n", - "def get_video_resolution(video_path):\n", - " \"\"\"Function to get the resolution of a video\"\"\"\n", - " import cv2\n", - " video = cv2.VideoCapture(video_path)\n", - " width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n", - " height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n", - " return (width, height)\n", - "\n", - "def resize_video(video_path, new_resolution):\n", - " \"\"\"Function to resize a video\"\"\"\n", - " import cv2\n", - " video = cv2.VideoCapture(video_path)\n", - " fourcc = int(video.get(cv2.CAP_PROP_FOURCC))\n", - " fps = video.get(cv2.CAP_PROP_FPS)\n", - " width, height = new_resolution\n", - " output_path = os.path.splitext(video_path)[0] + '_720p.mp4'\n", - " writer = cv2.VideoWriter(output_path, fourcc, fps, (width, height))\n", - " while True:\n", - " success, frame = video.read()\n", - " if not success:\n", - " break\n", - " resized_frame = cv2.resize(frame, new_resolution)\n", - " writer.write(resized_frame)\n", - " video.release()\n", - " writer.release()\n", - "\n", - "# Mount Google Drive if it's not already mounted\n", - "if not os.path.isdir(\"/content/drive/MyDrive\"):\n", - " drive.mount('/content/drive', force_remount=True)\n", - "\n", - "#@markdown ### Select an uploading method\n", - "upload_method = \"Upload\" #@param [\"Upload\", \"Custom Path\"]\n", - "\n", - "\n", - "# remove previous input video\n", - "if os.path.isfile('/content/sample_data/input_vid.mp4'):\n", - " os.remove('/content/sample_data/input_vid.mp4')\n", - "\n", - "if upload_method == \"Upload\":\n", - " uploaded = files.upload()\n", - " for filename in uploaded.keys():\n", - " os.rename(filename, '/content/sample_data/input_vid.mp4')\n", - " PATH_TO_YOUR_VIDEO = '/content/sample_data/input_vid.mp4'\n", - "\n", - "elif upload_method == 'Custom Path':\n", - " #@markdown ``Add the full path to your video on your Gdrive `` 👇\n", - " PATH_TO_YOUR_VIDEO = '/content/drive/MyDrive/test.mp4' #@param {type:\"string\"}\n", - " if not os.path.isfile(PATH_TO_YOUR_VIDEO):\n", - " print(\"ERROR: File not found!\")\n", - " raise SystemExit(0)\n", - "\n", - "#@markdown Notes:\n", - "\n", - "#@markdown . ``If your uploaded video is 1080p or higher resolution, this cell will resize it to 720p.``\n", - "\n", - "#@markdown . ``Do not upload videos longer than 60 seconds.``\n", - "\n", - "#@markdown ___\n", - "\n", - "video_duration = mp.VideoFileClip(PATH_TO_YOUR_VIDEO).duration\n", - "if video_duration > 60:\n", - " print(\"WARNING: Video duration exceeds 60 seconds. Please upload a shorter video.\")\n", - " raise SystemExit(0)\n", - "\n", - "video_resolution = get_video_resolution(PATH_TO_YOUR_VIDEO)\n", - "print(f\"Video resolution: {video_resolution}\")\n", - "if video_resolution[0] >= 1920 or video_resolution[1] >= 1080:\n", - " print(\"Resizing video to 720p...\")\n", - " os.system(f\"ffmpeg -i {PATH_TO_YOUR_VIDEO} -vf scale=1280:720 /content/sample_data/input_vid.mp4\")\n", - " PATH_TO_YOUR_VIDEO = \"/content/sample_data/input_vid.mp4\"\n", - " print(\"Video resized to 720p\")\n", - "else:\n", - " print(\"No resizing needed\")\n", - "\n", - "if upload_method == \"Upload\":\n", - " clear_output()\n", - " print(\"Input Video\")\n", - " showVideo(PATH_TO_YOUR_VIDEO)\n", - "else:\n", - " if os.path.isfile(PATH_TO_YOUR_VIDEO):\n", - " # Check if the source and destination files are the same\n", - " if PATH_TO_YOUR_VIDEO != \"/content/sample_data/input_vid.mp4\":\n", - " shutil.copyfile(PATH_TO_YOUR_VIDEO, \"/content/sample_data/input_vid.mp4\")\n", - " print(\"Video copied to destination.\")\n", - "\n", - " print(\"Input Video\")\n", - " # Display the video from the destination path\n", - " showVideo(\"/content/sample_data/input_vid.mp4\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "XgF4794r7sWK" - }, - "outputs": [], - "source": [ - "#@title STEP3: Select Audio (Record, Upload from local drive or Gdrive)\n", - "import os\n", - "from IPython.display import Audio\n", - "from IPython.core.display import display\n", - "\n", - "upload_method = 'Upload' #@param ['Record', 'Upload', 'Custom Path']\n", - "\n", - "#remove previous input audio\n", - "if os.path.isfile('/content/sample_data/input_audio.wav'):\n", - " os.remove('/content/sample_data/input_audio.wav')\n", - "\n", - "def displayAudio():\n", - " display(Audio('/content/sample_data/input_audio.wav'))\n", - "\n", - "if upload_method == 'Record':\n", - " audio, sr = get_audio()\n", - " import scipy\n", - " scipy.io.wavfile.write('/content/sample_data/input_audio.wav', sr, audio)\n", - "\n", - "elif upload_method == 'Upload':\n", - " from google.colab import files\n", - " uploaded = files.upload()\n", - " for fn in uploaded.keys():\n", - " print('User uploaded file \"{name}\" with length {length} bytes.'.format(\n", - " name=fn, length=len(uploaded[fn])))\n", - "\n", - " # Consider only the first file\n", - " PATH_TO_YOUR_AUDIO = str(list(uploaded.keys())[0])\n", - "\n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(PATH_TO_YOUR_AUDIO, sr=None)\n", - "\n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - "\n", - " clear_output()\n", - " displayAudio()\n", - "\n", - "else: # Custom Path\n", - " from google.colab import drive\n", - " drive.mount('/content/drive')\n", - " #@markdown ``Add the full path to your audio on your Gdrive`` 👇\n", - " PATH_TO_YOUR_AUDIO = '/content/drive/MyDrive/test.wav' #@param {type:\"string\"}\n", - "\n", - " # Load audio with specified sampling rate\n", - " import librosa\n", - " audio, sr = librosa.load(PATH_TO_YOUR_AUDIO, sr=None)\n", - "\n", - " # Save audio with specified sampling rate\n", - " import soundfile as sf\n", - " sf.write('/content/sample_data/input_audio.wav', audio, sr, format='wav')\n", - "\n", - " clear_output()\n", - " displayAudio()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "id": "ZgtO08V28ANf" - }, - "outputs": [], - "source": [ - "#@title STEP4: Start Crunching and Preview Output\n", - "#@markdown Note: Only change these, if you have to\n", - "\n", - "%cd /content/Wav2Lip\n", - "\n", - "# Set up paths and variables for the output file\n", - "output_file_path = '/content/Wav2Lip/results/result_voice.mp4'\n", - "\n", - "# Delete existing output file before processing, if any\n", - "if os.path.exists(output_file_path):\n", - " os.remove(output_file_path)\n", - "\n", - "pad_top = 0#@param {type:\"integer\"}\n", - "pad_bottom = 10#@param {type:\"integer\"}\n", - "pad_left = 0#@param {type:\"integer\"}\n", - "pad_right = 0#@param {type:\"integer\"}\n", - "rescaleFactor = 1#@param {type:\"integer\"}\n", - "nosmooth = True #@param {type:\"boolean\"}\n", - "#@markdown ___\n", - "#@markdown Model selection:\n", - "use_hd_model = False #@param {type:\"boolean\"}\n", - "checkpoint_path = 'checkpoints/wav2lip.pth' if not use_hd_model else 'checkpoints/wav2lip_gan.pth'\n", - "\n", - "\n", - "if nosmooth == False:\n", - " !python inference.py --checkpoint_path $checkpoint_path --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor\n", - "else:\n", - " !python inference.py --checkpoint_path $checkpoint_path --face \"../sample_data/input_vid.mp4\" --audio \"../sample_data/input_audio.wav\" --pads $pad_top $pad_bottom $pad_left $pad_right --resize_factor $rescaleFactor --nosmooth\n", - "\n", - "#Preview output video\n", - "if os.path.exists(output_file_path):\n", - " clear_output()\n", - " print(\"Final Video Preview\")\n", - " print(\"Download this video from\", output_file_path)\n", - " showVideo(output_file_path)\n", - "else:\n", - " print(\"Processing failed. Output video not found.\")" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "private_outputs": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/Wav2Lip/audio.py b/Wav2Lip/audio.py deleted file mode 100644 index 32ab5fabe8e505baa7431f7fb81e367aed1d0ac3..0000000000000000000000000000000000000000 --- a/Wav2Lip/audio.py +++ /dev/null @@ -1,136 +0,0 @@ -import librosa -import librosa.filters -import numpy as np -# import tensorflow as tf -from scipy import signal -from scipy.io import wavfile -from hparams import hparams as hp - -def load_wav(path, sr): - return librosa.core.load(path, sr=sr)[0] - -def save_wav(wav, path, sr): - wav *= 32767 / max(0.01, np.max(np.abs(wav))) - #proposed by @dsmiller - wavfile.write(path, sr, wav.astype(np.int16)) - -def save_wavenet_wav(wav, path, sr): - librosa.output.write_wav(path, wav, sr=sr) - -def preemphasis(wav, k, preemphasize=True): - if preemphasize: - return signal.lfilter([1, -k], [1], wav) - return wav - -def inv_preemphasis(wav, k, inv_preemphasize=True): - if inv_preemphasize: - return signal.lfilter([1], [1, -k], wav) - return wav - -def get_hop_size(): - hop_size = hp.hop_size - if hop_size is None: - assert hp.frame_shift_ms is not None - hop_size = int(hp.frame_shift_ms / 1000 * hp.sample_rate) - return hop_size - -def linearspectrogram(wav): - D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) - S = _amp_to_db(np.abs(D)) - hp.ref_level_db - - if hp.signal_normalization: - return _normalize(S) - return S - -def melspectrogram(wav): - D = _stft(preemphasis(wav, hp.preemphasis, hp.preemphasize)) - S = _amp_to_db(_linear_to_mel(np.abs(D))) - hp.ref_level_db - - if hp.signal_normalization: - return _normalize(S) - return S - -def _lws_processor(): - import lws - return lws.lws(hp.n_fft, get_hop_size(), fftsize=hp.win_size, mode="speech") - -def _stft(y): - if hp.use_lws: - return _lws_processor(hp).stft(y).T - else: - return librosa.stft(y=y, n_fft=hp.n_fft, hop_length=get_hop_size(), win_length=hp.win_size) - -########################################################## -#Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) -def num_frames(length, fsize, fshift): - """Compute number of time frames of spectrogram - """ - pad = (fsize - fshift) - if length % fshift == 0: - M = (length + pad * 2 - fsize) // fshift + 1 - else: - M = (length + pad * 2 - fsize) // fshift + 2 - return M - - -def pad_lr(x, fsize, fshift): - """Compute left and right padding - """ - M = num_frames(len(x), fsize, fshift) - pad = (fsize - fshift) - T = len(x) + 2 * pad - r = (M - 1) * fshift + fsize - T - return pad, pad + r -########################################################## -#Librosa correct padding -def librosa_pad_lr(x, fsize, fshift): - return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] - -# Conversions -_mel_basis = None - -def _linear_to_mel(spectogram): - global _mel_basis - if _mel_basis is None: - _mel_basis = _build_mel_basis() - return np.dot(_mel_basis, spectogram) - -def _build_mel_basis(): - assert hp.fmax <= hp.sample_rate // 2 - return librosa.filters.mel(sr=hp.sample_rate, n_fft=hp.n_fft, n_mels=hp.num_mels, - fmin=hp.fmin, fmax=hp.fmax) - -def _amp_to_db(x): - min_level = np.exp(hp.min_level_db / 20 * np.log(10)) - return 20 * np.log10(np.maximum(min_level, x)) - -def _db_to_amp(x): - return np.power(10.0, (x) * 0.05) - -def _normalize(S): - if hp.allow_clipping_in_normalization: - if hp.symmetric_mels: - return np.clip((2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value, - -hp.max_abs_value, hp.max_abs_value) - else: - return np.clip(hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)), 0, hp.max_abs_value) - - assert S.max() <= 0 and S.min() - hp.min_level_db >= 0 - if hp.symmetric_mels: - return (2 * hp.max_abs_value) * ((S - hp.min_level_db) / (-hp.min_level_db)) - hp.max_abs_value - else: - return hp.max_abs_value * ((S - hp.min_level_db) / (-hp.min_level_db)) - -def _denormalize(D): - if hp.allow_clipping_in_normalization: - if hp.symmetric_mels: - return (((np.clip(D, -hp.max_abs_value, - hp.max_abs_value) + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) - + hp.min_level_db) - else: - return ((np.clip(D, 0, hp.max_abs_value) * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) - - if hp.symmetric_mels: - return (((D + hp.max_abs_value) * -hp.min_level_db / (2 * hp.max_abs_value)) + hp.min_level_db) - else: - return ((D * -hp.min_level_db / hp.max_abs_value) + hp.min_level_db) diff --git a/Wav2Lip/checkpoints/README.md b/Wav2Lip/checkpoints/README.md deleted file mode 100644 index 80258ec8fb8e6fdce46f3d420bad25b58cd2ee12..0000000000000000000000000000000000000000 --- a/Wav2Lip/checkpoints/README.md +++ /dev/null @@ -1 +0,0 @@ -Place all your checkpoints (.pth files) here. \ No newline at end of file diff --git a/Wav2Lip/checkpoints/mobilenet.pth b/Wav2Lip/checkpoints/mobilenet.pth deleted file mode 100644 index 059e2c444b5abf257d527ff8856aa5ef91a3efef..0000000000000000000000000000000000000000 --- a/Wav2Lip/checkpoints/mobilenet.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2979b33ffafda5d74b6948cd7a5b9a7a62f62b949cef24e95fd15d2883a65220 -size 1789735 diff --git a/Wav2Lip/checkpoints/resnet50.pth b/Wav2Lip/checkpoints/resnet50.pth deleted file mode 100644 index 16546738ce0a00a9fd47585e0fc52744d31cc117..0000000000000000000000000000000000000000 --- a/Wav2Lip/checkpoints/resnet50.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d -size 109497761 diff --git a/Wav2Lip/checkpoints/wav2lip_gan.pth b/Wav2Lip/checkpoints/wav2lip_gan.pth deleted file mode 100644 index 0b8907735b1aef6adb297f2f6dcbcf8432823de4..0000000000000000000000000000000000000000 --- a/Wav2Lip/checkpoints/wav2lip_gan.pth +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ca9ab7b7b812c0e80a6e70a5977c545a1e8a365a6c49d5e533023c034d7ac3d8 -size 435801865 diff --git a/Wav2Lip/cog.yaml b/Wav2Lip/cog.yaml deleted file mode 100644 index f188727d7b56815c0e5dbc41616ee928d7a1fd68..0000000000000000000000000000000000000000 --- a/Wav2Lip/cog.yaml +++ /dev/null @@ -1,35 +0,0 @@ -# Configuration for Cog ⚙️ -# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md - -image: r8.im/devxpy/cog-wav2lip - -build: - # set to true if your model requires a GPU - gpu: true - cuda: "11.6.2" - - # a list of ubuntu apt packages to install - system_packages: - - ffmpeg - - cmake - - # python version in the form '3.8' or '3.8.12' - python_version: "3.8" - - # a list of packages in the format
-# Frame interpolation in PyTorch
-
-This is an unofficial PyTorch inference implementation
-of [FILM: Frame Interpolation for Large Motion, In ECCV 2022](https://film-net.github.io/).\
-[Original repository link](https://github.com/google-research/frame-interpolation)
-
-The project is focused on creating simple and TorchScript compilable inference interface for the original pretrained TF2
-model.
-
-# Quickstart
-
-Download a compiled model from [the release](https://github.com/dajes/frame-interpolation-pytorch/releases)
-and specify the path to the file in the following snippet:
-
-```python
-import torch
-
-device = torch.device('cuda')
-precision = torch.float16
-
-model = torch.jit.load(model_path, map_location='cpu')
-model.eval().to(device=device, dtype=precision)
-
-img1 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
-img3 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
-dt = img1.new_full((1, 1), .5)
-
-with torch.no_grad():
- img2 = model(img1, img3, dt) # Will be of the same shape as inputs (1, 3, 720, 1080)
-
-```
-
-# Exporting model by yourself
-
-You will need to install TensorFlow of the version specified in
-the [original repo](https://github.com/google-research/frame-interpolation#installation) and download SavedModel of "
-Style" network from [there](https://github.com/google-research/frame-interpolation#pre-trained-models)
-
-After you have downloaded the SavedModel and can load it via ```tf.compat.v2.saved_model.load(path)```:
-
-* Clone the repository
-
-```
-git clone https://github.com/dajes/frame-interpolation-pytorch
-cd frame-interpolation-pytorch
-```
-
-* Install dependencies
-
-```
-python -m pip install -r requirements.txt
-```
-
-* Run ```export.py```:
-
-```
-python export.py "model_path" "save_path" [--statedict] [--fp32] [--skiptest] [--gpu]
-```
-
-Argument list:
-
-* ```model_path``` Path to the TF SavedModel
-* ```save_path``` Path to save the PyTorch state dict
-* ```--statedict``` Export to state dict instead of TorchScript
-* ```--fp32``` Save weights at full precision
-* ```--skiptest``` Skip testing and save model immediately instead
-* ```--gpu``` Whether to attempt to use GPU for testing
-
-# Testing exported model
-The following script creates an MP4 video of interpolated frames between 2 input images:
-```
-python inference.py "model_path" "img1" "img2" [--save_path SAVE_PATH] [--gpu] [--fp16] [--frames FRAMES] [--fps FPS]
-```
-* ```model_path``` Path to the exported TorchScript checkpoint
-* ```img1``` Path to the first image
-* ```img2``` Path to the second image
-* ```--save_path SAVE_PATH``` Path to save the interpolated frames as a video, if absent it will be saved in the same directory as ```img1``` is located and named ```output.mp4```
-* ```--gpu``` Whether to attempt to use GPU for predictions
-* ```--fp16``` Whether to use fp16 for calculations, speeds inference up on GPUs with tensor cores
-* ```--frames FRAMES``` Number of frames to interpolate between the input images
-* ```--fps FPS``` FPS of the output video
-
-### Results on the 2 example photos from original repository:
-
- 
- 
-
diff --git a/frame-interpolation-pytorch/export.py b/frame-interpolation-pytorch/export.py
deleted file mode 100644
index 1414f768348f0412afa0f94a57ca5e68693c89e3..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/export.py
+++ /dev/null
@@ -1,155 +0,0 @@
-import warnings
-
-import numpy as np
-import tensorflow as tf
-import torch
-
-from interpolator import Interpolator
-
-
-def translate_state_dict(var_dict, state_dict):
- for name, (prev_name, weight) in zip(state_dict, var_dict.items()):
- print('Mapping', prev_name, '->', name)
- weight = torch.from_numpy(weight)
- if 'kernel' in prev_name:
- # Transpose the conv2d kernel weights, since TF uses (H, W, C, K) and PyTorch uses (K, C, H, W)
- weight = weight.permute(3, 2, 0, 1)
-
- assert state_dict[name].shape == weight.shape, f'Shape mismatch {state_dict[name].shape} != {weight.shape}'
-
- state_dict[name] = weight
-
-
-def import_state_dict(interpolator: Interpolator, saved_model):
- variables = saved_model.keras_api.variables
-
- extract_dict = interpolator.extract.state_dict()
- flow_dict = interpolator.predict_flow.state_dict()
- fuse_dict = interpolator.fuse.state_dict()
-
- extract_vars = {}
- _flow_vars = {}
- _fuse_vars = {}
-
- for var in variables:
- name = var.name
- if name.startswith('feat_net'):
- extract_vars[name[9:]] = var.numpy()
- elif name.startswith('predict_flow'):
- _flow_vars[name[13:]] = var.numpy()
- elif name.startswith('fusion'):
- _fuse_vars[name[7:]] = var.numpy()
-
- # reverse order of modules to allow jit export
- # TODO: improve this hack
- flow_vars = dict(sorted(_flow_vars.items(), key=lambda x: x[0].split('/')[0], reverse=True))
- fuse_vars = dict(sorted(_fuse_vars.items(), key=lambda x: int((x[0].split('/')[0].split('_')[1:] or [0])[0]) // 3, reverse=True))
-
- assert len(extract_vars) == len(extract_dict), f'{len(extract_vars)} != {len(extract_dict)}'
- assert len(flow_vars) == len(flow_dict), f'{len(flow_vars)} != {len(flow_dict)}'
- assert len(fuse_vars) == len(fuse_dict), f'{len(fuse_vars)} != {len(fuse_dict)}'
-
- for state_dict, var_dict in ((extract_dict, extract_vars), (flow_dict, flow_vars), (fuse_dict, fuse_vars)):
- translate_state_dict(var_dict, state_dict)
-
- interpolator.extract.load_state_dict(extract_dict)
- interpolator.predict_flow.load_state_dict(flow_dict)
- interpolator.fuse.load_state_dict(fuse_dict)
-
-
-def verify_debug_outputs(pt_outputs, tf_outputs):
- max_error = 0
- for name, predicted in pt_outputs.items():
- if name == 'image':
- continue
- pred_frfp = [f.permute(0, 2, 3, 1).detach().cpu().numpy() for f in predicted]
- true_frfp = [f.numpy() for f in tf_outputs[name]]
-
- for i, (pred, true) in enumerate(zip(pred_frfp, true_frfp)):
- assert pred.shape == true.shape, f'{name} {i} shape mismatch {pred.shape} != {true.shape}'
- error = np.max(np.abs(pred - true))
- max_error = max(max_error, error)
- assert error < 1, f'{name} {i} max error: {error}'
- print('Max intermediate error:', max_error)
-
-
-def test_model(interpolator, model, half=False, gpu=False):
- torch.manual_seed(0)
- time = torch.full((1, 1), .5)
- x0 = torch.rand(1, 3, 256, 256)
- x1 = torch.rand(1, 3, 256, 256)
-
- x0_ = tf.convert_to_tensor(x0.permute(0, 2, 3, 1).numpy(), dtype=tf.float32)
- x1_ = tf.convert_to_tensor(x1.permute(0, 2, 3, 1).numpy(), dtype=tf.float32)
- time_ = tf.convert_to_tensor(time.numpy(), dtype=tf.float32)
- tf_outputs = model({'x0': x0_, 'x1': x1_, 'time': time_}, training=False)
-
- if half:
- x0 = x0.half()
- x1 = x1.half()
- time = time.half()
-
- if gpu and torch.cuda.is_available():
- x0 = x0.cuda()
- x1 = x1.cuda()
- time = time.cuda()
-
- with torch.no_grad():
- pt_outputs = interpolator.debug_forward(x0, x1, time)
-
- verify_debug_outputs(pt_outputs, tf_outputs)
-
- with torch.no_grad():
- prediction = interpolator(x0, x1, time)
- output_color = prediction.permute(0, 2, 3, 1).detach().cpu().numpy()
- true_color = tf_outputs['image'].numpy()
- error = np.abs(output_color - true_color).max()
-
- print('Color max error:', error)
-
-
-def main(model_path, save_path, export_to_torchscript=True, use_gpu=False, fp16=True, skiptest=False):
- print(f'Exporting model to FP{["32", "16"][fp16]} {["state_dict", "torchscript"][export_to_torchscript]} '
- f'using {"CG"[use_gpu]}PU')
- model = tf.compat.v2.saved_model.load(model_path)
- interpolator = Interpolator()
- interpolator.eval()
- import_state_dict(interpolator, model)
-
- if use_gpu and torch.cuda.is_available():
- interpolator = interpolator.cuda()
- else:
- use_gpu = False
-
- if fp16:
- interpolator = interpolator.half()
- if export_to_torchscript:
- interpolator = torch.jit.script(interpolator)
- if export_to_torchscript:
- interpolator.save(save_path)
- else:
- torch.save(interpolator.state_dict(), save_path)
-
- if not skiptest:
- if not use_gpu and fp16:
- warnings.warn('Testing FP16 model on CPU is impossible, casting it back')
- interpolator = interpolator.float()
- fp16 = False
- test_model(interpolator, model, fp16, use_gpu)
-
-
-if __name__ == '__main__':
- import argparse
-
- parser = argparse.ArgumentParser(description='Export frame-interpolator model to PyTorch state dict')
-
- parser.add_argument('model_path', type=str, help='Path to the TF SavedModel')
- parser.add_argument('save_path', type=str, help='Path to save the PyTorch state dict')
- parser.add_argument('--statedict', action='store_true', help='Export to state dict instead of TorchScript')
- parser.add_argument('--fp32', action='store_true', help='Save at full precision')
- parser.add_argument('--skiptest', action='store_true', help='Skip testing and save model immediately instead')
- parser.add_argument('--gpu', action='store_true', help='Use GPU')
-
- args = parser.parse_args()
-
- main(args.model_path, args.save_path, not args.statedict, args.gpu, not args.fp32, args.skiptest)
diff --git a/frame-interpolation-pytorch/feature_extractor.py b/frame-interpolation-pytorch/feature_extractor.py
deleted file mode 100644
index 2b41975291c64173e4d98619d0ea5f2ca67f3240..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/feature_extractor.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""PyTorch layer for extracting image features for the film_net interpolator.
-
-The feature extractor implemented here converts an image pyramid into a pyramid
-of deep features. The feature pyramid serves a similar purpose as U-Net
-architecture's encoder, but we use a special cascaded architecture described in
-Multi-view Image Fusion [1].
-
-For comprehensiveness, below is a short description of the idea. While the
-description is a bit involved, the cascaded feature pyramid can be used just
-like any image feature pyramid.
-
-Why cascaded architeture?
-=========================
-To understand the concept it is worth reviewing a traditional feature pyramid
-first: *A traditional feature pyramid* as in U-net or in many optical flow
-networks is built by alternating between convolutions and pooling, starting
-from the input image.
-
-It is well known that early features of such architecture correspond to low
-level concepts such as edges in the image whereas later layers extract
-semantically higher level concepts such as object classes etc. In other words,
-the meaning of the filters in each resolution level is different. For problems
-such as semantic segmentation and many others this is a desirable property.
-
-However, the asymmetric features preclude sharing weights across resolution
-levels in the feature extractor itself and in any subsequent neural networks
-that follow. This can be a downside, since optical flow prediction, for
-instance is symmetric across resolution levels. The cascaded feature
-architecture addresses this shortcoming.
-
-How is it built?
-================
-The *cascaded* feature pyramid contains feature vectors that have constant
-length and meaning on each resolution level, except few of the finest ones. The
-advantage of this is that the subsequent optical flow layer can learn
-synergically from many resolutions. This means that coarse level prediction can
-benefit from finer resolution training examples, which can be useful with
-moderately sized datasets to avoid overfitting.
-
-The cascaded feature pyramid is built by extracting shallower subtree pyramids,
-each one of them similar to the traditional architecture. Each subtree
-pyramid S_i is extracted starting from each resolution level:
-
-image resolution 0 -> S_0
-image resolution 1 -> S_1
-image resolution 2 -> S_2
-...
-
-If we denote the features at level j of subtree i as S_i_j, the cascaded pyramid
-is constructed by concatenating features as follows (assuming subtree depth=3):
-
-lvl
-feat_0 = concat( S_0_0 )
-feat_1 = concat( S_1_0 S_0_1 )
-feat_2 = concat( S_2_0 S_1_1 S_0_2 )
-feat_3 = concat( S_3_0 S_2_1 S_1_2 )
-feat_4 = concat( S_4_0 S_3_1 S_2_2 )
-feat_5 = concat( S_5_0 S_4_1 S_3_2 )
- ....
-
-In above, all levels except feat_0 and feat_1 have the same number of features
-with similar semantic meaning. This enables training a single optical flow
-predictor module shared by levels 2,3,4,5... . For more details and evaluation
-see [1].
-
-[1] Multi-view Image Fusion, Trinidad et al. 2019
-"""
-from typing import List
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from util import Conv2d
-
-
-class SubTreeExtractor(nn.Module):
- """Extracts a hierarchical set of features from an image.
-
- This is a conventional, hierarchical image feature extractor, that extracts
- [k, k*2, k*4... ] filters for the image pyramid where k=options.sub_levels.
- Each level is followed by average pooling.
- """
-
- def __init__(self, in_channels=3, channels=64, n_layers=4):
- super().__init__()
- convs = []
- for i in range(n_layers):
- convs.append(nn.Sequential(
- Conv2d(in_channels, (channels << i), 3),
- Conv2d((channels << i), (channels << i), 3)
- ))
- in_channels = channels << i
- self.convs = nn.ModuleList(convs)
-
- def forward(self, image: torch.Tensor, n: int) -> List[torch.Tensor]:
- """Extracts a pyramid of features from the image.
-
- Args:
- image: TORCH.Tensor with shape BATCH_SIZE x HEIGHT x WIDTH x CHANNELS.
- n: number of pyramid levels to extract. This can be less or equal to
- options.sub_levels given in the __init__.
- Returns:
- The pyramid of features, starting from the finest level. Each element
- contains the output after the last convolution on the corresponding
- pyramid level.
- """
- head = image
- pyramid = []
- for i, layer in enumerate(self.convs):
- head = layer(head)
- pyramid.append(head)
- if i < n - 1:
- head = F.avg_pool2d(head, kernel_size=2, stride=2)
- return pyramid
-
-
-class FeatureExtractor(nn.Module):
- """Extracts features from an image pyramid using a cascaded architecture.
- """
-
- def __init__(self, in_channels=3, channels=64, sub_levels=4):
- super().__init__()
- self.extract_sublevels = SubTreeExtractor(in_channels, channels, sub_levels)
- self.sub_levels = sub_levels
-
- def forward(self, image_pyramid: List[torch.Tensor]) -> List[torch.Tensor]:
- """Extracts a cascaded feature pyramid.
-
- Args:
- image_pyramid: Image pyramid as a list, starting from the finest level.
- Returns:
- A pyramid of cascaded features.
- """
- sub_pyramids: List[List[torch.Tensor]] = []
- for i in range(len(image_pyramid)):
- # At each level of the image pyramid, creates a sub_pyramid of features
- # with 'sub_levels' pyramid levels, re-using the same SubTreeExtractor.
- # We use the same instance since we want to share the weights.
- #
- # However, we cap the depth of the sub_pyramid so we don't create features
- # that are beyond the coarsest level of the cascaded feature pyramid we
- # want to generate.
- capped_sub_levels = min(len(image_pyramid) - i, self.sub_levels)
- sub_pyramids.append(self.extract_sublevels(image_pyramid[i], capped_sub_levels))
- # Below we generate the cascades of features on each level of the feature
- # pyramid. Assuming sub_levels=3, The layout of the features will be
- # as shown in the example on file documentation above.
- feature_pyramid: List[torch.Tensor] = []
- for i in range(len(image_pyramid)):
- features = sub_pyramids[i][0]
- for j in range(1, self.sub_levels):
- if j <= i:
- features = torch.cat([features, sub_pyramids[i - j][j]], dim=1)
- feature_pyramid.append(features)
- return feature_pyramid
diff --git a/frame-interpolation-pytorch/film_net_fp16.pt b/frame-interpolation-pytorch/film_net_fp16.pt
deleted file mode 100644
index e2695211566846c6137de304743e5e4b5dd56739..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/film_net_fp16.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5d48a9c8f1032f046d7dfcbed40299d51e615b4bd8bbfbb36a83c9a49c76aca9
-size 69048401
diff --git a/frame-interpolation-pytorch/film_net_fp32.pt b/frame-interpolation-pytorch/film_net_fp32.pt
deleted file mode 100644
index 2691162477f27fe5e3cd4c69890fa2c28be27713..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/film_net_fp32.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f810cada26d0c288e50a27eac43af74446eb84b857ccbc77a22bb006f4d27240
-size 137922129
diff --git a/frame-interpolation-pytorch/fusion.py b/frame-interpolation-pytorch/fusion.py
deleted file mode 100644
index ca79661fdea7435a118e783cb74436f477faab2c..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/fusion.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""The final fusion stage for the film_net frame interpolator.
-
-The inputs to this module are the warped input images, image features and
-flow fields, all aligned to the target frame (often midway point between the
-two original inputs). The output is the final image. FILM has no explicit
-occlusion handling -- instead using the abovementioned information this module
-automatically decides how to best blend the inputs together to produce content
-in areas where the pixels can only be borrowed from one of the inputs.
-
-Similarly, this module also decides on how much to blend in each input in case
-of fractional timestep that is not at the halfway point. For example, if the two
-inputs images are at t=0 and t=1, and we were to synthesize a frame at t=0.1,
-it often makes most sense to favor the first input. However, this is not
-always the case -- in particular in occluded pixels.
-
-The architecture of the Fusion module follows U-net [1] architecture's decoder
-side, e.g. each pyramid level consists of concatenation with upsampled coarser
-level output, and two 3x3 convolutions.
-
-The upsampling is implemented as 'resize convolution', e.g. nearest neighbor
-upsampling followed by 2x2 convolution as explained in [2]. The classic U-net
-uses max-pooling which has a tendency to create checkerboard artifacts.
-
-[1] Ronneberger et al. U-Net: Convolutional Networks for Biomedical Image
- Segmentation, 2015, https://arxiv.org/pdf/1505.04597.pdf
-[2] https://distill.pub/2016/deconv-checkerboard/
-"""
-from typing import List
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from util import Conv2d
-
-_NUMBER_OF_COLOR_CHANNELS = 3
-
-
-def get_channels_at_level(level, filters):
- n_images = 2
- channels = _NUMBER_OF_COLOR_CHANNELS
- flows = 2
-
- return (sum(filters << i for i in range(level)) + channels + flows) * n_images
-
-
-class Fusion(nn.Module):
- """The decoder."""
-
- def __init__(self, n_layers=4, specialized_layers=3, filters=64):
- """
- Args:
- m: specialized levels
- """
- super().__init__()
-
- # The final convolution that outputs RGB:
- self.output_conv = nn.Conv2d(filters, 3, kernel_size=1)
-
- # Each item 'convs[i]' will contain the list of convolutions to be applied
- # for pyramid level 'i'.
- self.convs = nn.ModuleList()
-
- # Create the convolutions. Roughly following the feature extractor, we
- # double the number of filters when the resolution halves, but only up to
- # the specialized_levels, after which we use the same number of filters on
- # all levels.
- #
- # We create the convs in fine-to-coarse order, so that the array index
- # for the convs will correspond to our normal indexing (0=finest level).
- # in_channels: tuple = (128, 202, 256, 522, 512, 1162, 1930, 2442)
-
- in_channels = get_channels_at_level(n_layers, filters)
- increase = 0
- for i in range(n_layers)[::-1]:
- num_filters = (filters << i) if i < specialized_layers else (filters << specialized_layers)
- convs = nn.ModuleList([
- Conv2d(in_channels, num_filters, size=2, activation=None),
- Conv2d(in_channels + (increase or num_filters), num_filters, size=3),
- Conv2d(num_filters, num_filters, size=3)]
- )
- self.convs.append(convs)
- in_channels = num_filters
- increase = get_channels_at_level(i, filters) - num_filters // 2
-
- def forward(self, pyramid: List[torch.Tensor]) -> torch.Tensor:
- """Runs the fusion module.
-
- Args:
- pyramid: The input feature pyramid as list of tensors. Each tensor being
- in (B x H x W x C) format, with finest level tensor first.
-
- Returns:
- A batch of RGB images.
- Raises:
- ValueError, if len(pyramid) != config.fusion_pyramid_levels as provided in
- the constructor.
- """
-
- # As a slight difference to a conventional decoder (e.g. U-net), we don't
- # apply any extra convolutions to the coarsest level, but just pass it
- # to finer levels for concatenation. This choice has not been thoroughly
- # evaluated, but is motivated by the educated guess that the fusion part
- # probably does not need large spatial context, because at this point the
- # features are spatially aligned by the preceding warp.
- net = pyramid[-1]
-
- # Loop starting from the 2nd coarsest level:
- # for i in reversed(range(0, len(pyramid) - 1)):
- for k, layers in enumerate(self.convs):
- i = len(self.convs) - 1 - k
- # Resize the tensor from coarser level to match for concatenation.
- level_size = pyramid[i].shape[2:4]
- net = F.interpolate(net, size=level_size, mode='nearest')
- net = layers[0](net)
- net = torch.cat([pyramid[i], net], dim=1)
- net = layers[1](net)
- net = layers[2](net)
- net = self.output_conv(net)
- return net
diff --git a/frame-interpolation-pytorch/inference.py b/frame-interpolation-pytorch/inference.py
deleted file mode 100644
index 344d8bb62d81f02b60846cbf0865b28744047f33..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/inference.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import bisect
-import os
-from tqdm import tqdm
-import torch
-import numpy as np
-import cv2
-
-from util import load_image
-
-
-def inference(model_path, img1, img2, save_path, gpu, inter_frames, fps, half):
- model = torch.jit.load(model_path, map_location='cpu')
- model.eval()
- img_batch_1, crop_region_1 = load_image(img1)
- img_batch_2, crop_region_2 = load_image(img2)
-
- img_batch_1 = torch.from_numpy(img_batch_1).permute(0, 3, 1, 2)
- img_batch_2 = torch.from_numpy(img_batch_2).permute(0, 3, 1, 2)
-
- if not half:
- model.float()
-
- if gpu and torch.cuda.is_available():
- if half:
- model = model.half()
- else:
- model.float()
- model = model.cuda()
-
- if save_path == 'img1 folder':
- save_path = os.path.join(os.path.split(img1)[0], 'output.mp4')
-
- results = [
- img_batch_1,
- img_batch_2
- ]
-
- idxes = [0, inter_frames + 1]
- remains = list(range(1, inter_frames + 1))
-
- splits = torch.linspace(0, 1, inter_frames + 2)
-
- for _ in tqdm(range(len(remains)), 'Generating in-between frames'):
- starts = splits[idxes[:-1]]
- ends = splits[idxes[1:]]
- distances = ((splits[None, remains] - starts[:, None]) / (ends[:, None] - starts[:, None]) - .5).abs()
- matrix = torch.argmin(distances).item()
- start_i, step = np.unravel_index(matrix, distances.shape)
- end_i = start_i + 1
-
- x0 = results[start_i]
- x1 = results[end_i]
-
- if gpu and torch.cuda.is_available():
- if half:
- x0 = x0.half()
- x1 = x1.half()
- x0 = x0.cuda()
- x1 = x1.cuda()
-
- dt = x0.new_full((1, 1), (splits[remains[step]] - splits[idxes[start_i]])) / (splits[idxes[end_i]] - splits[idxes[start_i]])
-
- with torch.no_grad():
- prediction = model(x0, x1, dt)
- insert_position = bisect.bisect_left(idxes, remains[step])
- idxes.insert(insert_position, remains[step])
- results.insert(insert_position, prediction.clamp(0, 1).cpu().float())
- del remains[step]
-
- video_folder = os.path.split(save_path)[0]
- os.makedirs(video_folder, exist_ok=True)
-
- y1, x1, y2, x2 = crop_region_1
- frames = [(tensor[0] * 255).byte().flip(0).permute(1, 2, 0).numpy()[y1:y2, x1:x2].copy() for tensor in results]
-
- w, h = frames[0].shape[1::-1]
- fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
- writer = cv2.VideoWriter(save_path, fourcc, fps, (w, h))
- for frame in frames:
- writer.write(frame)
-
- for frame in frames[1:][::-1]:
- writer.write(frame)
-
- writer.release()
-
-
-if __name__ == '__main__':
- import argparse
-
- parser = argparse.ArgumentParser(description='Test frame interpolator model')
-
- parser.add_argument('model_path', type=str, help='Path to the TorchScript model')
- parser.add_argument('img1', type=str, help='Path to the first image')
- parser.add_argument('img2', type=str, help='Path to the second image')
-
- parser.add_argument('--save_path', type=str, default='img1 folder', help='Path to save the interpolated frames')
- parser.add_argument('--gpu', action='store_true', help='Use GPU')
- parser.add_argument('--fp16', action='store_true', help='Use FP16')
- parser.add_argument('--frames', type=int, default=18, help='Number of frames to interpolate')
- parser.add_argument('--fps', type=int, default=10, help='FPS of the output video')
-
- args = parser.parse_args()
-
- inference(args.model_path, args.img1, args.img2, args.save_path, args.gpu, args.frames, args.fps, args.fp16)
diff --git a/frame-interpolation-pytorch/interpolator.py b/frame-interpolation-pytorch/interpolator.py
deleted file mode 100644
index 707f8a69af0c6783a75766fed38c1353e96d1c16..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/interpolator.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""The film_net frame interpolator main model code.
-
-Basics
-======
-The film_net is an end-to-end learned neural frame interpolator implemented as
-a PyTorch model. It has the following inputs and outputs:
-
-Inputs:
- x0: image A.
- x1: image B.
- time: desired sub-frame time.
-
-Outputs:
- image: the predicted in-between image at the chosen time in range [0, 1].
-
-Additional outputs include forward and backward warped image pyramids, flow
-pyramids, etc., that can be visualized for debugging and analysis.
-
-Note that many training sets only contain triplets with ground truth at
-time=0.5. If a model has been trained with such training set, it will only work
-well for synthesizing frames at time=0.5. Such models can only generate more
-in-between frames using recursion.
-
-Architecture
-============
-The inference consists of three main stages: 1) feature extraction 2) warping
-3) fusion. On high-level, the architecture has similarities to Context-aware
-Synthesis for Video Frame Interpolation [1], but the exact architecture is
-closer to Multi-view Image Fusion [2] with some modifications for the frame
-interpolation use-case.
-
-Feature extraction stage employs the cascaded multi-scale architecture described
-in [2]. The advantage of this architecture is that coarse level flow prediction
-can be learned from finer resolution image samples. This is especially useful
-to avoid overfitting with moderately sized datasets.
-
-The warping stage uses a residual flow prediction idea that is similar to
-PWC-Net [3], Multi-view Image Fusion [2] and many others.
-
-The fusion stage is similar to U-Net's decoder where the skip connections are
-connected to warped image and feature pyramids. This is described in [2].
-
-Implementation Conventions
-====================
-Pyramids
---------
-Throughtout the model, all image and feature pyramids are stored as python lists
-with finest level first followed by downscaled versions obtained by successively
-halving the resolution. The depths of all pyramids are determined by
-options.pyramid_levels. The only exception to this is internal to the feature
-extractor, where smaller feature pyramids are temporarily constructed with depth
-options.sub_levels.
-
-Color ranges & gamma
---------------------
-The model code makes no assumptions on whether the images are in gamma or
-linearized space or what is the range of RGB color values. So a model can be
-trained with different choices. This does not mean that all the choices lead to
-similar results. In practice the model has been proven to work well with RGB
-scale = [0,1] with gamma-space images (i.e. not linearized).
-
-[1] Context-aware Synthesis for Video Frame Interpolation, Niklaus and Liu, 2018
-[2] Multi-view Image Fusion, Trinidad et al, 2019
-[3] PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
-"""
-from typing import Dict, List
-
-import torch
-from torch import nn
-
-import util
-from feature_extractor import FeatureExtractor
-from fusion import Fusion
-from pyramid_flow_estimator import PyramidFlowEstimator
-
-
-class Interpolator(nn.Module):
- def __init__(
- self,
- pyramid_levels=7,
- fusion_pyramid_levels=5,
- specialized_levels=3,
- sub_levels=4,
- filters=64,
- flow_convs=(3, 3, 3, 3),
- flow_filters=(32, 64, 128, 256),
- ):
- super().__init__()
- self.pyramid_levels = pyramid_levels
- self.fusion_pyramid_levels = fusion_pyramid_levels
-
- self.extract = FeatureExtractor(3, filters, sub_levels)
- self.predict_flow = PyramidFlowEstimator(filters, flow_convs, flow_filters)
- self.fuse = Fusion(sub_levels, specialized_levels, filters)
-
- def shuffle_images(self, x0, x1):
- return [
- util.build_image_pyramid(x0, self.pyramid_levels),
- util.build_image_pyramid(x1, self.pyramid_levels)
- ]
-
- def debug_forward(self, x0, x1, batch_dt) -> Dict[str, List[torch.Tensor]]:
- image_pyramids = self.shuffle_images(x0, x1)
-
- # Siamese feature pyramids:
- feature_pyramids = [self.extract(image_pyramids[0]), self.extract(image_pyramids[1])]
-
- # Predict forward flow.
- forward_residual_flow_pyramid = self.predict_flow(feature_pyramids[0], feature_pyramids[1])
-
- # Predict backward flow.
- backward_residual_flow_pyramid = self.predict_flow(feature_pyramids[1], feature_pyramids[0])
-
- # Concatenate features and images:
-
- # Note that we keep up to 'fusion_pyramid_levels' levels as only those
- # are used by the fusion module.
-
- forward_flow_pyramid = util.flow_pyramid_synthesis(forward_residual_flow_pyramid)[:self.fusion_pyramid_levels]
-
- backward_flow_pyramid = util.flow_pyramid_synthesis(backward_residual_flow_pyramid)[:self.fusion_pyramid_levels]
-
- # We multiply the flows with t and 1-t to warp to the desired fractional time.
- #
- # Note: In film_net we fix time to be 0.5, and recursively invoke the interpo-
- # lator for multi-frame interpolation. Below, we create a constant tensor of
- # shape [B]. We use the `time` tensor to infer the batch size.
- backward_flow = util.multiply_pyramid(backward_flow_pyramid, batch_dt)
- forward_flow = util.multiply_pyramid(forward_flow_pyramid, 1 - batch_dt)
-
- pyramids_to_warp = [
- util.concatenate_pyramids(image_pyramids[0][:self.fusion_pyramid_levels],
- feature_pyramids[0][:self.fusion_pyramid_levels]),
- util.concatenate_pyramids(image_pyramids[1][:self.fusion_pyramid_levels],
- feature_pyramids[1][:self.fusion_pyramid_levels])
- ]
-
- # Warp features and images using the flow. Note that we use backward warping
- # and backward flow is used to read from image 0 and forward flow from
- # image 1.
- forward_warped_pyramid = util.pyramid_warp(pyramids_to_warp[0], backward_flow)
- backward_warped_pyramid = util.pyramid_warp(pyramids_to_warp[1], forward_flow)
-
- aligned_pyramid = util.concatenate_pyramids(forward_warped_pyramid,
- backward_warped_pyramid)
- aligned_pyramid = util.concatenate_pyramids(aligned_pyramid, backward_flow)
- aligned_pyramid = util.concatenate_pyramids(aligned_pyramid, forward_flow)
-
- return {
- 'image': [self.fuse(aligned_pyramid)],
- 'forward_residual_flow_pyramid': forward_residual_flow_pyramid,
- 'backward_residual_flow_pyramid': backward_residual_flow_pyramid,
- 'forward_flow_pyramid': forward_flow_pyramid,
- 'backward_flow_pyramid': backward_flow_pyramid,
- }
-
- def forward(self, x0, x1, batch_dt) -> torch.Tensor:
- return self.debug_forward(x0, x1, batch_dt)['image'][0]
diff --git a/frame-interpolation-pytorch/photos/one.png b/frame-interpolation-pytorch/photos/one.png
deleted file mode 100644
index 044b61a95f23ff2b140c4deaf94230e10db2f7e2..0000000000000000000000000000000000000000
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+++ /dev/null
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-oid sha256:8bad1c97feb31a4bec60a809f808e1b0a26f55219fa991c4caa2e696bce8e81f
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diff --git a/frame-interpolation-pytorch/photos/two.png b/frame-interpolation-pytorch/photos/two.png
deleted file mode 100644
index c6aac8b76c7d8170987b380424facd2c3f30527f..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/photos/two.png
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
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diff --git a/frame-interpolation-pytorch/pyramid_flow_estimator.py b/frame-interpolation-pytorch/pyramid_flow_estimator.py
deleted file mode 100644
index 3083690a0737a19479a199ed633155cd6ff30163..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/pyramid_flow_estimator.py
+++ /dev/null
@@ -1,149 +0,0 @@
-"""PyTorch layer for estimating optical flow by a residual flow pyramid.
-
-This approach of estimating optical flow between two images can be traced back
-to [1], but is also used by later neural optical flow computation methods such
-as SpyNet [2] and PWC-Net [3].
-
-The basic idea is that the optical flow is first estimated in a coarse
-resolution, then the flow is upsampled to warp the higher resolution image and
-then a residual correction is computed and added to the estimated flow. This
-process is repeated in a pyramid on coarse to fine order to successively
-increase the resolution of both optical flow and the warped image.
-
-In here, the optical flow predictor is used as an internal component for the
-film_net frame interpolator, to warp the two input images into the inbetween,
-target frame.
-
-[1] F. Glazer, Hierarchical motion detection. PhD thesis, 1987.
-[2] A. Ranjan and M. J. Black, Optical Flow Estimation using a Spatial Pyramid
- Network. 2016
-[3] D. Sun X. Yang, M-Y. Liu and J. Kautz, PWC-Net: CNNs for Optical Flow Using
- Pyramid, Warping, and Cost Volume, 2017
-"""
-from typing import List
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-import util
-
-
-class FlowEstimator(nn.Module):
- """Small-receptive field predictor for computing the flow between two images.
-
- This is used to compute the residual flow fields in PyramidFlowEstimator.
-
- Note that while the number of 3x3 convolutions & filters to apply is
- configurable, two extra 1x1 convolutions are appended to extract the flow in
- the end.
-
- Attributes:
- name: The name of the layer
- num_convs: Number of 3x3 convolutions to apply
- num_filters: Number of filters in each 3x3 convolution
- """
-
- def __init__(self, in_channels: int, num_convs: int, num_filters: int):
- super(FlowEstimator, self).__init__()
-
- self._convs = nn.ModuleList()
- for i in range(num_convs):
- self._convs.append(util.Conv2d(in_channels=in_channels, out_channels=num_filters, size=3))
- in_channels = num_filters
- self._convs.append(util.Conv2d(in_channels, num_filters // 2, size=1))
- in_channels = num_filters // 2
- # For the final convolution, we want no activation at all to predict the
- # optical flow vector values. We have done extensive testing on explicitly
- # bounding these values using sigmoid, but it turned out that having no
- # activation gives better results.
- self._convs.append(util.Conv2d(in_channels, 2, size=1, activation=None))
-
- def forward(self, features_a: torch.Tensor, features_b: torch.Tensor) -> torch.Tensor:
- """Estimates optical flow between two images.
-
- Args:
- features_a: per pixel feature vectors for image A (B x H x W x C)
- features_b: per pixel feature vectors for image B (B x H x W x C)
-
- Returns:
- A tensor with optical flow from A to B
- """
- net = torch.cat([features_a, features_b], dim=1)
- for conv in self._convs:
- net = conv(net)
- return net
-
-
-class PyramidFlowEstimator(nn.Module):
- """Predicts optical flow by coarse-to-fine refinement.
- """
-
- def __init__(self, filters: int = 64,
- flow_convs: tuple = (3, 3, 3, 3),
- flow_filters: tuple = (32, 64, 128, 256)):
- super(PyramidFlowEstimator, self).__init__()
-
- in_channels = filters << 1
- predictors = []
- for i in range(len(flow_convs)):
- predictors.append(
- FlowEstimator(
- in_channels=in_channels,
- num_convs=flow_convs[i],
- num_filters=flow_filters[i]))
- in_channels += filters << (i + 2)
- self._predictor = predictors[-1]
- self._predictors = nn.ModuleList(predictors[:-1][::-1])
-
- def forward(self, feature_pyramid_a: List[torch.Tensor],
- feature_pyramid_b: List[torch.Tensor]) -> List[torch.Tensor]:
- """Estimates residual flow pyramids between two image pyramids.
-
- Each image pyramid is represented as a list of tensors in fine-to-coarse
- order. Each individual image is represented as a tensor where each pixel is
- a vector of image features.
-
- util.flow_pyramid_synthesis can be used to convert the residual flow
- pyramid returned by this method into a flow pyramid, where each level
- encodes the flow instead of a residual correction.
-
- Args:
- feature_pyramid_a: image pyramid as a list in fine-to-coarse order
- feature_pyramid_b: image pyramid as a list in fine-to-coarse order
-
- Returns:
- List of flow tensors, in fine-to-coarse order, each level encoding the
- difference against the bilinearly upsampled version from the coarser
- level. The coarsest flow tensor, e.g. the last element in the array is the
- 'DC-term', e.g. not a residual (alternatively you can think of it being a
- residual against zero).
- """
- levels = len(feature_pyramid_a)
- v = self._predictor(feature_pyramid_a[-1], feature_pyramid_b[-1])
- residuals = [v]
- for i in range(levels - 2, len(self._predictors) - 1, -1):
- # Upsamples the flow to match the current pyramid level. Also, scales the
- # magnitude by two to reflect the new size.
- level_size = feature_pyramid_a[i].shape[2:4]
- v = F.interpolate(2 * v, size=level_size, mode='bilinear')
- # Warp feature_pyramid_b[i] image based on the current flow estimate.
- warped = util.warp(feature_pyramid_b[i], v)
- # Estimate the residual flow between pyramid_a[i] and warped image:
- v_residual = self._predictor(feature_pyramid_a[i], warped)
- residuals.insert(0, v_residual)
- v = v_residual + v
-
- for k, predictor in enumerate(self._predictors):
- i = len(self._predictors) - 1 - k
- # Upsamples the flow to match the current pyramid level. Also, scales the
- # magnitude by two to reflect the new size.
- level_size = feature_pyramid_a[i].shape[2:4]
- v = F.interpolate(2 * v, size=level_size, mode='bilinear')
- # Warp feature_pyramid_b[i] image based on the current flow estimate.
- warped = util.warp(feature_pyramid_b[i], v)
- # Estimate the residual flow between pyramid_a[i] and warped image:
- v_residual = predictor(feature_pyramid_a[i], warped)
- residuals.insert(0, v_residual)
- v = v_residual + v
- return residuals
diff --git a/frame-interpolation-pytorch/requirements.txt b/frame-interpolation-pytorch/requirements.txt
deleted file mode 100644
index 8cf85fc758e71c0f018843b4e238b3198d89da30..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/requirements.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-opencv-python
-torch
-tqdm
\ No newline at end of file
diff --git a/frame-interpolation-pytorch/util.py b/frame-interpolation-pytorch/util.py
deleted file mode 100644
index 0eec1a11fa9d885a44081f35917783acda14d626..0000000000000000000000000000000000000000
--- a/frame-interpolation-pytorch/util.py
+++ /dev/null
@@ -1,166 +0,0 @@
-"""Various utilities used in the film_net frame interpolator model."""
-from typing import List, Optional
-
-import cv2
-import numpy as np
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-
-def pad_batch(batch, align):
- height, width = batch.shape[1:3]
- height_to_pad = (align - height % align) if height % align != 0 else 0
- width_to_pad = (align - width % align) if width % align != 0 else 0
-
- crop_region = [height_to_pad >> 1, width_to_pad >> 1, height + (height_to_pad >> 1), width + (width_to_pad >> 1)]
- batch = np.pad(batch, ((0, 0), (height_to_pad >> 1, height_to_pad - (height_to_pad >> 1)),
- (width_to_pad >> 1, width_to_pad - (width_to_pad >> 1)), (0, 0)), mode='constant')
- return batch, crop_region
-
-
-def load_image(path, align=64):
- image = cv2.cvtColor(cv2.imread(path), cv2.COLOR_BGR2RGB).astype(np.float32) / np.float32(255)
- image_batch, crop_region = pad_batch(np.expand_dims(image, axis=0), align)
- return image_batch, crop_region
-
-
-def build_image_pyramid(image: torch.Tensor, pyramid_levels: int = 3) -> List[torch.Tensor]:
- """Builds an image pyramid from a given image.
-
- The original image is included in the pyramid and the rest are generated by
- successively halving the resolution.
-
- Args:
- image: the input image.
- options: film_net options object
-
- Returns:
- A list of images starting from the finest with options.pyramid_levels items
- """
-
- pyramid = []
- for i in range(pyramid_levels):
- pyramid.append(image)
- if i < pyramid_levels - 1:
- image = F.avg_pool2d(image, 2, 2)
- return pyramid
-
-
-def warp(image: torch.Tensor, flow: torch.Tensor) -> torch.Tensor:
- """Backward warps the image using the given flow.
-
- Specifically, the output pixel in batch b, at position x, y will be computed
- as follows:
- (flowed_y, flowed_x) = (y+flow[b, y, x, 1], x+flow[b, y, x, 0])
- output[b, y, x] = bilinear_lookup(image, b, flowed_y, flowed_x)
-
- Note that the flow vectors are expected as [x, y], e.g. x in position 0 and
- y in position 1.
-
- Args:
- image: An image with shape BxHxWxC.
- flow: A flow with shape BxHxWx2, with the two channels denoting the relative
- offset in order: (dx, dy).
- Returns:
- A warped image.
- """
- flow = -flow.flip(1)
-
- dtype = flow.dtype
- device = flow.device
-
- # warped = tfa_image.dense_image_warp(image, flow)
- # Same as above but with pytorch
- ls1 = 1 - 1 / flow.shape[3]
- ls2 = 1 - 1 / flow.shape[2]
-
- normalized_flow2 = flow.permute(0, 2, 3, 1) / torch.tensor(
- [flow.shape[2] * .5, flow.shape[3] * .5], dtype=dtype, device=device)[None, None, None]
- normalized_flow2 = torch.stack([
- torch.linspace(-ls1, ls1, flow.shape[3], dtype=dtype, device=device)[None, None, :] - normalized_flow2[..., 1],
- torch.linspace(-ls2, ls2, flow.shape[2], dtype=dtype, device=device)[None, :, None] - normalized_flow2[..., 0],
- ], dim=3)
-
- warped = F.grid_sample(image, normalized_flow2,
- mode='bilinear', padding_mode='border', align_corners=False)
- return warped.reshape(image.shape)
-
-
-def multiply_pyramid(pyramid: List[torch.Tensor],
- scalar: torch.Tensor) -> List[torch.Tensor]:
- """Multiplies all image batches in the pyramid by a batch of scalars.
-
- Args:
- pyramid: Pyramid of image batches.
- scalar: Batch of scalars.
-
- Returns:
- An image pyramid with all images multiplied by the scalar.
- """
- # To multiply each image with its corresponding scalar, we first transpose
- # the batch of images from BxHxWxC-format to CxHxWxB. This can then be
- # multiplied with a batch of scalars, then we transpose back to the standard
- # BxHxWxC form.
- return [image * scalar[..., None, None] for image in pyramid]
-
-
-def flow_pyramid_synthesis(
- residual_pyramid: List[torch.Tensor]) -> List[torch.Tensor]:
- """Converts a residual flow pyramid into a flow pyramid."""
- flow = residual_pyramid[-1]
- flow_pyramid: List[torch.Tensor] = [flow]
- for residual_flow in residual_pyramid[:-1][::-1]:
- level_size = residual_flow.shape[2:4]
- flow = F.interpolate(2 * flow, size=level_size, mode='bilinear')
- flow = residual_flow + flow
- flow_pyramid.insert(0, flow)
- return flow_pyramid
-
-
-def pyramid_warp(feature_pyramid: List[torch.Tensor],
- flow_pyramid: List[torch.Tensor]) -> List[torch.Tensor]:
- """Warps the feature pyramid using the flow pyramid.
-
- Args:
- feature_pyramid: feature pyramid starting from the finest level.
- flow_pyramid: flow fields, starting from the finest level.
-
- Returns:
- Reverse warped feature pyramid.
- """
- warped_feature_pyramid = []
- for features, flow in zip(feature_pyramid, flow_pyramid):
- warped_feature_pyramid.append(warp(features, flow))
- return warped_feature_pyramid
-
-
-def concatenate_pyramids(pyramid1: List[torch.Tensor],
- pyramid2: List[torch.Tensor]) -> List[torch.Tensor]:
- """Concatenates each pyramid level together in the channel dimension."""
- result = []
- for features1, features2 in zip(pyramid1, pyramid2):
- result.append(torch.cat([features1, features2], dim=1))
- return result
-
-
-class Conv2d(nn.Sequential):
- def __init__(self, in_channels, out_channels, size, activation: Optional[str] = 'relu'):
- assert activation in (None, 'relu')
- super().__init__(
- nn.Conv2d(
- in_channels=in_channels,
- out_channels=out_channels,
- kernel_size=size,
- padding='same' if size % 2 else 0)
- )
- self.size = size
- self.activation = nn.LeakyReLU(.2) if activation == 'relu' else None
-
- def forward(self, x):
- if not self.size % 2:
- x = F.pad(x, (0, 1, 0, 1))
- y = self[0](x)
- if self.activation is not None:
- y = self.activation(y)
- return y