mirror of https://github.com/open-mmlab/mmcv.git
178 lines
11 KiB
Markdown
178 lines
11 KiB
Markdown
<div align="center">
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<img src="https://raw.githubusercontent.com/open-mmlab/mmcv/master/docs/en/mmcv-logo.png" width="300"/>
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<div> </div>
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<div align="center">
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<b><font size="5">OpenMMLab website</font></b>
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<sup>
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<a href="https://openmmlab.com">
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<i><font size="4">HOT</font></i>
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</a>
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</sup>
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<b><font size="5">OpenMMLab platform</font></b>
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<sup>
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<a href="https://platform.openmmlab.com">
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<i><font size="4">TRY IT OUT</font></i>
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</a>
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</sup>
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</div>
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<div> </div>
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</div>
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[](https://mmcv.readthedocs.io/en/latest/)
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[](https://mmcv.readthedocs.io/en/latest/get_started/installation.html)
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[](https://pypi.org/project/mmcv/)
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[](https://pytorch.org/get-started/previous-versions/)
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[](https://developer.nvidia.com/cuda-downloads)
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[](https://pypi.org/project/mmcv)
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[](https://github.com/open-mmlab/mmcv/actions)
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[](https://codecov.io/gh/open-mmlab/mmcv)
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[](https://github.com/open-mmlab/mmcv/blob/master/LICENSE)
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English | [简体中文](README_zh-CN.md)
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## Highlights
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The OpenMMLab team released a new generation of training engine [MMEngine](https://github.com/open-mmlab/mmengine) at the World Artificial Intelligence Conference on September 1, 2022. It is a foundational library for training deep learning models. Compared with MMCV, it provides a universal and powerful runner, an open architecture with a more unified interface, and a more customizable training process.
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At the same time, MMCV released [2.x](https://github.com/open-mmlab/mmcv/tree/2.x) release candidate version and will release 2.x official version on January 1, 2023.
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In version 2.x, it removed components related to the training process and added a data transformation module. Also, starting from 2.x, it renamed the package names **mmcv** to **mmcv-lite** and **mmcv-full** to **mmcv**. For details, see [Compatibility Documentation](docs/en/compatibility.md).
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MMCV will maintain both `1.x` and `2.x` versions. For details, see [Branch Maintenance Plan](README.md#branch-maintenance-plan).
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## Introduction
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MMCV is a foundational library for computer vision research and it provides the following functionalities:
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- [Universal IO APIs](https://mmcv.readthedocs.io/en/latest/understand_mmcv/io.html)
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- [Image/Video processing](https://mmcv.readthedocs.io/en/latest/understand_mmcv/data_process.html)
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- [Image and annotation visualization](https://mmcv.readthedocs.io/en/latest/understand_mmcv/visualization.html)
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- [Useful utilities (progress bar, timer, ...)](https://mmcv.readthedocs.io/en/latest/understand_mmcv/utils.html)
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- [PyTorch runner with hooking mechanism](https://mmcv.readthedocs.io/en/latest/understand_mmcv/runner.html)
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- [Various CNN architectures](https://mmcv.readthedocs.io/en/latest/understand_mmcv/cnn.html)
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- [High-quality implementation of common CPU and CUDA ops](https://mmcv.readthedocs.io/en/latest/understand_mmcv/ops.html)
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It supports the following systems:
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- Linux
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- Windows
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- macOS
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See the [documentation](http://mmcv.readthedocs.io/en/latest) for more features and usage.
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Note: MMCV requires Python 3.6+.
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## Installation
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There are two versions of MMCV:
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- **mmcv-full**: comprehensive, with full features and various CPU and CUDA ops out of the box. It takes longer time to build.
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- **mmcv**: lite, without CPU and CUDA ops but all other features, similar to mmcv\<1.0.0. It is useful when you do not need those CUDA ops.
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**Note**: Do not install both versions in the same environment, otherwise you may encounter errors like `ModuleNotFound`. You need to uninstall one before installing the other. `Installing the full version is highly recommended if CUDA is available`.
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### Install mmcv-full
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Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation).
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The command to install mmcv-full on Linux or Windows platforms is as follows (if your system is macOS, please refer to [build mmcv-full from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html#macos-mmcv-full))
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```bash
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pip install -U openmim
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mim install mmcv-full
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```
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If you need to specify the version of mmcv-full, you can use the following command
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```bash
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mim install mmcv-full==1.5.0
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```
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If you find that the above installation command does not use a pre-built package ending with `.whl` but a source package ending with `.tar.gz`, you may not have a pre-build package corresponding to the PyTorch or CUDA or mmcv-full version, in which case you can [build mmcv-full from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
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<details>
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<summary>Installation log using pre-built packages</summary>
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Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
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Collecting mmcv-full<br />
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<b>Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl</b>
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</details>
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<details>
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<summary>Installation log using source packages</summary>
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Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br />
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Collecting mmcv-full==1.6.0<br />
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<b>Downloading mmcv-full-1.6.0.tar.gz</b>
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</details>
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For more installation methods, please refer to the [Installation documentation](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).
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### Install mmcv
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If you need to use PyTorch-related modules, make sure PyTorch has been successfully installed in your environment by referring to the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation).
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```bash
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pip install -U openmim
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mim install mmcv
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```
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## Branch Maintenance Plan
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MMCV currently has two branches, the master and 2.x branches, which go through the following three phases.
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| Phase | Time | Branch | description |
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| -------------------- | --------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| RC Period | 2022/9/1 - 2022.12.31 | Release candidate code (2.x version) will be released on 2.x branch. Default master branch is still 1.x version | Master and 2.x branches iterate normally |
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| Compatibility Period | 2023/1/1 - 2023.12.31 | **Default master branch will be switched to 2.x branch**, and 1.x branch will correspond to 1.x version | We still maintain the old version 1.x, respond to user needs, but try not to introduce changes that break compatibility; master branch iterates normally |
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| Maintenance Period | From 2024/1/1 | Default master branch corresponds to 2.x version and 1.x branch is 1.x version | 1.x branch is in maintenance phase, no more new feature support; master branch is iterating normally |
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## Supported projects
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- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
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- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
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- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
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- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
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- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
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- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
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- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
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- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
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- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
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- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
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- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
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- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
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- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox.
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- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
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- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
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## FAQ
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If you face installation problems or runtime issues, you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html) to see if there is a solution. If the problem is still not solved, feel free to open an [issue](https://github.com/open-mmlab/mmcv/issues).
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## Citation
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If you find this project useful in your research, please consider cite:
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```latex
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@misc{mmcv,
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title={{MMCV: OpenMMLab} Computer Vision Foundation},
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author={MMCV Contributors},
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howpublished = {\url{https://github.com/open-mmlab/mmcv}},
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year={2018}
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}
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```
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## Contributing
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We appreciate all contributions to improve MMCV. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
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## License
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MMCV is released under the Apache 2.0 license, while some specific operations in this library are with other licenses. Please refer to [LICENSES.md](LICENSES.md) for the careful check, if you are using our code for commercial matters.
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