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.
MMCV v2.0.0 official version was released on April 6, 2023. 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).
MMCV will maintain both [1.x](https://github.com/open-mmlab/mmcv/tree/1.x)(corresponding to the original [master](https://github.com/open-mmlab/mmcv/tree/master) branch) and **2.x** (corresponding to the **main** branch, now the default branch) versions simultaneously. For details, see [Branch Maintenance Plan](README.md#branch-maintenance-plan).
## Introduction
MMCV is a foundational library for computer vision research and it provides the following functionalities:
-[High-quality implementation of common CPU and CUDA ops](https://mmcv.readthedocs.io/en/latest/understand_mmcv/ops.html)
It supports the following systems:
- Linux
- Windows
- macOS
See the [documentation](http://mmcv.readthedocs.io/en/latest) for more features and usage.
Note: MMCV requires Python 3.7+.
## Installation
There are two versions of MMCV:
-**mmcv**: comprehensive, with full features and various CUDA ops out of the box. It takes longer time to build.
-**mmcv-lite**: lite, without CUDA ops but all other features, similar to mmcv\<1.0.0. It is useful when you do not need those CUDA ops.
**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`.
### Install mmcv
Before installing mmcv, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation). For apple silicon users, please use PyTorch 1.13+.
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 version, in which case you can [build mmcv from source](https://mmcv.readthedocs.io/en/latest/get_started/build.html).
<details>
<summary>Installation log using pre-built packages</summary>
Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br/>
<summary>Installation log using source packages</summary>
Looking in links: https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html<br/>
Collecting mmcv==2.0.0<br/>
<b>Downloading mmcv-2.0.0.tar.gz</b>
</details>
For more installation methods, please refer to the [Installation documentation](https://mmcv.readthedocs.io/en/latest/get_started/installation.html).
### Install mmcv-lite
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).
```bash
pip install-U openmim
mim install mmcv-lite
```
```
1. 设置不转码编译环境变量
export FORCE_CUDA=1
source /usr/local/bin/fastpt -C
## FAQ
2. 编译whl包并安装
MMCV_WITH_OPS=1 python3 setup.py -v bdist_wheel
If you face some installation issues, CUDA related issues or RuntimeErrors,
pip install dist/mmcv*
you may first refer to this [Frequently Asked Questions](https://mmcv.readthedocs.io/en/latest/faq.html).
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).
## Citation
3. 源码编译安装
MMCV_WITH_OPS=1 python3 setup.py install
If you find this project useful in your research, please consider cite:
We appreciate all contributions to improve MMCV. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
## License
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.
## Branch Maintenance Plan
MMCV currently has four branches, namely main, 1.x, master, and 2.x, where 2.x is an alias for the main branch, and master is an alias for the 1.x branch. The 2.x and master branches will be deleted in the future. MMCV's branches go through the following three stages:
| RC Period | 2022.9.1 - 2023.4.5 | 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 |
| Compatibility Period | 2023.4.6 - 2023.12.31 | **The 2.x branch has been renamed to the main branch and set as the default 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; main branch iterates normally |
| Maintenance Period | From 2024/1/1 | Default main 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; main branch is iterating normally |
## Projects in OpenMMLab
## Known Issue
- 无
-[MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
## 参考资料
-[MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
