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<b><font size="5">OpenMMLab website</font></b>
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<a href="https://openmmlab.com">
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</sup>
<b><font size="5">OpenMMLab platform</font></b>
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<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
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[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmcv.readthedocs.io/en/latest/)
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English | [简体中文](README_zh-CN.md)
## Highlights
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.
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.
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` and `2.x` versions. 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:
- [Universal IO APIs](https://mmcv.readthedocs.io/en/latest/understand_mmcv/io.html)
- [Image/Video processing](https://mmcv.readthedocs.io/en/latest/understand_mmcv/data_process.html)
- [Image and annotation visualization](https://mmcv.readthedocs.io/en/latest/understand_mmcv/visualization.html)
- [Useful utilities (progress bar, timer, ...)](https://mmcv.readthedocs.io/en/latest/understand_mmcv/utils.html)
- [PyTorch runner with hooking mechanism](https://mmcv.readthedocs.io/en/latest/understand_mmcv/runner.html)
- [Various CNN architectures](https://mmcv.readthedocs.io/en/latest/understand_mmcv/cnn.html)
- [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 3.6+.
## Installation
There are two versions of MMCV:
- **mmcv-full**: comprehensive, with full features and various CPU and CUDA ops out of the box. It takes longer time to build.
- **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.
**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-full
Before installing mmcv-full, make sure that PyTorch has been successfully installed following the [PyTorch official installation guide](https://github.com/pytorch/pytorch#installation).
The command to install mmcv-full:
```bash
pip install -U openmim
mim install mmcv-full
```
If you need to specify the version of mmcv-full, you can use the following command:
```bash
mim install mmcv-full==1.7.0
```
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).
<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 />
Collecting mmcv-full<br />
<b>Downloading https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/mmcv_full-1.6.1-cp38-cp38-manylinux1_x86_64.whl</b>
</details>
<details>
<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-full==1.6.0<br />
<b>Downloading mmcv-full-1.6.0.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
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
```
## Branch Maintenance Plan
MMCV currently has two branches, the master and 2.x branches, which go through the following three phases.
| Phase | Time | Branch | description |
| -------------------- | --------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| 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 |
| 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 |
| 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 |
## Supported projects
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLa
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人脸识别-面向计算机视觉的基础库,支持 Linux、Windows 以及 MacOS 平台。它提供了众多功能,包括基于 PyTorch 的通用训练框架、高质量实现的常见 CUDA 算子、通用的 IO 接口、图像和视频处理、图像和标注结果可视化、多种 CNN 网络提供了如下众多功能: 通用的 IO 接口 图像和视频处理 图像和标注结果可视化 常用小工具(进度条,计时器等) 基于 PyTorch 的通用训练框架 多种 CNN 网络结构 高质量实现的常见 CUDA 算子 人脸识别-面向计算机视觉的基础库,支持 Linux、Windows 以及 MacOS 平台。它提供了众多功能,包括基于 PyTorch 的通用训练框架、高质量实现的常见 CUDA 算子、通用的 IO 接口、图像和视频处理、图像和标注结果可视化、多种 CNN 网络提供了如下众多功能:人脸识别-面向计算机视觉的基础库,支持 Linux、Windows 以及 MacOS 平台。它提供了众多功能,包括基于 PyTorch 的通用训练框架、高质量实现的常见 CUDA 算子、通用的 IO 接口、图像和视频处理、图像和标注结果可视化、多种
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人脸识别-面向计算机视觉的基础库.zip (1037个子文件)
make.bat 752B
make.bat 752B
ccattention_input.bin 253KB
ccattention_output.bin 253KB
psa_output_distribute.bin 64KB
psa_output_collect.bin 64KB
carafe_mask_grad.bin 28KB
carafe_mask.bin 28KB
carafe_output.bin 18KB
psa_input.bin 16KB
carafe_feat.bin 5KB
carafe_feat_grad.bin 5KB
a.bin 0B
CITATION.cff 254B
setup.cfg 809B
cudabind.cpp 95KB
cudabind.cpp 82KB
pybind.cpp 51KB
ms_deform_attn_mlu.cpp 25KB
roiaware_pool3d_mlu.cpp 21KB
deform_conv.cpp 21KB
deform_conv.cpp 21KB
modulated_deform_conv.cpp 20KB
roi_align.cpp 18KB
deform_conv.cpp 18KB
carafe_mlu.cpp 17KB
roi_align_rotated.cpp 17KB
deform_roi_pool_mlu.cpp 15KB
focal_loss_sigmoid_mlu.cpp 14KB
psamask_mlu.cpp 13KB
gridSample.cpp 13KB
voxelization_mlu.cpp 12KB
roi_pool_mlu.cpp 12KB
modulated_deform_conv.cpp 11KB
roi_align_rotated_mlu.cpp 11KB
trt_deform_conv.cpp 11KB
modulated_deform_conv.cpp 10KB
modulated_deform_conv.cpp 10KB
trt_modulated_deform_conv.cpp 10KB
masked_conv2d_mlu.cpp 10KB
deform_conv.cpp 10KB
trt_nms.cpp 10KB
roi_align.cpp 10KB
trt_roi_align.cpp 9KB
deform_conv_parrots.cpp 9KB
roi_align_rotated.cpp 9KB
psamask.cpp 9KB
rotated_feature_align.cpp 9KB
roi_align_mlu.cpp 9KB
tin_shift_mlu.cpp 9KB
trt_instance_norm.cpp 8KB
spconv_ops.cpp 8KB
roipoint_pool3d_mlu.cpp 8KB
trt_grid_sampler.cpp 8KB
trt_cummaxmin.cpp 8KB
modulated_deform_conv_parrots.cpp 7KB
nms.cpp 7KB
voxelization.cpp 7KB
reduce_ops.cpp 7KB
trt_corner_pool.cpp 7KB
trt_scatternd.cpp 6KB
nms_mlu.cpp 6KB
focal_loss_npu.cpp 6KB
correlation_parrots.cpp 6KB
iou3d_mlu.cpp 6KB
fused_bias_leakyrelu.cpp 5KB
fused_bias_leakyrelu.cpp 5KB
upfirdn2d.cpp 5KB
upfirdn2d.cpp 5KB
roi_align_parrots.cpp 5KB
roi_align_rotated_parrots.cpp 5KB
soft_nms.cpp 5KB
corner_pool.cpp 5KB
active_rotated_filter.cpp 5KB
pixel_group.cpp 5KB
psamask_parrots.cpp 4KB
rotated_feature_align.cpp 4KB
three_nn_mlu.cpp 4KB
nms_parrots.cpp 4KB
sync_bn_parrots.cpp 4KB
bbox_overlaps_mlu.cpp 4KB
voxelization_parrots.cpp 4KB
mlu_common_helper.cpp 4KB
group_points.cpp 4KB
sparse_indice.cpp 4KB
focal_loss_parrots.cpp 4KB
deform_roi_pool_parrots.cpp 4KB
roiaware_pool3d.cpp 4KB
roiaware_pool3d.cpp 4KB
voxelization.cpp 4KB
voxelization.cpp 4KB
nms.cpp 4KB
contour_expand.cpp 3KB
contour_expand.cpp 3KB
prroi_pool_parrots.cpp 3KB
sync_bn.cpp 3KB
sync_bn.cpp 3KB
rotated_feature_align_parrots.cpp 3KB
carafe_parrots.cpp 3KB
sparse_maxpool.cpp 3KB
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