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Dev 1.x #2176

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Dev 1.x #2176

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70fdd30
Bump version to v1.0.0 (#2169)
Tau-J Apr 6, 2023
6c6c12c
[Docs] Update Installation Guides (#2171)
Tau-J Apr 6, 2023
65a58ec
[Docs] add migration progress (#2175)
Tau-J Apr 6, 2023
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42 changes: 42 additions & 0 deletions README.md
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Expand Up @@ -123,6 +123,48 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-

Please refer to the [release notes](https://github.com/open-mmlab/mmpose/releases/tag/v1.0.0) for more updates brought by MMPose v1.0.0!

## 0.x -> 1.x 迁移进度

MMPose v1.0.0 is a major update, including many API and config file changes. Currently, a part of the algorithms have been migrated to v1.0.0, and the remaining algorithms will be completed in subsequent versions. We will show the migration progress in the following list.

<details close>
<summary><b>Migration Progress</b></summary>

| Algorithm | Status |
| :--- | :---: |
| MTUT (CVPR 2019) | |
| MSPN (ArXiv 2019)| done |
| InterNet (ECCV 2020) | |
| DEKR (CVPR 2021) | done |
| HigherHRNet (CVPR 2020) | |
| DeepPose (CVPR 2014) | done |
| RLE (ICCV 2021) | done |
| SoftWingloss (TIP 2021) | |
| VideoPose3D (CVPR 2019) | |
| Hourglass (ECCV 2016) | done |
| LiteHRNet (CVPR 2021) | done |
| AdaptiveWingloss (ICCV 2019) | done |
| SimpleBaseline2D (ECCV 2018) | done |
| PoseWarper (NeurIPS 2019) | |
| SimpleBaseline3D (ICCV 2017) | |
| HMR (CVPR 2018) | |
| UDP (CVPR 2020) | done |
| VIPNAS (CVPR 2021) | done |
| Wingloss (CVPR 2018) | |
| DarkPose (CVPR 2020) | done |
| Associative Embedding (NIPS 2017) | in progress |
| VoxelPose (ECCV 2020) | |
| RSN (ECCV 2020) | done |
| CID (CVPR 2022) | done |
| CPM (CVPR 2016) | done |
| HRNet (CVPR 2019) | done |
| HRNetv2 (TPAMI 2019) | done |
| SCNet (CVPR 2020) | done |

</details>

If your algorithm has not been migrated, you can continue to use the [0.x branch](https://github.com/open-mmlab/mmpose/tree/0.x) and [old documentation](https://mmpose.readthedocs.io/en/0.x/).

## Installation

Please refer to [installation.md](https://mmpose.readthedocs.io/en/latest/installation.html) for more detailed installation and dataset preparation.
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44 changes: 42 additions & 2 deletions README_CN.md
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Expand Up @@ -121,6 +121,47 @@ https://user-images.githubusercontent.com/15977946/124654387-0fd3c500-ded1-11eb-

请查看完整的 [版本说明](https://github.com/open-mmlab/mmpose/releases/tag/v1.0.0) 以了解更多 MMPose v1.0.0 带来的更新!

## 0.x / 1.x 迁移

MMPose v1.0.0 是一个重大更新，包括了大量的 API 和配置文件的变化。目前 v1.0.0 中已经完成了一部分算法的迁移工作，剩余的算法将在后续的版本中陆续完成，我们将在下面的列表中展示迁移进度。

<details close>
<summary><b>迁移进度</b></summary>

| 算法名称 | 迁移进度 |
| :--- | :---: |
| MTUT (CVPR 2019) | |
| MSPN (ArXiv 2019)| done |
| InterNet (ECCV 2020) | |
| DEKR (CVPR 2021) | done |
| HigherHRNet (CVPR 2020) | |
| DeepPose (CVPR 2014) | done |
| RLE (ICCV 2021) | done |
| SoftWingloss (TIP 2021) | |
| VideoPose3D (CVPR 2019) | |
| Hourglass (ECCV 2016) | done |
| LiteHRNet (CVPR 2021) | done |
| AdaptiveWingloss (ICCV 2019) | done |
| SimpleBaseline2D (ECCV 2018) | done |
| PoseWarper (NeurIPS 2019) | |
| SimpleBaseline3D (ICCV 2017) | |
| HMR (CVPR 2018) | |
| UDP (CVPR 2020) | done |
| VIPNAS (CVPR 2021) | done |
| Wingloss (CVPR 2018) | |
| DarkPose (CVPR 2020) | done |
| Associative Embedding (NIPS 2017) | in progress |
| VoxelPose (ECCV 2020) | |
| RSN (ECCV 2020) | done |
| CID (CVPR 2022) | done |
| CPM (CVPR 2016) | done |
| HRNet (CVPR 2019) | done |
| HRNetv2 (TPAMI 2019) | done |
| SCNet (CVPR 2020) | done |

</details>

如果您使用的算法还没有完成迁移，您也可以继续使用访问 [0.x 分支](https://github.com/open-mmlab/mmpose/tree/0.x) 和 [旧版文档](https://mmpose.readthedocs.io/zh_CN/0.x/)
## 安装

关于安装的详细说明请参考[安装文档](https://mmpose.readthedocs.io/zh_CN/latest/installation.html)。
Expand Down Expand Up @@ -321,8 +362,7 @@ MMPose 是一款由不同学校和公司共同贡献的开源项目。我们感
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](https://user-images.githubusercontent.com/25839884/205872898-e2e6009d-c6bb-4d27-8d07-117e697a3da8.jpg)或加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=K0QI8ByU)

<div align="center">
<img src="https://user-images.githubusercontent.com/25839884/205870927-39f4946d-8751-4219-a4c0-740117558fd7.jpg" height="400"><img src="https://user-images.githubusercontent.com/25839884/205872898-e2e6009d-c6bb-4d27-8d07-117e697a3da8.jpg" height="400"><img src="https://user-images.githubusercontent.com/25839884/203904835-62392033-02d4-4c73-a68c-c9e4c1e2b07f.jpg
" height="400">
<img src="https://user-images.githubusercontent.com/25839884/205870927-39f4946d-8751-4219-a4c0-740117558fd7.jpg" height="400"><img src="https://user-images.githubusercontent.com/25839884/205872898-e2e6009d-c6bb-4d27-8d07-117e697a3da8.jpg" height="400"><img src="https://user-images.githubusercontent.com/25839884/203904835-62392033-02d4-4c73-a68c-c9e4c1e2b07f.jpg" height="400">
</div>

我们会在 OpenMMLab 社区为大家
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12 changes: 6 additions & 6 deletions docs/en/installation.md
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Expand Up @@ -59,13 +59,13 @@ conda install pytorch torchvision cpuonly -c pytorch
```shell
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc4"
mim install "mmcv>=2.0.0"
```

Note that some of the demo scripts in MMPose require [MMDetection](https://github.com/open-mmlab/mmdetection) (mmdet) for human detection. If you want to run these demo scripts with mmdet, you can easily install mmdet as a dependency by running:

```shell
mim install "mmdet>=3.0.0rc6"
mim install "mmdet>=3.0.0"
```

## Best Practices
Expand All @@ -89,7 +89,7 @@ pip install -v -e .
To use mmpose as a dependency or third-party package, install it with pip:

```shell
mim install "mmpose>=1.0.0rc1"
mim install "mmpose>=1.0.0"
```

## Verify the installation
Expand Down Expand Up @@ -173,7 +173,7 @@ To install MMCV with pip instead of MIM, please follow [MMCV installation guides
For example, the following command install mmcv built for PyTorch 1.10.x and CUDA 11.3.

```shell
pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
pip install 'mmcv>=2.0.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
```

### Install on CPU-only platforms
Expand All @@ -192,7 +192,7 @@ thus we only need to install MMEngine, MMCV and MMPose with the following comman
```shell
!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.0rc1"
!mim install "mmcv>=2.0.0"
```

**Step 2.** Install MMPose from the source.
Expand All @@ -208,7 +208,7 @@ thus we only need to install MMEngine, MMCV and MMPose with the following comman
```python
import mmpose
print(mmpose.__version__)
# Example output: 1.0.0rc0
# Example output: 1.0.0
```

```{note}
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12 changes: 6 additions & 6 deletions docs/zh_cn/installation.md
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Expand Up @@ -57,13 +57,13 @@ conda install pytorch torchvision cpuonly -c pytorch
```shell
pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0rc1"
mim install "mmcv>=2.0.0"
```

请注意，MMPose 中的一些推理示例脚本需要使用 [MMDetection](https://github.com/open-mmlab/mmdetection) (mmdet) 检测人体。如果您想运行这些示例脚本，可以通过运行以下命令安装 mmdet:

```shell
mim install "mmdet>=3.0.0rc0"
mim install "mmdet>=3.0.0"
```

## 最佳实践
Expand All @@ -88,7 +88,7 @@ pip install -v -e .
如果只是希望调用 MMPose 的接口，或者在自己的项目中导入 MMPose 中的模块。直接使用 mim 安装即可。

```shell
mim install "mmpose>=1.0.0rc0"
mim install "mmpose>=1.0.0"
```

## 验证安装
Expand Down Expand Up @@ -180,7 +180,7 @@ MMCV 包含 C++ 和 CUDA 扩展，因此其对 PyTorch 的依赖比较复杂。M
举个例子，如下命令将会安装基于 PyTorch 1.10.x 和 CUDA 11.3 编译的 mmcv。

```shell
pip install 'mmcv>=2.0.0rc1' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
pip install 'mmcv>=2.0.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
```

### 在 CPU 环境中安装
Expand All @@ -198,7 +198,7 @@ MMPose 可以仅在 CPU 环境中安装，在 CPU 模式下，您可以完成训
```shell
!pip3 install openmim
!mim install mmengine
!mim install "mmcv>=2.0.0rc1"
!mim install "mmcv>=2.0.0"
```

**第 2 步** 从源码安装 mmpose
Expand All @@ -214,7 +214,7 @@ MMPose 可以仅在 CPU 环境中安装，在 CPU 模式下，您可以完成训
```python
import mmpose
print(mmpose.__version__)
# 预期输出： 1.0.0rc0
# 预期输出： 1.0.0
```

```{note}
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2 changes: 1 addition & 1 deletion mmpose/version.py
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@@ -1,6 +1,6 @@
# Copyright (c) Open-MMLab. All rights reserved.

__version__ = '1.0.0rc1'
__version__ = '1.0.0'
short_version = __version__


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