Merge pull request #2176 from open-mmlab/dev-1.x

Dev 1.x
open-mmlab · Apr 6, 2023 · b6419b4 · b6419b4
2 parents c7bfc9e + 65a58ec
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diff --git a/README.md b/README.md
@@ -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.

diff --git a/README_CN.md b/README_CN.md
@@ -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)。
@@ -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

diff --git a/docs/en/installation.md b/docs/en/installation.md
@@ -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
@@ -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
@@ -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
@@ -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.
@@ -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}

diff --git a/docs/zh_cn/installation.md b/docs/zh_cn/installation.md
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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}