Bump version to v2.27.0 (open-mmlab#9531)

* Add changelog of v2.27.0 * update rc5 * fix * fix * sync
chhluo · Jan 3, 2023 · dcac7dd · dcac7dd
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diff --git a/README.md b/README.md
@@ -77,23 +77,38 @@ Apart from MMDetection, we also released a library [mmcv](https://github.com/ope
 
 ### 💎 Stable version
 
-**2.26.0** was released in 23/11/2022:
+**2.27.0** was released in 26/12/2022:
 
-- Support training on [NPU](docs/en/device/npu.md).
+- Support receptive field search of CNN models([TPAMI 2022: RF-Next](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)).
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
 For compatibility changes between different versions of MMDetection, please refer to [compatibility.md](docs/en/compatibility.md).
 
 ### 🌟 Preview of 3.x version
 
-A brand new version of **MMDetection v3.0.0rc0** was released in 31/8/2022:
+#### Highlight
 
-- Unifies interfaces of all components based on [MMEngine](https://github.com/open-mmlab/mmengine).
-- Faster training and testing speed with complete support of mixed precision training.
-- Refactored and more flexible [architecture](https://mmdetection.readthedocs.io/en/v3.0.0rc0/overview.html).
-- Provides more strong baselines and a general semi-supervised object detection framework. See [tutorial of semi-supervised detection](https://mmdetection.readthedocs.io/en/v3.0.0rc0/user_guides/semi_det.html).
-- Allows any kind of single-stage model as an RPN in a two-stage model. See [tutorial](https://mmdetection.readthedocs.io/en/v3.0.0rc0/user_guides/single_stage_as_rpn.html).
+We are excited to announce our latest work on real-time object recognition tasks, **RTMDet**, a family of fully convolutional single-stage detectors. RTMDet not only achieves the best parameter-accuracy trade-off on object detection from tiny to extra-large model sizes but also obtains new state-of-the-art performance on instance segmentation and rotated object detection tasks. Details can be found in the [technical report](https://arxiv.org/abs/2212.07784). Pre-trained models are [here](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet).
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+A brand new version of **MMDetection v3.0.0rc5** was released in 26/12/2022:
+
+- Support [RTMDet](https://arxiv.org/abs/2212.07784) instance segmentation models. The technical report of RTMDet is on [arxiv](https://arxiv.org/abs/2212.07784)
+- Support SSHContextModule in paper [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979)
 
 Find more new features in [3.x branch](https://github.com/open-mmlab/mmdetection/tree/3.x). Issues and PRs are welcome!
 

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -76,22 +76,38 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 ### 💎 稳定版本
 
-最新的 **2.26.0** 版本已经在 2022.11.23 发布:
+最新的 **2.27.0** 版本已经在 2022.12.26 发布:
 
-- 支持在 [NPU](docs/en/device/npu.md) 上进行训练。
+- 支持 CNN 模型的感受野搜索 ([TPAMI 2022: RF-Next](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf))。
 
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
 
 如果想了解 MMDetection 不同版本之间的兼容性, 请参考[兼容性说明文档](docs/zh_cn/compatibility.md)。
 
 ### 🌟 3.x 预览版本
 
-全新的 **v3.0.0rc0** 版本已经在 2022.8.31 发布：
+#### 亮点
 
-- 基于 [MMEngine](https://github.com/open-mmlab/mmengine) 统一了各组件接口。
-- 全面支持混合精度，训练测试速度更快。
-- 提供了更强的基线模型，并支持了通用的半监督目标检测框架，详见[半监督目标检测教程](https://mmdetection.readthedocs.io/zh_CN/v3.0.0rc0/user_guides/semi_det.html)。
-- 支持使用任意单阶段检测器作为二阶段模型的 RPN，详见[教程](https://mmdetection.readthedocs.io/en/v3.0.0rc0/user_guides/single_stage_as_rpn.html)。
+我们很高兴向大家介绍我们在实时目标识别任务方面的最新成果 RTMDet，包含了一系列的全卷积单阶段检测模型。 RTMDet 不仅在从 tiny 到 extra-large 尺寸的目标检测模型上实现了最佳的参数量和精度的平衡，而且在实时实例分割和旋转目标检测任务上取得了最先进的成果。 更多细节请参阅[技术报告](https://arxiv.org/abs/2212.07784)。 预训练模型可以在[这里](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet)找到。
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/real-time-instance-segmentation-on-mscoco)](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-dota-1)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-dota-1?p=rtmdet-an-empirical-study-of-designing-real)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rtmdet-an-empirical-study-of-designing-real/object-detection-in-aerial-images-on-hrsc2016)](https://paperswithcode.com/sota/object-detection-in-aerial-images-on-hrsc2016?p=rtmdet-an-empirical-study-of-designing-real)
+
+| Task                     | Dataset | AP                                   | FPS(TRT FP16 BS1 3090) |
+| ------------------------ | ------- | ------------------------------------ | ---------------------- |
+| Object Detection         | COCO    | 52.8                                 | 322                    |
+| Instance Segmentation    | COCO    | 44.6                                 | 188                    |
+| Rotated Object Detection | DOTA    | 78.9(single-scale)/81.3(multi-scale) | 121                    |
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
+</div>
+
+全新的 **v3.0.0rc5** 版本已经在 2022.12.26 发布：
+
+- 支持了 [RTMDet](https://arxiv.org/abs/2212.07784) 的实例分割模型。RTMDet 的技术报告发布在了 [arxiv](https://arxiv.org/abs/2212.07784) 上。
+- 支持了 [SSH: Single Stage Headless Face Detector](https://arxiv.org/abs/1708.03979) 论文中的 SSHContextModule
 
 ## 安装
 

diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
@@ -4,7 +4,7 @@ ARG CUDNN="7"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="1.3.17"
-ARG MMDET="2.26.0"
+ARG MMDET="2.27.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docs/en/changelog.md b/docs/en/changelog.md
@@ -1,5 +1,31 @@
 ## Changelog
 
+### v2.27.0 (26/12/2022)
+
+#### Highlights
+
+- Support receptive field search of CNN models([TPAMI 2022: RF-Next](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)) (#8191)
+
+#### Bug Fixes
+
+- Fix deadlock issue related with MMDetWandbHook (#9476)
+
+#### Improvements
+
+- Add minimum GitHub token permissions for workflows (#8928)
+- Delete compatible code for parrots in roi extractor (#9503)
+- Deprecate np.bool Type Alias (#9498)
+
+#### Documents
+
+- Fix typo in docs/zh_cn/tutorials/config.md (#9416)
+- Fix Faster RCNN FP16 config link in README (#9366)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @gasvn, @lzyhha, @jbwang1997, @zachcoleman, @chenyuwang814, @MilkClouds, @Fizzez, @boahc077, @apatsekin, @zytx121, @DonggeunYu
+
 ### v2.26.0 (23/11/2022)
 
 #### Highlights

diff --git a/docs/en/faq.md b/docs/en/faq.md
@@ -11,6 +11,7 @@ We list some common troubles faced by many users and their corresponding solutio
 | MMDetection version |        MMCV version        |
 | :-----------------: | :------------------------: |
 |       master        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.27.0        | mmcv-full>=1.3.17, \<1.8.0 |
 |       2.26.0        | mmcv-full>=1.3.17, \<1.8.0 |
 |       2.25.3        | mmcv-full>=1.3.17, \<1.7.0 |
 |       2.25.2        | mmcv-full>=1.3.17, \<1.7.0 |

diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -12,6 +12,7 @@ MMDetection 和 MMCV 版本兼容性如下所示，需要安装正确的 MMCV
 | MMDetection 版本 |         MMCV 版本          |
 | :--------------: | :------------------------: |
 |      master      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.27.0      | mmcv-full>=1.3.17, \<1.8.0 |
 |      2.26.0      | mmcv-full>=1.3.17, \<1.8.0 |
 |      2.25.3      | mmcv-full>=1.3.17, \<1.7.0 |
 |      2.25.2      | mmcv-full>=1.3.17, \<1.7.0 |

diff --git a/mmdet/version.py b/mmdet/version.py
@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '2.26.0'
+__version__ = '2.27.0'
 short_version = __version__
 
 

diff --git a/requirements/optional.txt b/requirements/optional.txt
@@ -1,3 +1,3 @@
 cityscapesscripts
 imagecorruptions
-sklearn
+scikit-learn
diff --git a/setup.cfg b/setup.cfg
@@ -18,4 +18,4 @@ SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
 [codespell]
 skip = *.ipynb
 quiet-level = 3
-ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,nam
+ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,nam,dota,DOTA