Bump versions to v2.23.0 (#7555)

* Bump versions to v2.23.0

* update log

* replace two Efficientnet

* update mask2former log

* add maskformer and mask2former in README_zh-CN.md

* add efficientnet in README.md and  README_zh-CN.md

* update changelog

* simplify highlights

* update changelog.

* update changelog again

* add some highlights

Author: Czm369

README.md

@@ -74,11 +74,11 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-**2.22.0** was released in 24/2/2022:
+**2.23.0** was released in 28/3/2022:
 
-- Support [MaskFormer](configs/maskformer), [DyHead](configs/dyhead), [OpenImages Dataset](configs/openimages) and [TIMM backbone](configs/timm_example)
-- Support visualization for Panoptic Segmentation
-- Release a good recipe of using ResNet in object detectors pre-trained by [ResNet Strikes Back](https://arxiv.org/abs/2110.00476), which consistently brings about 3~4 mAP improvements over RetinaNet, Faster/Mask/Cascade Mask R-CNN
+- Support [Mask2Former](configs/mask2former) and [EfficientNet](configs/efficientnet)
+- Support setting data root through environment variable `MMDET_DATASETS`, users don't have to modify the corresponding path in config files anymore.
+- Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision.
 
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
@@ -164,6 +164,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <ul>
           <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
           <li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -228,6 +229,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
         <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
         <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
+        <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
       </ul>
       </td>
       <td>

README_zh-CN.md

@@ -73,13 +73,13 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 
 ## 更新日志
 
-最新的 **2.22.0** 版本已经在 2022.02.24 发布:
+最新的 **2.23.0** 版本已经在 2022.03.28 发布:
 
-- 支持 [MaskFormer](configs/maskformer)，[DyHead](configs/dyhead)，[OpenImages Dataset](configs/openimages) 和 [TIMM backbone](configs/timm_example)
-- 支持全景分割可视化
-- 发布了一个在目标检测任务中使用 ResNet 的好方法，它是由 [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) 预训练的，并且能稳定的在 RetinaNet, Faster/Mask/Cascade Mask R-CNN 上带来约 3-4 mAP 的提升
+- 支持 [Mask2Former](configs/mask2former) 和 [Efficientnet](configs/efficientnet)
+- 支持通环境变量 `MMDET_DATASETS` 设置数据根目录，因此无需修改配置文件中对应的路径。
+- 发现一个很好的方法来微调由 Torchvision 预训练的高精度 ResNet 主干。
 
-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/changelog.md)。
+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)。
 
@@ -162,6 +162,8 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
       <td>
         <ul>
           <li><a href="configs/panoptic_fpn">Panoptic FPN (CVPR'2019)</a></li>
+          <li><a href="configs/maskformer">MaskFormer (NeurIPS'2021)</a></li>
+          <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -226,6 +228,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
         <li><a href="configs/swin">Swin (CVPR'2021)</a></li>
         <li><a href="configs/pvt">PVTv2 (ArXiv'2021)</a></li>
         <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
+        <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
       </ul>
       </td>
       <td>

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="2.22.0"
+ARG MMDET="2.23.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

docs/en/changelog.md

@@ -1,5 +1,61 @@
 ## Changelog
 
+### v2.23.0 (28/3/2022)
+
+#### Highlights
+
+- Support Mask2Former: [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527)
+- Support EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
+- Support setting data root through environment variable `MMDET_DATASETS`, users don't have to modify the corresponding path in config files anymore.
+- Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision.
+
+#### New Features
+
+- Support [Mask2Former](configs/mask2former)(#6938)(#7466)(#7471)
+- Support [EfficientNet](configs/efficientnet) (#7514)
+- Support setting data root through environment variable `MMDET_DATASETS`, users don't have to modify the corresponding path in config files anymore. (#7386)
+- Support setting different seeds to different ranks (#7432)
+- Update the `dist_train.sh` so that the script can be used to support launching multi-node training on machines without slurm (#7415)
+- Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision (#7489)
+
+#### Bug Fixes
+
+- Fix bug in VOC unit test which removes the data directory (#7270)
+- Adjust the order of `get_classes` and `FileClient` (#7276)
+- Force the inputs of `get_bboxes` in yolox_head to float32 (#7324)
+- Fix misplaced arguments in LoadPanopticAnnotations (#7388)
+- Fix reduction=mean in CELoss. (#7449)
+- Update unit test of CrossEntropyCost (#7537)
+- Fix memory leaking in panpotic segmentation evaluation (#7538)
+- Fix the bug of shape broadcast in YOLOv3 (#7551)
+
+#### Improvements
+
+- Add Chinese version of onnx2tensorrt.md (#7219)
+- Update colab tutorials (#7310)
+- Update information about Localization Distillation (#7350)
+- Add Chinese version of `finetune.md` (#7178)
+- Update YOLOX log for non square input (#7235)
+- Add `nproc` in `coco_panoptic.py` for panoptic quality computing (#7315)
+- Allow to set channel_order in LoadImageFromFile (#7258)
+- Take point sample related functions out of mask_point_head (#7353)
+- Add instance evaluation for coco_panoptic (#7313)
+- Enhance the robustness of analyze_logs.py (#7407)
+- Supplementary notes of sync_random_seed (#7440)
+- Update docstring of cross entropy loss (#7472)
+- Update pascal voc result (#7503)
+- We create How-to documentation to record any questions about How to xxx. In this version, we added
+  - How to use Mosaic augmentation (#7507)
+  - How to use backbone in mmcls (#7438)
+  - How to produce and submit the prediction results of panoptic segmentation models on COCO test-dev set (#7430))
+
+#### Contributors
+
+A total of 27 developers contributed to this release.
+Thanks @ZwwWayne, @haofanwang, @shinya7y, @chhluo, @yangrisheng, @triple-Mu, @jbwang1997, @HikariTJU, @imflash217, @274869388, @zytx121, @matrixgame2018, @jamiechoi1995, @BIGWangYuDong, @JingweiZhang12, @Xiangxu-0103, @hhaAndroid, @jshilong, @osbm, @ceroytres, @bunge-bedstraw-herb, @Youth-Got, @daavoo, @jiangyitong, @RangiLyu, @CCODING04, @yarkable
+
+
+
 ### v2.22.0 (24/2/2022)
 
 #### Highlights

docs/en/get_started.md

@@ -12,6 +12,7 @@ Compatible MMDetection and MMCV versions are shown as below. Please install the
 | MMDetection version |       MMCV version        |
 |:-------------------:|:-------------------------:|
 |       master        | mmcv-full>=1.3.17, <1.5.0 |
+|       2.23.0        | mmcv-full>=1.3.17, <1.5.0 |
 |       2.22.0        | mmcv-full>=1.3.17, <1.5.0 |
 |       2.21.0        | mmcv-full>=1.3.17, <1.5.0 |
 |       2.20.0        | mmcv-full>=1.3.17, <1.5.0 |

docs/en/model_zoo.md

@@ -254,6 +254,14 @@ Please refer to [MaskFormer](https://github.com/open-mmlab/mmdetection/blob/mast
 
 Please refer to [DyHead](https://github.com/open-mmlab/mmdetection/blob/master/configs/dyhead) for details.
 
+### Mask2Former
+
+Please refer to [Mask2Former](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former) for details.
+
+### Efficientnet
+
+Please refer to [Efficientnet](https://github.com/open-mmlab/mmdetection/blob/master/configs/efficientnet) for details.
+
 ### Other datasets
 
 We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes), [OpenImages](https://github.com/open-mmlab/mmdetection/blob/master/configs/openimages) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face).

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, <1.5.0 |
+|     2.23.0     | mmcv-full>=1.3.17, <1.5.0 |
 |     2.22.0     | mmcv-full>=1.3.17, <1.5.0 |
 |     2.21.0     | mmcv-full>=1.3.17, <1.5.0 |
 |     2.20.0     | mmcv-full>=1.3.17, <1.5.0 |

mmdet/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '2.22.0'
+__version__ = '2.23.0'
 short_version = __version__