Bump version to 3.0.0rc6 (#9817)

Co-authored-by: Range King <RangeKingHZ@gmail.com>
open-mmlab · Feb 27, 2023 · 61dd8d5 · 61dd8d5
1 parent 33d073b
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diff --git a/.circleci/test.yml b/.circleci/test.yml
@@ -69,7 +69,7 @@ jobs:
           command: |
             python -m pip install git+ssh://git@github.com/open-mmlab/mmengine.git@main
             pip install -U openmim
-            mim install 'mmcv >= 2.0.0rc0'
+            mim install 'mmcv >= 2.0.0rc4'
             pip install -r requirements/tests.txt -r requirements/optional.txt
             pip install --force-reinstall pycocotools
             pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
@@ -117,7 +117,7 @@ jobs:
           command: |
             docker exec mmdetection pip install -e /mmengine
             docker exec mmdetection pip install -U openmim
-            docker exec mmdetection mim install 'mmcv >= 2.0.0rc0'
+            docker exec mmdetection mim install 'mmcv >= 2.0.0rc4'
             docker exec mmdetection pip install -r requirements/tests.txt -r requirements/optional.txt
             docker exec mmdetection pip install pycocotools
             docker exec mmdetection pip install albumentations>=0.3.2 --no-binary imgaug,albumentations

diff --git a/README.md b/README.md
@@ -108,10 +108,14 @@ We are excited to announce our latest work on real-time object recognition tasks
 <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
 </div>
 
-**v3.0.0rc5** was released in 26/12/2022:
+**v3.0.0rc6** was released in 24/2/2023:
 
-- 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)
+- Support [Boxinst](configs/boxinst), [Objects365 Dataset](configs/objects365), and [Separated and Occluded COCO metric](docs/en/user_guides/useful_tools.md#coco-separated--occluded-mask-metric)
+- Support [ConvNeXt-V2](projects/ConvNeXt-V2), [DiffusionDet](projects/DiffusionDet), and inference of [EfficientDet](projects/EfficientDet) and [Detic](projects/Detic) in `Projects`
+- Refactor [DETR](configs/detr) series and support [Conditional-DETR](configs/conditional_detr), [DAB-DETR](configs/dab_detr), and [DINO](configs/dino)
+- Support `DetInferencer` for inference, Test Time Augmentation, and automatically importing modules from registry
+- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](configs/rtmdet/README.md#deployment-tutorial)
+- Support [calculating FLOPs of detectors](docs/en/user_guides/useful_tools.md#Model-Complexity)
 
 ## Installation
 
@@ -220,6 +224,12 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
             <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
             <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
             <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+            <li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
+            <li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
+            <li><a href="configs/dino">DINO (ICLR'2023)</a></li>
+            <li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
+            <li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
+            <li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
       </ul>
       </td>
       <td>
@@ -237,9 +247,10 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
           <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
           <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
           <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
-          <li><a href="configs/condinst">CondInst (ECCV 2020)</a></li>
-          <li><a href="projects/SparseInst">SparseInst (CVPR 2022)</a></li>
+          <li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
+          <li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
           <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+          <li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -319,6 +330,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
         <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
         <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
         <li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
+        <li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
       </ul>
       </td>
       <td>

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -93,10 +93,14 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
 <img src="https://user-images.githubusercontent.com/12907710/208044554-1e8de6b5-48d8-44e4-a7b5-75076c7ebb71.png"/>
 </div>
 
-**v3.0.0rc5** 版本已经在 2022.12.26 发布：
+**v3.0.0rc6** 版本已经在 2023.2.24 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
+- 支持了 [Boxinst](configs/boxinst), [Objects365 Dataset](configs/objects365) 和 [Separated and Occluded COCO metric](docs/zh_cn/user_guides/useful_tools.md#coco-分离和遮挡实例分割性能评估)
+- 在 `Projects` 中支持了 [ConvNeXt-V2](projects/ConvNeXt-V2), [DiffusionDet](projects/DiffusionDet) 和 [EfficientDet](projects/EfficientDet), [Detic](projects/Detic) 的推理
+- 重构了 [DETR](configs/detr) 系列并支持了 [Conditional-DETR](configs/conditional_detr), [DAB-DETR](configs/dab_detr) 和 [DINO](configs/dino)
+- 支持了通过 `DetInferencer` 用于推理, Test Time Augmentation 以及从注册表(registry)自动导入模块
+- 支持了 RTMDet-Ins 的 ONNXRuntime 和 TensorRT [部署](configs/rtmdet/README.md#deployment-tutorial)
+- 支持了检测器[计算 FLOPS](docs/zh_cn/user_guides/useful_tools.md#模型复杂度)
 
 ## 安装
 
@@ -206,7 +210,13 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
             <li><a href="configs/deformable_detr">Deformable DETR (ICLR'2021)</a></li>
             <li><a href="configs/tood">TOOD (ICCV'2021)</a></li>
             <li><a href="configs/ddod">DDOD (ACM MM'2021)</a></li>
-            <li><a href="configs/RTMDet">RTMDet (ArXiv'2022)</a></li>
+            <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+            <li><a href="configs/conditional_detr">Conditional DETR (ICCV'2021)</a></li>
+            <li><a href="configs/dab_detr">DAB-DETR (ICLR'2022)</a></li>
+            <li><a href="configs/dino">DINO (ICLR'2023)</a></li>
+            <li><a href="projects/DiffusionDet">DiffusionDet (ArXiv'2023)</a></li>
+            <li><a href="projects/EfficientDet">EfficientDet (CVPR'2020)</a></li>
+            <li><a href="projects/Detic">Detic (ECCV'2022)</a></li>
       </ul>
       </td>
       <td>
@@ -224,9 +234,10 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
           <li><a href="configs/scnet">SCNet (AAAI'2021)</a></li>
           <li><a href="configs/queryinst">QueryInst (ICCV'2021)</a></li>
           <li><a href="configs/mask2former">Mask2Former (ArXiv'2021)</a></li>
-          <li><a href="configs/condinst">CondInst (ECCV 2020)</a></li>
-          <li><a href="projects/SparseInst">SparseInst (CVPR 2022)</a></li>
-          <li><a href="configs/RTMDet">RTMDet (ArXiv'2022)</a></li>
+          <li><a href="configs/condinst">CondInst (ECCV'2020)</a></li>
+          <li><a href="projects/SparseInst">SparseInst (CVPR'2022)</a></li>
+          <li><a href="configs/rtmdet">RTMDet (ArXiv'2022)</a></li>
+          <li><a href="configs/boxinst">BoxInst (CVPR'2021)</a></li>
         </ul>
       </td>
       <td>
@@ -306,6 +317,7 @@ MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [Ope
         <li><a href="configs/resnet_strikes_back">ResNet strikes back (ArXiv'2021)</a></li>
         <li><a href="configs/efficientnet">EfficientNet (ArXiv'2021)</a></li>
         <li><a href="configs/convnext">ConvNeXt (CVPR'2022)</a></li>
+        <li><a href="projects/ConvNeXt-V2">ConvNeXtv2 (ArXiv'2023)</a></li>
       </ul>
       </td>
       <td>

diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -29,7 +29,7 @@ RUN apt-get update \
 
 # Install MMEngine and MMCV
 RUN pip install openmim && \
-    mim install "mmengine==0.3.0" "mmcv>=2.0.0rc1"
+    mim install "mmengine>=0.6.0" "mmcv>=2.0.0rc4"
 
 # Install MMDetection
 RUN conda clean --all \

diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
@@ -3,8 +3,8 @@ ARG CUDA="11.1"
 ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
-ARG MMCV="2.0.0rc1"
-ARG MMDET="3.0.0rc5"
+ARG MMCV="2.0.0rc4"
+ARG MMDET="3.0.0rc6"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docker/serve_cn/Dockerfile b/docker/serve_cn/Dockerfile
@@ -3,8 +3,8 @@ ARG CUDA="11.1"
 ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
-ARG MMCV="2.0.0rc1"
-ARG MMDET="3.0.0rc5"
+ARG MMCV="2.0.0rc4"
+ARG MMDET="3.0.0rc6"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
@@ -1,5 +1,88 @@
 # Changelog of v3.x
 
+## v3.0.0rc6 (24/2/2023)
+
+### Highlights
+
+- Support [Boxinst](../../../configs/boxinst), [Objects365 Dataset](../../../configs/objects365), and [Separated and Occluded COCO metric](../user_guides/useful_tools.md#COCO-Separated-&-Occluded-Mask-Metric)
+- Support [ConvNeXt-V2](../../../projects/ConvNeXt-V2), [DiffusionDet](../../../projects/DiffusionDet), and inference of [EfficientDet](../../../projects/EfficientDet) and [Detic](../../../projects/Detic) in `Projects`
+- Refactor [DETR](../../../configs/detr) series and support [Conditional-DETR](../../../configs/conditional_detr), [DAB-DETR](../../../configs/dab_detr), and [DINO](../../../configs/detr)
+- Support `DetInferencer` for inference, Test Time Augmentation, and automatically importing modules from registry
+- Support RTMDet-Ins ONNXRuntime and TensorRT [deployment](../../../configs/rtmdet/README.md#deployment-tutorial)
+- Support [calculating FLOPs of detectors](../user_guides/useful_tools.md#Model-Complexity)
+
+### New Features
+
+- Support [Boxinst](https://arxiv.org/abs/2012.02310) (#9525)
+- Support [Objects365 Dataset](https://openaccess.thecvf.com/content_ICCV_2019/papers/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.pdf) (#9600)
+- Support [ConvNeXt-V2](http://arxiv.org/abs/2301.00808) in `Projects` (#9619)
+- Support [DiffusionDet](https://arxiv.org/abs/2211.09788) in `Projects` (#9639, #9768)
+- Support [Detic](http://arxiv.org/abs/2201.02605) inference in `Projects` (#9645)
+- Support [EfficientDet](https://arxiv.org/abs/1911.09070) inference in `Projects` (#9645)
+- Support [Separated and Occluded COCO metric](https://arxiv.org/abs/2210.10046) (#9710)
+- Support auto import modules from registry (#9143)
+- Refactor DETR series and support Conditional-DETR, DAB-DETR and DINO (#9646)
+- Support `DetInferencer` for inference (#9561)
+- Support Test Time Augmentation (#9452)
+- Support calculating FLOPs of detectors (#9777)
+
+### Bug Fixes
+
+- Fix deprecating old type alias due to new version of numpy (#9625, #9537)
+- Fix VOC metrics (#9784)
+- Fix the wrong link of RTMDet-x log (#9549)
+- Fix RTMDet link in README (#9575)
+- Fix MMDet get flops error (#9589)
+- Fix `use_depthwise` in RTMDet (#9624)
+- Fix `albumentations` augmentation post process with masks (#9551)
+- Fix DETR series Unit Test (#9647)
+- Fix `LoadPanopticAnnotations` bug (#9703)
+- Fix `isort` CI (#9680)
+- Fix amp pooling overflow (#9670)
+- Fix docstring about noise in DINO (#9747)
+- Fix potential bug in `MultiImageMixDataset` (#9764)
+
+### Improvements
+
+- Replace NumPy transpose with PyTorch permute to speed-up (#9762)
+- Deprecate `sklearn` (#9725)
+- Add RTMDet-Ins deployment guide (#9823)
+- Update RTMDet config and README (#9603)
+- Replace the models used in the tutorial document with RTMDet (#9843)
+- Adjust the minimum supported python version to 3.7 (#9602)
+- Support modifying palette through configuration (#9445)
+- Update README document in `Project` (#9599)
+- Replace `github` with `gitee` in `.pre-commit-config-zh-cn.yaml` file (#9586)
+- Use official `isort` in `.pre-commit-config.yaml` file (#9701)
+- Change MMCV minimum version to `2.0.0rc4` for `dev-3.x` (#9695)
+- Add Chinese version of single_stage_as_rpn.md and test_results_submission.md (#9434)
+- Add OpenDataLab download link (#9605, #9738)
+- Add type hints of several layers (#9346)
+- Add typehint for `DarknetBottleneck` (#9591)
+- Add dockerfile (#9659)
+- Add twitter, discord, medium, and youtube link (#9775)
+- Prepare for merging refactor-detr (#9656)
+- Add metafile to ConditionalDETR, DABDETR and DINO (#9715)
+- Support to modify `non_blocking` parameters (#9723)
+- Comment repeater visualizer register (#9740)
+- Update user guide: `finetune.md` and `inference.md` (#9578)
+
+### New Contributors
+
+- @NoFish-528 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9346>
+- @137208 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9434>
+- @lyviva made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9625>
+- @zwhus made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9589>
+- @zylo117 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9670>
+- @chg0901 made their first contribution in <https://github.com/open-mmlab/mmdetection/pull/9740>
+- @DanShouzhu made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9578
+
+### Contributors
+
+A total of 27 developers contributed to this release.
+
+Thanks @JosonChan1998, @RangeKing, @NoFish-528, @likyoo, @Xiangxu-0103, @137208, @PeterH0323, @tianleiSHI, @wufan-tb, @lyviva, @zwhus, @jshilong, @Li-Qingyun, @sanbuphy, @zylo117, @triple-Mu, @KeiChiTse, @LYMDLUT, @nijkah, @chg0901, @DanShouzhu, @zytx121, @vansin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne
+
 ## v3.0.0rc5 (26/12/2022)
 
 ### Highlights
@@ -25,6 +108,7 @@
 - Fix demo API in instance segmentation tutorial (#9226)
 - Fix `analyze_results` (#9380)
 - Fix the error that Readthedocs API cannot be displayed (#9510)
+- Fix the error when there are no prediction results and support visualize the groundtruth of TTA (#9840)
 
 ### Improvements
 

diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md
@@ -10,8 +10,8 @@ We list some common troubles faced by many users and their corresponding solutio
 
   | MMDetection version |      MMCV version       |     MMEngine version     |
   | :-----------------: | :---------------------: | :----------------------: |
-  |         3.x         | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.4.0, \<1.0.0 |
-  |      3.0.0rc6       | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
+  |         3.x         | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 |
+  |      3.0.0rc6       | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 |
   |      3.0.0rc5       | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
   |      3.0.0rc4       | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
   |      3.0.0rc3       | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |

diff --git a/docs/zh_cn/notes/faq.md b/docs/zh_cn/notes/faq.md
@@ -10,8 +10,8 @@
 
   | MMDetection 版本 |        MMCV 版本        |      MMEngine 版本       |
   | :--------------: | :---------------------: | :----------------------: |
-  |       3.x        | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.4.0, \<1.0.0 |
-  |     3.0.0rc6     | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
+  |       3.x        | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 |
+  |     3.0.0rc6     | mmcv>=2.0.0rc4, \<2.1.0 | mmengine>=0.6.0, \<1.0.0 |
   |     3.0.0rc5     | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
   |     3.0.0rc4     | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |
   |     3.0.0rc3     | mmcv>=2.0.0rc1, \<2.1.0 | mmengine>=0.3.0, \<1.0.0 |

diff --git a/mmdet/__init__.py b/mmdet/__init__.py
@@ -9,7 +9,7 @@
 mmcv_maximum_version = '2.1.0'
 mmcv_version = digit_version(mmcv.__version__)
 
-mmengine_minimum_version = '0.4.0'
+mmengine_minimum_version = '0.6.0'
 mmengine_maximum_version = '1.0.0'
 mmengine_version = digit_version(mmengine.__version__)
 

diff --git a/mmdet/version.py b/mmdet/version.py
@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 
-__version__ = '3.0.0rc5'
+__version__ = '3.0.0rc6'
 short_version = __version__