Bump to v1.2.0 (#2630)

* fix mmdet max version

* add readme

* fix changelog

* fix keep index bug

* fix open3d version bug

README.md

@@ -106,7 +106,13 @@ Like [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMCV](https:/
 
 **We have renamed the branch `1.1` to `main` and switched the default branch from `master` to `main`. We encourage users to migrate to the latest version, though it comes with some cost. Please refer to [Migration Guide](docs/en/migration.md) for more details.**
 
-We have constructed a comprehensive LiDAR semantic segmentation benchmark on SemanticKITTI, including Cylinder3D, MinkUNet and SPVCNN methods. Noteworthy, the improved MinkUNetv2 can achieve 70.3 mIoU on the validation set of SemanticKITTI. We have also supported the training of BEVFusion and an occupancy prediction method, TPVFormer, in our `projects`. More new features about 3D perception are on the way. Please stay tuned!
+We have constructed a comprehensive LiDAR semantic segmentation benchmark on SemanticKITTI, including Cylinder3D, MinkUNet and SPVCNN methods. Noteworthy, the improved MinkUNetv2 can achieve 70.3 mIoU on the validation set of SemanticKITTI. We have also supported the training of BEVFusion and an occupancy prediction method, TPVFomrer, in our `projects`. More new features about 3D perception are on the way. Please stay tuned!
+
+**v1.2.0** was released in 4/7/2023
+
+- Support [New Config Type](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) in `mmdet3d/config`
+- Support the inference of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`
+- Support downloading datasets from [OpenDataLab](https://opendatalab.com/) using `mim`
 
 **v1.1.1** was released in 30/5/2023:
 
@@ -336,7 +342,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 |     PETR      |   ✗    |   ✓    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
 |   TPVFormer   |   ✓    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
 
-**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
+**Note:** All the about **500+ models, methods of 90+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 ## FAQ
 

README_zh-CN.md

@@ -108,6 +108,12 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 我们在 SemanticKITTI 上构建了一个全面的点云语义分割基准，包括 Cylinder3D 、MinkUNet 和 SPVCNN 方法。其中，改进后的 MinkUNetv2 在验证集上可以达到 70.3 mIoU。我们还在 `projects` 中支持了 BEVFusion 的训练和全新的 3D 占有网格预测网络 TPVFormer。更多关于 3D 感知的新功能正在进行中。请继续关注！
 
+**v1.2.0** 版本已经在 2023.7.4 发布：
+
+- 在 `mmdet3d/config`中支持 [新Config样式](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta)
+- 在 `projects` 中支持 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的推理
+- 支持通过 `mim` 从 [OpenDataLab](https://opendatalab.com/) 下载数据集
+
 **v1.1.1** 版本已经在 2023.5.30 发布：
 
 - 在 `projects` 中支持 [TPVFormer](https://arxiv.org/pdf/2302.07817.pdf)

docs/en/notes/changelog.md

 # Changelog of v1.1
 
+### v1.2.0 (4/7/2023)
+
+#### Highlights
+
+- Support [New Config Type](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) in `mmdet3d/config`  (#2608)
+- Support the inference of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`  (#2606)
+- Support downloading datasets from [OpenDataLab](https://opendatalab.com/) using `mim`  (#2593)
+
+#### New Features
+
+- Support [New Config Type](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) in `mmdet3d/config`  (#2608)
+- Support the inference of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`  (#2606)
+- Support downloading datasets from [OpenDataLab](https://opendatalab.com/) using `mim`  (#2593)
+
+#### Improvements
+
+- Enhanced visualization in interactive form (#2611)
+- Update README.md and Model Zoo (#2599, #2600)
+- Speed up S3DIS data preparation (#2585)
+
+#### Bug Fixes
+
+- Remove PointRCNN in benchmark training (#2610)
+- Fix wrong indoor detection visualization (#2625)
+- Fix MinkUNet download link (#2590)
+- Fix the formula in the `readthedocs` (#2580)
+
+#### Contributors
+
+A total of 5 developers contributed to this release.
+
+@sunjiahao1999, @Xiangxu-0103, @JingweiZhang12, @col14m, @zhulf0804
+
 ### v1.1.1 (30/5/2023)
 
 #### Highlights

docs/en/notes/faq.md

@@ -9,13 +9,11 @@ We list some potential troubles encountered by users and developers, along with
 - The required versions of MMEngine, MMCV and MMDetection for different versions of MMDetection3D are as below. Please install the correct version of MMEngine, MMCV and MMDetection to avoid installation issues.
 
   | MMDetection3D version |     MMEngine version     |      MMCV version       |  MMDetection version  |
-  | --------------------- | :----------------------: | :---------------------: | :----------------------: |
-  | dev-1.x               | mmengine>=0.7.1, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 |  mmdet>=3.0.0, \<3.1.0   |
-  | main                  | mmengine>=0.7.1, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 |  mmdet>=3.0.0, \<3.1.0   |
+  | --------------------- | :----------------------: | :---------------------: | :-------------------: |
+  | dev-1.x               | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0, \<3.2.0 |
+  | main                  | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0, \<3.2.0 |
+  | v1.2.0                | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0, \<3.2.0 |
   | v1.1.1                | mmengine>=0.7.1, \<1.0.0 | mmcv>=2.0.0rc4, \<2.1.0 | mmdet>=3.0.0, \<3.1.0 |
-  | v1.1.0rc3             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
-  | v1.1.0rc2             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc3, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
-  | v1.1.0rc1             | mmengine>=0.1.0, \<1.0.0 | mmcv>=2.0.0rc0, \<2.1.0 | mmdet>=3.0.0rc0, \<3.1.0 |
 
   **Note:** If you want to install mmdet3d-v1.0.0rcx, the compatible MMDetection, MMSegmentation and MMCV versions table can be found at [here](https://mmdetection3d.readthedocs.io/en/latest/faq.html#mmcv-mmdet-mmdet3d-installation). Please choose the correct version of MMCV, MMDetection and MMSegmentation to avoid installation issues.
 

mmdet3d/__init__.py

@@ -10,12 +10,12 @@ mmcv_minimum_version = '2.0.0rc4'
 mmcv_maximum_version = '2.1.0'
 mmcv_version = digit_version(mmcv.__version__)
 
-mmengine_minimum_version = '0.7.1'
+mmengine_minimum_version = '0.8.0'
 mmengine_maximum_version = '1.0.0'
 mmengine_version = digit_version(mmengine.__version__)
 
 mmdet_minimum_version = '3.0.0'
-mmdet_maximum_version = '3.1.0'
+mmdet_maximum_version = '3.2.0'
 mmdet_version = digit_version(mmdet.__version__)
 
 assert (mmcv_version >= digit_version(mmcv_minimum_version)

mmdet3d/version.py

 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '1.1.1'
+__version__ = '1.2.0'
 short_version = __version__
 
 

mmdet3d/visualization/local_visualizer.py

@@ -964,6 +964,8 @@ class Det3DLocalVisualizer(DetLocalVisualizer):
         ignore_index = self.dataset_meta.get('ignore_index', None)
         if ignore_index is not None and 'gt_pts_seg' in data_sample and vis_task == 'lidar_seg':  # noqa: E501
             keep_index = data_sample.gt_pts_seg.pts_semantic_mask != ignore_index  # noqa: E501
+        else:
+            keep_index = None
 
         gt_data_3d = None
         pred_data_3d = None

requirements/mminstall.txt

 mmcv>=2.0.0rc4,<2.1.0
-mmdet>=3.0.0,<3.1.0
+mmdet>=3.0.0,<3.2.0
 mmengine>=0.7.1,<1.0.0

requirements/runtime.txt

@@ -3,7 +3,7 @@ networkx>=2.5
 numba # you should install numba==0.53.0 if your environment is cuda-9.0
 numpy
 nuscenes-devkit
-open3d
+open3d==0.16.0
 plyfile
 scikit-image
 # by default we also use tensorboard to log results