reset bump to v1.4

.circleci/test.yml

@@ -85,7 +85,7 @@ jobs:
         type: string
       cuda:
         type: enum
-        enum: ["11.1", "11.7"]
+        enum: ["10.2", "11.7"]
       cudnn:
         type: integer
         default: 8
@@ -173,7 +173,7 @@ workflows:
           torch: 1.8.1
           # Use double quotation mark to explicitly specify its type
           # as string instead of number
-          cuda: "11.1"
+          cuda: "10.2"
           requires:
             - hold
       - build_cuda:
@@ -190,7 +190,7 @@ workflows:
       - build_cuda:
           name: minimum_version_gpu
           torch: 1.8.1
-          cuda: "11.1"
+          cuda: "10.2"
           filters:
             branches:
               only:

README.md

@@ -104,9 +104,15 @@ Like [MMDetection](https://github.com/open-mmlab/mmdetection) and [MMCV](https:/
 
 ### Highlight
 
-**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.**
+In version 1.4, MMDetecion3D refactors the Waymo dataset and accelerates the preprocessing, training/testing setup, and evaluation of Waymo dataset. We also extends the support for camera-based, such as Monocular and BEV, 3D object detection models on Waymo. A detailed description of the Waymo data information is provided [here](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html).
 
-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!
+Besides, in version 1.4, MMDetection3D provides [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) to help community users get started with Waymo and use it for quick iterative development.
+
+**v1.4.0** was released in 8/1/2024：
+
+- Support the training of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects`
+- Support [Nerf-Det](https://arxiv.org/abs/2307.14620) in `projects`
+- Refactor Waymo dataset
 
 **v1.3.0** was released in 18/10/2023:
 

README_zh-CN.md

@@ -104,9 +104,15 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 ### 亮点
 
-**我们将 `1.1` 分支重命名为 `main` 并将默认分支从 `master` 切换到 `main`。我们鼓励用户迁移到最新版本，请参考 [迁移指南](docs/en/migration.md)以了解更多细节。**
+在1.4版本中，MMDetecion3D 重构了 Waymo 数据集, 加速了 Waymo 数据集的预处理、训练/测试启动、验证的速度。并且在 Waymo 上拓展了对 单目/BEV 等基于相机的三维目标检测模型的支持。在[这里](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html)提供了对 Waymo 数据信息的详细解读。
 
-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！
+此外，在1.4版本中，MMDetection3D 提供了 [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) 来帮助社区用户上手 Waymo 并用于快速迭代开发。
+
+**v1.4.0** 版本已经在 2024.1.8 发布：
+
+- 在 `projects` 中支持了 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的训练
+- 在 `projects` 中支持了 [Nerf-Det](https://arxiv.org/abs/2307.14620)
+- 重构了 Waymo 数据集
 
 **v1.3.0** 版本已经在 2023.10.18 发布：
 
@@ -171,8 +177,6 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代
 
 ## 基准测试和模型库
 
-## 基准测试和模型库
-
 测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
 
 <div align="center">

docker/serve/Dockerfile

@@ -4,8 +4,8 @@ ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="2.0.0rc4"
-ARG MMDET="3.1.0"
-ARG MMDET3D="1.3.0"
+ARG MMDET="3.3.0"
+ARG MMDET3D="1.4.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

docs/en/notes/changelog.md

@@ -1,5 +1,37 @@
 # Changelog of v1.1
 
+### v1.4.0 (8/1/2024)
+
+#### Highlights
+
+- Refactor Waymo dataset (#2836)
+- Support the training of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects` (#2738)
+- Support [Nerf-Det](https://arxiv.org/abs/2307.14620) in `projects` (#2732)
+
+#### New Features
+
+- Support the training of [DSVT](<(https://arxiv.org/abs/2301.06051)>) in `projects` (#2738)
+- Support [Nerf-Det](https://arxiv.org/abs/2307.14620) in `projects` (#2732)
+- Support [MV-FCOS3D++](https://arxiv.org/abs/2207.12716)
+- Refactor Waymo dataset (#2836)
+
+#### Improvements
+
+- Support [PGD](https://arxiv.org/abs/2107.14160)) (front-of-view / multi-view) on Waymo dataset (#2835)
+- Release new [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) for verify some methods or debug quickly (#2835)
+
+#### Bug Fixes
+
+- Fix MinkUNet and SPVCNN some wrong configs (#2854)
+- Fix incorrect number of arguments in PETR (#2800)
+- Delete unused files in `mmdet3d/configs` (#2773)
+
+#### Contributors
+
+A total of 5 developers contributed to this release.
+
+@sunjiahao1999, @WendellZ524, @Yanyirong, @JingweiZhang12, @Tai-Wang
+
 ### v1.3.0 (18/10/2023)
 
 #### Highlights

docs/en/notes/faq.md

@@ -10,8 +10,8 @@ We list some potential troubles encountered by users and developers, along with
 
   | MMDetection3D version |     MMEngine version     |      MMCV version       |   MMDetection version    |
   | --------------------- | :----------------------: | :---------------------: | :----------------------: |
-  | dev-1.x               | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
-  | main                  | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
+  | main                  | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.4.0 |
+  | v1.4.0                | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.4.0 |
   | v1.3.0                | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
   | 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   |

docs/zh_cn/notes/faq.md

@@ -10,8 +10,8 @@
 
   | MMDetection3D 版本 |      MMEngine 版本       |        MMCV 版本        |     MMDetection 版本     |
   | ------------------ | :----------------------: | :---------------------: | :----------------------: |
-  | dev-1.x            | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
-  | main               | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
+  | main               | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.4.0 |
+  | v1.4.0             | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.4.0 |
   | v1.3.0             | mmengine>=0.8.0, \<1.0.0 | mmcv>=2.0.0rc4, \<2.2.0 | mmdet>=3.0.0rc5, \<3.3.0 |
   | 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   |

mmdet3d/__init__.py

@@ -15,7 +15,7 @@
 mmengine_version = digit_version(mmengine.__version__)
 
 mmdet_minimum_version = '3.0.0rc5'
-mmdet_maximum_version = '3.3.0'
+mmdet_maximum_version = '3.4.0'
 mmdet_version = digit_version(mmdet.__version__)
 
 assert (mmcv_version >= digit_version(mmcv_minimum_version)

mmdet3d/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '1.3.0'
+__version__ = '1.4.0'
 short_version = __version__