Bump to v0.7.0 (#186)

* Update README.md

* Update __init__.py

* Update changelog.md

* Update changelog.md

* Fix a minor typo

* Fix minor typos

* Update changelog.md

README.md

@@ -8,7 +8,7 @@
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
 
 
-**News**: We released the codebase v0.6.1.
+**News**: We released the codebase v0.7.0.
 
 Documentation: https://mmdetection3d.readthedocs.io/
 
@@ -56,7 +56,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v0.6.1 was released in 11/10/2020.
+v0.7.0 was released in 1/11/2020.
 Please refer to [changelog.md](docs/changelog.md) for details and release history.
 
 ## Benchmark and model zoo

configs/pointpillars/README.md

@@ -59,4 +59,4 @@ We implement PointPillars and provide the results and checkpoints on KITTI, nuSc
 - **Data Split**: Here we provide several baselines for waymo dataset, among which D5 means that we divide the dataset into 5 folds and only use one fold for efficient experiments. Using the complete dataset can boost the performance a lot, especially for the detection of cyclist and pedestrian, where more than 5 mAP or mAPH improvement can be expected.
 - **Implementation Details**: We basically follow the implementation in the [paper](https://arxiv.org/pdf/1912.04838.pdf) in terms of the network architecture (having a
 stride of 1 for the first convolutional block). Different settings of voxelization, data augmentation and hyper parameters make these baselines outperform those in the paper by about 7 mAP for car and 4 mAP for pedestrian with only a subset of the whole dataset. All of these results are achieved without bells-and-whistles, e.g. ensemble, multi-scale training and test augmentation.
-- **License Aggrement**: To comply the [license aggrement of Waymo dataset](https://waymo.com/open/terms/), the pre-trained models on Waymo dataset are not released. We still release the training log as a reference to ease the future research.
+- **License Aggrement**: To comply the [license agreement of Waymo dataset](https://waymo.com/open/terms/), the pre-trained models on Waymo dataset are not released. We still release the training log as a reference to ease the future research.

docs/changelog.md

 ## Changelog
 
+### v0.7.0 (1/11/2020)
+
+#### Highlights
+
+- Support a new method [SSN](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700579.pdf) with benchmarks on nuScenes and Lyft datasets.
+- Update benchmarks for SECOND on Waymo, CenterPoint with TTA on nuScenes and models with mixed precision training on KITTI and nuScenes.
+- Support semantic segmentation on nuImages and provide [HTC](https://arxiv.org/abs/1901.07518) models with configurations and performance for reference.
+
+#### Bug Fixes
+
+- Fix incorrect code weights in anchor3d_head when introducing mixed precision training (#173)
+- Fix the incorrect label mapping on nuImages dataset (#155)
+
+#### New Features
+
+- Modified primitive head which can support the setting on SUN-RGBD dataset (#136)
+- Support semantic segmentation and [HTC](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/nuimages) with models for reference on nuImages dataset (#155)
+- Support [SSN](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/ssn) on nuScenes and Lyft datasets (#147, #174, #166, #182)
+- Support double flip for test time augmentation of CenterPoint with updated benchmark (#143)
+
+#### Improvements
+
+- Update [SECOND](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/second) benchmark with configurations for reference on Waymo (#166)
+- Delete checkpoints on Waymo to comply its specific license agreement (#180)
+- Update models and instructions with [mixed precision training](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/fp16) on KITTI and nuScenes (#178)
+
 ### v0.6.1 (11/10/2020)
 
 #### Highlights

mmdet3d/__init__.py

@@ -17,17 +17,17 @@ def digit_version(version_str):
 
 
 mmcv_minimum_version = '1.1.5'
-mmdet_maximum_version = '1.2'
+mmcv_maximum_version = '1.3.0'
 mmcv_version = digit_version(mmcv.__version__)
 
 
 assert (mmcv_version >= digit_version(mmcv_minimum_version)
-        and mmcv_version <= digit_version(mmdet_maximum_version)), \
+        and mmcv_version <= digit_version(mmcv_maximum_version)), \
     f'MMCV=={mmcv.__version__} is used but incompatible. ' \
-    f'Please install mmcv>={mmcv_minimum_version}, <={mmdet_maximum_version}.'
+    f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.'
 
 mmdet_minimum_version = '2.5.0'
-mmdet_maximum_version = '2.5.0'
+mmdet_maximum_version = '2.7.0'
 mmdet_version = digit_version(mmdet.__version__)
 assert (mmdet_version >= digit_version(mmdet_minimum_version)
         and mmdet_version <= digit_version(mmdet_maximum_version)), \