Bump to v2.9.0 (open-mmlab#4557)

* bump to v2.9.0

* update

README.md

@@ -42,7 +42,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v2.8.0 was released in 04/01/2021.
+v2.9.0 was released in 01/02/2021.
 Please refer to [changelog.md](docs/changelog.md) for details and release history.
 A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md).
 

docs/changelog.md

@@ -1,5 +1,49 @@
 ## Changelog
 
+### v2.9.0 (01/02/2021)
+
+#### Highlights
+
+- Support new methods: [SCNet](https://arxiv.org/abs/2012.10150), [Sparse R-CNN](https://arxiv.org/abs/2011.12450)
+- Move `train_cfg` and `test_cfg` into model in configs
+- Support to visualize results based on prediction quality
+
+#### New Features
+
+- Support [SCNet](https://arxiv.org/abs/2012.10150) (#4356)
+- Support [Sparse R-CNN](https://arxiv.org/abs/2011.12450) (#4219)
+- Support evaluate mAP by multiple IoUs (#4398)
+- Support concatenate dataset for testing (#4452)
+- Support to visualize results based on prediction quality (#4441)
+- Add ONNX simplify option to Pytorch2ONNX script (#4468)
+- Add hook for checking compatibility of class numbers in heads and datasets (#4508)
+
+#### Bug Fixes
+
+- Fix CPU inference bug of Cascade RPN (#4410)
+- Fix NMS error of CornerNet when there is no prediction box (#4409)
+- Fix TypeError in CornerNet inference (#4411)
+- Fix bug of PAA when training with background images (#4391)
+- Fix the error that the window data is not destroyed when `out_file is not None` and `show==False` (#4442)
+- Fix order of NMS `score_factor` that will decrease the performance of YOLOv3 (#4473)
+- Fix bug in HTC TTA when the number of detection boxes is 0 (#4516)
+- Fix resize error in mask data structures (#4520)
+
+#### Improvements
+
+- Allow to customize classes in LVIS dataset (#4382)
+- Add tutorials for building new models with existing datasets (#4396)
+- Add CPU compatibility information in documentation (#4405)
+- Add documentation of deprecated `ImageToTensor` for batch inference (#4408)
+- Add more details in documentation for customizing dataset (#4430)
+- Switch `imshow_det_bboxes` visualization backend from OpenCV to Matplotlib (#4389)
+- Deprecate `ImageToTensor` in `image_demo.py` (#4400)
+- Move train_cfg/test_cfg into model (#4347, #4489)
+- Update docstring for `reg_decoded_bbox` option in bbox heads (#4467)
+- Update dataset information in documentation (#4525)
+- Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495)
+- Add guidance for speed benchmark (#4537)
+
 ### v2.8.0 (04/01/2021)
 
 #### Highlights
@@ -121,7 +165,7 @@
 #### Backwards Incompatible Changes
 
 **FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)**
-Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.8.0.
+Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.10.0.
 
 **[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)**
 Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus `self.background_labels` in `dense_heads` is removed and all heads use `self.num_classes` to indicate the class index of background labels.

docs/get_started.md

@@ -12,6 +12,7 @@ The compatible MMDetection and MMCV versions are as below. Please install the co
 | MMDetection version |    MMCV version     |
 |:-------------------:|:-------------------:|
 | master              | mmcv-full>=1.2.4, <1.3|
+| 2.9.0               | mmcv-full>=1.2.4, <1.3|
 | 2.8.0               | mmcv-full>=1.2.4, <1.3|
 | 2.7.0               | mmcv-full>=1.1.5, <1.3|
 | 2.6.0               | mmcv-full>=1.1.5, <1.3|

docs/tutorials/customize_dataset.md

@@ -484,4 +484,4 @@ data = dict(
 - Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when `filter_empty_gt=True` and `test_mode=False`. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when `filter_empty_gt=True` and `test_mode=False`, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.
 - Since the middle format only has box labels and does not contain the class names, when using `CustomDataset`, users cannot filter out the empty GT images through configs but only do this offline.
 - Please remember to modify the `num_classes` in the head when specifying `classes` in dataset. We implemented [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the numbers are consistent since v2.9.0(after PR#4508).
-- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in v2.8.0 or v2.9.0 (depends on the progress).
+- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress).

mmdet/version.py

@@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '2.8.0'
+__version__ = '2.9.0'
 short_version = __version__