Mmsegmentation

**Highlights**

- Officially Support CPUs training and inference, please use the latest MMCV (1.4.4) to try it out.
- Support Segmenter: Transformer for Semantic Segmentation (ICCV'2021).
- Support ISPRS Potsdam and Vaihingen Dataset.
- Add Mosaic transform and `MultiImageMixDataset` class in `dataset_wrappers`.

**New Features**

- Support Segmenter: Transformer for Semantic Segmentation (ICCV'2021) ([955](https://github.com/open-mmlab/mmsegmentation/pull/955))
- Support ISPRS Potsdam and Vaihingen Dataset ([1097](https://github.com/open-mmlab/mmsegmentation/pull/1097), [#1171](https://github.com/open-mmlab/mmsegmentation/pull/1171))
- Add segformer‘s benchmark on cityscapes ([1155](https://github.com/open-mmlab/mmsegmentation/pull/1155))
- Add auto resume ([1172](https://github.com/open-mmlab/mmsegmentation/pull/1172))
- Add Mosaic transform and `MultiImageMixDataset` class in `dataset_wrappers` ([1093](https://github.com/open-mmlab/mmsegmentation/pull/1093), [#1105](https://github.com/open-mmlab/mmsegmentation/pull/1105))
- Add log collector ([1175](https://github.com/open-mmlab/mmsegmentation/pull/1175))

**Improvements**

- New-style CPU training and inference ([1251](https://github.com/open-mmlab/mmsegmentation/pull/1251))
- Add UNet benchmark with multiple losses supervision ([1143](https://github.com/open-mmlab/mmsegmentation/pull/1143))

**Bug Fixes**

- Fix the model statistics in doc for readthedoc ([1153](https://github.com/open-mmlab/mmsegmentation/pull/1153))
- Set random seed for `palette` if not given ([1152](https://github.com/open-mmlab/mmsegmentation/pull/1152))
- Add `COCOStuffDataset` in `class_names.py` ([1222](https://github.com/open-mmlab/mmsegmentation/pull/1222))
- Fix bug in non-distributed multi-gpu training/testing ([1247](https://github.com/open-mmlab/mmsegmentation/pull/1247))
- Delete unnecessary lines of STDCHead ([1231](https://github.com/open-mmlab/mmsegmentation/pull/1231))

**New Contributors**

- jbwang1997 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1152
- BeaverCC made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1206
- Echo-minn made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1214
- rstrudel made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/955

What's Changed
* [Fix] Revise `--option` to `--options` in https://github.com/open-mmlab/mmsegmentation/pull/1140.

Publish this version is to avoid BC-Breaking problem caused by v0.20.1.

Contributors:
RockeyCoss

**Improvements**

- Change options to cfg-options ([1129](https://github.com/open-mmlab/mmsegmentation/pull/1129))


**Bug Fixes**

- Fix `<!-- [ABSTRACT] -->` in metafile. ([1127](https://github.com/open-mmlab/mmsegmentation/pull/1127))
- Fix correct `num_classes` of HRNet in `LoveDA` dataset ([1136](https://github.com/open-mmlab/mmsegmentation/pull/1136))

Contributors
MengzhangLI
RockeyCoss

**Highlights**

- Support Twins ([989](https://github.com/open-mmlab/mmsegmentation/pull/989))
- Support a real-time segmentation model STDC ([995](https://github.com/open-mmlab/mmsegmentation/pull/995))
- Support a widely-used segmentation model in lane detection ERFNet ([960](https://github.com/open-mmlab/mmsegmentation/pull/960))
- Support A Remote Sensing Land-Cover Dataset LoveDA ([1028](https://github.com/open-mmlab/mmsegmentation/pull/1028))
- Support focal loss ([1024](https://github.com/open-mmlab/mmsegmentation/pull/1024))

**New Features**

- Support Twins ([989](https://github.com/open-mmlab/mmsegmentation/pull/989))
- Support a real-time segmentation model STDC ([995](https://github.com/open-mmlab/mmsegmentation/pull/995))
- Support a widely-used segmentation model in lane detection ERFNet ([960](https://github.com/open-mmlab/mmsegmentation/pull/960))
- Add SETR cityscapes benchmark ([1087](https://github.com/open-mmlab/mmsegmentation/pull/1087))
- Add BiSeNetV1 COCO-Stuff 164k benchmark ([1019](https://github.com/open-mmlab/mmsegmentation/pull/1019))
- Support focal loss ([1024](https://github.com/open-mmlab/mmsegmentation/pull/1024))
- Add Cutout transform ([1022](https://github.com/open-mmlab/mmsegmentation/pull/1022))

**Improvements**

- Set a random seed when the user does not set a seed ([1039](https://github.com/open-mmlab/mmsegmentation/pull/1039))
- Add CircleCI setup ([1086](https://github.com/open-mmlab/mmsegmentation/pull/1086))
- Skip CI on ignoring given paths ([1078](https://github.com/open-mmlab/mmsegmentation/pull/1078))
- Add abstract and image for every paper ([1060](https://github.com/open-mmlab/mmsegmentation/pull/1060))
- Create a symbolic link on windows ([1090](https://github.com/open-mmlab/mmsegmentation/pull/1090))
- Support video demo using trained model ([1014](https://github.com/open-mmlab/mmsegmentation/pull/1014))

**Bug Fixes**

- Fix incorrectly loading init_cfg or pretrained models of several transformer models ([999](https://github.com/open-mmlab/mmsegmentation/pull/999), [#1069](https://github.com/open-mmlab/mmsegmentation/pull/1069), [#1102](https://github.com/open-mmlab/mmsegmentation/pull/1102))
- Fix EfficientMultiheadAttention in SegFormer ([1003](https://github.com/open-mmlab/mmsegmentation/pull/1037))
- Remove `fp16` folder in `configs` ([1031](https://github.com/open-mmlab/mmsegmentation/pull/1031))
- Fix several typos in .yml file (Dice Metric [1041](https://github.com/open-mmlab/mmsegmentation/pull/1041), ADE20K dataset [#1120](https://github.com/open-mmlab/mmsegmentation/pull/1120), Training Memory (GB) [#1083](https://github.com/open-mmlab/mmsegmentation/pull/1083))
- Fix test error when using `--show-dir` ([1091](https://github.com/open-mmlab/mmsegmentation/pull/1091))
- Fix dist training infinite waiting issue ([1035](https://github.com/open-mmlab/mmsegmentation/pull/1035))
- Change the upper version of mmcv to 1.5.0 ([1096](https://github.com/open-mmlab/mmsegmentation/pull/1096))
- Fix symlink failure on Windows ([1038](https://github.com/open-mmlab/mmsegmentation/pull/1038))
- Cancel previous runs that are not completed ([1118](https://github.com/open-mmlab/mmsegmentation/pull/1118))
- Unified links of readthedocs in docs ([1119](https://github.com/open-mmlab/mmsegmentation/pull/1119))

**Contributors**

- Junjue-Wang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1028
- ddebby made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1066
- del-zhenwu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1078
- KangBK0120 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1106
- zergzzlun made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1091
- fingertap made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1035
- irvingzhang0512 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1014
- littleSunlxy made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/989
- lkm2835
- RockeyCoss
- MengzhangLI
- Junjun2016
- xiexinch
- xvjiarui

**Highlights**

- Support TIMMBackbone wrapper ([998](https://github.com/open-mmlab/mmsegmentation/pull/998))
- Support custom hook ([428](https://github.com/open-mmlab/mmsegmentation/pull/428))
- Add codespell pre-commit hook ([920](https://github.com/open-mmlab/mmsegmentation/pull/920))
- Add FastFCN benchmark on ADE20K ([972](https://github.com/open-mmlab/mmsegmentation/pull/972))

**New Features**

- Support TIMMBackbone wrapper ([998](https://github.com/open-mmlab/mmsegmentation/pull/998))
- Support custom hook ([428](https://github.com/open-mmlab/mmsegmentation/pull/428))
- Add FastFCN benchmark on ADE20K ([972](https://github.com/open-mmlab/mmsegmentation/pull/972))
- Add codespell pre-commit hook and fix typos ([920](https://github.com/open-mmlab/mmsegmentation/pull/920))

**Improvements**

- Make inputs & channels smaller in unittests ([1004](https://github.com/open-mmlab/mmsegmentation/pull/1004))
- Change `self.loss_decode` back to `dict` in Single Loss situation ([1002](https://github.com/open-mmlab/mmsegmentation/pull/1002))

**Bug Fixes**

- Fix typo in usage example ([1003](https://github.com/open-mmlab/mmsegmentation/pull/1003))
- Add contiguous after permutation in ViT ([992](https://github.com/open-mmlab/mmsegmentation/pull/992))
- Fix the invalid link ([985](https://github.com/open-mmlab/mmsegmentation/pull/985))
- Fix bug in CI with python 3.9 ([994](https://github.com/open-mmlab/mmsegmentation/pull/994))
- Fix bug when loading class name form file in custom dataset ([923](https://github.com/open-mmlab/mmsegmentation/pull/923))

**Contributors**

- ShoupingShan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/923
- RockeyCoss made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/954
- HarborYuan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/992
- lkm2835 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1003
- gszh made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/428
- xvjiarui
- VVsssssk
- MengzhangLI
- Junjun2016

**Highlights**

- Support three real-time segmentation models (ICNet [884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([833](https://github.com/open-mmlab/mmsegmentation/pull/833))
- Support loading GT for evaluation from multi-file backend ([867](https://github.com/open-mmlab/mmsegmentation/pull/867))

**New Features**

- Support three real-time segmentation models (ICNet [884](https://github.com/open-mmlab/mmsegmentation/pull/884), BiSeNetV1 [#851](https://github.com/open-mmlab/mmsegmentation/pull/851), and BiSeNetV2 [#804](https://github.com/open-mmlab/mmsegmentation/pull/804))
- Support one efficient segmentation model (FastFCN [885](https://github.com/open-mmlab/mmsegmentation/pull/885))
- Support one efficient non-local/self-attention based segmentation model (ISANet [70](https://github.com/open-mmlab/mmsegmentation/pull/70))
- Support COCO-Stuff 10k and 164k datasets ([625](https://github.com/open-mmlab/mmsegmentation/pull/625))
- Support evaluate concated dataset separately ([833](https://github.com/open-mmlab/mmsegmentation/pull/833))

**Improvements**

- Support loading GT for evaluation from multi-file backend ([867](https://github.com/open-mmlab/mmsegmentation/pull/867))
- Auto-convert SyncBN to BN when training on DP automatly([772](https://github.com/open-mmlab/mmsegmentation/pull/772))
- Refactor Swin-Transformer ([800](https://github.com/open-mmlab/mmsegmentation/pull/800))

**Bug Fixes**

- Update mmcv installation in dockerfile ([860](https://github.com/open-mmlab/mmsegmentation/pull/860))
- Fix number of iteration bug when resuming checkpoint in distributed train ([866](https://github.com/open-mmlab/mmsegmentation/pull/866))
- Fix parsing parse in val_step ([906](https://github.com/open-mmlab/mmsegmentation/pull/906))