Bump version to v0.24.0 (#1533)

* [WIP] Bump version to v0.24.0 * changelog * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * Update docs/en/changelog.md Co-authored-by: MengzhangLI <mcmong@pku.edu.cn> * words * typo Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
open-mmlab · Apr 29, 2022 · c4c0c1f · c4c0c1f
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diff --git a/README.md b/README.md
@@ -66,7 +66,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-v0.23.0 was released in 4/1/2022.
+v0.24.0 was released in 4/29/2022.
 Please refer to [changelog.md](docs/en/changelog.md) for details and release history.
 
 ## Benchmark and model zoo

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -65,7 +65,7 @@ MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 O
 
 ## 更新日志
 
-最新版本 v0.23.0 在 2022.4.1 发布。
+最新版本 v0.24.0 在 2022.4.29 发布。
 如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/en/changelog.md)。
 
 ## 基准测试和模型库

diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
@@ -4,7 +4,7 @@ ARG CUDNN="8"
 FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
 
 ARG MMCV="1.4.8"
-ARG MMSEG="0.23.0"
+ARG MMSEG="0.24.0"
 
 ENV PYTHONUNBUFFERED TRUE
 

diff --git a/docs/en/changelog.md b/docs/en/changelog.md
@@ -1,5 +1,68 @@
 ## Changelog
 
+### V0.24.0 (4/29/2022)
+
+**Highlights**
+
+- Support MAE: Masked Autoencoders Are Scalable Vision Learners
+- Support Resnet strikes back
+
+**New Features**
+
+- Support MAE: Masked Autoencoders Are Scalable Vision Learners ([1307](https://github.com/open-mmlab/mmsegmentation/pull/1307), [1523](https://github.com/open-mmlab/mmsegmentation/pull/1523))
+- Support Resnet strikes back ([1390](https://github.com/open-mmlab/mmsegmentation/pull/1390))
+- Support extra dataloader settings in configs ([1435](https://github.com/open-mmlab/mmsegmentation/pull/1435))
+
+**Bug Fixes**
+
+- Fix input previous results for the last cascade_decode_head ([#1450](https://github.com/open-mmlab/mmsegmentation/pull/1450))
+- Fix validation loss logging ([#1494](https://github.com/open-mmlab/mmsegmentation/pull/1494))
+- Fix the bug in binary_cross_entropy ([1527](https://github.com/open-mmlab/mmsegmentation/pull/1527))
+- Support single channel prediction for Binary Cross Entropy Loss ([#1454](https://github.com/open-mmlab/mmsegmentation/pull/1454))
+- Fix potential bugs in accuracy.py ([1496](https://github.com/open-mmlab/mmsegmentation/pull/1496))
+- Avoid converting label ids twice by label map during evaluation ([1417](https://github.com/open-mmlab/mmsegmentation/pull/1417))
+- Fix bug about label_map ([1445](https://github.com/open-mmlab/mmsegmentation/pull/1445))
+- Fix image save path bug in Windows ([1423](https://github.com/open-mmlab/mmsegmentation/pull/1423))
+- Fix MMSegmentation Colab demo ([1501](https://github.com/open-mmlab/mmsegmentation/pull/1501), [1452](https://github.com/open-mmlab/mmsegmentation/pull/1452))
+- Migrate azure blob for beit checkpoints ([1503](https://github.com/open-mmlab/mmsegmentation/pull/1503))
+- Fix bug in `tools/analyse_logs.py` caused by wrong plot_iter in some cases ([1428](https://github.com/open-mmlab/mmsegmentation/pull/1428))
+
+**Improvements**
+
+- Merge BEiT and ConvNext's LR decay optimizer constructors ([#1438](https://github.com/open-mmlab/mmsegmentation/pull/1438))
+- Register optimizer constructor with mmseg ([#1456](https://github.com/open-mmlab/mmsegmentation/pull/1456))
+- Refactor transformer encode layer in ViT and BEiT backbone ([#1481](https://github.com/open-mmlab/mmsegmentation/pull/1481))
+- Add `build_pos_embed` and `build_layers` for BEiT ([1517](https://github.com/open-mmlab/mmsegmentation/pull/1517))
+- Add `with_cp` to mit and vit ([1431](https://github.com/open-mmlab/mmsegmentation/pull/1431))
+- Fix inconsistent dtype of `seg_label` in stdc decode ([1463](https://github.com/open-mmlab/mmsegmentation/pull/1463))
+- Delete random seed for training in `dist_train.sh` ([1519](https://github.com/open-mmlab/mmsegmentation/pull/1519))
+- Revise high `workers_per_gpus` in config file ([#1506](https://github.com/open-mmlab/mmsegmentation/pull/1506))
+- Add GPG keys and del mmcv version in Dockerfile ([1534](https://github.com/open-mmlab/mmsegmentation/pull/1534))
+- Update checkpoint for model in deeplabv3plus ([#1487](https://github.com/open-mmlab/mmsegmentation/pull/1487))
+- Add `DistSamplerSeedHook` to set epoch number to dataloader when runner is `EpochBasedRunner` ([1449](https://github.com/open-mmlab/mmsegmentation/pull/1449))
+- Provide URLs of Swin Transformer pretrained models ([1389](https://github.com/open-mmlab/mmsegmentation/pull/1389))
+- Updating Dockerfiles From Docker Directory and `get_started.md` to reach latest stable version of Python, PyTorch and MMCV ([1446](https://github.com/open-mmlab/mmsegmentation/pull/1446))
+
+**Documentation**
+
+- Add more clearly statement of CPU training/inference ([1518](https://github.com/open-mmlab/mmsegmentation/pull/1518))
+
+**Contributors**
+
+* @jiangyitong made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1431
+* @kahkeng made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1447
+* @Nourollah made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1446
+* @androbaza made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1452
+* @Yzichen made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1445
+* @whu-pzhang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1423
+* @panfeng-hover made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1417
+* @Johnson-Wang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1496
+* @jere357 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1460
+* @mfernezir made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1494
+* @donglixp made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1503
+* @YuanLiuuuuuu made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1307
+* @Dawn-bin made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/1527
+
 ### V0.23.0 (4/1/2022)
 
 **Highlights**

diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -12,6 +12,7 @@ The compatible MMSegmentation and MMCV versions are as below. Please install the
 | MMSegmentation version |        MMCV version        | MMClassification version |
 |:----------------------:|:--------------------------:|:------------------------:|
 |         master         |  mmcv-full>=1.4.4, <=1.6.0  | mmcls>=0.20.1, <=1.0.0   |
+|      0.24.0            |  mmcv-full>=1.4.4, <=1.6.0  | mmcls>=0.20.1, <=1.0.0   |
 |         0.23.0         |  mmcv-full>=1.4.4, <=1.6.0  | mmcls>=0.20.1, <=1.0.0   |
 |         0.22.0         |  mmcv-full>=1.4.4, <=1.6.0  | mmcls>=0.20.1, <=1.0.0   |
 |         0.21.1         |  mmcv-full>=1.4.4, <=1.6.0  | Not required             |

diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
@@ -12,6 +12,7 @@
 | MMSegmentation 版本 |          MMCV 版本         | MMClassification 版本    |
 |:-----------------:|:--------------------------:|:------------------------:|
 |      master       |  mmcv-full>=1.4.4, <=1.6.0 | mmcls>=0.20.1, <=1.0.0   |
+|      0.24.0       |  mmcv-full>=1.4.4, <=1.6.0 | mmcls>=0.20.1, <=1.0.0   |
 |      0.23.0       |  mmcv-full>=1.4.4, <=1.6.0 | mmcls>=0.20.1, <=1.0.0   |
 |      0.22.0       |  mmcv-full>=1.4.4, <=1.6.0 | mmcls>=0.20.1, <=1.0.0   |
 |      0.21.1       |  mmcv-full>=1.4.4, <=1.6.0 | Not required             |

diff --git a/mmseg/version.py b/mmseg/version.py
@@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '0.23.0'
+__version__ = '0.24.0'
 
 
 def parse_version_info(version_str):