Bump1.1 (#3140)

Co-authored-by: CSH <40987381+csatsurnh@users.noreply.github.com>
2023-07-04 11:11:30 +08:00 · 2023-07-04 11:11:30 +08:00 · e4c1865a82
parent 8806b4e548
commit e4c1865a82
11 changed files with 105 additions and 15 deletions
--- a/README.md
+++ b/README.md
@ -88,12 +88,12 @@ MMSegmentation v1.x brings remarkable improvements over the 0.x release, offerin

 ## What's New

-v1.0.0 was released on 04/06/2023.
+v1.1.0 was released on 07/04/2023.
 Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.

- Add Mapillary Vistas Datasets support to MMSegmentation Core Package ([#2576](https://github.com/open-mmlab/mmsegmentation/pull/2576))
- Support PIDNet ([#2609](https://github.com/open-mmlab/mmsegmentation/pull/2609))
- Support SegNeXt ([#2654](https://github.com/open-mmlab/mmsegmentation/pull/2654))
+- Support 24 medical image datasets in [projects](./projects/medical/).
+- Add GDAL backend and support remote sensing datasets [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/pull/2903).
+- Support [DDRNet](https://github.com/open-mmlab/mmsegmentation/pull/2855).

 ## Installation

@ -252,6 +252,7 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
 - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isaid)
 - [x] [Mapillary Vistas](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#mapillary-vistas-datasets)
+- [x] [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd)

 </details>

--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -87,7 +87,7 @@ MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升，提供了

 ## 更新日志

-最新版本 v1.0.0 在 2023.04.06 发布。
+最新版本 v1.1.0 在 2023.07.04 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/notes/changelog.md)。

 ## 安装
@ -246,6 +246,7 @@ MMSegmentation v1.x 在 0.x 版本的基础上有了显著的提升，提供了
 - [x] [Vaihingen](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/dataset_prepare.md#isprs-vaihingen)
 - [x] [iSAID](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/zh_cn/dataset_prepare.md#isaid)
 - [x] [Mapillary Vistas](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#mapillary-vistas-datasets)
+- [x] [LEVIR-CD](https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd)

 </details>

--- a/configs/_base_/models/fpn_poolformer_s12.py
+++ b/configs/_base_/models/fpn_poolformer_s12.py
@ -1,9 +1,9 @@
 # model settings
 norm_cfg = dict(type='SyncBN', requires_grad=True)
 checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/poolformer/poolformer-s12_3rdparty_32xb128_in1k_20220414-f8d83051.pth'  # noqa
-# TODO: delete custom_imports after mmcls supports auto import
-# please install mmcls>=1.0
-# import mmcls.models to trigger register_module in mmcls
+# TODO: delete custom_imports after mmpretrain supports auto import
+# please install mmpretrain >= 1.0.0rc7
+# import mmpretrain.models to trigger register_module in mmpretrain
 custom_imports = dict(
    imports=['mmpretrain.models'], allow_failed_imports=False)
 data_preprocessor = dict(
--- a/configs/convnext/README.md
+++ b/configs/convnext/README.md
@ -27,7 +27,7 @@ The "Roaring 20s" of visual recognition began with the introduction of Vision Tr
 - ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.

 ```shell
-pip install mmcls>=0.20.1
+pip install mmpretrain>=1.0.0rc7
 ```

 ### Pre-trained Models
--- 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="2.0.0"
-ARG MMSEG="1.0.0"
+ARG MMSEG="1.1.0"

 ENV PYTHONUNBUFFERED TRUE

--- a/docs/en/get_started.md
+++ b/docs/en/get_started.md
@ -4,7 +4,7 @@

 In this section we demonstrate how to prepare an environment with PyTorch.

-MMSegmentation works on Linux, Windows and macOS. It requires Python 3.6+, CUDA 9.2+ and PyTorch 1.5+.
+MMSegmentation works on Linux, Windows and macOS. It requires Python 3.7+, CUDA 10.2+ and PyTorch 1.8+.

 **Note:**
 If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](##installation). Otherwise, you can follow these steps for the preparation.
--- a/docs/en/notes/changelog.md
+++ b/docs/en/notes/changelog.md
@ -1,5 +1,91 @@
 # Changelog of v1.x

+## v1.1.0(06/28/2023)
+
+## What's Changed
+
+### Features
+
+- Support albu transform ([#2943](https://github.com/open-mmlab/mmsegmentation/pull/2943))
+- Support DDRNet ([#2855](https://github.com/open-mmlab/mmsegmentation/pull/2855))
+- Add GDAL backend and Support LEVIR-CD Dataset ([#2903](https://github.com/open-mmlab/mmsegmentation/pull/2903))
+- Support DSDL Dataset ([#2925](https://github.com/open-mmlab/mmsegmentation/pull/2925))
+- huasdorff distance loss ([#2820](https://github.com/open-mmlab/mmsegmentation/pull/2820))
+
+### New Projects
+
+- Support SAM inferencer ([#2897](https://github.com/open-mmlab/mmsegmentation/pull/2897))
+- Added a supported for Visual Attention Network (VAN) ([#2987](https://github.com/open-mmlab/mmsegmentation/pull/2987))
+- add GID dataset ([#3038](https://github.com/open-mmlab/mmsegmentation/pull/3038))
+- add Medical semantic seg dataset: Bactteria ([#2568](https://github.com/open-mmlab/mmsegmentation/pull/2568))
+- add Medical semantic seg dataset: Vampire ([#2633](https://github.com/open-mmlab/mmsegmentation/pull/2633))
+- add Medical semantic seg dataset: Ravir ([#2635](https://github.com/open-mmlab/mmsegmentation/pull/2635))
+- add Medical semantic seg dataset: Cranium ([#2675](https://github.com/open-mmlab/mmsegmentation/pull/2675))
+- add Medical semantic seg dataset: bccs ([#2861](https://github.com/open-mmlab/mmsegmentation/pull/2861))
+- add Medical semantic seg dataset: Gamma Task3 dataset ([#2695](https://github.com/open-mmlab/mmsegmentation/pull/2695))
+- add Medical semantic seg dataset: consep ([#2724](https://github.com/open-mmlab/mmsegmentation/pull/2724))
+- add Medical semantic seg dataset: breast_cancer_cell_seg dataset ([#2726](https://github.com/open-mmlab/mmsegmentation/pull/2726))
+- add Medical semantic seg dataset: chest_image_pneum dataset ([#2727](https://github.com/open-mmlab/mmsegmentation/pull/2727))
+- add Medical semantic seg dataset: conic2022 ([#2725](https://github.com/open-mmlab/mmsegmentation/pull/2725))
+- add Medical semantic seg dataset: dr_hagis ([#2729](https://github.com/open-mmlab/mmsegmentation/pull/2729))
+- add Medical semantic seg dataset: orvs ([#2728](https://github.com/open-mmlab/mmsegmentation/pull/2728))
+- add Medical semantic seg dataset: ISIC-2016 Task1 ([#2708](https://github.com/open-mmlab/mmsegmentation/pull/2708))
+- add Medical semantic seg dataset: ISIC-2017 Task1 ([#2709](https://github.com/open-mmlab/mmsegmentation/pull/2709))
+- add Medical semantic seg dataset: Kvasir seg ([#2677](https://github.com/open-mmlab/mmsegmentation/pull/2677))
+- add Medical semantic seg dataset: Kvasir seg aliyun ([#2678](https://github.com/open-mmlab/mmsegmentation/pull/2678))
+- add Medical semantic seg dataset: Rite ([#2680](https://github.com/open-mmlab/mmsegmentation/pull/2680))
+- add Medical semantic seg dataset: Fusc2021 ([#2682](https://github.com/open-mmlab/mmsegmentation/pull/2682))
+- add Medical semantic seg dataset: 2pm vessel ([#2685](https://github.com/open-mmlab/mmsegmentation/pull/2685))
+- add Medical semantic seg dataset: Pcam ([#2684](https://github.com/open-mmlab/mmsegmentation/pull/2684))
+- add Medical semantic seg dataset: Pannuke ([#2683](https://github.com/open-mmlab/mmsegmentation/pull/2683))
+- add Medical semantic seg dataset: Covid 19 ct cxr ([#2688](https://github.com/open-mmlab/mmsegmentation/pull/2688))
+- add Medical semantic seg dataset: Crass ([#2690](https://github.com/open-mmlab/mmsegmentation/pull/2690))
+- add Medical semantic seg dataset: Chest x ray images with pneumothorax masks ([#2687](https://github.com/open-mmlab/mmsegmentation/pull/2687))
+
+### Enhancement
+
+- Robust mapping from image path to seg map path ([#3091](https://github.com/open-mmlab/mmsegmentation/pull/3091))
+- Change assertion logic inference cfg.model.test_cfg ([#3012](https://github.com/open-mmlab/mmsegmentation/pull/3012))
+- Refactor dice loss ([#3002](https://github.com/open-mmlab/mmsegmentation/pull/3002))
+- Update Dockerfile libgl1-mesa-dev ([#3095](https://github.com/open-mmlab/mmsegmentation/pull/3095))
+- Prevent passed `ann_file` from silently failing to load ([#2966](https://github.com/open-mmlab/mmsegmentation/pull/2966))
+- Update the translation of models documentation ([#2833](https://github.com/open-mmlab/mmsegmentation/pull/2833))
+- Add docs contents at README.md ([#3083](https://github.com/open-mmlab/mmsegmentation/pull/3083))
+- Enhance swin pretrained model loading ([#3097](https://github.com/open-mmlab/mmsegmentation/pull/3097))
+
+### Bug Fixes
+
+- Handle case where device is neither CPU nor CUDA in HamHead ([#2868](https://github.com/open-mmlab/mmsegmentation/pull/2868))
+- Fix bugs when out_channels==1 ([#2911](https://github.com/open-mmlab/mmsegmentation/pull/2911))
+- Fix binary C=1 focal loss & dataset fileio ([#2935](https://github.com/open-mmlab/mmsegmentation/pull/2935))
+- Fix isaid dataset pre-processing tool ([#3010](https://github.com/open-mmlab/mmsegmentation/pull/3010))
+- Fix bug cannot use both '--tta' and '--out' while testing ([#3067](https://github.com/open-mmlab/mmsegmentation/pull/3067))
+- Fix inferencer ut ([#3117](https://github.com/open-mmlab/mmsegmentation/pull/3117))
+- Fix document ([#2863](https://github.com/open-mmlab/mmsegmentation/pull/2863), [#2896](https://github.com/open-mmlab/mmsegmentation/pull/2896), [#2919](https://github.com/open-mmlab/mmsegmentation/pull/2919), [#2951](https://github.com/open-mmlab/mmsegmentation/pull/2951), [#2970](https://github.com/open-mmlab/mmsegmentation/pull/2970), [#2961](https://github.com/open-mmlab/mmsegmentation/pull/2961), [#3042](https://github.com/open-mmlab/mmsegmentation/pull/3042), )
+- Fix squeeze error when N=1 and C=1 ([#2933](https://github.com/open-mmlab/mmsegmentation/pull/2933))
+
+## New Contributors
+
+- @liu-mengyang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2896
+- @likyoo made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2911
+- @1qh made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2902
+- @JoshuaChou2018 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2951
+- @jts250 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2833
+- @MGAMZ made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2970
+- @tianbinli made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2568
+- @Provable0816 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2633
+- @Zoulinx made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2903
+- @wufan-tb made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2925
+- @haruishi43 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2966
+- @Masaaki-75 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2675
+- @tang576225574 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2987
+- @Kedreamix made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3010
+- @nightrain01 made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3067
+- @shigengtian made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3095
+- @SheffieldCao made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3097
+- @wangruohui made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3091
+- @LHamnett made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/3012
+
 ## v1.0.0(04/06/2023)

 ### Highlights
--- a/docs/en/notes/faq.md
+++ b/docs/en/notes/faq.md
@ -9,7 +9,8 @@ The compatible MMSegmentation, MMCV and MMEngine versions are as below. Please i
 | MMSegmentation version |          MMCV version          | MMEngine version  | MMClassification (optional) version | MMDetection (optional) version |
 | :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
 |     dev-1.x branch     |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
-|      main branch       |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
+|      main branch       |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
+|         1.1.0          |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
 |         1.0.0          |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
 |        1.0.0rc6        |        mmcv >= 2.0.0rc4        | MMEngine >= 0.5.0 |           mmcls>=1.0.0rc0           |       mmdet >= 3.0.0rc6        |
 |        1.0.0rc5        |        mmcv >= 2.0.0rc4        | MMEngine >= 0.2.0 |           mmcls>=1.0.0rc0           |        mmdet>=3.0.0rc6         |
--- a/docs/zh_cn/get_started.md
+++ b/docs/zh_cn/get_started.md
@ -4,7 +4,7 @@

 本教程中，我们将会演示如何使用 PyTorch 准备环境。

-MMSegmentation 可以在 Linux, Windows 和 macOS 系统上运行，并且需要安装 Python 3.6+, CUDA 9.2+ 和 PyTorch 1.5+
+MMSegmentation 可以在 Linux, Windows 和 macOS 系统上运行，并且需要安装 Python 3.7+, CUDA 10.2+ 和 PyTorch 1.8+

 **注意:**
 如果您已经安装了 PyTorch, 可以跳过该部分，直接到[下一小节](##安装)。否则，您可以按照以下步骤操作。
--- a/docs/zh_cn/notes/faq.md
+++ b/docs/zh_cn/notes/faq.md
@ -9,7 +9,8 @@
 | MMSegmentation version |          MMCV version          | MMEngine version  | MMClassification (optional) version | MMDetection (optional) version |
 | :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
 |     dev-1.x branch     |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
-|      main branch       |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
+|      main branch       |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
+|         1.1.0          |         mmcv >= 2.0.0          | MMEngine >= 0.7.4 |        mmpretrain>=1.0.0rc7         |         mmdet >= 3.0.0         |
 |         1.0.0          |        mmcv >= 2.0.0rc4        | MMEngine >= 0.7.1 |           mmcls==1.0.0rc6           |         mmdet >= 3.0.0         |
 |        1.0.0rc6        |        mmcv >= 2.0.0rc4        | MMEngine >= 0.5.0 |           mmcls>=1.0.0rc0           |       mmdet >= 3.0.0rc6        |
 |        1.0.0rc5        |        mmcv >= 2.0.0rc4        | MMEngine >= 0.2.0 |           mmcls>=1.0.0rc0           |        mmdet>=3.0.0rc6         |
--- a/mmseg/version.py
+++ b/mmseg/version.py
@ -1,6 +1,6 @@
 # Copyright (c) Open-MMLab. All rights reserved.

-__version__ = '1.0.0'
+__version__ = '1.1.0'


 def parse_version_info(version_str):