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Co-authored-by: CSH <40987381+csatsurnh@users.noreply.github.com>
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xiexinch and csatsurnh committed Jul 4, 2023
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9 changes: 5 additions & 4 deletions README.md
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Expand Up @@ -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

Expand Down Expand Up @@ -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>

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3 changes: 2 additions & 1 deletion README_zh-CN.md
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Expand Up @@ -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
Expand Down Expand Up @@ -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>

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6 changes: 3 additions & 3 deletions configs/_base_/models/fpn_poolformer_s12.py
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@@ -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(
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2 changes: 1 addition & 1 deletion configs/convnext/README.md
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Expand Up @@ -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
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2 changes: 1 addition & 1 deletion docker/serve/Dockerfile
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Expand Up @@ -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

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2 changes: 1 addition & 1 deletion docs/en/get_started.md
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Expand Up @@ -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.
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86 changes: 86 additions & 0 deletions docs/en/notes/changelog.md
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# 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
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3 changes: 2 additions & 1 deletion docs/en/notes/faq.md
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Expand Up @@ -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 |
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2 changes: 1 addition & 1 deletion docs/zh_cn/get_started.md
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Expand Up @@ -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。
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3 changes: 2 additions & 1 deletion docs/zh_cn/notes/faq.md
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Expand Up @@ -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 |
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2 changes: 1 addition & 1 deletion mmseg/version.py
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# Copyright (c) Open-MMLab. All rights reserved.

__version__ = '1.0.0'
__version__ = '1.1.0'


def parse_version_info(version_str):
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