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bumpv1.0.0 (#2849)

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README.md
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@ -88,11 +88,12 @@ MMSegmentation v1.x brings remarkable improvements over the 0.x release, offerin
## What's New
v1.0.0rc6 was released on 03/03/2023.
v1.0.0 was released on 04/06/2023.
Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
- Support MMSegInferencer ([#2413](https://github.com/open-mmlab/mmsegmentation/pull/2413), [#2658](https://github.com/open-mmlab/mmsegmentation/pull/2658))
- Support REFUGE dataset ([#2554](https://github.com/open-mmlab/mmsegmentation/pull/2554))
- 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))
## Installation

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README_zh-CN.md
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@ -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 v1.0.0rc6 在 2023.03.03 发布。
最新版本 v1.0.0 在 2023.04.06 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

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docker/Dockerfile
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@ -1,7 +1,7 @@
ARG PYTORCH="1.11.0"
ARG CUDA="11.3"
ARG CUDNN="8"
ARG MMCV="2.0.0rc4"
ARG MMCV="2.0.0"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel

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docker/serve/Dockerfile
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@ -3,8 +3,8 @@ ARG CUDA="11.3"
ARG CUDNN="8"
FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
ARG MMCV="2.0.0rc4"
ARG MMSEG="1.0.0rc6"
ARG MMCV="2.0.0"
ARG MMSEG="1.0.0"
ENV PYTHONUNBUFFERED TRUE

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docs/en/notes/changelog.md
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@ -1,5 +1,41 @@
# Changelog of v1.x
## v1.0.0(04/06/2023)
### Highlights
- 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))
### Features
- Support calculating FLOPs of segmentors ([#2706](https://github.com/open-mmlab/mmsegmentation/pull/2706))
- Support multi-band image for Mosaic ([#2748](https://github.com/open-mmlab/mmsegmentation/pull/2748))
- Support dump segment prediction ([#2712](https://github.com/open-mmlab/mmsegmentation/pull/2712))
### Bug fix
- Fix format_result and fix prefix param in cityscape metric, and rename CitysMetric to CityscapesMetric ([#2660](https://github.com/open-mmlab/mmsegmentation/pull/2660))
- Support input gt seg map is not 2D ([#2739](https://github.com/open-mmlab/mmsegmentation/pull/2739))
- Fix accepting an unexpected argument `local-rank` in PyTorch 2.0 ([#2812](https://github.com/open-mmlab/mmsegmentation/pull/2812))
### Documentation
- Add Chinese version of various documentation ([#2673](https://github.com/open-mmlab/mmsegmentation/pull/2673), [#2702](https://github.com/open-mmlab/mmsegmentation/pull/2702), [#2703](https://github.com/open-mmlab/mmsegmentation/pull/2703), [#2701](https://github.com/open-mmlab/mmsegmentation/pull/2701), [#2722](https://github.com/open-mmlab/mmsegmentation/pull/2722), [#2733](https://github.com/open-mmlab/mmsegmentation/pull/2733), [#2769](https://github.com/open-mmlab/mmsegmentation/pull/2769), [#2790](https://github.com/open-mmlab/mmsegmentation/pull/2790), [#2798](https://github.com/open-mmlab/mmsegmentation/pull/2798))
- Update and refine various English documentation ([#2715](https://github.com/open-mmlab/mmsegmentation/pull/2715), [#2755](https://github.com/open-mmlab/mmsegmentation/pull/2755), [#2745](https://github.com/open-mmlab/mmsegmentation/pull/2745), [#2797](https://github.com/open-mmlab/mmsegmentation/pull/2797), [#2799](https://github.com/open-mmlab/mmsegmentation/pull/2799), [#2821](https://github.com/open-mmlab/mmsegmentation/pull/2821), [#2827](https://github.com/open-mmlab/mmsegmentation/pull/2827), [#2831](https://github.com/open-mmlab/mmsegmentation/pull/2831))
- Add deeplabv3 model structure documentation ([#2426](https://github.com/open-mmlab/mmsegmentation/pull/2426))
- Add custom metrics documentation ([#2799](https://github.com/open-mmlab/mmsegmentation/pull/2799))
- Add faq in dev-1.x branch ([#2765](https://github.com/open-mmlab/mmsegmentation/pull/2765))
### New Contributors
- @liuruiqiang made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2554
- @wangjiangben-hw made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2569
- @jinxianwei made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2557
- @KKIEEK made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2747
- @Renzhihan made their first contribution in https://github.com/open-mmlab/mmsegmentation/pull/2765
## v1.0.0rc6(03/03/2023)
### Highlights

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docs/en/notes/faq.md
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@ -8,13 +8,14 @@ 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 >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
| 1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
| dev-1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
| main branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | 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 |
| 1.0.0rc4 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc3 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4 \<=3.0.0rc5 |
| 1.0.0rc2 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4 \<=3.0.0rc5 |
| 1.0.0rc3 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc2 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc1 | mmcv >= 2.0.0rc1, \<=2.0.0rc3> | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | Not required |
| 1.0.0rc0 | mmcv >= 2.0.0rc1, \<=2.0.0rc3> | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | Not required |
@ -61,7 +62,7 @@ But in binary segmentation task, there are two solutions:
In summary, to implement binary segmentation methods users should modify below parameters in the `decode_head` and `auxiliary_head` configs. Here is a modification example of [pspnet_unet_s5-d16.py](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/_base_/models/pspnet_unet_s5-d16.py):
- (1) `num_classes=2`, `out_channels=2` and `use_sigmoid=False` in `CrossEntropyLoss`.
- (1) `num_classes=2`, `out_channels=2` and `use_sigmoid=False` in `CrossEntropyLoss`.
```python
decode_head=dict(

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docs/zh_cn/notes/faq.md
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@ -8,13 +8,14 @@
| MMSegmentation version | MMCV version | MMEngine version | MMClassification (optional) version | MMDetection (optional) version |
| :--------------------: | :----------------------------: | :---------------: | :---------------------------------: | :----------------------------: |
| dev-1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
| 1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.5.0 | mmcls>=1.0.0rc0 | mmdet >= 3.0.0rc6 |
| dev-1.x branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | mmdet >= 3.0.0 |
| main branch | mmcv >= 2.0.0rc4 | MMEngine >= 0.7.1 | mmcls==1.0.0rc6 | 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 |
| 1.0.0rc4 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc3 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4 \<=3.0.0rc5 |
| 1.0.0rc2 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4 \<=3.0.0rc5 |
| 1.0.0rc3 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc2 | mmcv == 2.0.0rc3 | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | mmdet>=3.0.0rc4, \<=3.0.0rc5 |
| 1.0.0rc1 | mmcv >= 2.0.0rc1, \<=2.0.0rc3> | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | Not required |
| 1.0.0rc0 | mmcv >= 2.0.0rc1, \<=2.0.0rc3> | MMEngine >= 0.1.0 | mmcls>=1.0.0rc0 | Not required |
@ -112,7 +113,7 @@ if self.reduce_zero_label:
关于您的数据集是否需要使用 reduce_zero_label，有以下两类情况：
- 例如在 [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isprs-potsdam) 数据集上，有 0-不透水面、1-建筑、2-低矮植被、3-树、4-汽车、5-杂乱，六类。但该数据集提供了两种RGB标签，一种为图像边缘处有黑色像素的标签，另一种是没有黑色边缘的标签。对于有黑色边缘的标签，在 [dataset_converters.py](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/tools/dataset_converters/potsdam.py)中，其将黑色边缘转换为 label 0，其余标签分别为 1-不透水面、2-建筑、3-低矮植被、4-树、5-汽车、6-杂乱，那么此时，就应该在数据集 [potsdam.py](https://github.com/open-mmlab/mmsegmentation/blob/ff95416c3b5ce8d62b9289f743531398efce534f/mmseg/datasets/potsdam.py#L23) 中将`reduce_zero_label=True`。如果使用的是没有黑色边缘的标签，那么 mask label 中只有 0-5，此时就应该使`reduce_zero_label=False`。需要结合您的实际情况来使用。
- 例如在第 0 类为background类别的数据集上，如果您最终是需要将背景和您的其余类别分开时，是不需要使用`reduce_zero_label`的，此时在数据集中应该将其设置为`reduce_zero_label=False`
- 例如在 [Potsdam](https://github.com/open-mmlab/mmsegmentation/blob/1.x/docs/en/user_guides/2_dataset_prepare.md#isprs-potsdam) 数据集上，有 0-不透水面、1-建筑、2-低矮植被、3-树、4-汽车、5-杂乱，六类。但该数据集提供了两种 RGB 标签，一种为图像边缘处有黑色像素的标签，另一种是没有黑色边缘的标签。对于有黑色边缘的标签，在 [dataset_converters.py](https://github.com/open-mmlab/mmsegmentation/blob/dev-1.x/tools/dataset_converters/potsdam.py)中，其将黑色边缘转换为 label 0，其余标签分别为 1-不透水面、2-建筑、3-低矮植被、4-树、5-汽车、6-杂乱，那么此时，就应该在数据集 [potsdam.py](https://github.com/open-mmlab/mmsegmentation/blob/ff95416c3b5ce8d62b9289f743531398efce534f/mmseg/datasets/potsdam.py#L23) 中将`reduce_zero_label=True`。如果使用的是没有黑色边缘的标签，那么 mask label 中只有 0-5，此时就应该使`reduce_zero_label=False`。需要结合您的实际情况来使用。
- 例如在第 0 类为 background 类别的数据集上，如果您最终是需要将背景和您的其余类别分开时，是不需要使用`reduce_zero_label`的，此时在数据集中应该将其设置为`reduce_zero_label=False`
**注意:** 使用 `reduce_zero_label` 请确认数据集原始类别个数，如果只有两类，需要关闭 `reduce_zero_label` 即设置 `reduce_zero_label=False`。

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mmseg/version.py
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@ -1,6 +1,6 @@
# Copyright (c) Open-MMLab. All rights reserved.
__version__ = '1.0.0rc6'
__version__ = '1.0.0'
def parse_version_info(version_str):