bumpv1.0.0 (open-mmlab#2849)

xiexinch · web-flow · commit 892f9e19e747 · 2023-04-06T17:24:57.000+08:00
diff --git a/README.md b/README.md
@@ -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
 
diff --git 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 v1.0.0rc6 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
diff --git a/docker/Dockerfile b/docker/Dockerfile
@@ -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
 
diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
@@ -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
 
diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
@@ -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
diff --git a/docs/en/notes/faq.md b/docs/en/notes/faq.md
@@ -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(
diff --git a/docs/zh_cn/notes/faq.md b/docs/zh_cn/notes/faq.md
@@ -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`。
diff --git a/mmseg/version.py b/mmseg/version.py
@@ -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):
