Bump version to 1.0.0rc5 (#1662)

* Bump version to 1.0.0rc5

* fix

* update

README.md

@@ -69,7 +69,18 @@ The main branch works with **PyTorch 1.6+**.
 
   The toolbox provides a comprehensive set of utilities which can help users assess the performance of models. It includes visualizers which allow visualization of images, ground truths as well as predicted bounding boxes, and a validation tool for evaluating checkpoints during training.  It also includes data converters to demonstrate how to convert your own data to the annotation files which the toolbox supports.
 
-## What's New
+## Latest Updates
+
+v1.0.0rc5 was released in 2023-01-06.
+
+1. Two models, Aster and SVTR, are added to our model zoo. The full implementation of ABCNet is also available now.
+2. Dataset Preparer supports 5 more datasets: CocoTextV2, FUNSD, TextOCR, NAF, SROIE.
+3. We have 4 more text recognition transforms, and two more helper transforms.
+4. The transform, `FixInvalidPolygon`, is getting smarter at dealing with invalid polygons, and now capable of handling more weird annotations. As a result, a complete training cycle on TotalText dataset can be performed bug-free. The weights of DBNet and FCENet pretrained on TotalText are also released.
+
+Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!
+
+## What's New in MMOCR 1.0
 
 1. **New engines**. MMOCR 1.x is based on [MMEngine](https://github.com/open-mmlab/mmengine), which provides a general and powerful runner that allows more flexible customizations and significantly simplifies the entrypoints of high-level interfaces.
 
@@ -87,8 +98,6 @@ The main branch works with **PyTorch 1.6+**.
 
 8. **More models**. MMOCR 1.0 supports more tasks and more state-of-the-art models!
 
-Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!
-
 ## Installation
 
 MMOCR depends on [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) and [MMDetection](https://github.com/open-mmlab/mmdetection).

README_zh-CN.md

@@ -70,7 +70,18 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处
 
 该工具箱提供了一套全面的实用程序，可以帮助用户评估模型的性能。它包括可对图像，标注的真值以及预测结果进行可视化的可视化工具，以及用于在训练过程中评估模型的验证工具。它还包括数据转换器，演示了如何将用户自建的标注数据转换为 MMOCR 支持的标注文件。
 
-## 最新进展
+## 近期更新
+
+最新的版本 v1.0.0rc5 于 2023-01-06 发布。
+
+1. 新增了 Aster 和 SVTR 模型，并完整支持了 ABCNet 的训练与测试；
+2. Dataset Preparer 新支持了5个数据集：CocoTextV2, FUNSD, TextOCR, NAF, SROIE；
+3. 新增了4个文本识别以及2个辅助运行过程的数据变换。
+4. 数据变换 `FixInvalidPolygon` 现在可以处理更多种类的非法多边形，因此各模型如今也可以顺畅地在 TotalText 数据集上完成训练流程。我们同时也发布了 DBNet 和 FCENet 在 TotalText 上的预训练模型。
+
+阅读[更新日志](https://mmocr.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html)以获取更多信息。
+
+## MMOCR 1.0 更新汇总
 
 1. 架构升级：MMOCR 1.x 是基于 [MMEngine](https://github.com/open-mmlab/mmengine)，提供了一个通用的、强大的执行器，允许更灵活的定制，提供了统一的训练和测试入口。
 
@@ -88,8 +99,6 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处
 
 8. 更多新模型：MMOCR 1.0 支持了更多模型和模型种类。
 
-阅读[更新日志](https://mmocr.readthedocs.io/zh_CN/dev-1.x/notes/changelog.html)以获取更多信息。
-
 ## 安装
 
 MMOCR 依赖 [PyTorch](https://pytorch.org/), [MMEngine](https://github.com/open-mmlab/mmengine), [MMCV](https://github.com/open-mmlab/mmcv) 和 [MMDetection](https://github.com/open-mmlab/mmdetection)，以下是安装的简要步骤。

docs/en/get_started/install.md

@@ -194,5 +194,5 @@ MMOCR has different version requirements on MMEngine, MMCV and MMDetection at ea
 | MMOCR          | MMEngine                    | MMCV                       | MMDetection                 |
 | -------------- | --------------------------- | -------------------------- | --------------------------- |
 | dev-1.x        | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
-| 1.0.0rc4       | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
+| 1.0.0rc\[4-5\] | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
 | 1.0.0rc\[0-3\] | 0.0.0 \<= mmengine \< 0.2.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |

docs/en/notes/changelog.md

@@ -1,5 +1,68 @@
 # Changelog of v1.x
 
+## v1.0.0rc5 (06/01/2023)
+
+### Highlights
+
+1. Two models, Aster and SVTR, are added to our model zoo. The full implementation of ABCNet is also available now.
+2. Dataset Preparer supports 5 more datasets: CocoTextV2, FUNSD, TextOCR, NAF, SROIE.
+3. We have 4 more text recognition transforms, and two helper transforms. See https://github.com/open-mmlab/mmocr/pull/1646 https://github.com/open-mmlab/mmocr/pull/1632 https://github.com/open-mmlab/mmocr/pull/1645 for details.
+4. The transform, `FixInvalidPolygon`, is getting smarter at dealing with invalid polygons, and now capable of handling more weird annotations. As a result, a complete training cycle on TotalText dataset can be performed bug-free. The weights of DBNet and FCENet pretrained on TotalText are also released.
+
+### New Features & Enhancements
+
+- Update ic15 det config according to DataPrepare by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1617
+- Refactor icdardataset metainfo to lowercase. by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1620
+- Add ASTER Encoder by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1239
+- Add ASTER decoder by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1625
+- Add ASTER config by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1238
+- Update ASTER config by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1629
+- Support browse_dataset.py to visualize original dataset by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1503
+- Add CocoTextv2 to dataset preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1514
+- Add Funsd to dataset preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1550
+- Add TextOCR to Dataset Preparer by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1543
+- Refine example projects and readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1628
+- Enhance FixInvalidPolygon, add RemoveIgnored transform by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1632
+- ConditionApply by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1646
+- Add NAF to dataset preparer by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1609
+- Add SROIE to dataset preparer by @FerryHuang in https://github.com/open-mmlab/mmocr/pull/1639
+- Add svtr decoder by @willpat1213 in https://github.com/open-mmlab/mmocr/pull/1448
+- Add missing unit tests by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1651
+- Add svtr encoder by @willpat1213 in https://github.com/open-mmlab/mmocr/pull/1483
+- ABCNet train by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1610
+- Totaltext cfgs for DB and FCE by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1633
+- Add Aliases to models by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1611
+- SVTR transforms by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1645
+- Add SVTR framework and configs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1621
+- Issue Template by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1663
+
+### Docs
+
+- Add Chinese translation for browse_dataset.py by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1647
+- updata abcnet doc by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1658
+- update the dbnetpp\`s readme file by @zhuyue66 in https://github.com/open-mmlab/mmocr/pull/1626
+
+### Bug Fixes
+
+- nn.SmoothL1Loss beta can not be zero in PyTorch 1.13 version by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1616
+- ctc loss bug if target is empty by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1618
+- Add torch 1.13 by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1619
+- Remove outdated tutorial link by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1627
+- Dev 1.x  some doc mistakes by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1630
+- Support custom font to visualize some languages (e.g. Korean) by @ProtossDragoon in https://github.com/open-mmlab/mmocr/pull/1567
+- db_module_loss，negative number encountered in sqrt by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1640
+- Use int instead of np.int by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1636
+- Remove support for py3.6 by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1660
+
+### New Contributors
+
+- @zhuyue66 made their first contribution in https://github.com/open-mmlab/mmocr/pull/1626
+- @KevinNuNu made their first contribution in https://github.com/open-mmlab/mmocr/pull/1630
+- @FerryHuang made their first contribution in https://github.com/open-mmlab/mmocr/pull/1639
+- @willpat1213 made their first contribution in https://github.com/open-mmlab/mmocr/pull/1448
+
+**Full Changelog**: https://github.com/open-mmlab/mmocr/compare/v1.0.0rc4...v1.0.0rc5
+
 ## v1.0.0rc4 (06/12/2022)
 
 ### Highlights

docs/zh_cn/get_started/install.md

@@ -195,5 +195,5 @@ docker run --gpus all --shm-size=8g -it -v {实际数据目录}:/mmocr/data mmoc
 | MMOCR          | MMEngine                    | MMCV                       | MMDetection                 |
 | -------------- | --------------------------- | -------------------------- | --------------------------- |
 | dev-1.x        | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
-| 1.0.0rc4       | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
+| 1.0.0rc\[4-5\] | 0.1.0 \<= mmengine \< 1.0.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |
 | 1.0.0rc\[0-3\] | 0.0.0 \<= mmengine \< 0.2.0 | 2.0.0rc1 \<= mmcv \< 2.1.0 | 3.0.0rc0 \<= mmdet \< 3.1.0 |

mmocr/version.py

@@ -1,4 +1,4 @@
 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '1.0.0rc4'
+__version__ = '1.0.0rc5'
 short_version = __version__