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Bump version to 1.0.0rc6 (#1763) 

* Bump version to 1.0.0rc6

* fix

* update changelog

* fix

* fix
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Tong Gao 2023-03-07 20:22:54 +08:00 committed by GitHub
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README.md
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@ -54,6 +54,24 @@ English | [简体中文](README_zh-CN.md)
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a> <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
</div> </div>
## Latest Updates
**The default branch has been switched to `1.x` from `main`, and we encourage
users to migrate to the latest version, though it comes with some cost. Please refer to [Migration Guide](https://mmocr.readthedocs.io/en/dev-1.x/migration/overview.html) for more
details.**
v1.0.0rc6 was released in 2023-03-07.
1. Two new models, ABCNet v2 (inference only) and SPTS are added to `projects/` folder.
2. Announcing `Inferencer`, a unified inference interface in OpenMMLab for everyone's easy access and quick inference with all the pre-trained weights. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/inference.html)
3. Users can use test-time augmentation for text recognition tasks. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/train_test.html#test-time-augmentation)
4. Support [batch augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf) through [`BatchAugSampler`](https://github.com/open-mmlab/mmocr/pull/1757), which is a technique used in SPTS.
5. Dataset Preparer has been refactored to allow more flexible configurations. Besides, users are now able to prepare text recognition datasets in LMDB formats. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/data_prepare/dataset_preparer.html#lmdb-format)
6. Some textspotting datasets have been revised to enhance the correctness and consistency with the common practice.
7. Potential spurious warnings from `shapely` have been eliminated.
Read [Changelog](https://mmocr.readthedocs.io/en/dev-1.x/notes/changelog.html) for more details!
## Introduction ## Introduction
MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the [OpenMMLab](https://openmmlab.com/) project. MMOCR is an open-source toolbox based on PyTorch and mmdetection for text detection, text recognition, and the corresponding downstream tasks including key information extraction. It is part of the [OpenMMLab](https://openmmlab.com/) project.
@ -82,21 +100,6 @@ 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. 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.
## Latest Updates
**The default branch has been switched to `1.x` from `main`, and we encourage
users to migrate to the latest version, though it comes with some cost. Please refer to [Migration Guide](https://mmocr.readthedocs.io/en/dev-1.x/migration/overview.html) for more
details.**
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 ## 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. 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.

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@ -41,6 +41,28 @@
</div> </div>
## 近期更新
**默认分支已经从 `main` 切换到 `1.x`。我们鼓励用户迁移到最新版本，请参考 [迁移指南](https://mmocr.readthedocs.io/zh_CN/dev-1.x/migration/overview.html) 以了解更多细节。**
最新的版本 v1.0.0rc6 于 2023-03-07 发布。
1. 在 `projects/` 目录中新增了 ABCNet v2 (仅支持推理) 和 SPTS 模型；
2. 新增统一推理接口 `Inferencer`，用户可以方便直接地进行各任务的推理。[文档](https://mmocr.readthedocs.io/zh_CN/dev-1.x/user_guides/inference.html)
3. 支持了文本识别任务的测试时数据增强。[文档](https://mmocr.readthedocs.io/zh_CN/dev-1.x/user_guides/train_test.html#id15)
4. 通过 [`BatchAugSampler`](https://github.com/open-mmlab/mmocr/pull/1757) 支持了 [batch augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf) ，这是 SPTS 中使用的一种技巧。
5. 重构了 Dataset Preparer ，用户现在可以更灵活地配置数据集的预处理流程。除此之外，用户现在也可以将文本识别数据集转换为 LMDB 格式。[文档](https://mmocr.readthedocs.io/zh_CN/dev-1.x/user_guides/data_prepare/dataset_preparer.html#lmdb)
6. 修正了一些端到端数据集的标注，保证了数据集的正确性及与常见实践的一致性。
7. 减少了 `shapely` 中可能出现的一些错误警告。
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。
## 简介 ## 简介
MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检测，文本识别以及相应的下游任务，如关键信息提取。 它是 OpenMMLab 项目的一部分。 MMOCR 是基于 PyTorch 和 mmdetection 的开源工具箱，专注于文本检测，文本识别以及相应的下游任务，如关键信息提取。 它是 OpenMMLab 项目的一部分。
@ -69,19 +91,6 @@ MMOCR 的模块化设计使用户可以定义自己的优化器，数据预处
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。 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。
## 近期更新
**默认分支已经从 `main` 切换到 `1.x`。我们鼓励用户迁移到最新版本，请参考 [迁移指南](https://mmocr.readthedocs.io/zh_CN/dev-1.x/migration/overview.html) 以了解更多细节。**
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 ## 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。 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。

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@ -237,5 +237,6 @@ MMOCR has different version requirements on MMEngine, MMCV and MMDetection at ea
| MMOCR | MMEngine | MMCV | MMDetection | | MMOCR | MMEngine | MMCV | MMDetection |
| -------------- | --------------------------- | -------------------------- | --------------------------- | | -------------- | --------------------------- | -------------------------- | --------------------------- |
| dev-1.x | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= mmdet \< 3.1.0 | | dev-1.x | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= mmdet \< 3.1.0 |
| 1.0.0rc6 | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= 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\[4-5\] | 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 | | 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 |

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@ -1,5 +1,82 @@
# Changelog of v1.x # Changelog of v1.x
## v1.0.0rc5 (07/03/2023)
### Highlights
1. Two new models, ABCNet v2 (inference only) and SPTS are added to `projects/` folder.
2. Announcing `Inferencer`, a unified inference interface in OpenMMLab for everyone's easy access and quick inference with all the pre-trained weights. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/inference.html)
3. Users can use test-time augmentation for text recognition tasks. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/train_test.html#test-time-augmentation)
4. Support [batch augmentation](https://openaccess.thecvf.com/content_CVPR_2020/papers/Hoffer_Augment_Your_Batch_Improving_Generalization_Through_Instance_Repetition_CVPR_2020_paper.pdf) through [`BatchAugSampler`](https://github.com/open-mmlab/mmocr/pull/1757), which is a technique used in SPTS.
5. Dataset Preparer has been refactored to allow more flexible configurations. Besides, users are now able to prepare text recognition datasets in LMDB formats. [Docs](https://mmocr.readthedocs.io/en/dev-1.x/user_guides/data_prepare/dataset_preparer.html#lmdb-format)
6. Some textspotting datasets have been revised to enhance the correctness and consistency with the common practice.
7. Potential spurious warnings from `shapely` have been eliminated.
### Dependency
This version requires MMEngine >= 0.6.0, MMCV >= 2.0.0rc4 and MMDet >= 3.0.0rc5.
### New Features & Enhancements
- Discard deprecated lmdb dataset format and only support img+label now by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1681
- abcnetv2 inference by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1657
- Add RepeatAugSampler by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1678
- SPTS by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1696
- Refactor Inferencers by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1608
- Dynamic return type for rescale_polygons by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1702
- Revise upstream version limit by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1703
- TextRecogCropConverter add crop with opencv warpPersepective function by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1667
- change cudnn benchmark to false by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1705
- Add ST-pretrained DB-series models and logs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1635
- Only keep meta and state_dict when publish model by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1729
- Rec TTA by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1401
- Speedup formatting by replacing np.transpose with torch… by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1719
- Support auto import modules from registry. by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1731
- Support batch visualization & dumping in Inferencer by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1722
- add a new argument font_properties to set a specific font file in order to draw Chinese characters properly by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1709
- Refactor data converter and gather by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1707
- Support batch augmentation through BatchAugSampler by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1757
- Put all registry into registry.py by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1760
- train by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1756
- configs for regression benchmark by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1755
- Support lmdb format in Dataset Preparer by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1762
### Docs
- update the link of DBNet by @AllentDan in https://github.com/open-mmlab/mmocr/pull/1672
- Add notice for default branch switching by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1693
- docs: Add twitter discord medium youtube link by @vansin in https://github.com/open-mmlab/mmocr/pull/1724
- Remove unsupported datasets in docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1670
### Bug Fixes
- Update dockerfile by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1671
- Explicitly create np object array for compatibility by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1691
- Fix a minor error in docstring by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1685
- Fix lint by @triple-Mu in https://github.com/open-mmlab/mmocr/pull/1694
- Fix LoadOCRAnnotation ut by @Harold-lkk in https://github.com/open-mmlab/mmocr/pull/1695
- Fix isort pre-commit error by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1697
- Update owners by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1699
- Detect intersection before using shapley.intersection to eliminate spurious warnings by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1710
- Fix some inferencer bugs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1706
- Fix textocr ignore flag by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1712
- Add missing softmax in ASTER forward_test by @Mountchicken in https://github.com/open-mmlab/mmocr/pull/1718
- Fix head in readme by @vansin in https://github.com/open-mmlab/mmocr/pull/1727
- Fix some browse dataset script bugs and draw textdet gt instance with ignore flags by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1701
- icdar textrecog ann parser skip data with ignore flag by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1708
- bezier_to_polygon -> bezier2polygon by @double22a in https://github.com/open-mmlab/mmocr/pull/1739
- Fix docs recog CharMetric P/R error definition by @KevinNuNu in https://github.com/open-mmlab/mmocr/pull/1740
- Remove outdated resources in demo/ by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1747
- Fix wrong ic13 textspotting split data; add lexicons to ic13, ic15 and totaltext by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1758
- SPTS readme by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1761
### New Contributors
- @triple-Mu made their first contribution in https://github.com/open-mmlab/mmocr/pull/1694
- @double22a made their first contribution in https://github.com/open-mmlab/mmocr/pull/1739
**Full Changelog**: https://github.com/open-mmlab/mmocr/compare/v1.0.0rc5...v1.0.0rc6
## v1.0.0rc5 (06/01/2023) ## v1.0.0rc5 (06/01/2023)
### Highlights ### Highlights
@ -41,6 +118,7 @@
- Add Chinese translation for browse_dataset.py by @xinke-wang in https://github.com/open-mmlab/mmocr/pull/1647 - 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 - 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 - update the dbnetpp\`s readme file by @zhuyue66 in https://github.com/open-mmlab/mmocr/pull/1626
- Inferencer docs by @gaotongxiao in https://github.com/open-mmlab/mmocr/pull/1744
### Bug Fixes ### Bug Fixes

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@ -236,5 +236,6 @@ docker run --gpus all --shm-size=8g -it -v {实际数据目录}:/mmocr/data mmoc
| MMOCR | MMEngine | MMCV | MMDetection | | MMOCR | MMEngine | MMCV | MMDetection |
| -------------- | --------------------------- | -------------------------- | --------------------------- | | -------------- | --------------------------- | -------------------------- | --------------------------- |
| dev-1.x | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= mmdet \< 3.1.0 | | dev-1.x | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= mmdet \< 3.1.0 |
| 1.0.0rc6 | 0.6.0 \<= mmengine \< 1.0.0 | 2.0.0rc4 \<= mmcv \< 2.1.0 | 3.0.0rc5 \<= 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\[4-5\] | 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 | | 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 |

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# Copyright (c) Open-MMLab. All rights reserved. # Copyright (c) Open-MMLab. All rights reserved.
__version__ = '1.0.0rc5' __version__ = '1.0.0rc6'
short_version = __version__ short_version = __version__