Bump version to v1.0.0rc8 (#1583)

* Bump version to v1.0.0rc8 * Apply suggestions from code review Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com> * Update README.md --------- Co-authored-by: Yixiao Fang <36138628+fangyixiao18@users.noreply.github.com>
2023-05-23 11:22:51 +08:00 · 2023-05-23 11:22:51 +08:00 · 4dd8a86145
parent be389eb846
commit 4dd8a86145
10 changed files with 94 additions and 9 deletions
--- a/README.md
+++ b/README.md
@ -86,6 +86,12 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351

 ## What's new

+🌟 v1.0.0rc8 was released in 22/05/2023
+
+- Support multiple **multi-modal** algorithms and inferencers. You can explore these features by the [gradio demo](https://github.com/open-mmlab/mmpretrain/tree/main/projects/gradio_demo)!
+- Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones.
+- Register torchvision transforms into MMPretrain, you can now easily integrate torchvision's data augmentations in MMPretrain. See [the doc](https://mmpretrain.readthedocs.io/en/latest/api/data_process.html#torchvision-transforms)
+
 🌟 v1.0.0rc7 was released in 07/04/2023

 - Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.
@ -160,6 +166,9 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
      <td>
        <b>Self-supervised Learning</b>
      </td>
+      <td>
+        <b>Multi-Modality Algorithms</b>
+      </td>
      <td>
        <b>Others</b>
      </td>
@ -239,6 +248,15 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
        <li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
        </ul>
      </td>
+      <td>
+        <ul>
+        <li><a href="configs/blip">BLIP (arxiv'2022)</a></li>
+        <li><a href="configs/blip2">BLIP-2 (arxiv'2023)</a></li>
+        <li><a href="configs/ofa">OFA (CoRR'2022)</a></li>
+        <li><a href="configs/flamingo">Flamingo (NeurIPS'2022)</a></li>
+        <li><a href="configs/chinese_clip">Chinese CLIP (arxiv'2022)</a></li>
+        </ul>
+      </td>
      <td>
      Image Retrieval Task:
        <ul>
--- a/README_zh-CN.md
+++ b/README_zh-CN.md
@ -84,6 +84,12 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351

 ## 更新日志

+🌟 2023/5/22 发布了 v1.0.0rc8 版本
+
+- 支持多种多模态算法和推理器。您可以通过 [gradio demo](https://github.com/open-mmlab/mmpretrain/tree/main/projects/gradio_demo) 探索这些功能!
+- 新增 EVA-02，Dino-V2，ViT-SAM 和 GLIP 主干网络。
+- 将 torchvision 变换注册到 MMPretrain，现在您可以轻松地将 torchvision 的数据增强集成到 MMPretrain 中。
+
 🌟 2023/4/7 发布了 v1.0.0rc7 版本

 - 整和来自 MMSelfSup 的自监督学习算法，例如 `MAE`, `BEiT` 等
@ -157,6 +163,9 @@ mim install -e ".[multimodal]"
      <td>
        <b>自监督学习</b>
      </td>
+      <td>
+        <b>多模态算法</b>
+      </td>
      <td>
        <b>其它</b>
      </td>
@ -235,6 +244,15 @@ mim install -e ".[multimodal]"
        <li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
        </ul>
      </td>
+      <td>
+        <ul>
+        <li><a href="configs/blip">BLIP (arxiv'2022)</a></li>
+        <li><a href="configs/blip2">BLIP-2 (arxiv'2023)</a></li>
+        <li><a href="configs/ofa">OFA (CoRR'2022)</a></li>
+        <li><a href="configs/flamingo">Flamingo (NeurIPS'2022)</a></li>
+        <li><a href="configs/chinese_clip">Chinese CLIP (arxiv'2022)</a></li>
+        </ul>
+      </td>
      <td>
      图像检索任务：
        <ul>
--- a/docker/serve/Dockerfile
+++ b/docker/serve/Dockerfile
@ -3,7 +3,7 @@ ARG CUDA="11.3"
 ARG CUDNN="8"
 FROM pytorch/torchserve:latest-gpu

-ARG MMPRE="1.0.0rc5"
+ARG MMPRE="1.0.0rc8"

 ENV PYTHONUNBUFFERED TRUE

--- a/docs/en/get_started.md
+++ b/docs/en/get_started.md
@ -63,7 +63,7 @@ pip install -U openmim && mim install -e .
 Just install with mim.

 ```shell
-pip install -U openmim && mim install "mmpretrain>=1.0.0rc7"
+pip install -U openmim && mim install "mmpretrain>=1.0.0rc8"
 ```

 ```{note}
@ -80,7 +80,7 @@ can add `[multimodal]` during the installation. For example:
 mim install -e ".[multimodal]"

 # Install as a Python package
-mim install "mmpretrain[multimodal]>=1.0.0rc7"
+mim install "mmpretrain[multimodal]>=1.0.0rc8"
 ```

 ## Verify the installation
--- a/docs/en/notes/changelog.md
+++ b/docs/en/notes/changelog.md
@ -1,5 +1,52 @@
 # Changelog (MMPreTrain)

+## v1.0.0rc8(22/05/2023)
+
+### Highlights
+
+- Support multiple multi-modal algorithms and inferencers. You can explore these features by the [gradio demo](https://github.com/open-mmlab/mmpretrain/tree/main/projects/gradio_demo)!
+- Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones.
+- Register torchvision transforms into MMPretrain, you can now easily integrate torchvision's data augmentations in MMPretrain.
+
+### New Features
+
+- Support Chinese CLIP. ([#1576](https://github.com/open-mmlab/mmpretrain/pull/1576))
+- Add ScienceQA Metrics ([#1577](https://github.com/open-mmlab/mmpretrain/pull/1577))
+- Support multiple multi-modal algorithms and inferencers. ([#1561](https://github.com/open-mmlab/mmpretrain/pull/1561))
+- add eva02 backbone ([#1450](https://github.com/open-mmlab/mmpretrain/pull/1450))
+- Support dinov2 backbone ([#1522](https://github.com/open-mmlab/mmpretrain/pull/1522))
+- Support some downstream classification datasets. ([#1467](https://github.com/open-mmlab/mmpretrain/pull/1467))
+- Support GLIP ([#1308](https://github.com/open-mmlab/mmpretrain/pull/1308))
+- Register torchvision transforms into mmpretrain ([#1265](https://github.com/open-mmlab/mmpretrain/pull/1265))
+- Add ViT of SAM ([#1476](https://github.com/open-mmlab/mmpretrain/pull/1476))
+
+### Improvements
+
+- [Refactor] Support to freeze channel reduction and add layer decay function ([#1490](https://github.com/open-mmlab/mmpretrain/pull/1490))
+- [Refactor] Support resizing pos_embed while loading ckpt and format output ([#1488](https://github.com/open-mmlab/mmpretrain/pull/1488))
+
+### Bug Fixes
+
+- Fix scienceqa ([#1581](https://github.com/open-mmlab/mmpretrain/pull/1581))
+- Fix config of beit ([#1528](https://github.com/open-mmlab/mmpretrain/pull/1528))
+- Incorrect stage freeze on RIFormer Model ([#1573](https://github.com/open-mmlab/mmpretrain/pull/1573))
+- Fix ddp bugs caused by `out_type`. ([#1570](https://github.com/open-mmlab/mmpretrain/pull/1570))
+- Fix multi-task-head loss potential bug ([#1530](https://github.com/open-mmlab/mmpretrain/pull/1530))
+- Support bce loss without batch augmentations ([#1525](https://github.com/open-mmlab/mmpretrain/pull/1525))
+- Fix clip generator init bug ([#1518](https://github.com/open-mmlab/mmpretrain/pull/1518))
+- Fix the bug in binary cross entropy loss ([#1499](https://github.com/open-mmlab/mmpretrain/pull/1499))
+
+### Docs Update
+
+- Update PoolFormer citation to CVPR version ([#1505](https://github.com/open-mmlab/mmpretrain/pull/1505))
+- Refine Inference Doc ([#1489](https://github.com/open-mmlab/mmpretrain/pull/1489))
+- Add doc for usage of confusion matrix ([#1513](https://github.com/open-mmlab/mmpretrain/pull/1513))
+- Update MMagic link ([#1517](https://github.com/open-mmlab/mmpretrain/pull/1517))
+- Fix example_project README ([#1575](https://github.com/open-mmlab/mmpretrain/pull/1575))
+- Add NPU support page ([#1481](https://github.com/open-mmlab/mmpretrain/pull/1481))
+- train cfg: Removed old description ([#1473](https://github.com/open-mmlab/mmpretrain/pull/1473))
+- Fix typo in MultiLabelDataset docstring ([#1483](https://github.com/open-mmlab/mmpretrain/pull/1483))
+
 ## v1.0.0rc7(07/04/2023)

 ### Highlights
--- a/docs/en/notes/faq.md
+++ b/docs/en/notes/faq.md
@ -16,7 +16,8 @@ and make sure you fill in all required information in the template.

  | MMPretrain version | MMEngine version  |   MMCV version   |
  | :----------------: | :---------------: | :--------------: |
-  |  1.0.0rc7 (main)   | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
+  |  1.0.0rc8 (main)   | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
+  |      1.0.0rc7      | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |

  ```{note}
  Since the `dev` branch is under frequent development, the MMEngine and MMCV
--- a/docs/zh_CN/get_started.md
+++ b/docs/zh_CN/get_started.md
@ -67,7 +67,7 @@ pip install -U openmim && mim install -e .
 直接使用 mim 安装即可。

 ```shell
-pip install -U openmim && mim install "mmpretrain>=1.0.0rc7"
+pip install -U openmim && mim install "mmpretrain>=1.0.0rc8"
 ```

 ```{note}
@ -83,7 +83,7 @@ MMPretrain 中的多模态模型需要额外的依赖项，要安装这些依赖
 mim install -e ".[multimodal]"

 # 作为 Python 包安装
-mim install "mmpretrain[multimodal]>=1.0.0rc7"
+mim install "mmpretrain[multimodal]>=1.0.0rc8"
 ```

 ## 验证安装
--- a/docs/zh_CN/notes/faq.md
+++ b/docs/zh_CN/notes/faq.md
@ -13,7 +13,8 @@

  | MMPretrain 版本 |   MMEngine 版本   |    MMCV 版本     |
  | :-------------: | :---------------: | :--------------: |
-  | 1.0.0rc7 (main) | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
+  | 1.0.0rc8 (main) | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
+  |    1.0.0rc7     | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |

  ```{note}
  由于 `dev` 分支处于频繁开发中，MMEngine 和 MMCV 版本依赖可能不准确。如果您在使用
--- a/mmpretrain/init.py
+++ b/mmpretrain/init.py
@ -10,7 +10,7 @@ mmcv_minimum_version = '2.0.0rc4'
 mmcv_maximum_version = '2.1.0'
 mmcv_version = digit_version(mmcv.__version__)

-mmengine_minimum_version = '0.5.0'
+mmengine_minimum_version = '0.7.1'
 mmengine_maximum_version = '1.0.0'
 mmengine_version = digit_version(mmengine.__version__)

--- a/mmpretrain/version.py
+++ b/mmpretrain/version.py
@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved

-__version__ = '1.0.0rc7'
+__version__ = '1.0.0rc8'


 def parse_version_info(version_str):