Bump version to 1.0.0 (#1686)

* bump version to 1.0.0 * update * update * fix lint * update * update * update changelog * update
open-mmlab · Jul 5, 2023 · ae7a7b7 · ae7a7b7
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diff --git a/.dev_scripts/benchmark_options.py b/.dev_scripts/benchmark_options.py
diff --git a/.dev_scripts/benchmark_regression/bench_test.yml b/.dev_scripts/benchmark_regression/bench_test.yml
@@ -1,7 +1,7 @@
 - Name: convnext-base_32xb128_in1k
 - Name: convnext-v2-atto_fcmae-pre_3rdparty_in1k
 - Name: mobilenet-v2_8xb32_in1k
-- Name: mobilenet-v3-small-050_8xb128_in1k
+- Name: mobilenet-v3-small-050_3rdparty_in1k
 - Name: swin-tiny_16xb64_in1k
 - Name: swinv2-tiny-w8_3rdparty_in1k-256px
 - Name: vit-base-p16_32xb128-mae_in1k

diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
diff --git a/README.md b/README.md
@@ -86,28 +86,25 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351
 
 ## What's new
 
+🌟 v1.0.0 was released in 04/07/2023
+
+- Support inference of more **multi-modal** algorithms, such as [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.
+- Support around **10 multi-modal** datasets!
+- Add [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/) self-supervised learning algorithms.
+- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP with FlexibleRunner](./configs/mae/benchmarks/). Here are the documentation links of [New Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) and [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner).
+
 🌟 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
+Update of previous versions
 
 - Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.
 - Support **RIFormer**, a simple but effective vision backbone by removing token mixer.
-- Add t-SNE visualization.
 - Refactor dataset pipeline visualization.
-
-Update of previous versions
-
 - Support **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.
-- Reproduce the training accuracy of **ConvNeXt** and **RepVGG**.
-- Support confusion matrix calculation and plot.
-- Support **multi-task** training and testing.
-- Support Test-time Augmentation.
-- Upgrade API to get pre-defined models of MMPreTrain.
-- Refactor BEiT backbone and support v1/v2 inference.
 
 This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
 to try according to [the documentation](https://mmpretrain.readthedocs.io/en/latest/).
@@ -224,6 +221,10 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
         <li><a href="configs/levit">LeViT</a></li>
         <li><a href="configs/riformer">RIFormer</a></li>
         <li><a href="configs/glip">GLIP</a></li>
+        <li><a href="configs/sam">ViT SAM</a></li>
+        <li><a href="configs/eva02">EVA02</a></li>
+        <li><a href="configs/dinov2">DINO V2</a></li>
+        <li><a href="configs/hivit">HiViT</a></li>
         </ul>
       </td>
       <td>
@@ -246,6 +247,8 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
         <li><a href="configs/beitv2">BEiT V2 (arXiv'2022)</a></li>
         <li><a href="configs/eva">EVA (CVPR'2023)</a></li>
         <li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
+        <li><a href="configs/itpn">iTPN (CVPR'2023)</a></li>
+        <li><a href="configs/spark">SparK (ICLR'2023)</a></li>
         </ul>
       </td>
       <td>

diff --git a/README_zh-CN.md b/README_zh-CN.md
@@ -84,28 +84,25 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351
 
 ## 更新日志
 
+🌟 2023/7/4 发布了 v1.0.0 版本
+
+- 支持更多**多模态**算法的推理, 例如 [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/) 等。
+- 支持约 **10 个多模态**数据集!
+- 添加自监督学习算法 [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/)。
+- 提供[新配置文件](./mmpretrain/configs/)和 [DeepSpeed/FSDP](./configs/mae/benchmarks/) 的样例。这是[新配置文件](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) 和 [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner) 的文档链接。
+
 🌟 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
+之前版本更新内容
 
 - 整和来自 MMSelfSup 的自监督学习算法，例如 `MAE`, `BEiT` 等
 - 支持了 **RIFormer**，简单但有效的视觉主干网络，却移除了 token mixer
-- 支持 t-SNE 可视化
 - 重构数据管道可视化
-
-之前版本更新内容
-
 - 支持了 **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** 和 **MixMIM** 等骨干网络结构
-- 复现了 ConvNeXt 和 RepVGG 的训练精度。
-- 支持混淆矩阵计算和画图。
-- 支持了 **多任务** 训练和测试。
-- 支持了测试时增强（TTA）。
-- 更新了主要 API 接口，用以方便地获取 MMPreTrain 中预定义的模型。
-- 重构 BEiT 主干网络结构，并支持 v1 和 v2 模型的推理。
 
 这个版本引入一个全新的，可扩展性强的训练和测试引擎，但目前仍在开发中。欢迎根据 [文档](https://mmpretrain.readthedocs.io/zh_CN/latest/) 进行试用。
 
@@ -220,6 +217,10 @@ mim install -e ".[multimodal]"
         <li><a href="configs/levit">LeViT</a></li>
         <li><a href="configs/riformer">RIFormer</a></li>
         <li><a href="configs/glip">GLIP</a></li>
+        <li><a href="configs/sam">ViT SAM</a></li>
+        <li><a href="configs/eva02">EVA02</a></li>
+        <li><a href="configs/dinov2">DINO V2</a></li>
+        <li><a href="configs/hivit">HiViT</a></li>
         </ul>
       </td>
       <td>
@@ -242,6 +243,8 @@ mim install -e ".[multimodal]"
         <li><a href="configs/beitv2">BEiT V2 (arXiv'2022)</a></li>
         <li><a href="configs/eva">EVA (CVPR'2023)</a></li>
         <li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
+        <li><a href="configs/itpn">iTPN (CVPR'2023)</a></li>
+        <li><a href="configs/spark">SparK (ICLR'2023)</a></li>
         </ul>
       </td>
       <td>

diff --git a/configs/mae/benchmarks/vit-huge-p14_8xb128-ds-coslr-50e_in1k.py b/configs/mae/benchmarks/vit-huge-p14_8xb128-ds-coslr-50e_in1k.py
@@ -4,7 +4,6 @@
 optim_wrapper = dict(type='DeepSpeedOptimWrapper')
 
 # training strategy
-# Deepspeed with ZeRO3 + fp16
 strategy = dict(
     type='DeepSpeedStrategy',
     fp16=dict(

diff --git a/configs/mae/benchmarks/vit-large-p16_8xb128-ds-coslr-50e_in1k.py b/configs/mae/benchmarks/vit-large-p16_8xb128-ds-coslr-50e_in1k.py
@@ -4,7 +4,6 @@
 optim_wrapper = dict(type='DeepSpeedOptimWrapper')
 
 # training strategy
-# Deepspeed with ZeRO3 + fp16
 strategy = dict(
     type='DeepSpeedStrategy',
     fp16=dict(

diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md
@@ -1,5 +1,59 @@
 # Changelog (MMPreTrain)
 
+## v1.0.0(04/07/2023)
+
+### Highlights
+
+- Support inference of more **multi-modal** algorithms, such as **LLaVA**, **MiniGPT-4**, **Otter**, etc.
+- Support around **10 multi-modal datasets**!
+- Add **iTPN**, **SparK** self-supervised learning algorithms.
+- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP](./configs/mae/benchmarks/).
+
+### New Features
+
+- Transfer shape-bias tool from mmselfsup ([#1658](https://github.com/open-mmlab/mmpretrain/pull/1685))
+- Download dataset by using MIM&OpenDataLab ([#1630](https://github.com/open-mmlab/mmpretrain/pull/1630))
+- Support New Configs ([#1639](https://github.com/open-mmlab/mmpretrain/pull/1639), [#1647](https://github.com/open-mmlab/mmpretrain/pull/1647), [#1665](https://github.com/open-mmlab/mmpretrain/pull/1665))
+- Support Flickr30k Retrieval dataset ([#1625](https://github.com/open-mmlab/mmpretrain/pull/1625))
+- Support SparK ([#1531](https://github.com/open-mmlab/mmpretrain/pull/1531))
+- Support LLaVA ([#1652](https://github.com/open-mmlab/mmpretrain/pull/1652))
+- Support Otter ([#1651](https://github.com/open-mmlab/mmpretrain/pull/1651))
+- Support MiniGPT-4 ([#1642](https://github.com/open-mmlab/mmpretrain/pull/1642))
+- Add support for VizWiz dataset ([#1636](https://github.com/open-mmlab/mmpretrain/pull/1636))
+- Add support for vsr dataset ([#1634](https://github.com/open-mmlab/mmpretrain/pull/1634))
+- Add InternImage Classification project ([#1569](https://github.com/open-mmlab/mmpretrain/pull/1569))
+- Support OCR-VQA dataset ([#1621](https://github.com/open-mmlab/mmpretrain/pull/1621))
+- Support OK-VQA dataset ([#1615](https://github.com/open-mmlab/mmpretrain/pull/1615))
+- Support TextVQA dataset ([#1569](https://github.com/open-mmlab/mmpretrain/pull/1569))
+- Support iTPN and HiViT ([#1584](https://github.com/open-mmlab/mmpretrain/pull/1584))
+- Add retrieval mAP metric ([#1552](https://github.com/open-mmlab/mmpretrain/pull/1552))
+- Support NoCap dataset based on BLIP. ([#1582](https://github.com/open-mmlab/mmpretrain/pull/1582))
+- Add GQA dataset ([#1585](https://github.com/open-mmlab/mmpretrain/pull/1585))
+
+### Improvements
+
+- Update fsdp vit-huge and vit-large config ([#1675](https://github.com/open-mmlab/mmpretrain/pull/1675))
+- Support deepspeed with flexible runner ([#1673](https://github.com/open-mmlab/mmpretrain/pull/1673))
+- Update Otter and LLaVA docs and config. ([#1653](https://github.com/open-mmlab/mmpretrain/pull/1653))
+- Add image_only param of ScienceQA ([#1613](https://github.com/open-mmlab/mmpretrain/pull/1613))
+- Support to use "split" to specify training set/validation ([#1535](https://github.com/open-mmlab/mmpretrain/pull/1535))
+
+### Bug Fixes
+
+- Refactor \_prepare_pos_embed in ViT ([#1656](https://github.com/open-mmlab/mmpretrain/pull/1656)， [#1679](https://github.com/open-mmlab/mmpretrain/pull/1679))
+- Freeze pre norm in vision transformer ([#1672](https://github.com/open-mmlab/mmpretrain/pull/1672))
+- Fix bug loading IN1k dataset ([#1641](https://github.com/open-mmlab/mmpretrain/pull/1641))
+- Fix sam bug ([#1633](https://github.com/open-mmlab/mmpretrain/pull/1633))
+- Fixed circular import error for new transform ([#1609](https://github.com/open-mmlab/mmpretrain/pull/1609))
+- Update torchvision transform wrapper ([#1595](https://github.com/open-mmlab/mmpretrain/pull/1595))
+- Set default out_type in CAM visualization ([#1586](https://github.com/open-mmlab/mmpretrain/pull/1586))
+
+### Docs Update
+
+- Fix spelling ([#1681](https://github.com/open-mmlab/mmpretrain/pull/1681))
+- Fix doc typos ([#1671](https://github.com/open-mmlab/mmpretrain/pull/1671), [#1644](https://github.com/open-mmlab/mmpretrain/pull/1644), [#1629](https://github.com/open-mmlab/mmpretrain/pull/1629))
+- Add t-SNE visualization doc ([#1555](https://github.com/open-mmlab/mmpretrain/pull/1555))
+
 ## v1.0.0rc8(22/05/2023)
 
 ### Highlights

diff --git 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   |
   | :----------------: | :---------------: | :--------------: |
-  |  1.0.0rc8 (main)   | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
+  |    1.0.0 (main)    | mmengine >= 0.8.0 |  mmcv >= 2.0.0   |
+  |      1.0.0rc8      | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
   |      1.0.0rc7      | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
 
   ```{note}

diff --git 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     |
   | :-------------: | :---------------: | :--------------: |
-  | 1.0.0rc8 (main) | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
+  |  1.0.0 (main)   | mmengine >= 0.8.0 |  mmcv >= 2.0.0   |
+  |    1.0.0rc8     | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
   |    1.0.0rc7     | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
 
   ```{note}

diff --git a/mmpretrain/__init__.py b/mmpretrain/__init__.py
@@ -6,11 +6,11 @@
 from .apis import *  # noqa: F401, F403
 from .version import __version__
 
-mmcv_minimum_version = '2.0.0rc4'
+mmcv_minimum_version = '2.0.0'
 mmcv_maximum_version = '2.1.0'
 mmcv_version = digit_version(mmcv.__version__)
 
-mmengine_minimum_version = '0.7.3'
+mmengine_minimum_version = '0.8.0'
 mmengine_maximum_version = '1.0.0'
 mmengine_version = digit_version(mmengine.__version__)
 

diff --git a/mmpretrain/version.py b/mmpretrain/version.py
@@ -1,6 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved
 
-__version__ = '1.0.0rc8'
+__version__ = '1.0.0'
 
 
 def parse_version_info(version_str):

diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt
@@ -1,2 +1,2 @@
-mmcv>=2.0.0rc4,<2.1.0
-mmengine>=0.7.3,<1.0.0
+mmcv>=2.0.0,<2.1.0
+mmengine>=0.8.0,<1.0.0