Bump version to 1.0.0 (#1686)
* bump version to 1.0.0 * update * update * fix lint * update * update * update changelog * updatepull/1689/head 1.0.0
Yixiao Fang
GitHub
parent
0d80ab4650
commit
ae7a7b7560
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@ -1,10 +0,0 @@
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third_part_libs = [
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'pip install -r ../requirements.txt',
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]
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default_floating_range = 0.2
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model_floating_ranges = {
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'blip/blip-base_8xb32_retrieval.py': 1.0,
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'blip2/blip2-opt2.7b_8xb32_caption.py': 1.0,
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'ofa/ofa-base_finetuned_caption.py': 1.0,
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}
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@ -1,7 +1,7 @@
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- Name: convnext-base_32xb128_in1k
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- Name: convnext-v2-atto_fcmae-pre_3rdparty_in1k
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- Name: mobilenet-v2_8xb32_in1k
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- Name: mobilenet-v3-small-050_8xb128_in1k
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- Name: mobilenet-v3-small-050_3rdparty_in1k
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- Name: swin-tiny_16xb64_in1k
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- Name: swinv2-tiny-w8_3rdparty_in1k-256px
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- Name: vit-base-p16_32xb128-mae_in1k
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@ -1,22 +0,0 @@
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name: deploy
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on: push
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jobs:
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build-n-publish:
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runs-on: ubuntu-latest
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if: startsWith(github.event.ref, 'refs/tags')
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python 3.7
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uses: actions/setup-python@v4
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with:
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python-version: 3.7
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- name: Build MMPretrain
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run: |
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pip install wheel
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python setup.py sdist bdist_wheel
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- name: Publish distribution to PyPI
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run: |
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pip install twine
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twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}
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25
README.md
25
README.md
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@ -86,28 +86,25 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351
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## What's new
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🌟 v1.0.0 was released in 04/07/2023
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- Support inference of more **multi-modal** algorithms, such as [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.
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- Support around **10 multi-modal** datasets!
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- Add [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/) self-supervised learning algorithms.
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- 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).
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🌟 v1.0.0rc8 was released in 22/05/2023
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- 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)!
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- Add EVA-02, Dino-V2, ViT-SAM and GLIP backbones.
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- 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)
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🌟 v1.0.0rc7 was released in 07/04/2023
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Update of previous versions
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- Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.
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- Support **RIFormer**, a simple but effective vision backbone by removing token mixer.
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- Add t-SNE visualization.
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- Refactor dataset pipeline visualization.
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Update of previous versions
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- Support **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.
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- Reproduce the training accuracy of **ConvNeXt** and **RepVGG**.
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- Support confusion matrix calculation and plot.
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- Support **multi-task** training and testing.
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- Support Test-time Augmentation.
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- Upgrade API to get pre-defined models of MMPreTrain.
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- Refactor BEiT backbone and support v1/v2 inference.
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This release introduced a brand new and flexible training & test engine, but it's still in progress. Welcome
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to try according to [the documentation](https://mmpretrain.readthedocs.io/en/latest/).
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@ -224,6 +221,10 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
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<li><a href="configs/levit">LeViT</a></li>
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<li><a href="configs/riformer">RIFormer</a></li>
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<li><a href="configs/glip">GLIP</a></li>
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<li><a href="configs/sam">ViT SAM</a></li>
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<li><a href="configs/eva02">EVA02</a></li>
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<li><a href="configs/dinov2">DINO V2</a></li>
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<li><a href="configs/hivit">HiViT</a></li>
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</ul>
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</td>
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<td>
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@ -246,6 +247,8 @@ Results and models are available in the [model zoo](https://mmpretrain.readthedo
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<li><a href="configs/beitv2">BEiT V2 (arXiv'2022)</a></li>
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<li><a href="configs/eva">EVA (CVPR'2023)</a></li>
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<li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
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<li><a href="configs/itpn">iTPN (CVPR'2023)</a></li>
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<li><a href="configs/spark">SparK (ICLR'2023)</a></li>
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</ul>
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</td>
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<td>
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@ -84,28 +84,25 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351
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## 更新日志
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🌟 2023/7/4 发布了 v1.0.0 版本
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- 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。
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- 支持约 **10 个多模态**数据集!
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- 添加自监督学习算法 [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/)。
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- 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。
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🌟 2023/5/22 发布了 v1.0.0rc8 版本
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- 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!
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- 新增 EVA-02，Dino-V2，ViT-SAM 和 GLIP 主干网络。
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- 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。
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🌟 2023/4/7 发布了 v1.0.0rc7 版本
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之前版本更新内容
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- 整和来自 MMSelfSup 的自监督学习算法，例如 `MAE`, `BEiT` 等
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- 支持了 **RIFormer**，简单但有效的视觉主干网络，却移除了 token mixer
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- 支持 t-SNE 可视化
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- 重构数据管道可视化
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之前版本更新内容
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- 支持了 **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** 和 **MixMIM** 等骨干网络结构
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- 复现了 ConvNeXt 和 RepVGG 的训练精度。
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- 支持混淆矩阵计算和画图。
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- 支持了 **多任务** 训练和测试。
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- 支持了测试时增强（TTA）。
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- 更新了主要 API 接口，用以方便地获取 MMPreTrain 中预定义的模型。
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- 重构 BEiT 主干网络结构，并支持 v1 和 v2 模型的推理。
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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。
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@ -220,6 +217,10 @@ mim install -e ".[multimodal]"
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<li><a href="configs/levit">LeViT</a></li>
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<li><a href="configs/riformer">RIFormer</a></li>
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<li><a href="configs/glip">GLIP</a></li>
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<li><a href="configs/sam">ViT SAM</a></li>
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<li><a href="configs/eva02">EVA02</a></li>
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<li><a href="configs/dinov2">DINO V2</a></li>
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<li><a href="configs/hivit">HiViT</a></li>
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</ul>
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</td>
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<td>
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@ -242,6 +243,8 @@ mim install -e ".[multimodal]"
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<li><a href="configs/beitv2">BEiT V2 (arXiv'2022)</a></li>
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<li><a href="configs/eva">EVA (CVPR'2023)</a></li>
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<li><a href="configs/mixmim">MixMIM (arXiv'2022)</a></li>
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<li><a href="configs/itpn">iTPN (CVPR'2023)</a></li>
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<li><a href="configs/spark">SparK (ICLR'2023)</a></li>
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</ul>
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</td>
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<td>
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@ -4,7 +4,6 @@ _base_ = ['./vit-huge-p14_8xb128-coslr-50e_in1k.py']
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optim_wrapper = dict(type='DeepSpeedOptimWrapper')
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# training strategy
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# Deepspeed with ZeRO3 + fp16
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strategy = dict(
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type='DeepSpeedStrategy',
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fp16=dict(
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@ -4,7 +4,6 @@ _base_ = ['./vit-large-p16_8xb128-coslr-50e_in1k.py']
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optim_wrapper = dict(type='DeepSpeedOptimWrapper')
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# training strategy
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# Deepspeed with ZeRO3 + fp16
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strategy = dict(
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type='DeepSpeedStrategy',
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fp16=dict(
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@ -1,5 +1,59 @@
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# Changelog (MMPreTrain)
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## v1.0.0(04/07/2023)
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### Highlights
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- Support inference of more **multi-modal** algorithms, such as **LLaVA**, **MiniGPT-4**, **Otter**, etc.
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- Support around **10 multi-modal datasets**!
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- Add **iTPN**, **SparK** self-supervised learning algorithms.
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- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP](./configs/mae/benchmarks/).
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### New Features
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- Transfer shape-bias tool from mmselfsup ([#1658](https://github.com/open-mmlab/mmpretrain/pull/1685))
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- Download dataset by using MIM&OpenDataLab ([#1630](https://github.com/open-mmlab/mmpretrain/pull/1630))
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- 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))
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- Support Flickr30k Retrieval dataset ([#1625](https://github.com/open-mmlab/mmpretrain/pull/1625))
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- Support SparK ([#1531](https://github.com/open-mmlab/mmpretrain/pull/1531))
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- Support LLaVA ([#1652](https://github.com/open-mmlab/mmpretrain/pull/1652))
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- Support Otter ([#1651](https://github.com/open-mmlab/mmpretrain/pull/1651))
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- Support MiniGPT-4 ([#1642](https://github.com/open-mmlab/mmpretrain/pull/1642))
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- Add support for VizWiz dataset ([#1636](https://github.com/open-mmlab/mmpretrain/pull/1636))
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- Add support for vsr dataset ([#1634](https://github.com/open-mmlab/mmpretrain/pull/1634))
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- Add InternImage Classification project ([#1569](https://github.com/open-mmlab/mmpretrain/pull/1569))
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- Support OCR-VQA dataset ([#1621](https://github.com/open-mmlab/mmpretrain/pull/1621))
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- Support OK-VQA dataset ([#1615](https://github.com/open-mmlab/mmpretrain/pull/1615))
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- Support TextVQA dataset ([#1569](https://github.com/open-mmlab/mmpretrain/pull/1569))
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- Support iTPN and HiViT ([#1584](https://github.com/open-mmlab/mmpretrain/pull/1584))
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- Add retrieval mAP metric ([#1552](https://github.com/open-mmlab/mmpretrain/pull/1552))
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- Support NoCap dataset based on BLIP. ([#1582](https://github.com/open-mmlab/mmpretrain/pull/1582))
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- Add GQA dataset ([#1585](https://github.com/open-mmlab/mmpretrain/pull/1585))
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### Improvements
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- Update fsdp vit-huge and vit-large config ([#1675](https://github.com/open-mmlab/mmpretrain/pull/1675))
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- Support deepspeed with flexible runner ([#1673](https://github.com/open-mmlab/mmpretrain/pull/1673))
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- Update Otter and LLaVA docs and config. ([#1653](https://github.com/open-mmlab/mmpretrain/pull/1653))
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- Add image_only param of ScienceQA ([#1613](https://github.com/open-mmlab/mmpretrain/pull/1613))
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- Support to use "split" to specify training set/validation ([#1535](https://github.com/open-mmlab/mmpretrain/pull/1535))
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### Bug Fixes
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- Refactor \_prepare_pos_embed in ViT ([#1656](https://github.com/open-mmlab/mmpretrain/pull/1656)， [#1679](https://github.com/open-mmlab/mmpretrain/pull/1679))
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- Freeze pre norm in vision transformer ([#1672](https://github.com/open-mmlab/mmpretrain/pull/1672))
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- Fix bug loading IN1k dataset ([#1641](https://github.com/open-mmlab/mmpretrain/pull/1641))
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- Fix sam bug ([#1633](https://github.com/open-mmlab/mmpretrain/pull/1633))
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- Fixed circular import error for new transform ([#1609](https://github.com/open-mmlab/mmpretrain/pull/1609))
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- Update torchvision transform wrapper ([#1595](https://github.com/open-mmlab/mmpretrain/pull/1595))
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- Set default out_type in CAM visualization ([#1586](https://github.com/open-mmlab/mmpretrain/pull/1586))
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### Docs Update
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- Fix spelling ([#1681](https://github.com/open-mmlab/mmpretrain/pull/1681))
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- 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))
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- Add t-SNE visualization doc ([#1555](https://github.com/open-mmlab/mmpretrain/pull/1555))
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## v1.0.0rc8(22/05/2023)
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### Highlights
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@ -16,7 +16,8 @@ and make sure you fill in all required information in the template.
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| MMPretrain version | MMEngine version | MMCV version |
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| :----------------: | :---------------: | :--------------: |
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| 1.0.0rc8 (main) | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
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| 1.0.0 (main) | mmengine >= 0.8.0 | mmcv >= 2.0.0 |
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| 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
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| 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
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```{note}
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@ -13,7 +13,8 @@
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| MMPretrain 版本 | MMEngine 版本 | MMCV 版本 |
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| :-------------: | :---------------: | :--------------: |
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| 1.0.0rc8 (main) | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
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| 1.0.0 (main) | mmengine >= 0.8.0 | mmcv >= 2.0.0 |
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| 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
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| 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
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```{note}
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@ -6,11 +6,11 @@ from mmengine.utils import digit_version
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from .apis import * # noqa: F401, F403
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from .version import __version__
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mmcv_minimum_version = '2.0.0rc4'
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mmcv_minimum_version = '2.0.0'
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mmcv_maximum_version = '2.1.0'
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mmcv_version = digit_version(mmcv.__version__)
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mmengine_minimum_version = '0.7.3'
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mmengine_minimum_version = '0.8.0'
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mmengine_maximum_version = '1.0.0'
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mmengine_version = digit_version(mmengine.__version__)
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# Copyright (c) OpenMMLab. All rights reserved
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__version__ = '1.0.0rc8'
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__version__ = '1.0.0'
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def parse_version_info(version_str):
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mmcv>=2.0.0rc4,<2.1.0
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mmengine>=0.7.3,<1.0.0
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mmcv>=2.0.0,<2.1.0
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mmengine>=0.8.0,<1.0.0
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