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CITATION.cff 0 → 100644
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "OpenMMLab's Pre-training Toolbox and Benchmark"
authors:
- name: "MMPreTrain Contributors"
version: 0.15.0
date-released: 2023-04-06
repository-code: "https://github.com/open-mmlab/mmpretrain"
license: Apache-2.0
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# Contributing to MMPreTrain
- [Contributing to MMPreTrain](#contributing-to-mmpretrain)
- [Workflow](#workflow)
- [Code style](#code-style)
- [Python](#python)
- [C++ and CUDA](#c-and-cuda)
- [Pre-commit Hook](#pre-commit-hook)
Thanks for your interest in contributing to MMPreTrain! All kinds of contributions are welcome, including but not limited to the following.
- Fix typo or bugs
- Add documentation or translate the documentation into other languages
- Add new features and components
## Workflow
We recommend the potential contributors follow this workflow for contribution.
1. Fork and pull the latest MMPreTrain repository, follow [get started](https://mmpretrain.readthedocs.io/en/latest/get_started.html) to setup the environment.
2. Checkout a new branch (**do not use the master or dev branch** for PRs)
```bash
git checkout -b xxxx # xxxx is the name of new branch
```
3. Edit the related files follow the code style mentioned below
4. Use [pre-commit hook](https://pre-commit.com/) to check and format your changes.
5. Commit your changes
6. Create a PR with related information
## Code style
### Python
We adopt [PEP8](https://www.python.org/dev/peps/pep-0008/) as the preferred code style.
We use the following tools for linting and formatting:
- [flake8](https://github.com/PyCQA/flake8): A wrapper around some linter tools.
- [isort](https://github.com/timothycrosley/isort): A Python utility to sort imports.
- [yapf](https://github.com/google/yapf): A formatter for Python files.
- [codespell](https://github.com/codespell-project/codespell): A Python utility to fix common misspellings in text files.
- [mdformat](https://github.com/executablebooks/mdformat): Mdformat is an opinionated Markdown formatter that can be used to enforce a consistent style in Markdown files.
- [docformatter](https://github.com/myint/docformatter): A formatter to format docstring.
Style configurations of yapf and isort can be found in [setup.cfg](https://github.com/open-mmlab/mmpretrain/blob/main/setup.cfg).
### C++ and CUDA
We follow the [Google C++ Style Guide](https://google.github.io/styleguide/cppguide.html).
## Pre-commit Hook
We use [pre-commit hook](https://pre-commit.com/) that checks and formats for `flake8`, `yapf`, `isort`, `trailing whitespaces`, `markdown files`,
fixes `end-of-files`, `double-quoted-strings`, `python-encoding-pragma`, `mixed-line-ending`, sorts `requirments.txt` automatically on every commit.
The config for a pre-commit hook is stored in [.pre-commit-config](https://github.com/open-mmlab/mmpretrain/blob/main/.pre-commit-config.yaml).
After you clone the repository, you will need to install initialize pre-commit hook.
```shell
pip install -U pre-commit
```
From the repository folder
```shell
pre-commit install
```
After this on every commit check code linters and formatter will be enforced.
> Before you create a PR, make sure that your code lints and is formatted by yapf.
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include requirements/*.txt
include mmpretrain/.mim/model-index.yml
include mmpretrain/.mim/dataset-index.yml
recursive-include mmpretrain/.mim/configs *.py *.yml
recursive-include mmpretrain/.mim/tools *.py *.sh
README.md
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# Mobilenetv2
<div align="center">
<img src="resources/mmpt-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![PyPI](https://img.shields.io/pypi/v/mmpretrain)](https://pypi.org/project/mmpretrain)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmpretrain.readthedocs.io/en/latest/)
[![Build Status](https://github.com/open-mmlab/mmpretrain/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpretrain/actions)
[![codecov](https://codecov.io/gh/open-mmlab/mmpretrain/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmpretrain)
[![license](https://img.shields.io/github/license/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/blob/main/LICENSE)
[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)
[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)
[📘 Documentation](https://mmpretrain.readthedocs.io/en/latest/) |
[🛠️ Installation](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) |
[👀 Model Zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html) |
[🆕 Update News](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) |
[🤔 Reporting Issues](https://github.com/open-mmlab/mmpretrain/issues/new/choose)
<img src="https://user-images.githubusercontent.com/36138628/230307505-4727ad0a-7d71-4069-939d-b499c7e272b7.png" width="400"/>
English | [简体中文](/README_zh-CN.md)
</div>
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.gg/raweFPmdzG" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<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/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
</div>
## Introduction
MMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.
The `main` branch works with **PyTorch 1.8+**.
### Major features
- Various backbones and pretrained models
- Rich training strategies (supervised learning, self-supervised learning, multi-modality learning etc.)
- Bag of training tricks
- Large-scale training configs
- High efficiency and extensibility
- Powerful toolkits for model analysis and experiments
- Various out-of-box inference tasks.
- Image Classification
- Image Caption
- Visual Question Answering
- Visual Grounding
- Retrieval (Image-To-Image, Text-To-Image, Image-To-Text)
https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904
## What's new
🌟 v1.2.0 was released in 04/01/2023
- Support LLaVA 1.5.
- Implement of RAM with a gradio interface.
🌟 v1.1.0 was released in 12/10/2023
- Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B)
- Support zero-shot classification based on CLIP.
🌟 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).
🌟 Upgrade from MMClassification to MMPreTrain
- Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.
- Support **RIFormer**, a simple but effective vision backbone by removing token mixer.
- Refactor dataset pipeline visualization.
- Support **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.
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/).
And there are some BC-breaking changes. Please check [the migration tutorial](https://mmpretrain.readthedocs.io/en/latest/migration.html).
## 论文
Please refer to [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) for more details and other release history.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
## Installation
- https://openaccess.thecvf.com/content_cvpr_2018/papers/Sandler_MobileNetV2_Inverted_Residuals_CVPR_2018_paper.pdf
Below are quick steps for installation:
## 模型结构
MobileNetV2是一种轻量级的卷积神经网络模型，由Google在2018年提出。它是MobileNet系列中的第二个版本，主要用于移动设备和嵌入式设备等资源受限的环境中进行图像分类、目标检测等计算机视觉任务。
![d15a0e56517b4f7284a862f1d6eaef9a](./images/d15a0e56517b4f7284a862f1d6eaef9a.png)
## 算法原理
MobileNetV2的网络结构主要由两部分组成：特征提取层和分类器。
![20231124104337](./images/20231124104337.png)
## 环境配置
### Docker（方法一）
```python
git clone --recursive http://developer.hpccube.com/codes/modelzoo/mobilenetv2_mmcv.git
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:1.10.0-centos7.6-dtk-22.10.1-py37-latest
# <your IMAGE ID>用以上拉取的docker的镜像ID替换
docker run --shm-size 10g --network=host --name=mobilenetv2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/mobilenetv2_mmcv:/home/mobilenetv2_mmcv -it <your IMAGE ID> bash
cd mobilenetv2_mmcv/mmclassification-mmcv
pip install -r requirements.txt
```shell
conda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate open-mmlab
pip install openmim
git clone https://github.com/open-mmlab/mmpretrain.git
cd mmpretrain
mim install -e .
```
### Dockerfile（方法二）
```plaintext
cd mobilenetv2_mmcv/docker
docker build --no-cache -t mobilenetv2_mmcv:latest .
docker run --rm --shm-size 10g --network=host --name=mobilenetv2 --privileged --device=/dev/kfd --device=/dev/dri --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v $PWD/../../mobilenetv2_mmcv:/home/mobilenetv2_mmcv -it <your IMAGE ID> bash
# 若遇到Dockerfile启动的方式安装环境需要长时间等待，可注释掉里面的pip安装，启动容器后再安装python库：pip install -r requirements.txt
```
Please refer to [installation documentation](https://mmpretrain.readthedocs.io/en/latest/get_started.html) for more detailed installation and dataset preparation.
### Anaconda（方法三）
For multi-modality models support, please install the extra dependencies by:
1、关于本项目DCU显卡所需的特殊深度学习库可从光合开发者社区下载安装： https://developer.hpccube.com/tool/
```plaintext
DTK驱动：dtk22.10.1
python：python3.7
torch:1.10.0
torchvision:0.10.0
mmcv：1.6.1
Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应
```shell
mim install -e ".[multimodal]"
```
2、其它非特殊库参照requirements.txt安装
```plaintext
pip install -r requirements.txt
## User Guides
We provided a series of tutorials about the basic usage of MMPreTrain for new users:
- [Learn about Configs](https://mmpretrain.readthedocs.io/en/latest/user_guides/config.html)
- [Prepare Dataset](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html)
- [Inference with existing models](https://mmpretrain.readthedocs.io/en/latest/user_guides/inference.html)
- [Train](https://mmpretrain.readthedocs.io/en/latest/user_guides/train.html)
- [Test](https://mmpretrain.readthedocs.io/en/latest/user_guides/test.html)
- [Downstream tasks](https://mmpretrain.readthedocs.io/en/latest/user_guides/downstream.html)
For more information, please refer to [our documentation](https://mmpretrain.readthedocs.io/en/latest/).
## Model zoo
Results and models are available in the [model zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html).
<div align="center">
<b>Overview</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>Supported Backbones</b>
</td>
<td>
<b>Self-supervised Learning</b>
</td>
<td>
<b>Multi-Modality Algorithms</b>
</td>
<td>
<b>Others</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/vgg">VGG</a></li>
<li><a href="configs/resnet">ResNet</a></li>
<li><a href="configs/resnext">ResNeXt</a></li>
<li><a href="configs/seresnet">SE-ResNet</a></li>
<li><a href="configs/seresnet">SE-ResNeXt</a></li>
<li><a href="configs/regnet">RegNet</a></li>
<li><a href="configs/shufflenet_v1">ShuffleNet V1</a></li>
<li><a href="configs/shufflenet_v2">ShuffleNet V2</a></li>
<li><a href="configs/mobilenet_v2">MobileNet V2</a></li>
<li><a href="configs/mobilenet_v3">MobileNet V3</a></li>
<li><a href="configs/swin_transformer">Swin-Transformer</a></li>
<li><a href="configs/swin_transformer_v2">Swin-Transformer V2</a></li>
<li><a href="configs/repvgg">RepVGG</a></li>
<li><a href="configs/vision_transformer">Vision-Transformer</a></li>
<li><a href="configs/tnt">Transformer-in-Transformer</a></li>
<li><a href="configs/res2net">Res2Net</a></li>
<li><a href="configs/mlp_mixer">MLP-Mixer</a></li>
<li><a href="configs/deit">DeiT</a></li>
<li><a href="configs/deit3">DeiT-3</a></li>
<li><a href="configs/conformer">Conformer</a></li>
<li><a href="configs/t2t_vit">T2T-ViT</a></li>
<li><a href="configs/twins">Twins</a></li>
<li><a href="configs/efficientnet">EfficientNet</a></li>
<li><a href="configs/edgenext">EdgeNeXt</a></li>
<li><a href="configs/convnext">ConvNeXt</a></li>
<li><a href="configs/hrnet">HRNet</a></li>
<li><a href="configs/van">VAN</a></li>
<li><a href="configs/convmixer">ConvMixer</a></li>
<li><a href="configs/cspnet">CSPNet</a></li>
<li><a href="configs/poolformer">PoolFormer</a></li>
<li><a href="configs/inception_v3">Inception V3</a></li>
<li><a href="configs/mobileone">MobileOne</a></li>
<li><a href="configs/efficientformer">EfficientFormer</a></li>
<li><a href="configs/mvit">MViT</a></li>
<li><a href="configs/hornet">HorNet</a></li>
<li><a href="configs/mobilevit">MobileViT</a></li>
<li><a href="configs/davit">DaViT</a></li>
<li><a href="configs/replknet">RepLKNet</a></li>
<li><a href="configs/beit">BEiT</a></li>
<li><a href="configs/mixmim">MixMIM</a></li>
<li><a href="configs/efficientnet_v2">EfficientNet V2</a></li>
<li><a href="configs/revvit">RevViT</a></li>
<li><a href="configs/convnext_v2">ConvNeXt V2</a></li>
<li><a href="configs/vig">ViG</a></li>
<li><a href="configs/xcit">XCiT</a></li>
<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>
<ul>
<li><a href="configs/mocov2">MoCo V1 (CVPR'2020)</a></li>
<li><a href="configs/simclr">SimCLR (ICML'2020)</a></li>
<li><a href="configs/mocov2">MoCo V2 (arXiv'2020)</a></li>
<li><a href="configs/byol">BYOL (NeurIPS'2020)</a></li>
<li><a href="configs/swav">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/densecl">DenseCL (CVPR'2021)</a></li>
<li><a href="configs/simsiam">SimSiam (CVPR'2021)</a></li>
<li><a href="configs/barlowtwins">Barlow Twins (ICML'2021)</a></li>
<li><a href="configs/mocov3">MoCo V3 (ICCV'2021)</a></li>
<li><a href="configs/beit">BEiT (ICLR'2022)</a></li>
<li><a href="configs/mae">MAE (CVPR'2022)</a></li>
<li><a href="configs/simmim">SimMIM (CVPR'2022)</a></li>
<li><a href="configs/maskfeat">MaskFeat (CVPR'2022)</a></li>
<li><a href="configs/cae">CAE (arXiv'2022)</a></li>
<li><a href="configs/milan">MILAN (arXiv'2022)</a></li>
<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>
<li><a href="configs/mff">MFF (ICCV'2023)</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>
<li><a href="configs/minigpt4">MiniGPT-4 (arxiv'2023)</a></li>
<li><a href="configs/llava">LLaVA (arxiv'2023)</a></li>
<li><a href="configs/otter">Otter (arxiv'2023)</a></li>
</ul>
</td>
<td>
Image Retrieval Task:
<ul>
<li><a href="configs/arcface">ArcFace (CVPR'2019)</a></li>
</ul>
Training&Test Tips:
<ul>
<li><a href="https://arxiv.org/abs/1909.13719">RandAug</a></li>
<li><a href="https://arxiv.org/abs/1805.09501">AutoAug</a></li>
<li><a href="mmpretrain/datasets/samplers/repeat_aug.py">RepeatAugSampler</a></li>
<li><a href="mmpretrain/models/tta/score_tta.py">TTA</a></li>
<li>...</li>
</ul>
</td>
</tbody>
</table>
## Contributing
We appreciate all contributions to improve MMPreTrain.
Please refer to [CONTRUBUTING](https://mmpretrain.readthedocs.io/en/latest/notes/contribution_guide.html) for the contributing guideline.
## Acknowledgement
MMPreTrain is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.
We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and supporting their own academic research.
## Citation
If you find this project useful in your research, please consider cite:
```BibTeX
@misc{2023mmpretrain,
title={OpenMMLab's Pre-training Toolbox and Benchmark},
author={MMPreTrain Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpretrain}},
year={2023}
}
```
## 数据集
在本测试中可以使用ImageNet数据集。
下载ImageNet数据集：https://image-net.org/
下载val数据：链接：https://pan.baidu.com/s/1oXsmsYahGVG3uOZ8e535LA?pwd=c3bc 提取码：c3bc 替换ImageNet数据集中的val目录，处理后的数据结构如下：
```
data
├──imagenet
├── meta
├──val.txt
├──train.txt
...
├── train
├── val
```
SCNet快速下载链接[http://113.200.138.88:18080/aidatasets/project-dependency/imagenet-2012
](http://113.200.138.88:18080/aidatasets/project-dependency/imagenet-2012
)
## 训练
将训练数据解压到data目录下。
### 单机8卡
./mobilenetv2.sh
## result
![img](https://developer.hpccube.com/codes/modelzoo/vit_pytorch/-/raw/master/image/README/1695381570003.png)
### 精度
测试数据使用的是ImageNet数据集，使用的加速卡是DCU Z100L。
| 卡数 | 精度 |
| :--: | :-----------------------: |
| 8 | top1:0.71764;top5:0.90386 |
## 应用场景
### 算法类别
图像分类
### 热点行业
制造,能源,交通,网安
## 源码仓库及问题反馈
https://developer.hpccube.com/codes/modelzoo/mobilenetv2_mmcv
## 参考资料
https://github.com/open-mmlab/mmpretrain
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Projects in OpenMMLab
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
README_zh-CN.md 0 → 100644
View file @ 1b56329e
<div align="center">
<img src="resources/mmpt-logo.png" width="600"/>
<div>&nbsp;</div>
<div align="center">
<b><font size="5">OpenMMLab 官网</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
&nbsp;&nbsp;&nbsp;&nbsp;
<b><font size="5">OpenMMLab 开放平台</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div>&nbsp;</div>
[![PyPI](https://img.shields.io/pypi/v/mmpretrain)](https://pypi.org/project/mmpretrain)
[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmpretrain.readthedocs.io/zh_CN/latest/)
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[📘 中文文档](https://mmpretrain.readthedocs.io/zh_CN/latest/) |
[🛠️ 安装教程](https://mmpretrain.readthedocs.io/zh_CN/latest/get_started.html) |
[👀 模型库](https://mmpretrain.readthedocs.io/zh_CN/latest/modelzoo_statistics.html) |
[🆕 更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html) |
[🤔 报告问题](https://github.com/open-mmlab/mmpretrain/issues/new/choose)
<img src="https://user-images.githubusercontent.com/36138628/230307505-4727ad0a-7d71-4069-939d-b499c7e272b7.png" width="400"/>
[English](/README.md) | 简体中文
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<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
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</div>
## Introduction
MMPreTrain 是一款基于 PyTorch 的开源深度学习预训练工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
`主分支`代码目前支持 PyTorch 1.8 以上的版本。
### 主要特性
- 支持多样的主干网络与预训练模型
- 支持多种训练策略（有监督学习，无监督学习，多模态学习等）
- 提供多种训练技巧
- 大量的训练配置文件
- 高效率和高可扩展性
- 功能强大的工具箱，有助于模型分析和实验
- 支持多种开箱即用的推理任务
- 图像分类
- 图像描述（Image Caption）
- 视觉问答（Visual Question Answering）
- 视觉定位（Visual Grounding）
- 检索（图搜图，图搜文，文搜图）
https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904
## 更新日志
🌟 2024/01/04 发布了 v1.2.0 版本
- 支持了 LLaVA 1.5
- 实现了一个 RAM 模型的 gradio 推理例程
🌟 2023/10/12 发布了 v1.1.0 版本
- 支持 Mini-GPT4 训练并提供一个基于 Baichuan-7B 的中文模型
- 支持基于 CLIP 的零样本分类。
🌟 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。
🌟 从 MMClassification 升级到 MMPreTrain
- 整合来自 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
- 重构数据管道可视化
- 支持了 **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** 和 **MixMIM** 等骨干网络结构
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。
同时，新版本中存在一些与旧版本不兼容的修改。请查看 [迁移文档](https://mmpretrain.readthedocs.io/zh_CN/latest/migration.html) 来详细了解这些变动。
发布历史和更新细节请参考 [更新日志](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/changelog.html)。
## 安装
以下是安装的简要步骤：
```shell
conda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y
conda activate open-mmlab
pip3 install openmim
git clone https://github.com/open-mmlab/mmpretrain.git
cd mmpretrain
mim install -e .
```
更详细的步骤请参考 [安装指南](https://mmpretrain.readthedocs.io/zh_CN/latest/get_started.html) 进行安装。
如果需要多模态模型，请使用如下方式安装额外的依赖：
```shell
mim install -e ".[multimodal]"
```
## 基础教程
我们为新用户提供了一系列基础教程：
- [学习配置文件](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/config.html)
- [准备数据集](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
- [使用现有模型推理](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/inference.html)
- [训练](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/train.html)
- [测试](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/test.html)
- [下游任务](https://mmpretrain.readthedocs.io/zh_CN/latest/user_guides/downstream.html)
关于更多的信息，请查阅我们的 [相关文档](https://mmpretrain.readthedocs.io/zh_CN/latest/)。
## 模型库
相关结果和模型可在 [模型库](https://mmpretrain.readthedocs.io/zh_CN/latest/modelzoo_statistics.html) 中获得。
<div align="center">
<b>概览</b>
</div>
<table align="center">
<tbody>
<tr align="center" valign="bottom">
<td>
<b>支持的主干网络</b>
</td>
<td>
<b>自监督学习</b>
</td>
<td>
<b>多模态算法</b>
</td>
<td>
<b>其它</b>
</td>
</tr>
<tr valign="top">
<td>
<ul>
<li><a href="configs/vgg">VGG</a></li>
<li><a href="configs/resnet">ResNet</a></li>
<li><a href="configs/resnext">ResNeXt</a></li>
<li><a href="configs/seresnet">SE-ResNet</a></li>
<li><a href="configs/seresnet">SE-ResNeXt</a></li>
<li><a href="configs/regnet">RegNet</a></li>
<li><a href="configs/shufflenet_v1">ShuffleNet V1</a></li>
<li><a href="configs/shufflenet_v2">ShuffleNet V2</a></li>
<li><a href="configs/mobilenet_v2">MobileNet V2</a></li>
<li><a href="configs/mobilenet_v3">MobileNet V3</a></li>
<li><a href="configs/swin_transformer">Swin-Transformer</a></li>
<li><a href="configs/swin_transformer_v2">Swin-Transformer V2</a></li>
<li><a href="configs/repvgg">RepVGG</a></li>
<li><a href="configs/vision_transformer">Vision-Transformer</a></li>
<li><a href="configs/tnt">Transformer-in-Transformer</a></li>
<li><a href="configs/res2net">Res2Net</a></li>
<li><a href="configs/mlp_mixer">MLP-Mixer</a></li>
<li><a href="configs/deit">DeiT</a></li>
<li><a href="configs/deit3">DeiT-3</a></li>
<li><a href="configs/conformer">Conformer</a></li>
<li><a href="configs/t2t_vit">T2T-ViT</a></li>
<li><a href="configs/twins">Twins</a></li>
<li><a href="configs/efficientnet">EfficientNet</a></li>
<li><a href="configs/edgenext">EdgeNeXt</a></li>
<li><a href="configs/convnext">ConvNeXt</a></li>
<li><a href="configs/hrnet">HRNet</a></li>
<li><a href="configs/van">VAN</a></li>
<li><a href="configs/convmixer">ConvMixer</a></li>
<li><a href="configs/cspnet">CSPNet</a></li>
<li><a href="configs/poolformer">PoolFormer</a></li>
<li><a href="configs/inception_v3">Inception V3</a></li>
<li><a href="configs/mobileone">MobileOne</a></li>
<li><a href="configs/efficientformer">EfficientFormer</a></li>
<li><a href="configs/mvit">MViT</a></li>
<li><a href="configs/hornet">HorNet</a></li>
<li><a href="configs/mobilevit">MobileViT</a></li>
<li><a href="configs/davit">DaViT</a></li>
<li><a href="configs/replknet">RepLKNet</a></li>
<li><a href="configs/beit">BEiT</a></li>
<li><a href="configs/mixmim">MixMIM</a></li>
<li><a href="configs/revvit">RevViT</a></li>
<li><a href="configs/convnext_v2">ConvNeXt V2</a></li>
<li><a href="configs/vig">ViG</a></li>
<li><a href="configs/xcit">XCiT</a></li>
<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>
<ul>
<li><a href="configs/mocov2">MoCo V1 (CVPR'2020)</a></li>
<li><a href="configs/simclr">SimCLR (ICML'2020)</a></li>
<li><a href="configs/mocov2">MoCo V2 (arXiv'2020)</a></li>
<li><a href="configs/byol">BYOL (NeurIPS'2020)</a></li>
<li><a href="configs/swav">SwAV (NeurIPS'2020)</a></li>
<li><a href="configs/densecl">DenseCL (CVPR'2021)</a></li>
<li><a href="configs/simsiam">SimSiam (CVPR'2021)</a></li>
<li><a href="configs/barlowtwins">Barlow Twins (ICML'2021)</a></li>
<li><a href="configs/mocov3">MoCo V3 (ICCV'2021)</a></li>
<li><a href="configs/beit">BEiT (ICLR'2022)</a></li>
<li><a href="configs/mae">MAE (CVPR'2022)</a></li>
<li><a href="configs/simmim">SimMIM (CVPR'2022)</a></li>
<li><a href="configs/maskfeat">MaskFeat (CVPR'2022)</a></li>
<li><a href="configs/cae">CAE (arXiv'2022)</a></li>
<li><a href="configs/milan">MILAN (arXiv'2022)</a></li>
<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>
<li><a href="configs/mff">MFF (ICCV'2023)</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>
<li><a href="configs/minigpt4">MiniGPT-4 (arxiv'2023)</a></li>
<li><a href="configs/llava">LLaVA (arxiv'2023)</a></li>
<li><a href="configs/otter">Otter (arxiv'2023)</a></li>
</ul>
</td>
<td>
图像检索任务：
<ul>
<li><a href="configs/arcface">ArcFace (CVPR'2019)</a></li>
</ul>
训练和测试 Tips:
<ul>
<li><a href="https://arxiv.org/abs/1909.13719">RandAug</a></li>
<li><a href="https://arxiv.org/abs/1805.09501">AutoAug</a></li>
<li><a href="mmpretrain/datasets/samplers/repeat_aug.py">RepeatAugSampler</a></li>
<li><a href="mmpretrain/models/tta/score_tta.py">TTA</a></li>
<li>...</li>
</ul>
</td>
</tbody>
</table>
## 参与贡献
我们非常欢迎任何有助于提升 MMPreTrain 的贡献，请参考 [贡献指南](https://mmpretrain.readthedocs.io/zh_CN/latest/notes/contribution_guide.html) 来了解如何参与贡献。
## 致谢
MMPreTrain 是一款由不同学校和公司共同贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。
我们希望该工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现现有算法并开发自己的新模型，从而不断为开源社区提供贡献。
## 引用
如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMPreTrain。
```BibTeX
@misc{2023mmpretrain,
title={OpenMMLab's Pre-training Toolbox and Benchmark},
author={MMPreTrain Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmpretrain}},
year={2023}
}
```
## 许可证
该项目开源自 [Apache 2.0 license](LICENSE).
## OpenMMLab 的其他项目
- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
- [Playground](https://github.com/open-mmlab/playground): 收集和展示 OpenMMLab 相关的前沿、有趣的社区项目
## 欢迎加入 OpenMMLab 社区
扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMPretrain 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
<div align="center">
<img src="./resources/zhihu_qrcode.jpg" height="400"/> <img src="./resources/miaomiao_qrcode.jpg" height="400"/>
</div>
我们会在 OpenMMLab 社区为大家
- 📢 分享 AI 框架的前沿核心技术
- 💻 解读 PyTorch 常用模块源码
- 📰 发布 OpenMMLab 的相关新闻
- 🚀 介绍 OpenMMLab 开发的前沿算法
- 🏃 获取更高效的问题答疑和意见反馈
- 🔥 提供与各行各业开发者充分交流的平台
干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬
configs/_base_/datasets/cifar100_bs16.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'CIFAR100'
data_preprocessor = dict(
num_classes=100,
# RGB format normalization parameters
mean=[129.304, 124.070, 112.434],
std=[68.170, 65.392, 70.418],
# loaded images are already RGB format
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', crop_size=32, padding=4),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar100',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar100/',
split='test',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, ))
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/cifar10_bs16.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'CIFAR10'
data_preprocessor = dict(
num_classes=10,
# RGB format normalization parameters
mean=[125.307, 122.961, 113.8575],
std=[51.5865, 50.847, 51.255],
# loaded images are already RGB format
to_rgb=False)
train_pipeline = [
dict(type='RandomCrop', crop_size=32, padding=4),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar10',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=16,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/cifar10/',
split='test',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, ))
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_caption.py 0 → 100644
View file @ 1b56329e
# data settings
# coco caption annotations can be grabbed from LAVIS repo
# https://github.com/salesforce/LAVIS/blob/main/lavis/configs/datasets/coco/defaults_cap.yaml
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='CleanCaption', keys='gt_caption'),
dict(
type='PackInputs',
algorithm_keys=['gt_caption'],
meta_keys=['image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type='COCOCaption',
data_root='data/coco',
ann_file='annotations/coco_karpathy_train.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='COCOCaption',
data_root='data/coco',
ann_file='annotations/coco_karpathy_val.json',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(
type='COCOCaption',
ann_file='data/coco/annotations/coco_karpathy_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_okvqa.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/okvqa_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/okvqa_mscoco_train2014_annotations.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file=
'annotations/okvqa_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/okvqa_mscoco_val2014_annotations.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='VQAAcc')
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_retrieval.py 0 → 100644
View file @ 1b56329e
# data settings
# Here are the links to download the annotations for coco retrieval for conveniency # noqa
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_train2014.json
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_val2014.json
# https://download.openmmlab.com/mmclassification/datasets/coco_retrieval/caption_karpathy_test2014.json
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(
type='Brightness', magnitude_key='magnitude',
magnitude_range=(0, 0.0)),
dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='horizontal'),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='vertical'),
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies=rand_increasing_policies,
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'is_matched'],
meta_keys=['image_id']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=16,
dataset=dict(
type='COCORetrieval',
data_root='data/coco',
ann_file='annotations/caption_karpathy_train2014.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='COCORetrieval',
data_root='data/coco',
ann_file='annotations/caption_karpathy_val2014.json',
pipeline=test_pipeline,
# This is required for evaluation
test_mode=True,
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/coco_vg_vqa.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=(480, 480),
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies='simple_increasing', # slightly different from LAVIS
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys=['question', 'gt_answer']),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys=['question']),
dict(
type='PackInputs',
algorithm_keys=['question'],
meta_keys=['question_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=8,
dataset=dict(
type='ConcatDataset',
datasets=[
# VQAv2 train
dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/v2_mscoco_train2014_annotations.json',
pipeline=train_pipeline,
),
# VQAv2 val
dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file=
'annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=train_pipeline,
),
# Visual Genome
dict(
type='VisualGenomeQA',
data_root='visual_genome',
data_prefix='image',
ann_file='question_answers.json',
pipeline=train_pipeline,
)
]),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
test_dataloader = dict(
batch_size=32,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='test2015',
question_file=
'annotations/v2_OpenEnded_mscoco_test2015_questions.json', # noqa: E501
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
)
test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')
configs/_base_/datasets/coco_vqa.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='train2014',
question_file=
'annotations/v2_OpenEnded_mscoco_train2014_questions.json',
ann_file='annotations/v2_mscoco_train2014_annotations.json',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='val2014',
question_file='annotations/v2_OpenEnded_mscoco_val2014_questions.json',
ann_file='annotations/v2_mscoco_val2014_annotations.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='VQAAcc')
test_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='COCOVQA',
data_root='data/coco',
data_prefix='test2015',
question_file= # noqa: E251
'annotations/v2_OpenEnded_mscoco_test2015_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
test_evaluator = dict(type='ReportVQA', file_path='vqa_test.json')
configs/_base_/datasets/cub_bs8_384.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'CUB'
data_preprocessor = dict(
num_classes=200,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=510),
dict(type='RandomCrop', crop_size=384),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=510),
dict(type='CenterCrop', crop_size=384),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=8,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/CUB_200_2011',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=8,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/CUB_200_2011',
split='test',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, ))
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/cub_bs8_448.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'CUB'
data_preprocessor = dict(
num_classes=200,
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=600),
dict(type='RandomCrop', crop_size=448),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=600),
dict(type='CenterCrop', crop_size=448),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=8,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/CUB_200_2011',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=8,
num_workers=2,
dataset=dict(
type=dataset_type,
data_root='data/CUB_200_2011',
split='test',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, ))
test_dataloader = val_dataloader
test_evaluator = val_evaluator
configs/_base_/datasets/flickr30k_caption.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='CleanCaption', keys='gt_caption'),
dict(
type='PackInputs',
algorithm_keys=['gt_caption'],
meta_keys=['image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='PackInputs', meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='val',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
# refer tools/dataset_converters/convert_flickr30k_ann.py
val_evaluator = dict(
type='COCOCaption',
ann_file='data/flickr30k_val_gt.json',
)
# # If you want standard test, please manually configure the test dataset
test_dataloader = dict(
batch_size=16,
num_workers=5,
dataset=dict(
type='Flickr30kCaption',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='test',
pipeline=test_pipeline,
),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
# refer tools/dataset_converters/convert_flickr30k_ann.py
test_evaluator = dict(
type='COCOCaption',
ann_file='data/flickr30k_test_gt.json',
)
configs/_base_/datasets/flickr30k_retrieval.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
type='MultiModalDataPreprocessor',
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
rand_increasing_policies = [
dict(type='AutoContrast'),
dict(type='Equalize'),
dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)),
dict(
type='Brightness', magnitude_key='magnitude',
magnitude_range=(0, 0.0)),
dict(type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0)),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='horizontal'),
dict(
type='Shear',
magnitude_key='magnitude',
magnitude_range=(0, 0.3),
direction='vertical'),
]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
crop_ratio_range=(0.5, 1.0),
interpolation='bicubic'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='RandAugment',
policies=rand_increasing_policies,
num_policies=2,
magnitude_level=5),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'is_matched'],
meta_keys=['image_id']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(384, 384),
interpolation='bicubic',
backend='pillow'),
dict(type='CleanCaption', keys='text'),
dict(
type='PackInputs',
algorithm_keys=['text', 'gt_text_id', 'gt_image_id'],
meta_keys=['image_id']),
]
train_dataloader = dict(
batch_size=32,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='val',
pipeline=test_pipeline,
test_mode=True, # This is required for evaluation
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
val_evaluator = dict(type='RetrievalRecall', topk=(1, 5, 10))
# If you want standard test, please manually configure the test dataset
test_dataloader = dict(
batch_size=64,
num_workers=16,
dataset=dict(
type='Flickr30kRetrieval',
data_root='data/flickr30k',
ann_file='annotations/dataset_flickr30k.json',
data_prefix='images',
split='test',
pipeline=test_pipeline,
test_mode=True, # This is required for evaluation
),
sampler=dict(type='SequentialSampler', subsample_type='sequential'),
persistent_workers=True,
)
test_evaluator = val_evaluator
configs/_base_/datasets/gqa.py 0 → 100644
View file @ 1b56329e
# data settings
data_preprocessor = dict(
mean=[122.770938, 116.7460125, 104.09373615],
std=[68.5005327, 66.6321579, 70.32316305],
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='RandomResizedCrop',
scale=384,
interpolation='bicubic',
backend='pillow'),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='Resize',
scale=(480, 480),
interpolation='bicubic',
backend='pillow'),
dict(
type='CleanCaption',
keys=['question'],
),
dict(
type='PackInputs',
algorithm_keys=['question', 'gt_answer', 'gt_answer_weight'],
meta_keys=['question_id', 'image_id'],
),
]
train_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/train_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
drop_last=True,
)
val_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/testdev_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
val_evaluator = dict(type='GQAAcc')
test_dataloader = dict(
batch_size=16,
num_workers=8,
dataset=dict(
type='GQA',
data_root='data/gqa',
data_prefix='images',
ann_file='annotations/testdev_balanced_questions.json',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
persistent_workers=True,
)
test_evaluator = val_evaluator
configs/_base_/datasets/imagenet21k_bs128.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'ImageNet21k'
data_preprocessor = dict(
num_classes=21842,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet21k',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
configs/_base_/datasets/imagenet_bs128_mbv3.py 0 → 100644
View file @ 1b56329e
# dataset settings
dataset_type = 'ImageNet'
data_preprocessor = dict(
num_classes=1000,
# RGB format normalization parameters
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
# convert image from BGR to RGB
to_rgb=True,
)
bgr_mean = data_preprocessor['mean'][::-1]
bgr_std = data_preprocessor['std'][::-1]
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='RandomResizedCrop', scale=224, backend='pillow'),
dict(type='RandomFlip', prob=0.5, direction='horizontal'),
dict(
type='AutoAugment',
policies='imagenet',
hparams=dict(pad_val=[round(x) for x in bgr_mean])),
dict(
type='RandomErasing',
erase_prob=0.2,
mode='rand',
min_area_ratio=0.02,
max_area_ratio=1 / 3,
fill_color=bgr_mean,
fill_std=bgr_std),
dict(type='PackInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='ResizeEdge', scale=256, edge='short', backend='pillow'),
dict(type='CenterCrop', crop_size=224),
dict(type='PackInputs'),
]
train_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
split='train',
pipeline=train_pipeline),
sampler=dict(type='DefaultSampler', shuffle=True),
)
val_dataloader = dict(
batch_size=128,
num_workers=5,
dataset=dict(
type=dataset_type,
data_root='data/imagenet',
split='val',
pipeline=test_pipeline),
sampler=dict(type='DefaultSampler', shuffle=False),
)
val_evaluator = dict(type='Accuracy', topk=(1, 5))
# If you want standard test, please manually configure the test dataset
test_dataloader = val_dataloader
test_evaluator = val_evaluator
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