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# Qwen2-VL
## 论文
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
-https://arxiv.org/abs/2409.12191
## 模型结构
整体上Qwen2-VL仍然延续了 Qwen-VL 中 ViT 加 Qwen2 的串联结构，在三个不同尺度的模型上，都采用 600M 规模大小的 ViT，并且支持图像和视频统一输入。
<div align=center>
<img src="./assets/qwen2_vl_framework.jpg"/>
</div>
## 算法原理
为了让模型更清楚地感知视觉信息和理解视频，还进行了以下升级：
- Qwen2-VL 在架构上的一大改进是实现了对原生动态分辨率的全面支持。与上一代模型相比，Qwen2-VL 能够处理任意分辨率的图像输入，不同大小图片被转换为动态数量的 tokens，
最小只占 4 个 tokens。这种设计不仅确保了模型输入与图像原始信息之间的高度一致性，更是模拟了人类视觉感知的自然方式，赋予模型处理任意尺寸图像的强大能力，
使其在图像处理领域展现出更加灵活和高效的表现。
- Qwen2-VL 在架构上的另一重要创新则是多模态旋转位置嵌入（M-ROPE）。传统的旋转位置嵌入只能捕捉一维序列的位置信息，而 M-ROPE 通过将原始旋转嵌入分解为代表时间、高度和宽度的三个部分，
使得大规模语言模型能够同时捕捉和整合一维文本序列、二维视觉图像以及三维视频的位置信息。这一创新赋予了语言模型强大的多模态处理和推理能力，能够更好地理解和建模复杂的多模态数据。
<div align=center>
<img src="./assets/mrope.png"/>
</div>
## 环境配置
### Docker（方法一）
推荐使用docker方式运行， 此处提供[光源](https://www.sourcefind.cn/#/service-details)拉取docker镜像的地址与使用步骤
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/pytorch:2.3.0-py3.10-dtk24.04.3-ubuntu20.04-vllm0.6
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen2_vl_pytorch <your IMAGE ID> bash # <your IMAGE ID>为以上拉取的docker的镜像ID替换，本镜像为：b030eb4a853a
cd /path/your_code_data/
pip install qwen-vl-utils[decord]
git clone http://developer.sourcefind.cn/codes/OpenDAS/llama-factory.git
cd llama-factory
pip install -e ".[torch,metrics]"
pip install timm
```
Tips:以上dtk驱动、python、torch、vllm等DCU相关工具版本需要严格一一对应。
### Dockerfile（方法二）
此处提供dockerfile的使用方法
```
docker build -t qwen2.5:latest .
docker run -it --shm-size=1024G -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal:/opt/hyhal --privileged=true --device=/dev/kfd --device=/dev/dri/ --group-add video --name qwen2.5_pytorch qwen2.5 bash
cd /path/your_code_data/
pip install qwen-vl-utils[decord]
git clone http://developer.sourcefind.cn/codes/OpenDAS/llama-factory.git
cd llama-factory
pip install -e ".[torch,metrics]"
pip install timm
```
### Anaconda（方法三）
此处提供本地配置、编译的详细步骤，例如：
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.hpccube.com/tool/)开发者社区下载安装。
```
DTK驱动:dtk24.04.3
python:3.10
torch:2.3.0
flash-attn:2.6.1
vllm:0.6.2
lmslim:0.1.2
xformers:0.0.25
triton:2.1.0
deepspeed:0.14.2
apx:1.3.0
```
`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
其它非深度学习库参照requirement.txt安装：
```
cd /path/your_code_data/
pip install qwen-vl-utils[decord]
git clone http://developer.sourcefind.cn/codes/OpenDAS/llama-factory.git
cd llama-factory
pip install -e ".[torch,metrics]"
pip install timm
```
## 数据集
使用mllm_demo,identity,mllm_video_demo数据集，已经包含在data目录中
训练数据目录结构如下，用于正常训练的完整数据集请按此目录结构进行制备：
```
── data
├── mllm_demo.json
├── identity.json
├── mllm_video_demo.json
└── ...
```
如果您正在使用自定义数据集，请按以下方式准备您的数据集。
将数据组织成一个 JSON 文件，并将数据放入 data 文件夹中。LLaMA-Factory 支持以 sharegpt 格式的多模态数据集。 sharegpt 格式的数据集应遵循以下格式：
```
[
{
"messages": [
{
"content": "<image>Who are they?",
"role": "user"
},
{
"content": "They're Kane and Gretzka from Bayern Munich.",
"role": "assistant"
},
{
"content": "What are they doing?<image>",
"role": "user"
},
{
"content": "They are celebrating on the soccer field.",
"role": "assistant"
}
],
"images": [
"mllm_demo_data/1.jpg",
"mllm_demo_data/1.jpg"
]
},
]
```
请按照以下格式在 data/dataset_info.json 中提供您的数据集定义。
对于 sharegpt 格式的数据集，dataset_info.json 中的列应包括：
```
"dataset_name": {
"file_name": "dataset_name.json",
"formatting": "sharegpt",
"columns": {
"messages": "messages",
"images": "images"
},
"tags": {
"role_tag": "role",
"content_tag": "content",
"user_tag": "user",
"assistant_tag": "assistant"
}
}
```
## 训练
使用LLaMA-Factory框架微调
### 单机单卡（LoRA-finetune）
```
# 注意：根据自己的模型切换.yaml文件中的模型位置并调整其他参数
cd /path/your_code_data/
cd llama-factory
HIP_VISIBLE_DEVICES=0 llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
```
### 单机多卡(LoRA-finetune）
4卡微调
```
HIP_VISIBLE_DEVICES=0,1,2,3 llamafactory-cli train examples/train_lora/qwen2vl_lora_sft.yaml
```
## 推理
使用vllm框架推理
### 单机单卡
- inference:
```
#注意：根据自己的模型切换文件中的模型位置并调整其他参数
cd /path/your_code_data/
python ./inference_vllm/single_dcu_inference.py
```
- OpenAI api服务推理:
运行以下命令来启动与 OpenAI 兼容的 API 服务：
```
python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-VL-7B-Instruct --model Qwen/Qwen2-VL-7B-Instruct
```
然后您可以按如下方式使用聊天 API（通过 curl 或 Python API）：
```
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Qwen2-VL-7B-Instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
{"type": "text", "text": "What is the text in the illustrate?"}
]}
]
}'
```
```
from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
chat_response = client.chat.completions.create(
model="Qwen2-VL-7B-Instruct",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"
},
},
{"type": "text", "text": "What is the text in the illustrate?"},
],
},
],
)
print("Chat response:", chat_response)
```
### 单机多卡
```
python ./inference_vllm/multi_dcu_inference.py
```
其中，MODEL_PATH为模型路径，tensor_parallel_size=4为使用卡数,messages为需要输入的内容。
## result
messages:
- "image"：https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png
<div align=left>
<img src="./assets/qwen.png"/>
</div>
- text:"What is the text in the illustrate?"
result:
<div align=left>
<img src="./assets/infer_result2.png"/>
</div>
### 精度
精度测试使用视觉-语言模型评估工具VLMEvalkit：
- 模型:Qwen2-VL-7B-Instruct
```
#可根据自己的要求切换模型和其他设置，调整卡的数量等
#4卡测试样例：
cd VLMEvalKit
torchrun --nproc-per-node=4 --master-port=29501 run.py --data MMMU_DEV_VAL DocVQA_VAL MMBench_DEV_EN --model Qwen2-VL-7B-Instruct --verbose
```
| Model Size | MMMU | DocVQA | MMBench |
| --- |-------| --- |---------|
| Qwen2-VL-7B-Instruct | 50.66 | 93.82 | 81.61 |
## 应用场景
### 算法类别
`对话问答`
### 热点应用行业
`科研,教育,政府,金融`
## 预训练权重
[Qwen2-VL-2B-Instruct模型下载SCNet链接](http://113.200.138.88:18080/aimodels/qwen/Qwen2-VL-2B-Instruct)
[Qwen2-VL-7B-Instruct模型下载SCNet链接](http://113.200.138.88:18080/aimodels/qwen/Qwen2-VL-7B-Instruct)
[Qwen2-VL-72B-Instruct模型下载SCNet链接](http://113.200.138.88:18080/aimodels/qwen/Qwen2-VL-72B-Instruct)
其他size的模型可在[SCNet](http://113.200.138.88:18080/aimodels/)进行搜索下载
## 源码仓库及问题反馈
- http://developer.hpccube.com/codes/modelzoo/qwen2_vl_pytorch.git
## 参考资料
- https://github.com/hiyouga/LLaMA-Factory
- https://github.com/QwenLM/Qwen2-VL
- https://github.com/open-compass/VLMEvalKit
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cancel-in-progress: true
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uses: actions/setup-python@v2
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python-version: 3.10.15
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run: |
pip install pre-commit
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(?x)^(
scripts/|
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)
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rev: v3.1.0
hooks:
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args: ["--remove"]
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![LOGO](http://opencompass.openxlab.space/utils/MMLB.jpg)
<b>A Toolkit for Evaluating Large Vision-Language Models. </b>
[![][github-contributors-shield]][github-contributors-link] • [![][github-forks-shield]][github-forks-link] • [![][github-stars-shield]][github-stars-link] • [![][github-issues-shield]][github-issues-link] • [![][github-license-shield]][github-license-link]
English | [简体中文](/docs/zh-CN/README_zh-CN.md) | [日本語](/docs/ja/README_ja.md)
<a href="https://rank.opencompass.org.cn/leaderboard-multimodal">🏆 OC Learderboard </a> •
<a href="#%EF%B8%8F-quickstart">🏗️Quickstart </a> •
<a href="#-datasets-models-and-evaluation-results">📊Datasets & Models </a> •
<a href="#%EF%B8%8F-development-guide">🛠️Development </a> •
<a href="#-the-goal-of-vlmevalkit">🎯Goal </a> •
<a href="#%EF%B8%8F-citation">🖊️Citation </a>
<a href="https://huggingface.co/spaces/opencompass/open_vlm_leaderboard">🤗 HF Leaderboard</a> •
<a href="https://huggingface.co/datasets/VLMEval/OpenVLMRecords">🤗 Evaluation Records</a> •
<a href="https://huggingface.co/spaces/opencompass/openvlm_video_leaderboard">🤗 HF Video Leaderboard</a> •
<a href="https://discord.gg/evDT4GZmxN">🔊 Discord</a> •
<a href="https://www.arxiv.org/abs/2407.11691">📝 Report</a>
</div>
**VLMEvalKit** (the python package name is **vlmeval**) is an **open-source evaluation toolkit** of **large vision-language models (LVLMs)**. It enables **one-command evaluation** of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt **generation-based evaluation** for all LVLMs, and provide the evaluation results obtained with both **exact matching** and **LLM-based answer extraction**.
## 🆕 News
> We have presented a [**comprehensive survey**](https://arxiv.org/pdf/2411.15296) on the evaluation of large multi-modality models, jointly with [**MME Team**](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models) and [**LMMs-Lab**](https://lmms-lab.github.io) 🔥🔥🔥
- **[2024-12-11]** Supported [**NaturalBench**](https://huggingface.co/datasets/BaiqiL/NaturalBench), a vision-centric VQA benchmark (NeurIPS'24) that challenges vision-language models with simple questions about natural imagery.
- **[2024-12-02]** Supported [**VisOnlyQA**](https://github.com/psunlpgroup/VisOnlyQA/), a benchmark for evaluating the visual perception capabilities 🔥🔥🔥
- **[2024-11-26]** Supported [**Ovis1.6-Gemma2-27B**](https://huggingface.co/AIDC-AI/Ovis1.6-Gemma2-27B), thanks to [**runninglsy**](https://github.com/runninglsy) 🔥🔥🔥
- **[2024-11-25]** Create a new flag `VLMEVALKIT_USE_MODELSCOPE`. By setting this environment variable, you can download the video benchmarks supported from [**modelscope**](https://www.modelscope.cn) 🔥🔥🔥
- **[2024-11-25]** Supported [**VizWiz**](https://vizwiz.org/tasks/vqa/) benchmark 🔥🔥🔥
- **[2024-11-22]** Supported the inference of [**MMGenBench**](https://mmgenbench.alsoai.com), thanks [**lerogo**](https://github.com/lerogo) 🔥🔥🔥
- **[2024-11-22]** Supported [**Dynamath**](https://huggingface.co/datasets/DynaMath/DynaMath_Sample), a multimodal math benchmark comprising of 501 SEED problems and 10 variants generated based on random seeds. The benchmark can be used to measure the robustness of MLLMs in multi-modal math solving 🔥🔥🔥
- **[2024-11-21]** Integrated a new config system to enable more flexible evaluation settings. Check the [Document](/docs/en/ConfigSystem.md) or run `python run.py --help` for more details 🔥🔥🔥
- **[2024-11-21]** Supported [**QSpatial**](https://andrewliao11.github.io/spatial_prompt/), a multimodal benchmark for Quantitative Spatial Reasoning (determine the size / distance, e.g.), thanks [**andrewliao11**](https://github.com/andrewliao11) for providing the official support 🔥🔥🔥
- **[2024-11-21]** Supported [**MM-Math**](https://github.com/kge-sun/mm-math), a new multimodal math benchmark comprising of ~6K middle school multi-modal reasoning math problems. GPT-4o-20240806 achieces 22.5% accuracy on this benchmark 🔥🔥🔥
## 🏗️ QuickStart
See [[QuickStart](/docs/en/Quickstart.md) | [快速开始](/docs/zh-CN/Quickstart.md)] for a quick start guide.
## 📊 Datasets, Models, and Evaluation Results
### Evaluation Results
**The performance numbers on our official multi-modal leaderboards can be downloaded from here!**
[**OpenVLM Leaderboard**](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard): [**Download All DETAILED Results**](http://opencompass.openxlab.space/assets/OpenVLM.json).
Check **Supported Benchmarks** Tab in [**VLMEvalKit Features**](https://aicarrier.feishu.cn/wiki/Qp7wwSzQ9iK1Y6kNUJVcr6zTnPe?table=tblsdEpLieDoCxtb) to view all supported image & video benchmarks (70+).
Check **Supported LMMs** Tab in [**VLMEvalKit Features**](https://aicarrier.feishu.cn/wiki/Qp7wwSzQ9iK1Y6kNUJVcr6zTnPe?table=tblsdEpLieDoCxtb) to view all supported LMMs, including commercial APIs, open-source models, and more (200+).
**Transformers Version Recommendation:**
Note that some VLMs may not be able to run under certain transformer versions, we recommend the following settings to evaluate each VLM:
- **Please use** `transformers==4.33.0` **for**: `Qwen series`, `Monkey series`, `InternLM-XComposer Series`, `mPLUG-Owl2`, `OpenFlamingo v2`, `IDEFICS series`, `VisualGLM`, `MMAlaya`, `ShareCaptioner`, `MiniGPT-4 series`, `InstructBLIP series`, `PandaGPT`, `VXVERSE`.
- **Please use** `transformers==4.36.2` **for**: `Moondream1`.
- **Please use** `transformers==4.37.0` **for**: `LLaVA series`, `ShareGPT4V series`, `TransCore-M`, `LLaVA (XTuner)`, `CogVLM Series`, `EMU2 Series`, `Yi-VL Series`, `MiniCPM-[V1/V2]`, `OmniLMM-12B`, `DeepSeek-VL series`, `InternVL series`, `Cambrian Series`, `VILA Series`, `Llama-3-MixSenseV1_1`, `Parrot-7B`, `PLLaVA Series`.
- **Please use** `transformers==4.40.0` **for**: `IDEFICS2`, `Bunny-Llama3`, `MiniCPM-Llama3-V2.5`, `360VL-70B`, `Phi-3-Vision`, `WeMM`.
- **Please use** `transformers==4.44.0` **for**: `Moondream2`, `H2OVL series`.
- **Please use** `transformers==4.45.0` **for**: `Aria`.
- **Please use** `transformers==latest` **for**: `LLaVA-Next series`, `PaliGemma-3B`, `Chameleon series`, `Video-LLaVA-7B-HF`, `Ovis series`, `Mantis series`, `MiniCPM-V2.6`, `OmChat-v2.0-13B-sinlge-beta`, `Idefics-3`, `GLM-4v-9B`, `VideoChat2-HD`, `RBDash_72b`, `Llama-3.2 series`, `Kosmos series`.
**Torchvision Version Recommendation:**
Note that some VLMs may not be able to run under certain torchvision versions, we recommend the following settings to evaluate each VLM:
- **Please use** `torchvision>=0.16` **for**: `Moondream series` and `Aria`
**Flash-attn Version Recommendation:**
Note that some VLMs may not be able to run under certain flash-attention versions, we recommend the following settings to evaluate each VLM:
- **Please use** `pip install flash-attn --no-build-isolation` **for**: `Aria`
```python
# Demo
from vlmeval.config import supported_VLM
model = supported_VLM['idefics_9b_instruct']()
# Forward Single Image
ret = model.generate(['assets/apple.jpg', 'What is in this image?'])
print(ret) # The image features a red apple with a leaf on it.
# Forward Multiple Images
ret = model.generate(['assets/apple.jpg', 'assets/apple.jpg', 'How many apples are there in the provided images? '])
print(ret) # There are two apples in the provided images.
```
## 🛠️ Development Guide
To develop custom benchmarks, VLMs, or simply contribute other codes to **VLMEvalKit**, please refer to [[Development_Guide](/docs/en/Development.md) | [开发指南](/docs/zh-CN/Development.md)].
**Call for contributions**
To promote the contribution from the community and share the corresponding credit (in the next report update):
- All Contributions will be acknowledged in the report.
- Contributors with 3 or more major contributions (implementing an MLLM, benchmark, or major feature) can join the author list of [VLMEvalKit Technical Report](https://www.arxiv.org/abs/2407.11691) on ArXiv. Eligible contributors can create an issue or dm kennyutc in [VLMEvalKit Discord Channel](https://discord.com/invite/evDT4GZmxN).
Here is a [contributor list](/docs/en/Contributors.md) we curated based on the records.
## 🎯 The Goal of VLMEvalKit
**The codebase is designed to:**
1. Provide an **easy-to-use**, **opensource evaluation toolkit** to make it convenient for researchers & developers to evaluate existing LVLMs and make evaluation results **easy to reproduce**.
2. Make it easy for VLM developers to evaluate their own models. To evaluate the VLM on multiple supported benchmarks, one just need to **implement a single `generate_inner()` function**, all other workloads (data downloading, data preprocessing, prediction inference, metric calculation) are handled by the codebase.
**The codebase is not designed to:**
1. Reproduce the exact accuracy number reported in the original papers of all **3rd party benchmarks**. The reason can be two-fold:
1. VLMEvalKit uses **generation-based evaluation** for all VLMs (and optionally with **LLM-based answer extraction**). Meanwhile, some benchmarks may use different approaches (SEEDBench uses PPL-based evaluation, *eg.*). For those benchmarks, we compare both scores in the corresponding result. We encourage developers to support other evaluation paradigms in the codebase.
2. By default, we use the same prompt template for all VLMs to evaluate on a benchmark. Meanwhile, **some VLMs may have their specific prompt templates** (some may not covered by the codebase at this time). We encourage VLM developers to implement their own prompt template in VLMEvalKit, if that is not covered currently. That will help to improve the reproducibility.
## 🖊️ Citation
If you find this work helpful, please consider to **star🌟** this repo. Thanks for your support!
[![Stargazers repo roster for @open-compass/VLMEvalKit](https://reporoster.com/stars/open-compass/VLMEvalKit)](https://github.com/open-compass/VLMEvalKit/stargazers)
If you use VLMEvalKit in your research or wish to refer to published OpenSource evaluation results, please use the following BibTeX entry and the BibTex entry corresponding to the specific VLM / benchmark you used.
```bib
@inproceedings{duan2024vlmevalkit,
title={Vlmevalkit: An open-source toolkit for evaluating large multi-modality models},
author={Duan, Haodong and Yang, Junming and Qiao, Yuxuan and Fang, Xinyu and Chen, Lin and Liu, Yuan and Dong, Xiaoyi and Zang, Yuhang and Zhang, Pan and Wang, Jiaqi and others},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={11198--11201},
year={2024}
}
```
<p align="right"><a href="#top">🔝Back to top</a></p>
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[github-forks-shield]: https://img.shields.io/github/forks/open-compass/VLMEvalKit?color=8ae8ff&labelColor=black&style=flat-square
[github-issues-link]: https://github.com/open-compass/VLMEvalKit/issues
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version: 2
# Set the version of Python and other tools you might need
build:
os: ubuntu-22.04
tools:
python: "3.8"
formats:
- epub
sphinx:
configuration: docs/en/conf.py
python:
install:
- requirements: requirements/docs.txt
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# Config System
By default, VLMEvalKit launches the evaluation by setting the model name(s) (defined in `/vlmeval/config.py`) and dataset name(s) (defined in `vlmeval/dataset/__init__.py` or `vlmeval/dataset/video_dataset_config.py`) in the `run.py` script with the `--model` and `--data` arguments. Such approach is simple and efficient in most scenarios, however, it may not be flexible enough when the user wants to evaluate multiple models / datasets with different settings.
To address this, VLMEvalKit provides a more flexible config system. The user can specify the model and dataset settings in a json file, and pass the path to the config file to the `run.py` script with the `--config` argument. Here is a sample config json:
```json
{
"model": {
"GPT4o_20240806_T00_HIGH": {
"class": "GPT4V",
"model": "gpt-4o-2024-08-06",
"temperature": 0,
"img_detail": "high"
},
"GPT4o_20240806_T10_Low": {
"class": "GPT4V",
"model": "gpt-4o-2024-08-06",
"temperature": 1.0,
"img_detail": "low"
},
"GPT4o_20241120": {}
},
"data": {
"MME-RealWorld-Lite": {
"class": "MMERealWorld",
"dataset": "MME-RealWorld-Lite"
},
"MMBench_DEV_EN_V11": {
"class": "ImageMCQDataset",
"dataset": "MMBench_DEV_EN_V11"
},
"MMBench_Video_8frame_nopack":{},
"Video-MME_16frame_subs": {
"class": "VideoMME",
"dataset": "Video-MME",
"nframe": 16,
"use_subtitle": true
},
}
}
```
Explanation of the config json:
1. Now we support two fields: `model` and `data`, each of which is a dictionary. The key of the dictionary is the name of the model / dataset (set by the user), and the value is the setting of the model / dataset.
2. For items in `model`, the value is a dictionary containing the following keys:
- `class`: The class name of the model, which should be a class name defined in `vlmeval/vlm/__init__.py` (open-source models) or `vlmeval/api/__init__.py` (API models).
- Other kwargs: Other kwargs are model-specific parameters, please refer to the definition of the model class for detailed usage. For example, `model`, `temperature`, `img_detail` are arguments of the `GPT4V` class. It's noteworthy that the `model` argument is required by most model classes.
- Tip: The defined model in the `supported_VLM` of `vlmeval/config.py` can be used as a shortcut, for example, `GPT4o_20241120: {}` is equivalent to `GPT4o_20241120: {'class': 'GPT4V', 'model': 'gpt-4o-2024-11-20', 'temperature': 0, 'img_size': -1, 'img_detail': 'high', 'retry': 10, 'verbose': False}`
3. For the dictionary `data`, we suggest users to use the official dataset name as the key (or part of the key), since we frequently determine the post-processing / judging settings based on the dataset name. For items in `data`, the value is a dictionary containing the following keys:
- `class`: The class name of the dataset, which should be a class name defined in `vlmeval/dataset/__init__.py`.
- Other kwargs: Other kwargs are dataset-specific parameters, please refer to the definition of the dataset class for detailed usage. Typically, the `dataset` argument is required by most dataset classes. It's noteworthy that the `nframe` argument or `fps` argument is required by most video dataset classes.
- Tip: The defined dataset in the `supported_video_datasets` of `vlmeval/dataset/video_dataset_config.py` can be used as a shortcut, for example, `MMBench_Video_8frame_nopack: {}` is equivalent to `MMBench_Video_8frame_nopack: {'class': 'MMBenchVideo', 'dataset': 'MMBench-Video', 'nframe': 8, 'pack': False}`.
Saving the example config json to `config.json`, you can launch the evaluation by:
```bash
python run.py --config config.json
```
That will generate the following output files under the working directory `$WORK_DIR` (Following the format `{$WORK_DIR}/{$MODEL_NAME}/{$MODEL_NAME}_{$DATASET_NAME}_*`):
- `$WORK_DIR/GPT4o_20240806_T00_HIGH/GPT4o_20240806_T00_HIGH_MME-RealWorld-Lite*`
- `$WORK_DIR/GPT4o_20240806_T10_Low/GPT4o_20240806_T10_Low_MME-RealWorld-Lite*`
- `$WORK_DIR/GPT4o_20240806_T00_HIGH/GPT4o_20240806_T00_HIGH_MMBench_DEV_EN_V11*`
- `$WORK_DIR/GPT4o_20240806_T10_Low/GPT4o_20240806_T10_Low_MMBench_DEV_EN_V11*`
...
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# Contributors
## Contributors w. 3+ Major Contributions
> In this section, we list all the contributors who have made significant contributions (3+) to the development of VLMEvalKit.
New Qualified Contributors (2024.09):
1. [amitbcp](https://github.com/amitbcp): The contributor helped support MUIRBench, Phi-3.5, Idefics3, VILA, and xGen-MM
2. [czczup](https://github.com/czczup): The contributor helped support the InternVL Series (V1.5, Mini-InternVL, V2, etc.)
3. [DseidLi](https://github.com/DseidLi): The contributor helped support LLaVA-OneVision, GQA, and developed the readthedocs site for VLMEvalKit
4. [mayubo2333](https://github.com/mayubo2333): The contributor helped support MMLongBench, SlideVQA, and DUDE
5. [sun-hailong](https://github.com/sun-hailong): The contributor helped support A-OKVQA, Parrot, MMMB, and MTL-MMBench
6. [PhoenixZ810](https://github.com/PhoenixZ810): The contributor helped support Video-ChatGPT, Chat-UniVI, and Llama-VID
7. [Cuiunbo](https://github.com/Cuiunbo): The contributor helped support OmniLMM-12B, MiniCPM-V Series (V1, V2, V2.5)
## Full Contributor List
> In this section, we list all the contributors as well as their corresponding contributions to the development of VLMEvalKit.
TBD.
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# Develop new Benchmark / MLLM
> 🛠️ How to implement a new Benchmark / VLM in VLMEvalKit?
## Implement a new benchmark
Example PR: **Math-Vision Benchmark** ([#292](https://github.com/open-compass/VLMEvalKit/pull/292/files))
In VLMEvalKit, benchmarks are organized as dataset classes. When you try to implement a new benchmark, you can either reuse existing dataset classes (*e.g.*, You can reuse `ImageMCQDataset` when implementing a new multi-choice benchmark), or support a new dataset class. Each dataset must have the following two member functions (either reuse the one of the parent class or implement your own):
- `build_prompt(self, line)`: The function input `line` is an integer (the sample index) or a `pd.Series` object (the raw record of the sample). The function outputs a `multi-modal message`, serving as the input of an MLLM. The `multi-modal message` is an interleaved list of multi-modal messages adopting the following format (the example includes an image and a text message): `[dict(type='image', value=IMAGE_PTH), dict(type='text', value=prompt)]`.
- `evaluate(self, eval_file, **judge_kwargs)`: The function input `eval_file` is the MLLM prediction (typically in `.xlsx` format). If the benchmark requires an external LLM (typically GPT) for evaluation, then `judge_kwargs` can pass the arguments for the LLM. The function outputs the benchmark evaluation results (metrics) in the form of `dict` or `pd.DataFrame`.
We then brief the typical steps to implement a new benchmark under VLMEvalKit:
### 1. Prepare your benchmark tsv file
Currently, we organize a benchmark as one single TSV file. During inference, the data file will be automatically downloaded from the definited `DATASET_URL` link to `$LMUData` file (default path is `$HOME/LMUData`, if not set explicitly). You can upload the prepared TSV file to a downloadable address (e.g., Huggingface) or send it to us at <opencompass@pjlab.org.cn>. We will assist in uploading the dataset to the server. You can also customize `LMUData` path in the environment variable `LMUData=/path/to/your/data`.
The contents of the TSV file consist of:
| Dataset Name \ Fields | index | image | image_path | question | hint | multi-choice<br>options | answer | category | l2-category | split |
| --------------------------------------- | ----- | ----- | ---------- | -------- | ---- | ----------------------- | ------ | -------- | ----------- | ----- |
| MMBench_DEV_[CN/EN] | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
| MMBench_TEST_[CN/EN] | ✅ | ✅ | | ✅ | ✅ | ✅ | | ✅ | ✅ | ✅ |
| CCBench | ✅ | ✅ | | ✅ | | ✅ | ✅ | ✅ | | |
| SEEDBench_IMG | ✅ | ✅ | | ✅ | | ✅ | ✅ | ✅ | | |
| MME | ✅ | ✅ | | ✅ | | | ✅ | ✅ | | |
| CORE_MM | ✅ | ✅ | ✅ | ✅ | | | | ✅ | | |
| MMVet | ✅ | ✅ | | ✅ | | | ✅ | ✅ | | |
| MMMU_DEV_VAL | ✅ | ✅ | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ |
| COCO_VAL | ✅ | ✅ | | | | | ✅ | | | |
| OCRVQA_[TEST/TESTCORE] | ✅ | ✅ | | ✅ | | | ✅ | | | |
| TextVQA_VAL | ✅ | ✅ | | ✅ | | | ✅ | | | |
| VCR_[EN/ZH]\_[EASY/HARD]\_[ALL/500/100] | ✅ | ✅ | | ✅ | | | ✅ | | | |
| MMMB_[en/cn/pt/ar/tr/ru] | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | |✅ |
| MMBench_dev_[en/cn/pt/ar/tr/ru] | ✅ | ✅ | | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |✅ |
<div align="center"><b>Table 1. TSV fields of supported datasets.</b></div>
**Intro to mandatory fields in the `TSV` file:**
- **index:** Integer, Unique for each line in `tsv`
- **image:** The base64 of the image, you can use APIs implemented in `vlmeval/smp/vlm.py` for encoding and decoding:
- Encoding: `encode_image_to_base64 `(for PIL Image) / `encode_image_file_to_base64` (for image file path)
- Decoding: `decode_base64_to_image`(for PIL Image) / `decode_base64_to_image_file` (for image file path)
- **question**: The question corresponding to the image, a string
- **answer**: The answer to the question, a string. The `test` split does not need this field
### 2. Cutomize your benchmark prompt
`ImageBaseDataset` defines the default prompt format. If you need to add prompts specific to the dataset or input data in the `Interleave` format to the model, you can implement this through the `build_prompt(line)` function. This function takes a line from a TSV file as input, containing fields such as index, image, question, etc. The function returns a dictionary list of multimodal messages `msg` in the format `[dict(type='image', value=IMAGE_PTH), dict(type='text', value=prompt)]`, including the image path and the text prompt to be input into VLMs. For interleave type inputs, you can directly place the dictionary of the image path at the image token position.
### 3. Cutomize your benchmark metrics
To add evaluation for a new benchmark, you need to customize a class object to implement the dataset’s metrics calculation. Multimodal datasets inherit from the `ImageBaseDataset` object in `vlmeval/dataset/image_base.py`. The TYPE defines the type of dataset, `DATASET_URL` is the download address of the dataset, and `DATASET_MD5` is the MD5 checksum for consistency checking of the dataset file.
In this class, **you need to implement** the `evaluate(eval_file, **judge_kwargs)` class function to calculate metrics and output results for the custom dataset. The function input `eval_file` is the path to the model prediction results file `{model_name}_{dataset}.xlsx`. This file can be read as a pandas.DataFrame using the `load(eval_file)` method, containing fields such as index, question, answer, category, prediction, etc. The judge_kwargs will pass a dictionary related to evaluation, such as the name of the `judge model`, the number of API request threads, etc. **The return value** of the function is the calculated accuracy and other metrics, formatted as a dictionary composed of lists, organized into a pandas.DataFrame.
## Implement a new model
Example PR: **Support LLaVA-Next-Interleave** ([#294](https://github.com/open-compass/VLMEvalKit/pull/294))
**1. Support `generate_inner` API (mandatory).**
All existing models are implemented in `vlmeval/vlm`. For a minimal model, your model class **must implement the method** `generate_inner(msgs, dataset=None)`. In this function, you feed a multi-modal message to your VLM and return the VLM prediction (which is a string). The optional argument `dataset` can be used as the flag for the model to switch among various inference strategies.
The multi-modal messages `msgs` is a list of dictionaries, each dictionary has two keys: type and value:
- `type`: We currently support two types, choices are ["image", "text"].
- `value`: When type=='text' , the value is the text message (a single string); when type=='image', the value can be the local path of an image file, or the image URL.
Currently a multi-modal message may contain arbitrarily interleaved images and texts. If your model do not support that, a practice can be taking the 1st image and concatenated text messages as the input. You can set the `INTERLEAVE = False` in your model class and use `self.message_to_promptimg(message, dataset=dataset)` to build your prompt and the first image's path.
Here are some examples of multi-modal messages:
```python
IMAGE_PTH = 'assets/apple.jpg'
IMAGE_URL = 'https://raw.githubusercontent.com/open-compass/VLMEvalKit/main/assets/apple.jpg'
msg1 = [
dict(type='image', value=IMAGE_PTH),
dict(type='text', value='What is in this image?')
]
msg2 = [
dict(type='image', value=IMAGE_URL),
dict(type='image', value=IMAGE_URL),
dict(type='text', value='How many apples are there in these images?')
]
response = model.generate(msg1)
```
For convenience sake, we also support to take a list of string as inputs. In that case, we will check if a string is an image path or image URL and automatically convert it to the list[dict] format:
```python
IMAGE_PTH = 'assets/apple.jpg'
IMAGE_URL = 'https://raw.githubusercontent.com/open-compass/VLMEvalKit/main/assets/apple.jpg'
msg1 = [IMAGE_PTH, 'What is in this image?']
msg2 = [IMAGE_URL, IMAGE_URL, 'How many apples are there in these images?']
response = model.generate(msg1)
```
**Support Custom Prompt (optional).**
Besides, your model can support **custom prompt building** by implementing two optional methods: `use_custom_prompt(dataset)` and `build_prompt(line, dataset=None)`.
Both functions take the dataset name as the input：
- `use_custom_prompt(dataset)` returns a boolean flag, indicating whether the model should use the custom prompt building strategy.
- If `use_custom_prompt(dataset)` returns True, `build_prompt(line, dataset)` should return a customly bulit multimodal message for the corresponding `dataset`, given `line`, which is a dictionary that includes the necessary information of a data sample. If `use_custom_prompt(dataset)` returns False, the default prompt building strategy will be used.
**Support multi-turn chatting (optional).**
You can also support the multi-turn chatting and evaluation with your VLM by supporting the `chat_inner(message, dataset)` function. The function outputs a single string response, and the `message` is a list of chat history, following the below format.
```python
# Assume msg1, msg2, msg3, ... are multi-modal messages following the previously described format
# `chat_inner` take the following chat history list as input:
message = [
dict(role='user', content=msg1),
dict(role='assistant', content=msg2),
dict(role='user', content=msg3),
dict(role='assistant', content=msg4),
......
dict(role='user', content=msgn),
]
# `message` should contain an odd number of chat utterances, the role of utterances should be interleaved "user" and "assistant", with the role of the last utterance to be "user".
# The chat function will call `chat_inner`
response = model.chat(message)
```
### Example PRs:
- VLM that doesn't support interleaved images and texts, and does not use custom prompts: [[Model] Support glm-4v-9b](https://github.com/open-compass/VLMEvalKit/pull/221)
- VLM that supports interleaved images and texts and custom prompts: [Add MiniCPM-Llama3-V-2.5](https://github.com/open-compass/VLMEvalKit/pull/205)
- VLM API: [Feature add glmv](https://github.com/open-compass/VLMEvalKit/pull/201)
## Contribute to VLMEvalKit
If you want to contribute codes to **VLMEvalKit**, please do the pre-commit check before you submit a PR. That helps to keep the code tidy.
```bash
# Under the directory of VLMEvalKit, install the pre-commit hook:
pip install pre-commit
pre-commit install
pre-commit run --all-files
# Then you can commit your code.
```
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# Using LMDeploy to Accelerate Evaluation and Inference
VLMEvalKit supports testing VLM models deployed by LMDeploy. Below, we use InternVL2-8B as an example to show how to test the model.
## Step 0: Install LMDeploy
```bash
pip install lmdeploy
```
For other installation methods, you can refer to LMDeploy's [documentation](https://github.com/InternLM/lmdeploy).
## Step 1: Start the Inference Service
```bash
lmdeploy serve api_server OpenGVLab/InternVL2-8B --model-name InternVL2-8B
```
> [!IMPORTANT]
> Since models in VLMEvalKit may have custom behaviors when building prompts for different datasets, such as InternVL2's handling of HallusionBench, it is necessary to specify `--model-name` when starting the server. This allows the VLMEvalKit to select appropriate prompt construction strategy based on the name when using the LMDeploy API.
>
> If `--server-port`, is specified, the corresponding environment variable `LMDEPLOY_API_BASE` needs to be set.
## Step 2: Evaluation
```bash
python run.py --data MMStar --model lmdeploy --verbose --nproc 64
```
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# Minimal makefile for Sphinx documentation
#
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SPHINXOPTS ?=
SPHINXBUILD ?= sphinx-build
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BUILDDIR = _build
# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
.PHONY: help Makefile
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# Quickstart
Before running the evaluation script, you need to **configure** the VLMs and set the model_paths properly.
After that, you can use a single script `run.py` to inference and evaluate multiple VLMs and benchmarks at a same time.
## Step 0. Installation & Setup essential keys
**Installation.**
```bash
git clone https://github.com/open-compass/VLMEvalKit.git
cd VLMEvalKit
pip install -e .
```
**Setup Keys.**
To infer with API models (GPT-4v, Gemini-Pro-V, etc.) or use LLM APIs as the **judge or choice extractor**, you need to first setup API keys. VLMEvalKit will use an judge **LLM** to extract answer from the output if you set the key, otherwise it uses the **exact matching** mode (find "Yes", "No", "A", "B", "C"... in the output strings). **The exact matching can only be applied to the Yes-or-No tasks and the Multi-choice tasks.**
- You can place the required keys in `$VLMEvalKit/.env` or directly set them as the environment variable. If you choose to create a `.env` file, its content will look like:
```bash
# The .env file, place it under $VLMEvalKit
# API Keys of Proprietary VLMs
# QwenVL APIs
DASHSCOPE_API_KEY=
# Gemini w. Google Cloud Backends
GOOGLE_API_KEY=
# OpenAI API
OPENAI_API_KEY=
OPENAI_API_BASE=
# StepAI API
STEPAI_API_KEY=
# REKA API
REKA_API_KEY=
# GLMV API
GLMV_API_KEY=
# CongRong API
CW_API_BASE=
CW_API_KEY=
# SenseChat-V API
SENSECHAT_AK=
SENSECHAT_SK=
# Hunyuan-Vision API
HUNYUAN_SECRET_KEY=
HUNYUAN_SECRET_ID=
# LMDeploy API
LMDEPLOY_API_BASE=
# You can also set a proxy for calling api models during the evaluation stage
EVAL_PROXY=
```
- Fill the blanks with your API keys (if necessary). Those API keys will be automatically loaded when doing the inference and evaluation.
## Step 1. Configuration
**VLM Configuration**: All VLMs are configured in `vlmeval/config.py`. Few legacy VLMs (like MiniGPT-4, LLaVA-v1-7B) requires additional configuration (configuring the code / model_weight root in the config file). During evaluation, you should use the model name specified in `supported_VLM` in `vlmeval/config.py` to select the VLM. Make sure you can successfully infer with the VLM before starting the evaluation with the following command `vlmutil check {MODEL_NAME}`.
## Step 2. Evaluation
**New!!!** We integrated a new config system to enable more flexible evaluation settings. Check the [Document](/docs/en/ConfigSystem.md) or run `python run.py --help` for more details 🔥🔥🔥
We use `run.py` for evaluation. To use the script, you can use `$VLMEvalKit/run.py` or create a soft-link of the script (to use the script anywhere):
**Arguments**
- `--data (list[str])`: Set the dataset names that are supported in VLMEvalKit (names can be found in the codebase README).
- `--model (list[str])`: Set the VLM names that are supported in VLMEvalKit (defined in `supported_VLM` in `vlmeval/config.py`).
- `--mode (str, default to 'all', choices are ['all', 'infer'])`: When `mode` set to "all", will perform both inference and evaluation; when set to "infer", will only perform the inference.
- `--nproc (int, default to 4)`: The number of threads for OpenAI API calling.
- `--work-dir (str, default to '.')`: The directory to save evaluation results.
**Command for Evaluating Image Benchmarks **
You can run the script with `python` or `torchrun`:
```bash
# When running with `python`, only one VLM instance is instantiated, and it might use multiple GPUs (depending on its default behavior).
# That is recommended for evaluating very large VLMs (like IDEFICS-80B-Instruct).
# IDEFICS-80B-Instruct on MMBench_DEV_EN, MME, and SEEDBench_IMG, Inference and Evalution
python run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct --verbose
# IDEFICS-80B-Instruct on MMBench_DEV_EN, MME, and SEEDBench_IMG, Inference only
python run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct --verbose --mode infer
# When running with `torchrun`, one VLM instance is instantiated on each GPU. It can speed up the inference.
# However, that is only suitable for VLMs that consume small amounts of GPU memory.
# IDEFICS-9B-Instruct, Qwen-VL-Chat, mPLUG-Owl2 on MMBench_DEV_EN, MME, and SEEDBench_IMG. On a node with 8 GPU. Inference and Evaluation.
torchrun --nproc-per-node=8 run.py --data MMBench_DEV_EN MME SEEDBench_IMG --model idefics_80b_instruct qwen_chat mPLUG-Owl2 --verbose
# Qwen-VL-Chat on MME. On a node with 2 GPU. Inference and Evaluation.
torchrun --nproc-per-node=2 run.py --data MME --model qwen_chat --verbose
```
**Command for Evaluating Video Benchmarks**
```bash
# When running with `python`, only one VLM instance is instantiated, and it might use multiple GPUs (depending on its default behavior).
# That is recommended for evaluating very large VLMs (like IDEFICS-80B-Instruct).
# IDEFICS2-8B on MMBench-Video, with 8 frames as inputs and vanilla evaluation. On a node with 8 GPUs. MMBench_Video_8frame_nopack is a defined dataset setting in `vlmeval/dataset/video_dataset_config.py`.
torchrun --nproc-per-node=8 run.py --data MMBench_Video_8frame_nopack --model idefics2_8
# GPT-4o (API model) on MMBench-Video, with 1 frame per second as inputs and pack evaluation (all questions of a video in a single query).
python run.py --data MMBench_Video_1fps_pack --model GPT4o
```
The evaluation results will be printed as logs, besides. **Result Files** will also be generated in the directory `$YOUR_WORKING_DIRECTORY/{model_name}`. Files ending with `.csv` contain the evaluated metrics.
## Deploy a local language model as the judge / choice extractor
The default setting mentioned above uses OpenAI's GPT as the judge LLM. However, you can also deploy a local judge LLM with [LMDeploy](https://github.com/InternLM/lmdeploy).
First install:
```
pip install lmdeploy openai
```
And then deploy a local judge LLM with the single line of code. LMDeploy will automatically download the model from Huggingface. Assuming we use internlm2-chat-1_8b as the judge, port 23333, and the key sk-123456 (the key must start with "sk-" and follow with any number you like):
```
lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333
```
You need to get the model name registered by LMDeploy with the following python code:
```
from openai import OpenAI
client = OpenAI(
api_key='sk-123456',
base_url="http://0.0.0.0:23333/v1"
)
model_name = client.models.list().data[0].id
```
Now set some environment variables to tell VLMEvalKit how to use the local judge LLM. As mentioned above, you can also set them in `$VLMEvalKit/.env` file:
```
OPENAI_API_KEY=sk-123456
OPENAI_API_BASE=http://0.0.0.0:23333/v1/chat/completions
LOCAL_LLM=<model_name you get>
```
Finally, you can run the commands in step 2 to evaluate your VLM with the local judge LLM.
Note that
- If you hope to deploy the judge LLM in a single GPU and evaluate your VLM on other GPUs because of limited GPU memory, try `CUDA_VISIBLE_DEVICES=x` like
```
CUDA_VISIBLE_DEVICES=0 lmdeploy serve api_server internlm/internlm2-chat-1_8b --server-port 23333
CUDA_VISIBLE_DEVICES=1,2,3 torchrun --nproc-per-node=3 run.py --data HallusionBench --model qwen_chat --verbose
```
- If the local judge LLM is not good enough in following the instructions, the evaluation may fail. Please report such failures (e.g., by issues).
- It's possible to deploy the judge LLM in different ways, e.g., use a private LLM (not from HuggingFace) or use a quantized LLM. Please refer to the [LMDeploy doc](https://lmdeploy.readthedocs.io/en/latest/serving/api_server.html). You can use any other deployment framework if they support OpenAI API.
### Using LMDeploy to Accelerate Evaluation and Inference
You can refer this [doc](/docs/en/EvalByLMDeploy.md)
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