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**/__pycache__
checkpoints/
.ipynb_checkpoints/
wandb/
THU-Cloud-Downloader/
data/
feat/
backup/
proxy.sh
\ No newline at end of file
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README.md 0 → 100644
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# VTimeLLM
## 论文
`VTimeLLM: Empower LLM to Grasp Video Moments`
- https://arxiv.org/abs/2311.18445
## 模型结构
VTimeLLM有以下两个部分组成：1、一个视觉编码器和一个视觉适配器来处理输入视频；2、 一个特制的LLM过三阶段预训练来使模型同时具有grounding和chat能力
阶段一：图文对齐，通过图片-文本对训练将视觉特征与LLM在语义空间对齐；
阶段二：设计了密集Video Caption的单轮QA任务和包括片段描述&时序grounding的多轮的QA任务，使VTimeLLM具有时序感知的能力，可以定位视频的segmentation；
阶段三：创造了一个高质量的对话数据集来指令微调，来和人类意图对齐。
<div align=center>
<img src="./doc/VTimeLLM.PNG"/>
</div>
## 算法原理
Visual Encoder：利用CLIP ViT-L/14模型对每一帧获取cls token的feature和每个patch的feature，其中采用cls token的特征v_cls作为图片的feature
Visual Adapter：一个线性层，对每一帧的v_cls做变换，映射到LLM空间，最后视频由N*d的特征Z表示（N为帧数，d为LLM的隐层维度），这里均匀采样100帧
Vicuna：即LLM，用<video>来代表视频内容，将视觉特征Z嵌入到text的embedding中间
<div align=center>
<img src="./doc/ExpNet.PNG"/>
</div>
<div align=center>
<img src="./doc/PoseVAE.PNG"/>
</div>
<div align=center>
<img src="./doc/FaceRender.PNG"/>
</div>
## 环境配置
### Docker（方法一）
```
docker pull image.sourcefind.cn:5000/dcu/admin/base/jupyterlab-pytorch:2.1.0-ubuntu20.04-dtk24.04.2-py3.8
docker run -it --name=SadTalker --network=host --privileged=true --device=/dev/kfd --device=/dev/dri --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /path/your_code_data:/path/SadTalker -v /opt/hyhal/:/opt/hyhal/:ro <imageID> bash # <imageID>为以上拉取的docker的镜像ID替换
cd SadTalker
# 安装ffmpeg：格式转换相关
apt update
apt install ffmpeg
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install tb-nightly -i https://mirrors.aliyun.com/pypi/simple
pip install -r requirements.txt
```
### Dockerfile（方法二）
```
docker build --no-cache -t sadtalker:latest .
docker run -it --name=SadTalker --network=host --privileged=true --device=/dev/kfd --device=/dev/dri --shm-size=16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /path/your_code_data:/path/SadTalker -v /opt/hyhal/:/opt/hyhal/:ro sadtalker /bin/bash
cd SadTalker
# 安装ffmpeg：格式转换相关
apt update
apt install ffmpeg
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install tb-nightly -i https://mirrors.aliyun.com/pypi/simple
pip install -r requirements.txt
```
### Anaconda（方法三）
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/
```
DTK软件栈：dtk24.04.2
python：python3.8
pytorch：2.1.0
torchvision：
torchaudio：
```
`Tips：以上dtk软件栈、python、pytorch等DCU相关工具版本需要严格一一对应`
2、其他非特殊库直接按照下面步骤进行安装
```
cd SadTalker
# 安装ffmpeg：格式转换相关
apt update
apt install ffmpeg
# 安装依赖
pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
pip install tb-nightly -i https://mirrors.aliyun.com/pypi/simple
pip install -r requirements.txt
```
## 数据集
推理测试所用数据已保存在SadTalker/dataset/下，目录结构如下：
```
── dataset
│   ├── bus_chinese.wav
│   └── image.png
```
## 训练
官方暂未开放
## 推理
模型可通过[scnet](http://113.200.138.88:18080/aimodels/findsource-dependency/sadtalker)或以下方式进行下载：
1-1、Pre-Trained Models
* [Google Drive](https://drive.google.com/file/d/1gwWh45pF7aelNP_P78uDJL8Sycep-K7j/view?usp=sharing)
* [GitHub Releases](https://github.com/OpenTalker/SadTalker/releases)
* [Baidu (百度云盘)](https://pan.baidu.com/s/1kb1BCPaLOWX1JJb9Czbn6w?pwd=sadt) (Password: `sadt`)
1-2、GFPGAN Offline Patch
* [Google Drive](https://drive.google.com/file/d/19AIBsmfcHW6BRJmeqSFlG5fL445Xmsyi?usp=sharing)
* [GitHub Releases](https://github.com/OpenTalker/SadTalker/releases)
* [Baidu (百度云盘)](https://pan.baidu.com/s/1P4fRgk9gaSutZnn8YW034Q?pwd=sadt) (Password: `sadt`)
2、运行自动下载（GitHub Releases）：
```
cd SadTalker
sh scripts/download_models.sh
```
模型目录结构如下，checkpoints是预训练模型，gfpgan是人脸检测和增强模型：
```
── checkpoints
│   └── ...
── gfpgan
│   └── weights
│    └── ...
```
推理运行代码：
```
HIP_VISIBLE_DEVICES=0 python inference.py \
--driven_audio dataset/bus_chinese.wav \
--source_image dataset/image.png \
--still \
--preprocess full \
--enhancer gfpgan \
--result_dir result/
# --driven_audio 音频数据的路径
# --source_image 图片数据的路径
# --still 使用与原始图像相同的姿势参数，头部运动较少
# --preprocess full 对图像进行['crop', 'extcrop', 'resize', 'full', 'extfull']预处理
# --enhancer 使用或通过人脸修复网络[gfpgan, RestoreFormer]增强生成的人脸
# --result_dir 输出路径
# 更多参数设置可参考inference.py的parser注释和docs/best_practice.md
```
## result
推理运行的默认推理结果为：
<div align=center>
<video src="./doc/inference_result.mp4"/>
</div>
### 精度
无
## 应用场景
### 算法类别
`视频生成`
### 热点应用行业
`家具,电商,医疗,广媒,教育`
## 预训练权重
-
- http://113.200.138.88:18080/aimodels/vicuna-7b-v1.5.git (vicuna-7b-v1.5)
http://113.200.138.88:18080/aimodels/chatglm3-6b (chatglm3-6b)
## 源码仓库及问题反馈
-
## 参考资料
- https://github.com/huangb23/VTimeLLM
README_origin.md 0 → 100644
View file @ fef630ee
# VTimeLLM \[[Paper](https://arxiv.org/pdf/2311.18445.pdf)\]
Official PyTorch implementation of the paper "VTimeLLM: Empower LLM to Grasp Video Moments".
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance-4)](https://paperswithcode.com/sota/video-based-generative-performance-4?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/dense-video-captioning-on-activitynet)](https://paperswithcode.com/sota/dense-video-captioning-on-activitynet?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance-3)](https://paperswithcode.com/sota/video-based-generative-performance-3?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance-1)](https://paperswithcode.com/sota/video-based-generative-performance-1?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance-5)](https://paperswithcode.com/sota/video-based-generative-performance-5?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance-2)](https://paperswithcode.com/sota/video-based-generative-performance-2?p=vtimellm-empower-llm-to-grasp-video-moments)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/vtimellm-empower-llm-to-grasp-video-moments/video-based-generative-performance)](https://paperswithcode.com/sota/video-based-generative-performance?p=vtimellm-empower-llm-to-grasp-video-moments)
---
## :loudspeaker: Latest Updates
- **Jan-2**: Thanks to [Xiao Xia](https://github.com/Rishubi) , [Shengbo Tong](https://github.com/tsb-19) and [Beining Wang](https://github.com/Benson0704), we have refactored the code to now support both the LLAMA and ChatGLM3 architectures. We translated the training data into Chinese and fine-tuned a Chinese version based on the ChatGLM3-6b.
- **Dec-14**: Released the training code and data. All the resources including models, datasets and extracted features are available
[here](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2F&mode=list). :fire::fire:
- **Dec-4**: VTimeLLM: demo released.
---
## VTimeLLM Overview :bulb:
VTimeLLM is a novel Video LLM designed for fine-grained video moment understanding and reasoning with respect to time boundary.
VTimeLLM adopts a boundary-aware three-stage training strategy, which respectively utilizes image-text pairs for feature alignment, multiple-event videos to increase temporal-boundary awareness, and high-quality video-instruction tuning to further improve temporal understanding ability as well as align with human intents.
![framework](images/framework.png)
---
## Contributions :trophy:
- We propose VTimeLLM, the first boundary-aware Video LLM, to the best of our knowledge.
- We propose the boundary-aware three-stage training strategy, which consecutively leverages i) large-scale image-text data for feature alignment, ii) large-scale multi-event video-text data together with the temporal-related single-turn and multi-turn QA to enhance the awareness of time boundary, and iii) instruction tuning on the high-quality dialog dataset for better temporal reasoning ability.
- We conduct extensive experiments to demonstrate that the proposed VTimeLLM significantly outperforms existing Video LLMs in various fine-grained temporal-related video tasks, showing its superior ability for video understanding and reasoning.
---
## Installation :wrench:
We recommend setting up a conda environment for the project:
```shell
conda create --name=vtimellm python=3.10
conda activate vtimellm
git clone https://github.com/huangb23/VTimeLLM.git
cd VTimeLLM
pip install -r requirements.txt
```
Additionally, install additional packages for training cases.
```shell
pip install ninja
pip install flash-attn --no-build-isolation
```
## Running Demo Offline :cd:
To run the demo offline, please refer to the instructions in [offline_demo.md](docs/offline_demo.md).
## Training :train:
For training instructions, check out [train.md](docs/train.md).
## Qualitative Analysis :mag:
A Comprehensive Evaluation of VTimeLLM's Performance across Multiple Tasks.
### Video Understanding and Conversational Tasks :speech_balloon:
![0](images/ex.png)
---
### Creative Tasks :paintbrush:
![1](images/ex1.png)
---
### Fine-grained Understanding Tasks :globe_with_meridians:
![2](images/ex2.png)
---
### Video Reasoning Tasks :question:
![3](images/ex3.png)
---
## Acknowledgements :pray:
We are grateful for the following awesome projects our VTimeLLM arising from:
* [LLaVA](https://github.com/haotian-liu/LLaVA): Large Language and Vision Assistant
* [FastChat](https://github.com/lm-sys/FastChat): An Open Platform for Training, Serving, and Evaluating Large Language Model based Chatbots
* [Video-ChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT): Towards Detailed Video Understanding via Large Vision and Language Models
* [LLaMA](https://github.com/facebookresearch/llama): Open and Efficient Foundation Language Models
* [Vid2seq](https://github.com/google-research/scenic/tree/main/scenic/projects/vid2seq): Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning
* [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid): A Large-scale Video-Text dataset
If you're using VTimeLLM in your research or applications, please cite using this BibTeX:
```bibtex
@inproceedings{huang2024vtimellm,
title={Vtimellm: Empower llm to grasp video moments},
author={Huang, Bin and Wang, Xin and Chen, Hong and Song, Zihan and Zhu, Wenwu},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={14271--14280},
year={2024}
}
```
## License :scroll:
<a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-nd/4.0/80x15.png" /></a>
This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/4.0/">Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License</a>.
Looking forward to your feedback, contributions, and stars! :star2:
docs/data.md 0 → 100644
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Our dataset can be downloaded from [here](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Fdata&mode=list). The data is organized in the following JSON format:
* source: The original sources of the dataset. This field is ['internvid'](https://huggingface.co/datasets/OpenGVLab/InternVid) in stage 2 and ['anet'](http://activity-net.org/download.html) or ['didemo'](https://drive.google.com/drive/u/0/folders/1_oyJ5rQiZboipbMl6tkhY8v0s9zDkvJc) in stage 3.
* id: The ID of the video in the original dataset.
* conversations: The conversation data used for training. We utilize special tokens (such as `<s0>`, `<e0>`) to indicate timestamps. These need to be replaced during training using information from the 'meta' field.
* meta:
* split: refers to the extraction of a segment from the original video as input, with two numbers denoting the start and end timestamps of the extraction. If this field does not exist, the entire video is used as input.
* duration: indicates the length of the input video.
* token: represents the timestamp of special tokens appearing in 'conversations'.
Here is an example:
```json
{
"source": "internvid",
"id": "3n3oCNerzV0",
"conversations": [
{
"from": "human",
"value": "<video>\nI'd appreciate it if you could provide a detailed account of the events that occurred at different timestamps in the video."
},
{
"from": "gpt",
"value": "From <s0> to <e0>, woman is counting money with a pen on a white table. From <s1> to <e1>, two people shaking hands in front of a desk."
}
],
"meta": {
"split": [
129.8,
184.0
],
"duration": 54.2,
"token": {
"<s0>": 0,
"<e0>": 9.4,
"<s1>": 49.4,
"<e1>": 53.2
}
}
}
```
Due to Internvid's data being sourced from YouTube, the 'id' field provides the YouTube ID of the video. We download the [original video](https://www.youtube.com/watch?v=3n3oCNerzV0) and extract the segment from 129.8s to 184.0s as input.
If we sample $N=100$ frames as input, then `<s0>` should be replaced by $\frac{0}{54.2} * N = 00$, `<e0>` should be replaced by $\frac{9.4}{54.2} * N = 17$. and `<s1>` and `<e1>` should be replaced in a similar manner. Finally, in this example, the question from 'human' remains unchanged, while the 'answer' from 'gpt' is shown as below:
```
From 00 to 17, woman is counting money with a pen on a white table. From 91 to 98, two people shaking hands in front of a desk.
```
In the Stage 3 data, approximately 20k data originate from [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT). This subset of data does not contain special tokens and the 'meta' field.
docs/eval.md 0 → 100644
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# VTimeLLM-Vicuna Evaluation
We provide evaluation code for VTimeLLM-Vicuna on dense video captioning and temporal video grounding tasks. Before proceeding, you should be able to run the inference code (refer to [offline_demo.md](offline_demo.md)). Below, we outline the evaluation process using the [ActivityNet Captions](https://cs.stanford.edu/people/ranjaykrishna/densevid/) dataset as an example.
- Download the annotation JSON file for the dataset. For the ActivityNet Captions dataset, the test set annotation file is `val_2.json`. For other datasets, you need to preprocess them to match the JSON format of this dataset.
- Download the raw videos of the dataset and store them in a specific folder.
- (Optional) Pre-extract video features (refer to inference.py) and store them in a specific folder. (For the ActivityNet Captions dataset, we have extracted features for approximately 80% of the test set videos, placed in the [feat folder of the stage3 training](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Ffeat&mode=list). You can use them for incomplete testing.)
- Run the evaluation code, and record the model's responses in a log file. (Specify at least one of `feat_folder` and `video_folder`) :
```bash
python vtimellm/eval/eval.py \
--data_path /path/to/val_2.json \
--feat_folder /path/to/feat \
--video_folder /path/to/video \
--log_path /path/to/log \
--model_base <path to the Vicuna v1.5 weights>
```
* In order to compute metrics for the dense video captioning task, you need to install `pycocoevalcap` and `Java`.
```bash
pip install pycocoevalcap
conda install conda-forge::openjdk
```
* Parse the log file and obtain the corresponding metrics.
```bash
python vtimellm/eval/metric.py \
--data_path /path/to/val_2.json \
--log_path /path/to/log
```
\ No newline at end of file
docs/inference.ipynb 0 → 100644
View file @ fef630ee
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "6401fb4e-c559-49e5-bc88-874a74dd54c4",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"root_dir = os.path.join(os.getcwd(), \"..\")\n",
"import sys\n",
"sys.path.append(root_dir)\n",
"from vtimellm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN\n",
"from vtimellm.conversation import conv_templates, SeparatorStyle\n",
"from vtimellm.model.builder import load_pretrained_model, load_lora\n",
"from vtimellm.utils import disable_torch_init\n",
"from vtimellm.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria, VideoExtractor\n",
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"from transformers import TextStreamer\n",
"from easydict import EasyDict as edict\n",
"try:\n",
" from torchvision.transforms import InterpolationMode\n",
" BICUBIC = InterpolationMode.BICUBIC\n",
"except ImportError:\n",
" from PIL import Image\n",
" BICUBIC = Image.BICUBIC\n",
"from torchvision.transforms import Compose, Resize, CenterCrop, Normalize\n",
"import numpy as np\n",
"import clip\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a05c755d-ae5c-4e93-ae6f-4e4a6e795b14",
"metadata": {},
"outputs": [],
"source": [
"args = edict()\n",
"args.model_base = \"/path/to/vicuna-7b-v1.5\"\n",
"args.clip_path = os.path.join(root_dir, \"checkpoints/clip/ViT-L-14.pt\")\n",
"args.pretrain_mm_mlp_adapter = os.path.join(root_dir, \"checkpoints/vtimellm-vicuna-v1-5-7b-stage1/mm_projector.bin\")\n",
"args.stage2 = os.path.join(root_dir, \"checkpoints/vtimellm-vicuna-v1-5-7b-stage2\")\n",
"args.stage3 = os.path.join(root_dir, \"checkpoints/vtimellm-vicuna-v1-5-7b-stage3\")\n",
"args.video_path = os.path.join(root_dir, \"images/demo.mp4\")\n",
"args.temperature = 0.05"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "d3815e10-d5a5-4fdf-a98f-ae9050f23b97",
"metadata": {},
"outputs": [],
"source": [
"def inference(model, tokenizer, context_len, image, args):\n",
" conv = conv_templates['v1'].copy()\n",
" roles = conv.roles\n",
" first = True\n",
" while True:\n",
" try:\n",
" inp = input(f\"{roles[0]}: \")\n",
" except EOFError:\n",
" inp = \"\"\n",
" if not inp:\n",
" print(\"exit...\")\n",
" break\n",
"\n",
" print(f\"{roles[1]}: \", end=\"\")\n",
"\n",
" if first:\n",
" # first message\n",
" inp = DEFAULT_IMAGE_TOKEN + '\\n' + inp\n",
" conv.append_message(conv.roles[0], inp)\n",
" first = False\n",
" else:\n",
" # later messages\n",
" conv.append_message(conv.roles[0], inp)\n",
" conv.append_message(conv.roles[1], None)\n",
" prompt = conv.get_prompt()\n",
"\n",
" input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()\n",
" stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 # plain:sep(###) v1:sep2(None)\n",
" keywords = [stop_str]\n",
" stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)\n",
" streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
"\n",
" with torch.inference_mode():\n",
" output_ids = model.generate(\n",
" input_ids,\n",
" images=image[None,].cuda(),\n",
" do_sample=True,\n",
" temperature=args.temperature,\n",
" max_new_tokens=1024,\n",
" streamer=streamer,\n",
" use_cache=True,\n",
" stopping_criteria=[stopping_criteria]\n",
" )\n",
"\n",
" outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()\n",
" conv.messages[-1][-1] = outputs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a29e94ff-620e-4c86-adee-47206b67b606",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are using a model of type llama to instantiate a model of type VTimeLLM. This is not supported for all configurations of models and can yield errors.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading VTimeLLM from base model...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "8c13771904c8424abf87e3e2507e4bd8",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bhuang/miniconda3/envs/vtime/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:381: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.9` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
" warnings.warn(\n",
"/home/bhuang/miniconda3/envs/vtime/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:386: UserWarning: `do_sample` is set to `False`. However, `top_p` is set to `0.6` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_p`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"load mlp: /DATA/DATANAS2/bhuang/link/gitlab/vtimellm/docs/../checkpoints/vtimellm-vicuna-v1-5-7b-stage1/mm_projector.bin\n",
"Loading stage2 weights...\n",
"Loading LoRA weights...\n",
"Merging stage2 weights...\n",
"Loading stage3 weights...\n",
"Loading LoRA weights...\n",
"Merging stage3 weights...\n"
]
}
],
"source": [
"disable_torch_init()\n",
"tokenizer, model, context_len = load_pretrained_model(args, args.stage2, args.stage3)\n",
"model = model.cuda()\n",
"model = model.to(torch.float16)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "f8165baa-775b-4106-88aa-3c723bd97c12",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/bhuang/miniconda3/envs/vtime/lib/python3.10/site-packages/torchvision/transforms/functional.py:1603: UserWarning: The default value of the antialias parameter of all the resizing transforms (Resize(), RandomResizedCrop(), etc.) will change from None to True in v0.17, in order to be consistent across the PIL and Tensor backends. To suppress this warning, directly pass antialias=True (recommended, future default), antialias=None (current default, which means False for Tensors and True for PIL), or antialias=False (only works on Tensors - PIL will still use antialiasing). This also applies if you are using the inference transforms from the models weights: update the call to weights.transforms(antialias=True).\n",
" warnings.warn(\n"
]
}
],
"source": [
"clip_model, _ = clip.load(args.clip_path)\n",
"clip_model.eval()\n",
"clip_model = clip_model.cuda()\n",
"\n",
"video_loader = VideoExtractor(N=100)\n",
"_, images = video_loader.extract({'id': None, 'video': args.video_path})\n",
"\n",
"transform = Compose([\n",
" Resize(224, interpolation=BICUBIC),\n",
" CenterCrop(224),\n",
" Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),\n",
"])\n",
"\n",
"# print(images.shape) # <N, 3, H, W>\n",
"images = transform(images / 255.0)\n",
"images = images.to(torch.float16)\n",
"with torch.no_grad():\n",
" features = clip_model.encode_image(images.to('cuda'))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "010a986d-6121-43f2-9971-619472c0732c",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"USER: Explain why this video is funny.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASSISTANT: The video is funny because the bear is dancing to the music and moving its arms and legs in a funny way. The bear's movements are exaggerated and comical, making it difficult for the person to keep up with the beat. The bear's facial expressions and body language add to the humor of the video.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"USER: Is it a real bear?\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASSISTANT: No, it is not a real bear. It is a costume worn by a person.\n"
]
},
{
"name": "stdin",
"output_type": "stream",
"text": [
"USER: \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"exit...\n"
]
}
],
"source": [
"inference(model, tokenizer, context_len, features, args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef73e662-cab9-46fa-9df8-b45e2b1d9f5b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/inference_for_glm.ipynb 0 → 100644
View file @ fef630ee
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "6401fb4e-c559-49e5-bc88-874a74dd54c4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/DATA/DATANAS2/bhuang/link/gitlab/vtimellm/docs/..\n"
]
}
],
"source": [
"import os\n",
"os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n",
"root_dir = os.path.join(os.getcwd(), \"..\")\n",
"print(root_dir)\n",
"import sys\n",
"sys.path.append(root_dir)\n",
"\n",
"from vtimellm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN\n",
"from vtimellm.conversation import conv_templates, SeparatorStyle\n",
"from vtimellm.model.builder import load_pretrained_model, load_lora\n",
"from vtimellm.train.dataset import preprocess_glm\n",
"from vtimellm.utils import disable_torch_init\n",
"from vtimellm.mm_utils import tokenizer_image_token, KeywordsStoppingCriteria, VideoExtractor\n",
"from PIL import Image\n",
"import requests\n",
"from io import BytesIO\n",
"from transformers import TextStreamer\n",
"from easydict import EasyDict as edict\n",
"try:\n",
" from torchvision.transforms import InterpolationMode\n",
" BICUBIC = InterpolationMode.BICUBIC\n",
"except ImportError:\n",
" from PIL import Image\n",
" BICUBIC = Image.BICUBIC\n",
"from torchvision.transforms import Compose, Resize, CenterCrop, Normalize\n",
"import numpy as np\n",
"import clip\n",
"import torch"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a05c755d-ae5c-4e93-ae6f-4e4a6e795b14",
"metadata": {},
"outputs": [],
"source": [
"model_version = 'chatglm3-6b' # vicuna-v1-5-7b\n",
"args = edict()\n",
"args.model_base = \"/DATA/DATANAS2/bhuang/data/vicuna-7b-v1.5\"\n",
"if model_version == 'chatglm3-6b':\n",
" args.model_base = os.path.join(root_dir, 'checkpoints/' + model_version)\n",
"args.clip_path = os.path.join(root_dir, \"checkpoints/clip/ViT-L-14.pt\")\n",
"args.pretrain_mm_mlp_adapter = os.path.join(root_dir, f\"checkpoints/vtimellm-{model_version}-stage1/mm_projector.bin\")\n",
"args.stage2 = os.path.join(root_dir, f\"checkpoints/vtimellm-{model_version}-stage2\")\n",
"args.stage3 = os.path.join(root_dir, f\"checkpoints/vtimellm-{model_version}-stage3\")\n",
"args.temperature = 0.05"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7cb39bb4",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"You are using a model of type chatglm to instantiate a model of type VTimeLLM_ChatGLM. This is not supported for all configurations of models and can yield errors.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading VTimeLLM from base model...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "20832fd2d2a44b7f8d540c32069df157",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/7 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"load mlp: /DATA/DATANAS2/bhuang/link/gitlab/vtimellm/docs/../checkpoints/vtimellm-chatglm3-6b-stage1/mm_projector.bin\n",
"Loading stage2 weights...\n",
"Loading LoRA weights...\n",
"Merging stage2 weights...\n",
"Loading stage3 weights...\n",
"Loading LoRA weights...\n",
"Merging stage3 weights...\n"
]
}
],
"source": [
"disable_torch_init()\n",
"tokenizer, model, context_len = load_pretrained_model(args, args.stage2, args.stage3)\n",
"model = model.cuda()\n",
"model = model.to(torch.float16)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "f8165baa-775b-4106-88aa-3c723bd97c12",
"metadata": {},
"outputs": [],
"source": [
"clip_model, _ = clip.load(args.clip_path)\n",
"clip_model.eval()\n",
"clip_model = clip_model.cuda()\n",
"\n",
"video_loader = VideoExtractor(N=100)\n",
"\n",
"transform = Compose([\n",
" Resize(224, interpolation=BICUBIC),\n",
" CenterCrop(224),\n",
" Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),\n",
"])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b0038bac",
"metadata": {},
"outputs": [],
"source": [
"args.video_path = '/DATA/DATANAS2/bhuang/link/1.mp4'\n",
"_, images = video_loader.extract({'id': None, 'video': args.video_path})\n",
"# print(images.shape) # <N, 3, H, W>\n",
"images = transform(images / 255.0)\n",
"images = images.to(torch.float16)\n",
"with torch.no_grad():\n",
" features = clip_model.encode_image(images.to('cuda'))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "7b42d546",
"metadata": {},
"outputs": [],
"source": [
"def inference(model, tokenizer, context_len, image, args):\n",
" source = []\n",
" first = True\n",
" while True:\n",
" try:\n",
" inp = input(f\"USER: \")\n",
" except EOFError:\n",
" inp = \"\"\n",
" if not inp:\n",
" print(\"exit...\")\n",
" break\n",
"\n",
" print(f\"ASSISTANT:\", end=\"\")\n",
"\n",
" if first:\n",
" # first message\n",
" inp = DEFAULT_IMAGE_TOKEN + '\\n' + inp\n",
" first = False\n",
" \n",
" source.append({\n",
" 'from': \"human\",\n",
" 'value': inp\n",
" })\n",
" input_ids = preprocess_glm([source], tokenizer)['input_ids'].cuda()\n",
" input_ids[0][-1] = tokenizer.get_command(\"<|assistant|>\")\n",
" streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
"\n",
" with torch.inference_mode():\n",
" output_ids = model.generate(\n",
" input_ids,\n",
" images=image[None,].cuda(),\n",
" do_sample=True,\n",
" temperature=args.temperature,\n",
" max_new_tokens=1024,\n",
" streamer=streamer,\n",
" use_cache=True,\n",
" eos_token_id=[tokenizer.get_command(\"<|user|>\"), tokenizer.eos_token_id],\n",
" )\n",
"\n",
" outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:-1]).strip()\n",
" # print(outputs)\n",
" source.append({\n",
" 'from': \"gpt\",\n",
" 'value': outputs\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "010a986d-6121-43f2-9971-619472c0732c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ASSISTANT:视频中，一名男子在黑暗的房间里，手里拿着一个装满东西的盒子。他打开盒子，里面装满了各种物品。然后，该男子爬上一座高高的建筑物，并从窗户跳入水中。<|user|>\n",
"exit...\n"
]
}
],
"source": [
"inference(model, tokenizer, context_len, features, args)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef73e662-cab9-46fa-9df8-b45e2b1d9f5b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
docs/offline_demo.md 0 → 100644
View file @ fef630ee
# Running VTimeLLM inference Offline
Please follow the instructions below to run the VTimeLLM inference on your local GPU machine.
Note: Our demo requires approximately 18 GB of GPU memory.
### Clone the VTimeLLM repository
```shell
conda create --name=vtimellm python=3.10
conda activate vtimellm
git clone https://github.com/huangb23/VTimeLLM.git
cd VTimeLLM
pip install -r requirements.txt
```
### Download weights
* Download clip model and vtimellm model from the [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Fcheckpoints&mode=list) and place them into the 'checkpoints' directory.
* Download [Vicuna v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) or [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) weights.
### Run the inference code
```shell
python -m vtimellm.inference --model_base <path to the Vicuna v1.5 weights>
```
Alternatively, you can also choose to conduct multi-turn conversations in [Jupyter Notebook](inference.ipynb). Similarly, you need to set 'args.model_base' to the path of Vicuna v1.5.
If you want to run the VTimeLLM-ChatGLM version, please refer to the code in [inference_for_glm.ipynb](inference_for_glm.ipynb).
### Run the gradio demo
We have provided an offline gradio demo as follows:
```shell
cd vtimellm
python demo_gradio.py --model_base <path to the Vicuna v1.5 weights>
```
docs/train.md 0 → 100644
View file @ fef630ee
# Training VTimeLLM
VTimeLLM adopts a three-stage training strategy. Please follow the instructions below to train VTimeLLM-7B model.
* Download [clip](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Fcheckpoints&mode=list) and [Vicuna v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) weights, and place them into the 'checkpoints' directory.
* Download stage1 dataset from [this link](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain/blob/main/blip_laion_cc_sbu_558k.json), and download stage2 and stage3 dataset from the [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Fdata&mode=list). Place them into the 'data' directory.
```markdown
- VTimeLLM
- checkpoints
- clip
- ViT-L-14.pt
- vicuna-7b-v1.5
- pytorch_model-00001-of-00002.bin
- ...
- data
- blip_laion_cc_sbu_558k.json
- stage2.json
- stage3.json
- scripts
- stage1.sh
- stage2.sh
- stage3.sh
- ...
- vtimellm
- ...
```
If you want to train a Chinese version, you can download the [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) model and the translated Chinese [dataset](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Fdata&mode=list).
* Download the pre-extracted features from the [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/6db5d02883124826aa6f/?p=%2Ffeat&mode=list).
```shell
tar -xzvf stage1.tar.gz
cat stage2_part_* > stage2.tar.gz
tar -xzvf stage2.tar.gz
tar -xzvf stage3.tar.gz
```
* Train in three stages sequentially, and make sure to modify '--feat_folder' in the script to the corresponding feature folder for each stage.
```shell
cd VTimeLLM
bash scripts/stage1.sh
bash scripts/stage2.sh
bash scripts/stage3.sh
```
\ No newline at end of file
images/demo.mp4 0 → 100644
View file @ fef630ee
File added
requirements.txt 0 → 100644
View file @ fef630ee
# torch
# flash-attn
# torchvision
# deepspeed
decord
easydict
einops
gradio
numpy
pandas>=2.0.3
peft>=0.4.0
Pillow
tqdm
transformers==4.31.0
git+https://github.com/openai/CLIP.git
sentencepiece
protobuf
wandb
ninja
huggingface_hub
scripts/stage1.sh 0 → 100644
View file @ fef630ee
#!/bin/bash
MODEL_VERSION=vicuna-v1-5-7b
gpu_vis=0,1 # per_device_train_batch_size * gradient_accumulation_steps * n_gpus = 128
MASTER_PORT=29029
deepspeed --include localhost:$gpu_vis --master_port $MASTER_PORT vtimellm/train/train_mem.py \
--deepspeed ./scripts/zero3.json \
--model_name_or_path ./checkpoints/vicuna-7b-v1.5 \
--version plain \
--data_path ./data/blip_laion_cc_sbu_558k.json \
--feat_folder ./feat/558k_clip_feat \
--tune_mm_mlp_adapter True \
--output_dir ./checkpoints/vtimellm-$MODEL_VERSION-stage1_test \
--bf16 True \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--gradient_accumulation_steps 4 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 200 \
--save_total_limit 1 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--tf32 True \
--logging_steps 1 \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 4 \
--lazy_preprocess True \
--report_to wandb
\ No newline at end of file
scripts/stage1_glm.sh 0 → 100755
View file @ fef630ee
#!/bin/bash
MODEL_VERSION=chatglm3-6b
gpu_vis=0 # per_device_train_batch_size * gradient_accumulation_steps * n_gpus = 128
MASTER_PORT=29570
deepspeed --include localhost:$gpu_vis --master_port $MASTER_PORT vtimellm/train/train.py \
--deepspeed ./scripts/zero3.json \
--model_name_or_path ./checkpoints/$MODEL_VERSION \
--version plain \
--data_path ./data/blip_laion_cc_sbu_558k_chinese.json \
--feat_folder /path/to/stage1_feat \
--tune_mm_mlp_adapter True \
--output_dir ./checkpoints/vtimellm-$MODEL_VERSION-stage1 \
--bf16 True \
--num_train_epochs 1 \
--per_device_train_batch_size 16 \
--gradient_accumulation_steps 8 \
--evaluation_strategy "no" \
--save_strategy "steps" \
--save_steps 24000 \
--save_total_limit 1 \
--learning_rate 1e-3 \
--weight_decay 0. \
--warmup_ratio 0.03 \
--lr_scheduler_type "cosine" \
--logging_steps 1 \
--tf32 True \
--model_max_length 2048 \
--gradient_checkpointing True \
--dataloader_num_workers 4 \
--lazy_preprocess True \
--report_to wandb
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