The videollama2 from arthurwish

VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

If you like our project, please give us a star ⭐ on GitHub for the latest update.

💡 Some other multimodal-LLM projects from our team may interest you ✨.

Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Hang Zhang, Xin Li, Lidong Bing

VCD: Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding
Sicong Leng, Hang Zhang, Guanzheng Chen, Xin Li, Shijian Lu, Chunyan Miao, Lidong Bing

demo_video.webm

📰 News

[2024.06.03] Release training, evaluation, and serving codes of VideoLLaMA 2.

🛠️ Requirements and Installation

Basic Dependencies:

Python >= 3.8
Pytorch >= 2.0.1
CUDA Version >= 11.7
transformers >= 4.37.2

[Online Mode] Install required packages (better for development):

git clone https://github.com/DAMO-NLP-SG/VideoLLaMA2
cd VideoLLaMA2
pip install -r requirements.txt
pip install flash-attn --no-build-isolation

[Offline Mode] Install VideoLLaMA2 as a Python package (better for direct use):

git clone https://github.com/DAMO-NLP-SG/VideoLLaMA2
cd VideoLLaMA2
pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install flash-attn --no-build-isolation

🚀 Main Results

Multi-Choice Video QA & Video Captioning

Open-Ended Video QA

🌎 Model Zoo

Model Name	Model Type	Visual Encoder	Language Decoder	# Training Frames
VideoLLaMA2-7B-Base	Base	clip-vit-large-patch14-336	Mistral-7B-Instruct-v0.2	8
VideoLLaMA2-7B	Chat	clip-vit-large-patch14-336	Mistral-7B-Instruct-v0.2	8
VideoLLaMA2-7B-16F-Base	Base	clip-vit-large-patch14-336	Mistral-7B-Instruct-v0.2	16
VideoLLaMA2-7B-16F	Chat	clip-vit-large-patch14-336	Mistral-7B-Instruct-v0.2	16

🗝️ Training & Evaluation

Quick Start

To facilitate further development on top of our codebase, we provide a quick-start guide on how to train a customized VideoLLaMA2 with VideoLLaVA dataset and evaluate the trained model on the mainstream video-llm benchmarks.

Training Data Structure:

VideoLLaMA2
├── datasets
│   ├── videollava_pt
|   |   ├── llava_image/ # Available at: https://pan.baidu.com/s/17GYcE69FcJjjUM0e4Gad2w?pwd=9ga3
|   |   ├── valley/      # Available at: https://pan.baidu.com/s/1jluOimE7mmihEBfnpwwCew?pwd=jyjz
|   |   └── valley_llavaimage.json # Available at: https://drive.google.com/file/d/1zGRyVSUMoczGq6cjQFmT0prH67bu2wXD/view, including 703K video-text and 558K image-text pairs
│   ├── videollava_sft
|   |   ├── llava_image_tune/  # Available at: https://pan.baidu.com/s/1l-jT6t_DlN5DTklwArsqGw?pwd=o6ko
|   |   ├── videochatgpt_tune/ # Available at: https://pan.baidu.com/s/10hJ_U7wVmYTUo75YHc_n8g?pwd=g1hf
|   |   └── videochatgpt_llavaimage_tune.json # Available at: https://drive.google.com/file/d/1zGRyVSUMoczGq6cjQFmT0prH67bu2wXD/view, including 100K video-centric, 625K image-centric and 40K text-only conversations

Command:

# VideoLLaMA2-vllava pretraining
bash scripts/vllava/stc/pretrain.sh
# VideoLLaMA2-vllava finetuning
bash scripts/vllava/stc/finetune.sh

Evaluation Data Structure:

VideoLLaMA2
├── eval
│   ├── egoschema # Official website: https://github.com/egoschema/EgoSchema
|   |   ├── good_clips_git/ # Available at: https://drive.google.com/drive/folders/1SS0VVz8rML1e5gWq7D7VtP1oxE2UtmhQ
|   |   └── questions.json  # Available at: https://github.com/egoschema/EgoSchema/blob/main/questions.json
│   ├── mvbench # Official website: https://huggingface.co/datasets/OpenGVLab/MVBench
|   |   ├── video/
|   |   |   ├── clever/
|   |   |   └── ...
|   |   └── json/
|   |   |   ├── action_antonym.json
|   |   |   └── ...
│   ├── perception_test_mcqa # Official website: https://huggingface.co/datasets/OpenGVLab/MVBench
|   |   ├── videos/ # Available at: https://storage.googleapis.com/dm-perception-test/zip_data/test_videos.zip
|   |   └── mc_question_test.json # Download from https://storage.googleapis.com/dm-perception-test/zip_data/mc_question_test_annotations.zip
│   ├── Activitynet_Zero_Shot_QA # Official website: https://github.com/MILVLG/activitynet-qa
|   |   ├── all_test/   # Available at: https://mbzuaiac-my.sharepoint.com/:u:/g/personal/hanoona_bangalath_mbzuai_ac_ae/EatOpE7j68tLm2XAd0u6b8ABGGdVAwLMN6rqlDGM_DwhVA?e=90WIuW
|   |   ├── test_q.json # Available at: https://github.com/MILVLG/activitynet-qa/tree/master/dataset
|   |   └── test_a.json # Available at: https://github.com/MILVLG/activitynet-qa/tree/master/dataset
│   ├── MSVD_Zero_Shot_QA # Official website: https://github.com/xudejing/video-question-answering
|   |   ├── videos/     
|   |   ├── test_q.json 
|   |   └── test_a.json
│   ├── videochatgpt_gen # Official website: https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main/quantitative_evaluation
|   |   ├── Test_Videos/ # Available at: https://mbzuaiac-my.sharepoint.com/:u:/g/personal/hanoona_bangalath_mbzuai_ac_ae/EatOpE7j68tLm2XAd0u6b8ABGGdVAwLMN6rqlDGM_DwhVA?e=90WIuW
|   |   ├── Test_Human_Annotated_Captions/ # Available at: https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking%2FTest%5FHuman%5FAnnotated%5FCaptions%2Ezip&parent=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking&ga=1
|   |   ├── generic_qa.json     # These three json files available at: https://mbzuaiac-my.sharepoint.com/personal/hanoona_bangalath_mbzuai_ac_ae/_layouts/15/onedrive.aspx?id=%2Fpersonal%2Fhanoona%5Fbangalath%5Fmbzuai%5Fac%5Fae%2FDocuments%2FVideo%2DChatGPT%2FData%5FCode%5FModel%5FRelease%2FQuantitative%5FEvaluation%2Fbenchamarking%2FBenchmarking%5FQA&ga=1
|   |   ├── temporal_qa.json
|   |   └── consistency_qa.json

Command:

# mvbench evaluation
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh
# activitynet-qa evaluation (need to set azure openai key/endpoint/deployname)
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash scripts/eval/eval_video_qa_mvbench.sh

Data Format

If you want to train a video-llm on your data, you need to follow the procedures below to prepare the video/image sft data:

Suppose your data structure is like:

VideoLLaMA2
├── datasets
│   ├── custom_sft
│   |   ├── images
│   |   ├── videos
|   |   └── custom.json

Then you should re-organize the annotated video/image sft data according to the following format:

[
    {
        "id": 0,
        "video": "images/xxx.jpg",
        "conversations": [
            {
                "from": "human",
                "value": "<image>\nWhat are the colors of the bus in the image?"
            },
            {
                "from": "gpt",
                "value": "The bus in the image is white and red."
            },
            ...
        ],
    }
    {
        "id": 1,
        "video": "videos/xxx.mp4",
        "conversations": [
            {
                "from": "human",
                "value": "<video>\nWhat are the main activities that take place in the video?"
            },
            {
                "from": "gpt",
                "value": "The main activities that take place in the video are the preparation of camera equipment by a man, a group of men riding a helicopter, and a man sailing a boat through the water."
            },
            ...
        ],
    },
    ...
]

Modify the scripts/custom/finetune.sh:

...
--data_path datasets/custom_sft/custom.json
--data_folder datasets/custom_sft/
--pretrain_mm_mlp_adapter CONNECTOR_DOWNLOAD_PATH (e.g., DAMO-NLP-SG/VideoLLaMA2-7B-Base)
...

🤖 Inference

Video/Image Inference:

import torch
import transformers

import sys
sys.path.append('./')
from videollama2.conversation import conv_templates
from videollama2.constants import DEFAULT_MMODAL_TOKEN, MMODAL_TOKEN_INDEX
from videollama2.mm_utils import get_model_name_from_path, tokenizer_MMODAL_token, process_video, process_image
from videollama2.model.builder import load_pretrained_model


def inference():
    # Video Inference
    paths = ['assets/cat_and_chicken.mp4']
    questions = ['What animals are in the video, what are they doing, and how does the video feel?']
    # Reply:
    # The video features a kitten and a baby chick playing together. The kitten is seen laying on the floor while the baby chick hops around. The two animals interact playfully with each other, and the video has a cute and heartwarming feel to it.
    modal_list = ['video']

    # Video Inference
    paths = ['assets/sora.mp4']
    questions = ['Please describe this video.']
    # Reply:
    # The video features a series of colorful kites flying in the sky. The kites are first seen flying over trees, and then they are shown flying in the sky. The kites come in various shapes and colors, including red, green, blue, and yellow. The video captures the kites soaring gracefully through the air, with some kites flying higher than others. The sky is clear and blue, and the trees below are lush and green. The kites are the main focus of the video, and their vibrant colors and intricate designs are highlighted against the backdrop of the sky and trees. Overall, the video showcases the beauty and artistry of kite-flying, and it is a delight to watch the kites dance and glide through the air.
    modal_list = ['video']

    # Image Inference
    paths = ['assets/sora.png']
    questions = ['What is the woman wearing, what is she doing, and how does the image feel?']
    # Reply:
    # The woman in the image is wearing a black coat and sunglasses, and she is walking down a rain-soaked city street. The image feels vibrant and lively, with the bright city lights reflecting off the wet pavement, creating a visually appealing atmosphere. The woman's presence adds a sense of style and confidence to the scene, as she navigates the bustling urban environment.
    modal_list = ['image']

    # 1. Initialize the model.
    model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B'
    # Base model inference (only need to replace model_path)
    # model_path = 'DAMO-NLP-SG/VideoLLaMA2-7B-Base'
    model_name = get_model_name_from_path(model_path)
    tokenizer, model, processor, context_len = load_pretrained_model(model_path, None, model_name)
    model = model.to('cuda:0')
    conv_mode = 'llama_2'

    # 2. Visual preprocess (load & transform image or video).
    if modal_list[0] == 'video':
        tensor = process_video(paths[0], processor, model.config.image_aspect_ratio).to(dtype=torch.float16, device='cuda', non_blocking=True)
        default_mm_token = DEFAULT_MMODAL_TOKEN["VIDEO"]
        modal_token_index = MMODAL_TOKEN_INDEX["VIDEO"]
    else:
        tensor = process_image(paths[0], processor, model.config.image_aspect_ratio)[0].to(dtype=torch.float16, device='cuda', non_blocking=True)
        default_mm_token = DEFAULT_MMODAL_TOKEN["IMAGE"]
        modal_token_index = MMODAL_TOKEN_INDEX["IMAGE"]
    tensor = [tensor]

    # 3. text preprocess (tag process & generate prompt).
    question = default_mm_token + "\n" + questions[0]
    conv = conv_templates[conv_mode].copy()
    conv.append_message(conv.roles[0], question)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    input_ids = tokenizer_MMODAL_token(prompt, tokenizer, modal_token_index, return_tensors='pt').unsqueeze(0).to('cuda:0')

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images_or_videos=tensor,
            modal_list=modal_list,
            do_sample=True,
            temperature=0.2,
            max_new_tokens=1024,
            use_cache=True,
        )

    outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    print(outputs[0])


if __name__ == "__main__":
    inference()

🤗 Demo

To run a video-based LLM (Large Language Model) web demonstration on your device, you will first need to ensure that you have the necessary model checkpoints prepared, followed by adhering to the steps outlined to successfully launch the demo.

Launch a global controller

cd /path/to/VideoLLaMA2
python -m videollama2.serve.controller --host 0.0.0.0 --port 10000

Launch a gradio webserver

python -m videollama2.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload

Launch one or multiple model workers

#  export HF_ENDPOINT=https://hf-mirror.com  # If you are unable to access Hugging Face, try to uncomment this line.
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path /PATH/TO/MODEL1
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40001 --worker http://localhost:40001 --model-path /PATH/TO/MODEL2
python -m videollama2.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40002 --worker http://localhost:40002 --model-path /PATH/TO/MODEL3
...

👍 Acknowledgement

🔒 License

This project is released under the Apache 2.0 license as found in the LICENSE file. The service is a research preview intended for non-commercial use ONLY, subject to the model Licenses of LLaMA and Mistral, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Please get in touch with us if you find any potential violations.

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