|
--- |
|
license: mit |
|
pipeline_tag: video-text-to-text |
|
extra_gated_prompt: >- |
|
You agree to not use the model to conduct experiments that cause harm to human |
|
subjects. |
|
extra_gated_fields: |
|
Name: text |
|
Company/Organization: text |
|
Country: text |
|
E-Mail: text |
|
language: |
|
- en |
|
tags: |
|
- video |
|
--- |
|
|
|
# InternVideo2-Chat-8B |
|
|
|
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2) [\[📜 Tech Report\]](https://arxiv.org/abs/2403.15377) [\[🗨️ Chat Demo\]](https://vchat.opengvlab.com/) |
|
|
|
To further enrich the semantics embedded in **InternVideo2** and improve its user-friendly in human communications, we tune InternVideo2 by incorporating it into a VideoLLM with a LLM and a video BLIP. We employ the progressive learning scheme in [VideoChat](https://arxiv.org/abs/2311.17005) by using InternVideo2 as the video encoder and train a video blip for |
|
communicating with open-sourced LLM. In training, the video encoder will be updated. Detailed training recipts are in [VideoChat](https://arxiv.org/abs/2311.17005). |
|
|
|
The BaseLLM of this model is Mistral-7B.**Before using it, please ensure that you have obtained the access permission of Mistral-7B**, if not yet obtained, please go to[Mistral-7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) to obtain the access permission and add your `HF_token` to the environment variable. |
|
|
|
## 📈 Performance |
|
| Model | MVBench | VideoMME(w/o sub)| |
|
| --- | --- | --- | |
|
|[InternVideo2-Chat-8B](https://huggingface.co/OpenGVLab/InternVideo2-Chat-8B)| 60.3 | 41.9 | |
|
|[InternVideo2-Chat-8B-HD](https://huggingface.co/OpenGVLab/InternVideo2_chat_8B_HD) | 65.4 | 46.1| |
|
|InternVideo2-Chat-8B-HD-F16 | 67.5 | 49.4| |
|
|[InternVideo2-Chat-8B-InternLM](https://huggingface.co/OpenGVLab/InternVideo2_Chat_8B_InternLM2_5)| 61.9| 49.1| |
|
|
|
## 🚀 How to use the model |
|
|
|
1. Apply for the permission of this project and the base LLM permission |
|
|
|
2. Fill the HF user access token into the environment variable |
|
|
|
```shell |
|
export HF_TOKEN=hf_.... |
|
``` |
|
If you don't know how to obtain the token starting with "hf_", please refer to: [How to Get HF User access Token](https://huggingface.co/docs/hub/security-tokens#user-access-tokens) |
|
|
|
3. make sure to have `transformers >= 4.39.0, peft==0.5.0` |
|
|
|
``` |
|
pip install transformers==4.39.1 |
|
pip install peft==0.5.0 |
|
pip install timm easydict einops |
|
``` |
|
|
|
Install the requisite Python packages from [pip_requirements](https://huggingface.co/OpenGVLab/InternVideo2_chat_8B_HD/blob/main/requirements.txt) |
|
|
|
4. Inference with Video input |
|
|
|
```Python |
|
import os |
|
token = os.environ['HF_TOKEN'] |
|
import torch |
|
|
|
tokenizer = AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2-Chat-8B', trust_remote_code=True, use_fast=False) |
|
|
|
from transformers import AutoTokenizer, AutoModel |
|
model = AutoModel.from_pretrained( |
|
'OpenGVLab/InternVideo2-Chat-8B', |
|
torch_dtype=torch.bfloat16, |
|
trust_remote_code=True).cuda() |
|
|
|
from decord import VideoReader, cpu |
|
from PIL import Image |
|
import numpy as np |
|
import numpy as np |
|
import decord |
|
from decord import VideoReader, cpu |
|
import torch.nn.functional as F |
|
import torchvision.transforms as T |
|
from torchvision.transforms import PILToTensor |
|
from torchvision import transforms |
|
from torchvision.transforms.functional import InterpolationMode |
|
decord.bridge.set_bridge("torch") |
|
|
|
def get_index(num_frames, num_segments): |
|
seg_size = float(num_frames - 1) / num_segments |
|
start = int(seg_size / 2) |
|
offsets = np.array([ |
|
start + int(np.round(seg_size * idx)) for idx in range(num_segments) |
|
]) |
|
return offsets |
|
|
|
|
|
def load_video(video_path, num_segments=8, return_msg=False, resolution=224, hd_num=4, padding=False): |
|
vr = VideoReader(video_path, ctx=cpu(0), num_threads=1) |
|
num_frames = len(vr) |
|
frame_indices = get_index(num_frames, num_segments) |
|
|
|
mean = (0.485, 0.456, 0.406) |
|
std = (0.229, 0.224, 0.225) |
|
|
|
transform = transforms.Compose([ |
|
transforms.Lambda(lambda x: x.float().div(255.0)), |
|
transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC), |
|
transforms.CenterCrop(224), |
|
transforms.Normalize(mean, std) |
|
]) |
|
|
|
frames = vr.get_batch(frame_indices) |
|
frames = frames.permute(0, 3, 1, 2) |
|
frames = transform(frames) |
|
|
|
T_, C, H, W = frames.shape |
|
|
|
if return_msg: |
|
fps = float(vr.get_avg_fps()) |
|
sec = ", ".join([str(round(f / fps, 1)) for f in frame_indices]) |
|
# " " should be added in the start and end |
|
msg = f"The video contains {len(frame_indices)} frames sampled at {sec} seconds." |
|
return frames, msg |
|
else: |
|
return frames |
|
|
|
video_path = "yoga.mp4" |
|
# sample uniformly 8 frames from the video |
|
video_tensor = load_video(video_path, num_segments=8, return_msg=False) |
|
video_tensor = video_tensor.to(model.device) |
|
|
|
chat_history= [] |
|
response, chat_history = model.chat(tokenizer, '', 'describe the action step by step.', media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False}) |
|
print(response) |
|
# The video shows a woman performing yoga on a rooftop with a beautiful view of the mountains in the background. She starts by standing on her hands and knees, then moves into a downward dog position, and finally ends with a standing position. Throughout the video, she maintains a steady and fluid movement, focusing on her breath and alignment. The video is a great example of how yoga can be practiced in different environments and how it can be a great way to connect with nature and find inner peace. |
|
|
|
response, chat_history = model.chat(tokenizer, '', 'What is she wearing?', media_type='video', media_tensor=video_tensor, chat_history= chat_history, return_history=True,generation_config={'do_sample':False}) |
|
# The woman in the video is wearing a black tank top and grey yoga pants. |
|
print(response) |
|
``` |
|
|
|
## ✏️ Citation |
|
If this work is helpful for your research, please consider citing InternVideo and VideoChat. |
|
|
|
``` |
|
@article{wang2024internvideo2, |
|
title={Internvideo2: Scaling video foundation models for multimodal video understanding}, |
|
author={Wang, Yi and Li, Kunchang and Li, Xinhao and Yu, Jiashuo and He, Yinan and Wang, Chenting and Chen, Guo and Pei, Baoqi and Zheng, Rongkun and Xu, Jilan and Wang, Zun and others}, |
|
journal={arXiv preprint arXiv:2403.15377}, |
|
year={2024} |
|
} |
|
|
|
@article{li2023videochat, |
|
title={Videochat: Chat-centric video understanding}, |
|
author={Li, KunChang and He, Yinan and Wang, Yi and Li, Yizhuo and Wang, Wenhai and Luo, Ping and Wang, Yali and Wang, Limin and Qiao, Yu}, |
|
journal={arXiv preprint arXiv:2305.06355}, |
|
year={2023} |
|
} |
|
``` |
