InternVideo2 Chat 8B HD F16

InternVideo2-Chat-8B-HD-f16

[📂 GitHub] [📜 Tech Report]

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 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. This model has HD training.

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 to obtain the access permission and add your HF_token to the environment variable.

📈 Performance

Model	MVBench	VideoMME(w/o sub)
InternVideo2-Chat-8B	60.3	41.9
InternVideo2-Chat-8B-HD	65.4	46.1
InternVideo2-Chat-8B-HD-F16	67.5	49.4
InternVideo2-Chat-8B-InternLM	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

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

3. make sure to have transformers >= 4.38.0

Install the requisite Python packages from pip_requirements

4. Inference with Video input

import os
token = os.environ['HF_TOKEN']
import torch

from transformers import AutoTokenizer, AutoModel

tokenizer =  AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2_chat_8B_HD_F16',
    trust_remote_code=True,
    use_fast=False,
    token=token)
if torch.cuda.is_available():
  model = AutoModel.from_pretrained(
      'OpenGVLab/InternVideo2_chat_8B_HD_F16',
      torch_dtype=torch.bfloat16,
      trust_remote_code=True).cuda()
else:
  model = AutoModel.from_pretrained(
      'OpenGVLab/InternVideo2_chat_8B_HD_F16',
      torch_dtype=torch.bfloat16,
      trust_remote_code=True)


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.Normalize(mean, std)
    ])

    frames = vr.get_batch(frame_indices)
    frames = frames.permute(0, 3, 1, 2)

    if padding:
        frames = HD_transform_padding(frames.float(), image_size=resolution, hd_num=hd_num)
    else:
        frames = HD_transform_no_padding(frames.float(), image_size=resolution, hd_num=hd_num)

    frames = transform(frames)
    # print(frames.shape)
    T_, C, H, W = frames.shape

    sub_img = frames.reshape(
        1, T_, 3, H//resolution, resolution, W//resolution, resolution
    ).permute(0, 3, 5, 1, 2, 4, 6).reshape(-1, T_, 3, resolution, resolution).contiguous()

    glb_img = F.interpolate(
        frames.float(), size=(resolution, resolution), mode='bicubic', align_corners=False
    ).to(sub_img.dtype).unsqueeze(0)

    frames = torch.cat([sub_img, glb_img]).unsqueeze(0)

    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

def HD_transform_padding(frames, image_size=224, hd_num=6):
    def _padding_224(frames):
        _, _, H, W = frames.shape
        tar = int(np.ceil(H / 224) * 224)
        top_padding = (tar - H) // 2
        bottom_padding = tar - H - top_padding
        left_padding = 0
        right_padding = 0

        padded_frames = F.pad(
            frames,
            pad=[left_padding, right_padding, top_padding, bottom_padding],
            mode='constant', value=255
        )
        return padded_frames

    _, _, H, W = frames.shape
    trans = False
    if W < H:
        frames = frames.flip(-2, -1)
        trans = True
        width, height = H, W
    else:
        width, height = W, H

    ratio = width / height
    scale = 1
    while scale * np.ceil(scale / ratio) <= hd_num:
        scale += 1
    scale -= 1
    new_w = int(scale * image_size)
    new_h = int(new_w / ratio)

    resized_frames = F.interpolate(
        frames, size=(new_h, new_w),
        mode='bicubic',
        align_corners=False
    )
    padded_frames = _padding_224(resized_frames)

    if trans:
        padded_frames = padded_frames.flip(-2, -1)

    return padded_frames

def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
        best_ratio_diff = float('inf')
        best_ratio = (1, 1)
        area = width * height
        for ratio in target_ratios:
            target_aspect_ratio = ratio[0] / ratio[1]
            ratio_diff = abs(aspect_ratio - target_aspect_ratio)
            if ratio_diff < best_ratio_diff:
                best_ratio_diff = ratio_diff
                best_ratio = ratio
            elif ratio_diff == best_ratio_diff:
                if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                    best_ratio = ratio
        return best_ratio


def HD_transform_no_padding(frames, image_size=224, hd_num=6, fix_ratio=(2,1)):
    min_num = 1
    max_num = hd_num
    _, _, orig_height, orig_width = frames.shape
    aspect_ratio = orig_width / orig_height

    # calculate the existing video aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    if fix_ratio:
        target_aspect_ratio = fix_ratio
    else:
        target_aspect_ratio = find_closest_aspect_ratio(
            aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the frames
    resized_frame = F.interpolate(
        frames, size=(target_height, target_width),
        mode='bicubic', align_corners=False
    )
    return resized_frame

video_path = "yoga.mp4"
# sample uniformly 16 frames from the video
video_tensor = load_video(video_path, num_segments=16, return_msg=False, resolution=224, hd_num=6)
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)

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})

✏️ 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}
}