Internvl 2 5 HiCo R16

🚀 📕InternVL2.5_HiCo_R16⚡

InternVL2.5_HiCo_R16 是基于 InternVideo2.5 构建的视频多模态大语言模型（MLLM），增强了长而丰富上下文（LRC）建模能力。它通过直接偏好优化（TPO）进行密集视觉任务注释，以及通过自适应分层令牌压缩（HiCo）实现紧凑的时空表示，显著提升了现有 MLLMs 感知细粒度细节和捕捉长形式时间结构的能力。该模型是 InternVideo2.5 消融实验的一个变体，仅基于 HiCo 技术构建（R16 表示每帧 16 个令牌）。

🚀 快速开始

安装依赖

首先，你需要安装 flash attention2 和其他一些模块。以下是一个简单的安装示例：

pip install transformers==4.40.1
pip install av
pip install imageio
pip install decord
pip install opencv-python
pip install flash-attn --no-build-isolation

使用模型

import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer


# model setting
model_path = 'OpenGVLab/InternVL_2_5_HiCo_R16'

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)

def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img), T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), T.ToTensor(), T.Normalize(mean=MEAN, std=STD)])
    return transform


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 dynamic_preprocess(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image 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
    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 image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = ((i % (target_width // image_size)) * image_size, (i // (target_width // image_size)) * image_size, ((i % (target_width // image_size)) + 1) * image_size, ((i // (target_width // image_size)) + 1) * image_size)
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def load_image(image, input_size=448, max_num=6):
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values


def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
    if bound:
        start, end = bound[0], bound[1]
    else:
        start, end = -100000, 100000
    start_idx = max(first_idx, round(start * fps))
    end_idx = min(round(end * fps), max_frame)
    seg_size = float(end_idx - start_idx) / num_segments
    frame_indices = np.array([int(start_idx + (seg_size / 2) + np.round(seg_size * idx)) for idx in range(num_segments)])
    return frame_indices

def get_num_frames_by_duration(duration):
        local_num_frames = 4        
        num_segments = int(duration // local_num_frames)
        if num_segments == 0:
            num_frames = local_num_frames
        else:
            num_frames = local_num_frames * num_segments
        
        num_frames = min(512, num_frames)
        num_frames = max(128, num_frames)

        return num_frames

def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32, get_frame_by_duration = False):
    vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
    max_frame = len(vr) - 1
    fps = float(vr.get_avg_fps())

    pixel_values_list, num_patches_list = [], []
    transform = build_transform(input_size=input_size)
    if get_frame_by_duration:
        duration = max_frame / fps
        num_segments = get_num_frames_by_duration(duration)
    frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
    for frame_index in frame_indices:
        img = Image.fromarray(vr[frame_index].asnumpy()).convert("RGB")
        img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
        pixel_values = [transform(tile) for tile in img]
        pixel_values = torch.stack(pixel_values)
        num_patches_list.append(pixel_values.shape[0])
        pixel_values_list.append(pixel_values)
    pixel_values = torch.cat(pixel_values_list)
    return pixel_values, num_patches_list

# evaluation setting
max_num_frames = 512
generation_config = dict(
    do_sample=False,
    temperature=0.0,
    max_new_tokens=1024,
    top_p=0.1,
    num_beams=1
)
video_path = "your_video.mp4"
num_segments=128


with torch.no_grad():
  
  pixel_values, num_patches_list = load_video(video_path, num_segments=num_segments, max_num=1, get_frame_by_duration=False)
  pixel_values = pixel_values.to(torch.bfloat16).to(model.device)
  video_prefix = "".join([f"Frame{i+1}: <image>\n" for i in range(len(num_patches_list))])
  # single-turn conversation
  question1 = "Describe this video in detail."
  question = video_prefix + question1
  output1, chat_history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=None, return_history=True)
  print(output1)
  
  # multi-turn conversation
  question2 = "How many people appear in the video?"
  output2, chat_history = model.chat(tokenizer, pixel_values, question, generation_config, num_patches_list=num_patches_list, history=chat_history, return_history=True)
  
  print(output2)

✨ 主要特性

• 长上下文建模：增强了长而丰富上下文（LRC）建模能力，能够更好地处理长视频内容。
• 细粒度感知：通过直接偏好优化（TPO）进行密集视觉任务注释，提升了感知细粒度细节的能力。
• 紧凑时空表示：利用自适应分层令牌压缩（HiCo）实现紧凑的时空表示，有助于捕捉长形式时间结构。

📈 性能表现

✏️ 引用信息

@article{wang2025internvideo,
  title={InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling},
  author={Wang, Yi and Li, Xinhao and Yan, Ziang and He, Yinan and Yu, Jiashuo and Zeng, Xiangyu and Wang, Chenting and Ma, Changlian and Huang, Haian and Gao, Jianfei and Dou, Min and Chen, Kai and Wang, Wenhai and Qiao, Yu and Wang, Yali and Wang, Limin},
  journal={arXiv preprint arXiv:2501.12386},
  year={2025}
}


@article{li2024videochatflash,
  title={VideoChat-Flash: Hierarchical Compression for Long-Context Video Modeling},
  author={Li, Xinhao and Wang, Yi and Yu, Jiashuo and Zeng, Xiangyu and Zhu, Yuhan and Huang, Haian and Gao, Jianfei and Li, Kunchang and He, Yinan and Wang, Chenting and others},
  journal={arXiv preprint arXiv:2501.00574},
  year={2024}
}

📄 许可证

本项目采用 Apache-2.0 许可证。