| ---
|
| license: mit
|
| base_model:
|
| - OpenGVLab/InternViT-300M-448px
|
| - internlm/internlm2_5-7b-chat
|
| base_model_relation: merge
|
| language:
|
| - multilingual
|
| pipeline_tag: image-text-to-text
|
| library_name: transformers
|
| tags:
|
| - internvl
|
| - video
|
| - token compression
|
| ---
|
|
|
| # PVC-InternVL2-8B
|
|
|
| [\[📜 Paper\]](https://arxiv.org/abs/2412.09613)
|
| [\[📂 GitHub\]](https://github.com/OpenGVLab/PVC)
|
| [\[🚀 Quick Start\]](#quick-start)
|
|
|
| ## Introduction
|
|
|
| We introduce the **Progressive Visual Token Compression (PVC)** in large vision-language models (VLMs), which unifies the visual inputs as videos and progressively compresses vision tokens across video frames. Our PVC achieves:
|
|
|
| * Preserve spatial details and temporal dynamics for both images and videos.
|
| * Effectively reduce the tokens used for each video frame and image tile.
|
| * SoTA performance on various video benchmarks, including long and fine-grained short video tasks.
|
| * No performance loss on image benchmarks, especially on detail-sensitive tasks.
|
|
|
| <div style="text-align: center;">
|
| <img src="./assets/overview.png" width="70%"/>
|
| </div>
|
|
|
| ## Results
|
|
|
| Our implementation is based on the [InternVL2](https://github.com/OpenGVLab/InternVL) model, referred to as **PVC<sub>InternVL2</sub>**
|
|
|
| ### Video Understanding Benckmarks
|
|
|
| | Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVC<sub>InternVL2</sub>-8B |
|
| | :--------------: | :--: | :--: | :--: | :--: |
|
| | \# token/frame | 196 | - | 256 | 64 |
|
| | | | | | |
|
| | MVbench | 56.7 | 67.0 | 66.4 | 73.8 |
|
| | VideoMME w/o-sub | 58.2 | 63.3 | 54.0 | 64.1 |
|
| | VideoMME w-sub | 61.5 | 69.0 | 56.9 | 69.7 |
|
| | MLVU | 64.7 | - | 52.0 | 72.4 |
|
| | LongVideoBench | 56.5 | - | - | 59.2 |
|
| | NextQA | 79.4 | - | - | 82.0 |
|
| | Egoschema | 60.1 | 66.7 | 55.0 | 59.6 |
|
| | PercepTest | 57.1 | 62.3 | 52.0 | 68.4 |
|
| | AcNet-QA | 56.6 | - | - | 57.1 |
|
|
|
| ### Image Understanding Benckmarks
|
|
|
| | Model | LLaVA-OneVision-7B | Qwen2-VL-7B | InternVL2-8B | PVC<sub>InternVL2</sub>-8B |
|
| | :--------------------: | :--: | :--: | :--: | :--: |
|
| | \# token/image tile | 729 | - | 256 | 64 |
|
| | | | | | |
|
| | AI2D<sub>test</sub> | 81.4 | 83.0 | 83.8 | 83.8 |
|
| | ChartQA<sub>test</sub> | 80.0 | 83.0 | 83.3 | 84.1 |
|
| | DocVQA<sub>test</sub> | 87.5 | 94.5 | 91.6 | 92.5 |
|
| | InfoVQA<sub>test</sub> | 68.8 | 76.5 | 74.8 | 75.0 |
|
| | SQA<sub>test</sub> | 96.0 | - | 97.1 | 97.7 |
|
| | TextVQA<sub>val</sub> | - | 84.3 | 77.4 | 80.0 |
|
| | MMB<sub>en-test</sub> | - | 83.0 | 81.7 | 83.9 |
|
| | MME<sub>sum</sub> | 1998 | 2327 | 2210 | 2282 |
|
| | MMMU<sub>val</sub> | 48.8 | 54.1 | 49.3 | 50.9 |
|
| | SEED<sub>I</sub> | 75.4 | - | 76.2 | 77.2 |
|
| | OCRBench | - | 866 | 794 | 807 |
|
|
|
| ## Quick Start
|
|
|
| ```python
|
| 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
|
|
|
| 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=12, 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_file, input_size=448, max_num=12):
|
| image = Image.open(image_file).convert('RGB')
|
| 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 load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
|
| 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)
|
| 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
|
|
|
|
|
| path = 'OpenGVLab/PVC-InternVL2-8B'
|
| model = AutoModel.from_pretrained(
|
| path,
|
| torch_dtype=torch.bfloat16,
|
| low_cpu_mem_usage=True,
|
| trust_remote_code=True).eval().cuda()
|
| tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
|
| generation_config = dict(max_new_tokens=1024, do_sample=True)
|
|
|
| # single-image conversation
|
| pixel_values = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
|
| data_flag = torch.tensor([1], dtype=torch.long).cuda()
|
|
|
| question = '<image>\nWhat is in the image?'
|
| response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag)
|
| print(f'User: {question}\nAssistant: {response}')
|
|
|
| # multi-image conversation
|
| pixel_values1 = load_image('./assets/example_image1.jpg', max_num=12).to(torch.bfloat16).cuda()
|
| pixel_values2 = load_image('./assets/example_image2.jpg', max_num=12).to(torch.bfloat16).cuda()
|
| pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
|
| data_flag = torch.tensor([2], dtype=torch.long).cuda()
|
| num_patches_list = [pixel_values1.shape[0], pixel_values2.shape[0]]
|
|
|
| question = 'Image-1: <image>\nImage-2: <image>\nWhat are the similarities and differences between these two images.'
|
| response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
|
| print(f'User: {question}\nAssistant: {response}')
|
|
|
| # video conversation
|
| pixel_values, num_patches_list = load_video('./assets/example_video.mp4', num_segments=64, max_num=1)
|
| pixel_values = pixel_values.to(torch.bfloat16).cuda()
|
| video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
|
| # Frame1: <image>\nFrame2: <image>\n...\nFrameN: <image>\n{question}
|
| data_flag = torch.tensor([3], dtype=torch.long).cuda()
|
|
|
| question = video_prefix + 'Describe this video in detail.'
|
| response = model.chat(tokenizer, pixel_values, question, generation_config, data_flag=data_flag, num_patches_list=num_patches_list)
|
| print(f'User: {question}\nAssistant: {response}')
|
| ```
|
|
|
| ## Evaluation
|
|
|
| Please refer to our [Github Repo](https://github.com/OpenGVLab/PVC?tab=readme-ov-file#-evaluation).
|
|
|
| ## Citation
|
|
|
| If you find this work helpful in your research, please consider citing:
|
|
|
| ```bibtex
|
| @article{yang2024pvc,
|
| title={PVC: Progressive Visual Token Compression for Unified Image and Video Processing in Large Vision-Language Models},
|
| author={Yang, Chenyu and Dong, Xuan and Zhu, Xizhou and Su, Weijie and Wang, Jiahao and Tian, Hao and Chen, Zhe and Wang, Wenhai and Lu, Lewei and and Dai, Jifeng},
|
| journal={arXiv preprint arXiv:2412.09613},
|
| year={2024}
|
| }
|
| ```
|
|
|
| ## License
|
|
|
| This project is released under the MIT license. Parts of this project contain code and models from other sources, which are subject to their respective licenses.
|
|
|
