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OpenGVLab
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Model Details
Model Provider
OpenGVLab
Model Tree
OpenGVLab/InternVL2-4B
Input Modalities
Output Modalities
Supported Functionality
We release the adaptation models for the specific domains: autonomous driving, medical images, and remote sensing.
These models are built upon Mini-InternVL and fine-tuned using a unified adaptation framework, achieving good performance on tasks in specific domains.
The script for evaluation is in the document.
General domain dataset:
ShareGPT4V, AllSeeingV2, LLaVA-Instruct-ZH, DVQA, ChartQA, AI2D, DocVQA, GeoQA+, SynthDoG-EN
Medicalt dataset:
PMC-OA, MedICaT, Open-i, MedPix, Quilt-1M, RP3D, MIMIC-CXR, Retina Image Bank and others
We provide an example code to run Mini-InternVL2-4B using transformers.
Please use transformers>=4.37.2 to ensure the model works normally.
python
import numpy as npimport torchimport torchvision.transforms as Tfrom decord import VideoReader, cpufrom PIL import Imagefrom torchvision.transforms.functional import InterpolationModefrom transformers import AutoModel, AutoTokenizerIMAGENET_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_STDtransform = 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 transformdef find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):best_ratio_diff = float('inf')best_ratio = (1, 1)area = width * heightfor 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_diffbest_ratio = ratioelif ratio_diff == best_ratio_diff:if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:best_ratio = ratioreturn best_ratiodef dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):orig_width, orig_height = image.sizeaspect_ratio = orig_width / orig_height# calculate the existing image aspect ratiotarget_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) ifi * 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 targettarget_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)# calculate the target width and heighttarget_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 imageresized_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 imagesplit_img = resized_img.crop(box)processed_images.append(split_img)assert len(processed_images) == blocksif use_thumbnail and len(processed_images) != 1:thumbnail_img = image.resize((image_size, image_size))processed_images.append(thumbnail_img)return processed_imagesdef 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# If you want to load a model using multiple GPUs, please refer to the `Multiple GPUs` section.path = 'OpenGVLab/Mini-InternVL2-4B-DA-Medical'model = AutoModel.from_pretrained(path,torch_dtype=torch.bfloat16,low_cpu_mem_usage=True,use_flash_attn=True,trust_remote_code=True).eval().cuda()tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)# set the max number of tiles in `max_num`pixel_values = load_image('path/to/image.jpg', max_num=12).to(torch.bfloat16).cuda()generation_config = dict(max_new_tokens=1024, do_sample=True)# pure-text conversation (纯文本对话)question = 'Hello, who are you?'response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)print(f'User: {question}\nAssistant: {response}')question = 'Can you tell me a story?'response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)print(f'User: {question}\nAssistant: {response}')# single-image single-round conversation (单图单轮对话)question = '<image>\nPlease describe the image shortly.'response = model.chat(tokenizer, pixel_values, question, generation_config)print(f'User: {question}\nAssistant: {response}')# single-image multi-round conversation (单图多轮对话)question = '<image>\nPlease describe the image in detail.'response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)print(f'User: {question}\nAssistant: {response}')question = 'Please write a poem according to the image.'response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)print(f'User: {question}\nAssistant: {response}')# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)question = '<image>\nDescribe the two images in detail.'response, history = model.chat(tokenizer, pixel_values, question, generation_config,history=None, return_history=True)print(f'User: {question}\nAssistant: {response}')question = 'What are the similarities and differences between these two images.'response, history = model.chat(tokenizer, pixel_values, question, generation_config,history=history, return_history=True)print(f'User: {question}\nAssistant: {response}')# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()pixel_values2 = load_image('path/to/image2.jpg', max_num=12).to(torch.bfloat16).cuda()pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'response, history = model.chat(tokenizer, pixel_values, question, generation_config,num_patches_list=num_patches_list,history=None, return_history=True)print(f'User: {question}\nAssistant: {response}')question = 'What are the similarities and differences between these two images.'response, history = model.chat(tokenizer, pixel_values, question, generation_config,num_patches_list=num_patches_list,history=history, return_history=True)print(f'User: {question}\nAssistant: {response}')# batch inference, single image per sample (单图批处理)pixel_values1 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()pixel_values2 = load_image('path/to/image1.jpg', max_num=12).to(torch.bfloat16).cuda()num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)responses = model.batch_chat(tokenizer, pixel_values,num_patches_list=num_patches_list,questions=questions,generation_config=generation_config)for question, response in zip(questions, responses):print(f'User: {question}\nAssistant: {response}')
If you find this project useful in your research, please consider citing:
BibTeX
@article{gao2024mini,title={Mini-internvl: A flexible-transfer pocket multimodal model with 5\% parameters and 90\% performance},author={Gao, Zhangwei and Chen, Zhe and Cui, Erfei and Ren, Yiming and Wang, Weiyun and Zhu, Jinguo and Tian, Hao and Ye, Shenglong and He, Junjun and Zhu, Xizhou and others},journal={arXiv preprint arXiv:2410.16261},year={2024}}@article{chen2024expanding,title={Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling},author={Chen, Zhe and Wang, Weiyun and Cao, Yue and Liu, Yangzhou and Gao, Zhangwei and Cui, Erfei and Zhu, Jinguo and Ye, Shenglong and Tian, Hao and Liu, Zhaoyang and others},journal={arXiv preprint arXiv:2412.05271},year={2024}}@article{chen2024far,title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},journal={arXiv preprint arXiv:2404.16821},year={2024}}@inproceedings{chen2024internvl,title={Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks},author={Chen, Zhe and Wu, Jiannan and Wang, Wenhai and Su, Weijie and Chen, Guo and Xing, Sen and Zhong, Muyan and Zhang, Qinglong and Zhu, Xizhou and Lu, Lewei and others},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={24185--24198},year={2024}}