--- 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 --- # InternVideo2-Chat-8B-HD [\[📂 GitHub\]](https://github.com/OpenGVLab/InternVideo/tree/main/InternVideo2) [\[📜 Tech Report\]](https://arxiv.org/abs/2403.15377) 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).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](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 >= 4.38.0` 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 from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('OpenGVLab/InternVideo2_chat_8B_HD', trust_remote_code=True, use_fast=False, token=token) if torch.cuda.is_available(): model = AutoModel.from_pretrained( 'OpenGVLab/InternVideo2_chat_8B_HD', torch_dtype=torch.bfloat16, trust_remote_code=True).cuda() else: model = AutoModel.from_pretrained( 'OpenGVLab/InternVideo2_chat_8B_HD', 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 8 frames from the video video_tensor = load_video(video_path, num_segments=8, 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} } ```