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| 1 | +# LLaVA Video |
| 2 | + |
| 3 | +## Table of Contents |
| 4 | + |
| 5 | +1. [Model Summary](##model-summary) |
| 6 | +2. [Inference](##inference) |
| 7 | +3. [Training](##training) |
| 8 | +4. [Evaluation](##evaluation-guidance) |
| 9 | +6. [Citation](##citation) |
| 10 | + |
| 11 | +## Model Summary |
| 12 | + |
| 13 | +The LLaVA-Video models are 7/72B parameter models trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), based on Qwen2 language model with a context window of 32K tokens. |
| 14 | + |
| 15 | + |
| 16 | +## Inference |
| 17 | + |
| 18 | +We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT). |
| 19 | + |
| 20 | +```python |
| 21 | +# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git |
| 22 | +from llava.model.builder import load_pretrained_model |
| 23 | +from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token |
| 24 | +from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX |
| 25 | +from llava.conversation import conv_templates, SeparatorStyle |
| 26 | +from PIL import Image |
| 27 | +import requests |
| 28 | +import copy |
| 29 | +import torch |
| 30 | +import sys |
| 31 | +import warnings |
| 32 | +from decord import VideoReader, cpu |
| 33 | +import numpy as np |
| 34 | +warnings.filterwarnings("ignore") |
| 35 | +def load_video(self, video_path, max_frames_num,fps=1,force_sample=False): |
| 36 | + if max_frames_num == 0: |
| 37 | + return np.zeros((1, 336, 336, 3)) |
| 38 | + vr = VideoReader(video_path, ctx=cpu(0),num_threads=1) |
| 39 | + total_frame_num = len(vr) |
| 40 | + video_time = total_frame_num / vr.get_avg_fps() |
| 41 | + fps = round(vr.get_avg_fps()/fps) |
| 42 | + frame_idx = [i for i in range(0, len(vr), fps)] |
| 43 | + frame_time = [i/fps for i in frame_idx] |
| 44 | + if len(frame_idx) > max_frames_num or force_sample: |
| 45 | + sample_fps = max_frames_num |
| 46 | + uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int) |
| 47 | + frame_idx = uniform_sampled_frames.tolist() |
| 48 | + frame_time = [i/vr.get_avg_fps() for i in frame_idx] |
| 49 | + frame_time = ",".join([f"{i:.2f}s" for i in frame_time]) |
| 50 | + spare_frames = vr.get_batch(frame_idx).asnumpy() |
| 51 | + # import pdb;pdb.set_trace() |
| 52 | + return spare_frames,frame_time,video_time |
| 53 | +pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2" |
| 54 | +model_name = "llava_qwen" |
| 55 | +device = "cuda" |
| 56 | +device_map = "auto" |
| 57 | +tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args |
| 58 | +model.eval() |
| 59 | +video_path = "XXXX" |
| 60 | +max_frames_num = "64" |
| 61 | +video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True) |
| 62 | +video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().bfloat16() |
| 63 | +video = [video] |
| 64 | +conv_template = "qwen_1_5" # Make sure you use correct chat template for different models |
| 65 | +time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video." |
| 66 | +question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\nPlease describe this video in detail." |
| 67 | +conv = copy.deepcopy(conv_templates[conv_template]) |
| 68 | +conv.append_message(conv.roles[0], question) |
| 69 | +conv.append_message(conv.roles[1], None) |
| 70 | +prompt_question = conv.get_prompt() |
| 71 | +input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device) |
| 72 | +cont = model.generate( |
| 73 | + input_ids, |
| 74 | + images=video, |
| 75 | + modalities= ["video"], |
| 76 | + do_sample=False, |
| 77 | + temperature=0, |
| 78 | + max_new_tokens=4096, |
| 79 | +) |
| 80 | +text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() |
| 81 | +print(text_outputs) |
| 82 | +``` |
| 83 | + |
| 84 | + |
| 85 | +## Training |
| 86 | + |
| 87 | +[[Scripts]](/Users/zhangyuanhan/Desktop/LLaVA-NeXT/scripts/video/train): Start training models on your single-image/multi-image/video data. |
| 88 | + |
| 89 | + |
| 90 | +## Evaluation Guidance |
| 91 | + |
| 92 | +We use the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) toolkit to evaluate our models. Ensure you have installed the LLaVA-NeXT model files as per the instructions in the main README.md. |
| 93 | + |
| 94 | +Install lmms-eval: |
| 95 | + |
| 96 | +> pip install git+https://github.com/EvolvingLMMs-Lab/lmms-eval.git |
| 97 | +
|
| 98 | +### Reproducing Evaluation Results |
| 99 | + |
| 100 | +Our models' evaluation results can be fully reproduced using the lmms-eval toolkit. After installing lmms-eval and llava, you can run the evaluation using the following commands. |
| 101 | + |
| 102 | +Note: These commands require flash-attn. If you prefer not to install it, disable flash-attn by adding `attn_implementation=None` to the `--model_args` parameter. |
| 103 | + |
| 104 | +Important: Different torch versions may cause slight variations in results. By default in `lmms-eval`, the requirement for torch version is set to the latest version. In `llava` repo, the torch version is set to `2.1.2`. Torch version `2.1.2` would be stable for both `llava` and `lmms-eval` |
| 105 | + |
| 106 | +### Evaluating LLaVA-Video on multiple datasets |
| 107 | + |
| 108 | +We recommend the developers and researchers to thoroughly evaluate the models on more datasets to get a comprehensive understanding of their performance in different scenarios. So we provide a comprehensive list of datasets for evaluation, and welcome to incoporate more evaluation tasks. Please refer to the [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) for more details. |
| 109 | + |
| 110 | +```bash |
| 111 | +# video tasks |
| 112 | +accelerate launch --num_processes=8 \ |
| 113 | +-m lmms_eval \ |
| 114 | +--model llava_vid \ |
| 115 | +--model_args pretrained=lmms-lab/LLaVA-Video-7B-Qwen2,conv_template=qwen_1_5,max_frames_num=64,mm_spatial_pool_mode=average \ |
| 116 | +--tasks activitynetqa,videochatgpt,nextqa_mc_test,egoschema,video_dc499,videmme,videomme_w_subtitle,perceptiontest_val_mc \ |
| 117 | +--batch_size 1 \ |
| 118 | +--log_samples \ |
| 119 | +--log_samples_suffix llava_vid \ |
| 120 | +--output_path ./logs/ |
| 121 | +``` |
| 122 | + |
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