| |
| from llava.model.builder import load_pretrained_model |
| from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token |
| from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX |
| from llava.conversation import conv_templates, SeparatorStyle |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from PIL import Image |
| from tqdm import tqdm |
| import requests |
| import copy |
| import torch |
| import sys |
| import warnings |
| import re |
| from decord import VideoReader, cpu |
| import numpy as np |
| import json |
| import os |
| import argparse |
| from vbench2.utils import load_dimension_info |
| from tqdm import tqdm |
| warnings.filterwarnings("ignore") |
|
|
| sys_prompt_sum = """You are Qwen, created by Alibaba Cloud. You are a helpful assistant and a brilliant plot summarizer. |
| You need to summerize and divide the detailed <video_caption> into several key plots up to the given <template>, each plot should be a complete sentence. |
| """ |
|
|
| sys_prompt = """You are Qwen, created by Alibaba Cloud. You are a helpful assistant and a brilliant plot consistency judger. |
| You need to judge whether prompt2 contains the key elements described in prompt1 , similar semantic should be compromised. |
| Note that you should not care about the detailed name in prompt when judging the consistency, it is trival. |
| Simple reasoning is permitted, such as 'A girl is in the forest and a ghost is in the side' is consistent with 'The girl's father was captured by a monster, and the girl ventures into the forest to find the monster and rescue her father.'. |
| Note that you should only focus on the person or creatures mentioned in prompt1 when assessing the prompt2, unrelated contents should not be judged and considered, such background and the clothes details. |
| Be sensitive to the consistency, "the runner from one team accelerated in the second-to-last turn, widening the gap and winning the race" is not consistent with "running" only. "holding the swords" only is not consistent with "holding the swords to fight the enermy". |
| First return yes or no, then giving the reason. |
| """ |
|
|
| def judge(prompt, sysp, tokenizer, model): |
| messages = [ |
| { |
| "role": "system", |
| "content": sysp |
| }, |
| { |
| "role": "user", |
| "content": prompt |
| } |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| generated_ids = model.generate( |
| **model_inputs, |
| do_sample=False, |
| temperature=0, |
| max_new_tokens=512 |
| ) |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| ] |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| return response |
|
|
| def split_by_numbered_list(text): |
| pattern = r'\d+\.\s*' |
| parts = re.split(pattern, text) |
| parts = [part.strip() for part in parts if part.strip()] |
| return parts |
|
|
| def load_video(video_path, max_frames_num,fps=1,force_sample=False): |
| if max_frames_num == 0: |
| return np.zeros((1, 336, 336, 3)) |
| vr = VideoReader(video_path, ctx=cpu(0),num_threads=1) |
| total_frame_num = len(vr) |
| video_time = total_frame_num / vr.get_avg_fps() |
| fps = round(vr.get_avg_fps()/fps) |
| frame_idx = [i for i in range(0, len(vr), fps)] |
| frame_time = [i/fps for i in frame_idx] |
| if len(frame_idx) > max_frames_num or force_sample: |
| sample_fps = max_frames_num |
| uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int) |
| frame_idx = uniform_sampled_frames.tolist() |
| frame_time = [i/vr.get_avg_fps() for i in frame_idx] |
| frame_time = ",".join([f"{i:.2f}s" for i in frame_time]) |
| spare_frames = vr.get_batch(frame_idx).asnumpy() |
| return spare_frames,frame_time,video_time |
|
|
| def LLaVA_Video(prompt_dict_ls, llava_model, llava_tokenizer, image_processor, qwen_model, qwen_tokenizer, device): |
| final_score = 0 |
| valid_num = 0 |
| processed_json=[] |
| for prompt_dict in tqdm(prompt_dict_ls): |
| video_paths = prompt_dict['video_list'] |
| ground_truth_text = prompt_dict['auxiliary_info'] |
| length = len(ground_truth_text) |
| |
| for video_path in video_paths: |
| new_item = { |
| "video_path": video_path, |
| } |
| |
| max_frames_num = 64 |
| video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True) |
| video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].to(device).bfloat16() |
| video = [video] |
| conv_template = "qwen_1_5" |
| 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}. Return the plot in video. Here is the template.." |
| if length==5: |
| template = "1. ; 2. ; 3. ; 4. ; 5. ." |
| elif length==4: |
| template = "1. ; 2. ; 3. ; 4. ." |
| question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\n"+f"{template}" |
| conv = copy.deepcopy(conv_templates[conv_template]) |
| conv.append_message(conv.roles[0], question) |
| conv.append_message(conv.roles[1], None) |
| prompt_question = conv.get_prompt() |
| input_ids = tokenizer_image_token(prompt_question, llava_tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device) |
| cont = llava_model.generate( |
| input_ids, |
| images=video, |
| modalities= ["video"], |
| do_sample=False, |
| temperature=0, |
| max_new_tokens=4096, |
| ) |
| answer_llava = llava_tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() |
| score=0 |
| if '1. ' not in answer_llava: |
| for q, item in enumerate(ground_truth_text): |
| prompt1 = item.strip() |
| prompt = f""" |
| prompt1: {prompt1} |
| prompt2: {answer_llava} |
| """ |
| response = judge(prompt, sys_prompt, qwen_tokenizer, qwen_model) |
| if 'yes' in response.lower(): |
| score+=1 |
| else: |
| break |
| else: |
| prompt_list = split_by_numbered_list(answer_llava) |
| if len(prompt_list) > length: |
| 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}. Describe the video in detail." |
| question = DEFAULT_IMAGE_TOKEN + f"{time_instruciton}\n" |
| conv = copy.deepcopy(conv_templates[conv_template]) |
| conv.append_message(conv.roles[0], question) |
| conv.append_message(conv.roles[1], None) |
| prompt_question = conv.get_prompt() |
| input_ids = tokenizer_image_token(prompt_question, llava_tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device) |
| cont = llava_model.generate( |
| input_ids, |
| images=video, |
| modalities= ["video"], |
| do_sample=False, |
| temperature=0, |
| max_new_tokens=4096, |
| ) |
| answer_llava = llava_tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() |
| prompt = f""" |
| video_caption: {answer_llava} |
| template: {template} |
| """ |
| response = judge(prompt, sys_prompt_sum, qwen_tokenizer, qwen_model) |
| prompt_list = split_by_numbered_list(response) |
| for q, item in enumerate(prompt_list): |
| prompt1 = ground_truth_text[q] |
| prompt2 = item.strip() |
| prompt = f""" |
| prompt1: {prompt1} |
| prompt2: {prompt2} |
| """ |
| response = judge(prompt, sys_prompt, qwen_tokenizer, qwen_model) |
| if 'yes' in response.lower(): |
| score+=1 |
| else: |
| break |
| valid_num+=1 |
| new_item["video_results"] = score/len(ground_truth_text) |
| final_score+=score/len(ground_truth_text) |
| processed_json.append(new_item) |
| |
| return final_score/valid_num, processed_json |
| |
| |
| def compute_complex_plot(json_dir, device, submodules_dict, **kwargs): |
| _, prompt_dict_ls = load_dimension_info(json_dir, dimension='complex_plot', lang='en') |
| |
| model_name = "llava_qwen" |
| device_map = "auto" |
| try: |
| pretrained = submodules_dict['llava'] |
| llava_tokenizer, llava_model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) |
| except: |
| pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2" |
| llava_tokenizer, llava_model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) |
| llava_model.eval() |
| |
| try: |
| qwen_model_name = submodules_dict['qwen'] |
| qwen_model = AutoModelForCausalLM.from_pretrained( |
| qwen_model_name, |
| torch_dtype="auto", |
| device_map="auto", |
| cache_dir=submodules_dict['qwen'] |
| ) |
| qwen_tokenizer = AutoTokenizer.from_pretrained(qwen_model_name, cache_dir=submodules_dict['qwen']) |
| except: |
| qwen_model_name = 'Qwen/Qwen2.5-7B-Instruct' |
| qwen_model = AutoModelForCausalLM.from_pretrained( |
| qwen_model_name, |
| torch_dtype="auto", |
| device_map="auto", |
| cache_dir=submodules_dict['qwen'] |
| ) |
| qwen_tokenizer = AutoTokenizer.from_pretrained(qwen_model_name, cache_dir=submodules_dict['qwen']) |
| |
| all_results, video_results = LLaVA_Video(prompt_dict_ls, llava_model, llava_tokenizer, image_processor, qwen_model, qwen_tokenizer, device) |
| all_results = sum([d['video_results'] for d in video_results]) / len(video_results) |
| return all_results, video_results |
