import re, time, os from tqdm import tqdm import json from datetime import datetime import argparse import Levenshtein from base_agent import BaseAgent_SFT, BaseAgent_Open from system_prompts import sys_prompts from tools import ToolCalling from vbench_leaderboard import VBenchLeaderboard import pandas as pd from process import * def parse_args(): parser = argparse.ArgumentParser(description='Eval-Agent-VBench', formatter_class=argparse.RawTextHelpFormatter) parser.add_argument( "--user_query", type=str, required=True, help="user query", ) parser.add_argument( "--model", type=str, default="latte1", help="target model", ) parser.add_argument( "--recommend", action="store_true", help="recommend model", ) args = parser.parse_args() return args class EvalAgent: def __init__(self, sample_model="latte1", save_mode="video", refer_file="vbench_dimension_scores.tsv", recommend=False): self.tools = ToolCalling(sample_model=sample_model, save_mode=save_mode) self.sample_model = sample_model self.user_query = "" self.tsv_file_path = refer_file self.recommend = recommend def init_agent(self): self.eval_agent = BaseAgent_SFT(system_prompt=sys_prompts["eval-agent-vbench-training-sys"], use_history=True, temp=0.5) # self.prompt_agent = BaseAgent_Open(system_prompt=sys_prompts["vbench-prompt-sys"], use_history=True, temp=0.5) self.prompt_agent = BaseAgent_SFT(system_prompt=sys_prompts["vbench-prompt-sys-open"], use_history=True, temp=0.5, model_name_or_path="http://0.0.0.0:12334/v1/chat/completions") def recommend_model(self, query): leaderboard = VBenchLeaderboard() recommendations = leaderboard.recommend_model(query, top_k=3) report = leaderboard.generate_recommendation_report(query, recommendations) return report def search_auxiliary(self, designed_prompts, prompt): for _, value in designed_prompts.items(): if value['prompt'] == prompt: return value["auxiliary_info"] raise "Didn't find auxiliary info, please check your json." def sample_and_eval(self, designed_prompts, save_path, tool_name): try: prompts = [item["prompt"] for _, item in designed_prompts.items()] except: designed_prompts = parse_json(designed_prompts) if isinstance(designed_prompts, list): prompts = [item["prompt"] for item in designed_prompts] else: prompts = [item["prompt"] for _, item in designed_prompts.items()] video_pairs = self.tools.sample(prompts, save_path) if 'auxiliary_info' in designed_prompts["Step 1"]: for item in video_pairs: item["auxiliary_info"] = self.search_auxiliary(designed_prompts, item["prompt"]) eval_results = self.tools.eval(tool_name, video_pairs) return eval_results def reference_prompt(self, search_dim): file_path = "./eval_tools/vbench/VBench_full_info.json" data = json.load(open(file_path, "r")) results = [] for item in data: if search_dim in item["dimension"]: item.pop("dimension") item["Prompt"] = item.pop("prompt_en") if 'auxiliary_info' in item and search_dim in item['auxiliary_info']: item["auxiliary_info"] = list(item["auxiliary_info"][search_dim].values())[0] results.append(item) return results # def format_eval_result(self, results, reference_table): # question = results["Sub-aspect"] # tool_name = results["Tool"] # average_score = results["eval_results"]["score"][0] # video_results = results["eval_results"]["score"][1] # output = f"Sub-aspect: {question}\n" # output += f"The score categorization table for the numerical results evaluated by the '{tool_name}' is as follows:\n{reference_table}\n\n" # output += f"Observation: The evaluation results using '{tool_name}' are summarized below.\n" # output += f"Average Score: {average_score:.4f}\n" # output += "Detailed Results:\n" # for i, video in enumerate(video_results, 1): # prompt = video["prompt"] # score = video["video_results"] # output += f"\t{i}. Prompt: {prompt}\n" # output += f"\tScore: {score:.4f}\n" # return output def format_eval_results(self, results, reference_table): tool_name = results["tool"] average_score = results["eval_results"]["score"][0] video_results = results["eval_results"]["score"][1] # More concise and structured format for SFT output = f"Scoring Reference Table of '{tool_name}': {reference_table}\n\n" output += f"Results:\n" output += f"- Overall score: {average_score:.4f}\n" output += f"- Per-prompt scores:\n" for video in video_results: prompt = video["prompt"] score = video["video_results"] output += f" • \"{prompt}\": {score:.4f}\n" return output def update_info(self): # folder_name = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + "-" + self.user_query.replace(" ", "_") folder_name = os.environ["FOLDER_NAME"] if "FOLDER_NAME" in os.environ else datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + "-" + self.user_query.replace(" ", "_") # the environment folder name categorizes model into different rounds self.save_path = f"./eval_vbench_results/{self.sample_model}/{folder_name}" os.makedirs(self.save_path, exist_ok=True) self.video_folder = os.path.join(self.save_path, "videos") self.file_name = os.path.join(self.save_path, f"eval_results.json") def explore(self, query, all_chat=[]): self.user_query = query self.update_info() self.init_agent() df = pd.read_csv(self.tsv_file_path, sep='\t') plan_query = query all_chat.append(plan_query) n = 0 start_time = time.time() while True: plans_str = self.eval_agent(plan_query) plans = format_plans(plans_str) if '' in plans_str: print(f"Finish! Time: {time.time() - start_time:.2f}s") plans["eval_time"] = time.time() - start_time if self.recommend: print("Generating recommendation report...") report = self.recommend_model(query) plans["recommendation_report"] = report print(f"\nQuery: {query}") print("-" * 40) print(report) print("\n" + "="*80) all_chat.append(plans) break for _ in range(3): try: tool = plans.get('tool', None) if tool and tool_existence(tool): plans["tool"] = tool_existence(tool) break else: # If tool does not exist, regenerate plan_str plans_str = self.eval_agent(plan_query) plans = format_plans(plans_str) except Exception as e: # Safe error message that doesn't assume 'tool' key exists tool_name = plans.get("tool", "UNKNOWN") print(f"❌ Tool '{tool_name}' not found or not valid.") print(f"Generated plan: {plans_str[:200]}...") print(f"Parsed result: {plans}") print(f"Error: {e}") continue # Try again # tool_name_ori = plans["tool"] # reference_table = format_dimension_as_string(df, tool_name_ori) reference_table = format_dimension_as_string(df, plans["tool"]) # tool_name = tool_name_ori.replace(" ", "_").lower() # Subject Consistency -> subject_consistency prompt_list = self.reference_prompt(plans["tool"]) prompt_query = f"## Context:\n{json.dumps(plans)}\n\n ## Prompt list:\n{json.dumps(prompt_list)}" designed_prompts = self.prompt_agent(prompt_query) plans["eval_results"] = self.sample_and_eval(designed_prompts, self.video_folder, plans["tool"]) plan_query = self.format_eval_results(plans, reference_table=reference_table) # NEW plan query, simpler all_chat.append(plans) if n > 9: break n += 1 all_chat.append(self.eval_agent.messages) save_json(all_chat, self.file_name) def main(): args = parse_args() user_query = args.user_query eval_agent = EvalAgent(sample_model=args.model, save_mode="video", recommend=args.recommend) eval_agent.explore(user_query) if __name__ == "__main__": main()