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import re, time, os |
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from tqdm import tqdm |
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import json |
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from datetime import datetime |
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import argparse |
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import Levenshtein |
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from base_agent import BaseAgent |
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from system_prompts import sys_prompts |
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from tools import ToolCalling |
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from process import * |
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import pandas as pd |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='Eval-Agent-VBench', formatter_class=argparse.RawTextHelpFormatter) |
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parser.add_argument( |
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"--user_query", |
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type=str, |
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required=True, |
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help="user query", |
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) |
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parser.add_argument( |
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"--model", |
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type=str, |
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default="latte1", |
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help="target model", |
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) |
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args = parser.parse_args() |
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return args |
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def most_similar_string(prompt, string_list): |
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similarities = [Levenshtein.distance(prompt, item["Prompt"]) for item in string_list] |
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most_similar_idx = similarities.index(min(similarities)) |
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return string_list[most_similar_idx] |
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def check_and_fix_prompt(chosed_prompts, prompt_list): |
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results_dict={} |
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for key, item in chosed_prompts.items(): |
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thought = item["Thought"] |
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sim_item = most_similar_string(item["Prompt"], prompt_list) |
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sim_item["Thought"] = thought |
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results_dict[key] = sim_item |
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return results_dict |
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def format_dimension_as_string(df, dimension_name): |
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row = df.loc[df['Dimension'] == dimension_name] |
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if row.empty: |
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return f"No data found for dimension: {dimension_name}" |
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formatted_string = ( |
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f"{row['Dimension'].values[0]}: " |
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f"Very High -> {row['Very High'].values[0]}, " |
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f"High -> {row['High'].values[0]}, " |
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f"Moderate -> {row['Moderate'].values[0]}, " |
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f"Low -> {row['Low'].values[0]}, " |
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f"Very Low -> {row['Very Low'].values[0]}" |
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) |
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return formatted_string |
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class EvalAgent: |
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def __init__(self, sample_model="latte1", save_mode="video", refer_file="vbench_dimension_scores.tsv"): |
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self.sample_model = sample_model |
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self.user_query = "" |
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self.tsv_file_path = refer_file |
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def init_agent(self): |
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self.prompt_agent = BaseAgent(system_prompt=sys_prompts["vbench-prompt-sys"], use_history=False, temp=0.7) |
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self.plan_agent = BaseAgent(system_prompt=sys_prompts["vbench-plan-sys"], use_history=True, temp=0.7) |
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def search_auxiliary(self, designed_prompts, prompt): |
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for _, value in designed_prompts.items(): |
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if value['Prompt'] == prompt: |
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return value["auxiliary_info"] |
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raise "Didn't find auxiliary info, please check your json." |
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def sample_and_eval(self, designed_prompts, save_path, tool_name): |
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prompts = [item["Prompt"] for _, item in designed_prompts.items()] |
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video_pairs = self.tools.sample(prompts, save_path) |
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if 'auxiliary_info' in designed_prompts["Step 1"]: |
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for item in video_pairs: |
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item["auxiliary_info"] = self.search_auxiliary(designed_prompts, item["prompt"]) |
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eval_results = self.tools.eval(tool_name, video_pairs) |
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return eval_results |
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def reference_prompt(self, search_dim): |
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file_path = "./eval_tools/vbench/VBench_full_info.json" |
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data = json.load(open(file_path, "r")) |
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results = [] |
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for item in data: |
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if search_dim in item["dimension"]: |
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item.pop("dimension") |
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item["Prompt"] = item.pop("prompt_en") |
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if 'auxiliary_info' in item and search_dim in item['auxiliary_info']: |
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item["auxiliary_info"] = list(item["auxiliary_info"][search_dim].values())[0] |
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results.append(item) |
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return results |
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def format_eval_result(self, results, reference_table): |
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question = results["Sub-aspect"] |
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tool_name = results["Tool"] |
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average_score = results["eval_results"]["score"][0] |
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video_results = results["eval_results"]["score"][1] |
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output = f"Sub-aspect: {question}\n" |
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output += f"The score categorization table for the numerical results evaluated by the '{tool_name}' is as follows:\n{reference_table}\n\n" |
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output += f"Observation: The evaluation results using '{tool_name}' are summarized below.\n" |
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output += f"Average Score: {average_score:.4f}\n" |
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output += "Detailed Results:\n" |
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for i, video in enumerate(video_results, 1): |
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prompt = video["prompt"] |
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score = video["video_results"] |
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output += f"\t{i}. Prompt: {prompt}\n" |
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output += f"\tScore: {score:.4f}\n" |
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return output |
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def update_info(self): |
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folder_name = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + "-" + self.user_query.replace(" ", "_") |
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self.save_path = f"./eval_vbench_results/{self.sample_model}/{folder_name}" |
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os.makedirs(self.save_path, exist_ok=True) |
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self.video_folder = os.path.join(self.save_path, "videos") |
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self.file_name = os.path.join(self.save_path, f"eval_results.json") |
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def explore(self, query, all_chat=[]): |
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self.user_query = query |
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self.update_info() |
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self.init_agent() |
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df = pd.read_csv(self.tsv_file_path, sep='\t') |
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plan_query = query |
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all_chat.append(plan_query) |
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n = 0 |
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while True: |
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breakpoint() |
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plans = self.plan_agent(plan_query, parse=True) |
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if plans.get("Analysis"): |
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all_chat.append(plans) |
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print("Finish!") |
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break |
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tool_name = plans["Tool"].lower().strip().replace(" ", "_") |
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reference_table = format_dimension_as_string(df, plans["Tool"]) |
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prompt_query = json.dumps(plans) |
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prompt_list = self.reference_prompt(tool_name) |
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prompt_query = f"Context:\n{prompt_query}\n\nPrompt list:\n{json.dumps(prompt_list)}" |
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designed_prompts = self.prompt_agent(prompt_query, parse=True) |
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designed_prompts = check_and_fix_prompt(designed_prompts, prompt_list) |
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plans["eval_results"] = self.sample_and_eval(designed_prompts, self.video_folder, tool_name) |
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plan_query = self.format_eval_result(plans, reference_table=reference_table) |
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all_chat.append(plans) |
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if n > 9: |
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break |
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n += 1 |
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all_chat.append(self.plan_agent.messages) |
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save_json(all_chat, self.file_name) |
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def main(): |
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args = parse_args() |
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user_query = args.user_query |
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eval_agent = EvalAgent(sample_model=args.model, save_mode="video") |
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eval_agent.explore(user_query) |
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if __name__ == "__main__": |
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main() |
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