| import os | |
| import json | |
| from datetime import datetime | |
| import argparse | |
| import Levenshtein | |
| from base_agent import BaseAgent | |
| from system_prompts import sys_prompts | |
| from tools import ToolCalling | |
| from process import * | |
| import pandas as pd | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Eval-Agent-T2I-CompBench', formatter_class=argparse.RawTextHelpFormatter) | |
| parser.add_argument( | |
| "--user_query", | |
| type=str, | |
| required=True, | |
| help="user query", | |
| ) | |
| parser.add_argument( | |
| "--model", | |
| type=str, | |
| required=True, | |
| default="sdxl-1", | |
| help="model", | |
| ) | |
| args = parser.parse_args() | |
| return args | |
| def most_similar_string(prompt, string_list): | |
| similarities = [Levenshtein.distance(prompt, item) for item in string_list] | |
| most_similar_idx = similarities.index(min(similarities)) | |
| return string_list[most_similar_idx] | |
| def check_and_fix_prompt(chosed_prompts, prompt_list): | |
| results_dict={} | |
| for key, item in chosed_prompts.items(): | |
| item["Prompt"] = most_similar_string(item["Prompt"], prompt_list) | |
| results_dict[key] = item | |
| return results_dict | |
| def format_dimension_as_string(df, dimension_name): | |
| row = df.loc[df['Dimension'] == dimension_name] | |
| if row.empty: | |
| return f"No data found for dimension: {dimension_name}" | |
| formatted_string = ( | |
| f"{row['Dimension'].values[0]}: " | |
| f"Very High -> {row['Very High'].values[0]}, " | |
| f"High -> {row['High'].values[0]}, " | |
| f"Moderate -> {row['Moderate'].values[0]}, " | |
| f"Low -> {row['Low'].values[0]}, " | |
| f"Very Low -> {row['Very Low'].values[0]}" | |
| ) | |
| return formatted_string | |
| class EvalAgent: | |
| def __init__(self, sample_model="sdxl-1", save_mode="img", refer_file="t2i_comp_dimension_scores.tsv"): | |
| 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 | |
| def init_agent(self): | |
| self.prompt_agent = BaseAgent(system_prompt=sys_prompts["t2i-comp-prompt-sys"], use_history=False, temp=0.7) | |
| self.plan_agent = BaseAgent(system_prompt=sys_prompts["t2i-comp-plan-sys"], use_history=True, temp=0.7) | |
| def sample_and_eval(self, designed_prompts, save_path, tool_name): | |
| prompts = [item["Prompt"] for _, item in designed_prompts.items()] | |
| video_pairs = self.tools.sample(prompts, save_path) | |
| eval_results = self.tools.eval(tool_name, video_pairs) | |
| return eval_results | |
| def reference_prompt(self, search_dim): | |
| search_item = search_dim.replace("_binding", "") | |
| file_path = f"./eval_tools/t2i_comp/prompt_file/{search_item}_val.txt" | |
| with open(file_path, "r") as f: | |
| lines = f.readlines() | |
| lines = [line.strip() for line in lines] | |
| return lines | |
| 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["image_results"] | |
| output += f"\t{i}. Prompt: {prompt}\n" | |
| output += f"\tScore: {score:.4f}\n" | |
| return output | |
| def update_info(self): | |
| folder_name = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + "-" + self.user_query.replace(" ", "_") | |
| self.save_path = f"./eval_t2i_comp_results/{self.sample_model}/{folder_name}" | |
| os.makedirs(self.save_path, exist_ok=True) | |
| self.image_folder = os.path.join(self.save_path, "images") | |
| 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 | |
| while True: | |
| plans = self.plan_agent(plan_query, parse=True) | |
| if plans.get("Analysis"): | |
| all_chat.append(plans) | |
| print("Finish!") | |
| break | |
| tool_name = plans["Tool"].lower().strip().replace(" ", "_") | |
| reference_table = format_dimension_as_string(df, plans["Tool"]) | |
| prompt_query = json.dumps(plans) | |
| prompt_list = self.reference_prompt(tool_name) | |
| prompt_query = f"Context:\n{prompt_query}\n\nPrompt list:\n{json.dumps(prompt_list)}" | |
| designed_prompts = self.prompt_agent(prompt_query, parse=True) | |
| designed_prompts = check_and_fix_prompt(designed_prompts, prompt_list) | |
| plans["eval_results"] = self.sample_and_eval(designed_prompts, self.image_folder, tool_name) | |
| plan_query = self.format_eval_result(plans, reference_table=reference_table) | |
| all_chat.append(plans) | |
| if n > 9: | |
| break | |
| n += 1 | |
| all_chat.append(self.plan_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="img") | |
| eval_agent.explore(user_query) | |
| if __name__ == "__main__": | |
| main() | |