File size: 9,325 Bytes
9f3bc09 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 |
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 '</summary>' 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()
|