File size: 6,809 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 |
import re, time, os
from tqdm import tqdm
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-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",
)
args = parser.parse_args()
return args
def most_similar_string(prompt, string_list):
similarities = [Levenshtein.distance(prompt, item["Prompt"]) 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():
thought = item["Thought"]
sim_item = most_similar_string(item["Prompt"], prompt_list)
sim_item["Thought"] = thought
results_dict[key] = sim_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="latte1", save_mode="video", refer_file="vbench_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["vbench-prompt-sys"], use_history=False, temp=0.7)
self.plan_agent = BaseAgent(system_prompt=sys_prompts["vbench-plan-sys"], use_history=True, temp=0.7)
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):
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 update_info(self):
folder_name = datetime.now().strftime('%Y-%m-%d-%H:%M:%S') + "-" + self.user_query.replace(" ", "_")
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
while True:
breakpoint()
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.video_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="video")
eval_agent.explore(user_query)
if __name__ == "__main__":
main()
|