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Running
on
Zero
File size: 7,078 Bytes
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import argparse
import json
import os
import re
import time
import openai
from tqdm import tqdm
NUM_SECONDS_TO_SLEEP = 0.5
VOCAB_IMAGE_W = 1000
VOCAB_IMAGE_H = 1000
# Define Azure OpenAI details
model_name = "gpt-4o-2024-11-20"
max_tokens = 1000 # range: [1, 4095]
# Initialize the Azure client
client = openai.AzureOpenAI(
azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
api_key=os.getenv("AZURE_OPENAI_KEY"),
api_version="2024-03-01-preview",
)
def get_eval(content: str, max_tokens: int):
while True:
try:
completion = client.chat.completions.create(
model=model_name,
messages=[
{
"role": "system",
"content": "You are a helpful and precise assistant for checking the quality of the answer.",
},
{
"role": "user",
"content": content,
},
],
max_tokens=max_tokens,
temperature=0,
)
ret = completion.choices[0].message.content
break
except openai.error.RateLimitError:
pass
except Exception as e:
print(e)
time.sleep(NUM_SECONDS_TO_SLEEP)
return ret
def postprocess_answer(answer, category):
if category == "refer_desc" or category == "refer_reason":
pattern = r"\[.*?\]"
matches = re.findall(pattern, answer)
for match in matches:
answer = answer.replace(" " + match, "")
elif category == "ground_conv":
pattern = r"\[.*?\]"
matches = re.findall(pattern, answer)
for match in matches:
coor_cur = match.replace("[", "")
coor_cur = coor_cur.replace("]", "")
coor_cur = coor_cur.split(",")
coor_cur = [float(i.strip()) for i in coor_cur]
try:
assert len(coor_cur) == 4
except:
print("Found a exception when parsing coordinates")
answer = answer.replace(match, "")
converted_box_coor = [
coor_cur[0] / VOCAB_IMAGE_W,
coor_cur[1] / VOCAB_IMAGE_H,
coor_cur[2] / VOCAB_IMAGE_W,
coor_cur[3] / VOCAB_IMAGE_H,
]
answer = answer.replace(
match,
f"[{converted_box_coor[0]:.3f}, {converted_box_coor[1]:.3f}, {converted_box_coor[2]:.3f}, {converted_box_coor[3]:.3f}]",
)
return answer
def parse_score(review):
try:
score_pair = review.split("\n")[0]
score_pair = score_pair.replace(",", " ")
sp = score_pair.split(" ")
print("score:", sp)
return [float(sp[0]), float(sp[1])]
except Exception as e:
print(e)
print("error", review)
return [-1, -1]
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="ChatGPT-based QA evaluation.")
parser.add_argument("-q", "--question")
parser.add_argument("-c", "--context")
parser.add_argument("-a", "--answer-list", nargs="+", default=[])
parser.add_argument("-r", "--rule")
parser.add_argument("-o", "--output")
parser.add_argument(
"--max-tokens",
type=int,
default=1024,
help="maximum number of tokens produced in the output",
)
parser.add_argument("--source-file", type=str, required=True)
args = parser.parse_args()
f_q = open(os.path.expanduser(args.question))
f_ans1 = open(os.path.expanduser(args.answer_list[0]))
# convert first
target_path = os.path.expanduser(args.answer_list[1])
with open(args.source_file, "r") as file:
source_file = json.load(file)
with open(target_path, "w") as file:
for idx, item in enumerate(source_file):
info = {
"question_id": idx,
"image": item["image_path"].split("/")[-1],
"category": "refer_desc",
"text": item["caption"],
}
json.dump(info, file, ensure_ascii=False)
file.write("\n")
f_ans2 = open(os.path.expanduser(args.answer_list[1]))
rule_dict = json.load(open(os.path.expanduser(args.rule), "r"))
if os.path.isfile(os.path.expanduser(args.output)):
cur_reviews = [
json.loads(line) for line in open(os.path.expanduser(args.output))
]
else:
cur_reviews = []
review_file = open(f"{args.output}", "a")
context_list = [json.loads(line) for line in open(os.path.expanduser(args.context))]
image_to_context = {context["image"]: context for context in context_list}
handles = []
idx = 0
for ques_js, ans1_js, ans2_js in tqdm(zip(f_q, f_ans1, f_ans2)):
ques = json.loads(ques_js)
ans1 = json.loads(ans1_js)
ans2 = json.loads(ans2_js)
inst = image_to_context[ques["image"]]
# cap_str = '\n'.join(inst['captions'])
# box_str = '\n'.join([f'{instance["category"]}: {instance["bbox"]}' for instance in inst['instances']])
category = json.loads(ques_js)["category"]
if category in rule_dict:
rule = rule_dict[category]
else:
assert False, f"Visual QA category not found in rule file: {category}."
# Assume ans2 is the predicted one.
processed_answer = postprocess_answer(ans2["text"], category)
# pdb.set_trace()
ans2["text"] = processed_answer
# if category == 'refer_desc':
prompt = rule["prompt"]
role = rule["role"]
content = (
f'[Context]\{inst["text"]}\n\n'
f'[Question]\n{ques["text"]}\n\n'
f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
f"[System]\n{prompt}\n\n"
)
# content = (f'[Context]\n{cap_str}\n\n{box_str}\n\n'
# f'[Question]\n{ques["text"]}\n\n'
# f'[{role} 1]\n{ans1["text"]}\n\n[End of {role} 1]\n\n'
# f'[{role} 2]\n{ans2["text"]}\n\n[End of {role} 2]\n\n'
# f'[System]\n{prompt}\n\n')
cur_js = {
"id": idx + 1,
"question_id": ques["question_id"],
"answer1_id": ans1.get("answer_id", ans1["question_id"]),
"answer2_id": ans2.get("answer_id", ans2["question_id"]),
"category": category,
}
if idx >= len(cur_reviews):
review = get_eval(content, args.max_tokens)
scores = parse_score(review)
cur_js["content"] = review
cur_js["tuple"] = scores
cur_js["answer1"] = ans1["text"]
cur_js["answer2"] = ans2["text"]
review_file.write(json.dumps(cur_js) + "\n")
review_file.flush()
else:
print(f"Skipping {idx} as we already have it.")
idx += 1
print(idx)
review_file.close()
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