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import json
import os
import time
from copy import deepcopy
from pathlib import Path
import numpy as np
import openai
import requests
import yaml
from loguru import logger as eval_logger
from openai import OpenAI
NUM_SECONDS_TO_SLEEP = 0.5
FERRET_W_METRICS = ["gpt_eval_ferret_refer_desc", "gpt_eval_ferret_refer_reason", "gpt_eval_ferret_ground_conv"]
rule_dict = json.load(open(os.path.join(os.path.dirname(os.path.abspath(__file__)), "rule.json"), "r"))
with open(Path(__file__).parent / "ferret.yaml", "r") as f:
raw_data = f.readlines()
safe_data = []
for i, line in enumerate(raw_data):
# remove function definition since yaml load cannot handle it
if "!function" not in line:
safe_data.append(line)
config = yaml.safe_load("".join(safe_data))
GPT_EVAL_MODEL_NAME = os.getenv("MODEL_VERSION", "gpt-4o-2024-11-20")
API_TYPE = os.getenv("API_TYPE", "openai")
if API_TYPE == "openai":
API_URL = os.getenv("OPENAI_API_URL", "https://api.openai.com/v1/chat/completions")
API_KEY = os.getenv("OPENAI_API_KEY", "YOUR_API_KEY")
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
}
elif API_TYPE == "azure":
API_URL = os.getenv("AZURE_ENDPOINT", "https://api.cognitive.microsoft.com/sts/v1.0/issueToken")
API_KEY = os.getenv("AZURE_API_KEY", "YOUR_API_KEY")
headers = {
"api-key": API_KEY,
"Content-Type": "application/json",
}
def get_eval(content: str, max_tokens: int, retries: int = 3):
global headers
messages = [
{
"role": "system",
"content": "You are a helpful and precise assistant for checking the quality of the answer.",
},
{"role": "user", "content": content},
]
payload = {
"model": GPT_EVAL_MODEL_NAME,
"messages": messages,
"temperature": 0.2,
"max_tokens": max_tokens,
}
for attempt in range(retries):
try:
response = requests.post(API_URL, headers=headers, json=payload)
response.raise_for_status()
response_data = response.json()
content = response_data["choices"][0]["message"]["content"].strip()
if content != "":
return content, response_data["model"]
break # If successful, break out of the loop
except Exception as e:
eval_logger.info(f"Attempt {attempt + 1} failed with error: {e}")
if attempt < retries - 1: # If we have retries left, sleep and then continue to next attempt
time.sleep(NUM_SECONDS_TO_SLEEP)
else: # If this was the last attempt, log and return empty
eval_logger.error(f"All {retries} attempts failed. Last error message: {e}")
return "", ""
return "", ""
def parse_score(review):
try:
score_pair = review.split("\n")[0].strip()
score_pair = score_pair.replace(",", " ")
sp = score_pair.split(" ")
if len(sp) == 2:
return [float(sp[0]), float(sp[1])]
else:
eval_logger.debug(f"Can not split: {review}. Returning [-1, -1]")
return [-1, -1]
except Exception as e:
eval_logger.debug(f"Error: {e}. Returning [-1, -1]")
return [-1, -1]
def ferret_doc_to_visual(doc):
return [doc["image"].convert("RGB")]
def ferret_doc_to_text(doc, lmms_eval_specific_kwargs=None):
if lmms_eval_specific_kwargs is None:
lmms_eval_specific_kwargs = {}
pre_prompt = lmms_eval_specific_kwargs.get("pre_prompt", "")
post_prompt = lmms_eval_specific_kwargs.get("post_prompt", "")
question = f"{pre_prompt}{doc['question']}{post_prompt}"
return question
def ferret_process_results(doc, result):
"""
Args:
doc: a instance of the eval dataset
results: [pred]
Returns:
a dictionary with key: metric name (in this case coco_bleu), value: metric value
"""
try:
question = doc.get("question", "")
ans1 = doc.get("gpt_answer", "")
ans2 = result[0] if result else ""
context = doc.get("context", [])
context = "\n".join(context) if isinstance(context, list) else context
category = doc.get("category", "")
rule = rule_dict.get(category, {})
prompt = rule.get("prompt", "")
role = rule.get("role", "user")
content = f"[Context]\n{context}\n\n" f"[Question]\n{question}\n\n" f"[{role} 1]\n{ans1}\n\n[End of {role} 1]\n\n" f"[{role} 2]\n{ans2}\n\n[End of {role} 2]\n\n" f"[System]\n{prompt}\n\n"
review, model_name = get_eval(content, 1024)
scores = parse_score(review)
except Exception as e:
eval_logger.error(f"Error for Question ID: {doc.get('question_id', 'Unknown')}: {e}")
review = "Failed to Get a Proper Review."
model_name = "Failed Request"
scores = [-1, -1]
metric = f"gpt_eval_ferret_{doc.get('category', 'all')}"
category_review_dict = {
"question": question,
"ans1": ans1,
"ans2": ans2,
"context": context,
"category": category,
"review": review,
"scores": scores,
"eval_model": model_name,
}
non_category_review_dict = deepcopy(category_review_dict)
non_category_review_dict["scores"] = [-999, -999]
data_dict = {}
for m in FERRET_W_METRICS:
if m == metric:
data_dict[m] = category_review_dict
else:
data_dict[m] = non_category_review_dict
data_dict["gpt_eval_ferret_all"] = category_review_dict
# return {"gpt_eval_ferret_all": review_dict}
return data_dict
def ferret_refer_desc_aggregation(results):
return ferret_aggregation(results, "refer_desc")
def ferret_refer_reason_aggregation(results):
return ferret_aggregation(results, "refer_reason")
def ferret_ground_conv_aggregation(results):
return ferret_aggregation(results, "ground_conv")
def ferret_all_aggregation(results):
return ferret_aggregation(results, "all")
def ferret_aggregation(results, category):
try:
scores = []
for result in results:
if -999 in result["scores"]:
continue
scores.append(result["scores"])
stats = np.asarray(scores).mean(0).tolist()
stats = [round(x, 3) for x in stats]
# gpt4_score_percentage = stats[0] * 10
# model_score_percentage = stats[1] * 10
# eval_logger.info(f"Category: {category}")
# eval_logger.info(f"GPT4 Score: {gpt4_score_percentage:.1f}%")
# eval_logger.info(f"Model Score: {model_score_percentage:.1f}%")
# eval_logger.info("=========================")
return round(stats[1] / stats[0] * 100, 1)
except Exception as e:
eval_logger.info(f"Error in ferret_aggregation: {e}, and in category: {category}")
return None
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