File size: 3,954 Bytes
797df36 | 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 | import os
import sys
import json
import base64
import asyncio
import aiofiles
from tqdm.asyncio import tqdm_asyncio
from openai import AsyncOpenAI
from rouge import Rouge
# CogReasoner
# UI-TARs
Test_Model = "Qwen2.5-VL-7B" # 模型名称
# ===== 配置区 =====
TEST_JSON_PATH = "/code/CogReasoner/Test/VisualWebBench_element_ocr.json" # 测试数据路径
OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Test_Model}-VisualWebBench_Element_Ocr.json" # 推理结果保存路径
MAX_SAMPLE = 10 # 测试样本上限
MAX_CONCURRENT_REQUESTS = 5 # 最大并发量
ACCURACY_PRINT_INTERVAL = 10 # 每多少步打印一次中间结果
MODEL_NAME = "qwen2vl" # 使用的大模型名称
BASE_URL = "http://localhost:8080/v1" # vLLM兼容API地址
# ===== 初始化 openai 客户端 =====
client = AsyncOpenAI(
api_key="EMPTY",
base_url=BASE_URL,
)
# ===== 正式测评指标函数 =====
def eval_heading_ocr(preds, golds, **kwargs):
assert len(preds) == len(golds)
for i in range(len(preds)):
if not preds[i]:
preds[i] = " " # 避免ROUGE计算异常
rouge = Rouge(metrics=['rouge-1', 'rouge-2', 'rouge-l'])
scores = rouge.get_scores(preds, golds, avg=True)
return dict(
rouge_1=scores['rouge-1']['f'] * 100,
rouge_2=scores['rouge-2']['f'] * 100,
rouge_l=scores['rouge-l']['f'] * 100
)
# ===== 单条样本推理函数 =====
async def process_item(index, item, sem):
async with sem:
image_path = item["images"][0]
ground_truth = item["messages"][1]["content"].strip()
user_prompt = item["messages"][0]["content"]
# 读取并编码图片
async with aiofiles.open(image_path, "rb") as f:
content = await f.read()
encoded_image = base64.b64encode(content).decode("utf-8")
image_data_uri = f"data:image;base64,{encoded_image}"
try:
response = await client.chat.completions.create(
model=MODEL_NAME,
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": image_data_uri}},
{"type": "text", "text": user_prompt},
],
},
],
temperature=0.1,
top_p=0.95,
max_tokens=1024,
)
pred_text = response.choices[0].message.content.strip()
except Exception as e:
pred_text = f"[ERROR] {str(e)}"
return {
"image": image_path,
"ground_truth": ground_truth,
"prediction": pred_text,
}
# ===== 主函数 =====
async def main():
with open(TEST_JSON_PATH, "r", encoding="utf-8") as f:
test_data = json.load(f)[:MAX_SAMPLE]
sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)
tasks = [process_item(i, item, sem) for i, item in enumerate(test_data)]
print(f"\n🚀 Starting evaluation on {len(tasks)} samples...\n")
results = await tqdm_asyncio.gather(*tasks)
predictions = [r["prediction"] for r in results]
references = [r["ground_truth"] for r in results]
metrics = eval_heading_ocr(predictions, references)
output = {
"metrics": metrics,
"results": results,
}
# 保存结果
os.makedirs(os.path.dirname(OUTPUT_JSON_PATH), exist_ok=True)
with open(OUTPUT_JSON_PATH, "w", encoding="utf-8") as f:
json.dump(output, f, indent=2, ensure_ascii=False)
print(f"\n✅ Evaluation Complete!")
print(f"📊 Metrics: {json.dumps(metrics, indent=2)}")
print(f"📁 Results saved at: {OUTPUT_JSON_PATH}")
await client.close()
# ===== 启动入口 =====
if __name__ == "__main__":
asyncio.run(main())
sys.exit(0) |