import os import sys import json import base64 import re import asyncio import aiofiles from tqdm.asyncio import tqdm_asyncio from openai import AsyncOpenAI Model_name = "UI-TARs" # 模型名称 # ===== 配置项 ===== # 请确保这里的路径和模型名是正确的 TEST_JSON_PATH = "/code/CogReasoner/Test/MultiStep_Selected_OnePerSite_step01_FinalAction.json" MODEL_NAME = "qwen2vl" MAX_SAMPLE = 70 # 您可以根据需要调整测试样本数，None表示测试全部 MAX_CONCURRENT_REQUESTS = 5 ACCURACY_PRINT_INTERVAL = 10 OUTPUT_JSON_PATH = f"/code/CogReasoner/Code/Evalaute/Result/Test-{Model_name}-Single_Step.json" # ===== 初始化 OpenAI 客户端 ===== client = AsyncOpenAI( api_key="EMPTY", base_url="http://localhost:8080/v1", ) # ===== 答案提取函数 (无变化) ===== def extract_action(text: str): if not text: return None type_match = re.search(r"Action:\s+type\s+\[(\d+)\]\s+\[(.*?)\]", text, re.IGNORECASE) if type_match: node_id = type_match.group(1) value = type_match.group(2) return f"TYPE({node_id}, {value})" simple_match = re.search(r"Action:\s+(\w+)\s+\[(\d+)\]", text, re.IGNORECASE) if simple_match: action = simple_match.group(1).upper() node_id = simple_match.group(2) return f"{action}({node_id})" return None # ===== 解析多答案的 Ground Truth (无变化) ===== def parse_ground_truth(gt_content: str): action_parts = gt_content.split(';') parsed_actions = [extract_action(part.strip()) for part in action_parts] return [action for action in parsed_actions if action is not None] # ===== 关键修改 1: 新增智能比较函数 ===== def compare_actions(prediction: str, ground_truth_list: list) -> bool: """ 智能比较预测动作和真实动作列表。 规则: 1. 如果预测为空，则不匹配。 2. 对于任何动作，如果预测与列表中的任何一个真实动作完全相同，则匹配。 3. **特殊规则**: 如果预测动作和真实动作都是 TYPE 类型， 只要它们的 node_id 相同，就认为它们匹配，忽略输入的具体文本。 Args: prediction (str): 标准化后的预测动作，例如 "TYPE(6, LYHNCNCT)"。 ground_truth_list (list): 标准化后的真实动作列表，例如 ["TYPE(6, LYNCT)"]。 Returns: bool: 如果匹配则返回 True，否则返回 False。 """ if not prediction: return False # 预先解析预测动作的类型和ID pred_match = re.match(r"(\w+)\((\d+)", prediction) if not pred_match: return False # 预测格式不正确 pred_action_type = pred_match.group(1) pred_node_id = pred_match.group(2) for gt_action in ground_truth_list: # 规则 2: 完全匹配总是正确的 if prediction == gt_action: return True # 规则 3: 对 TYPE 动作的特殊处理 if pred_action_type == "TYPE" and gt_action.startswith("TYPE("): gt_match = re.match(r"TYPE\((\d+)", gt_action) if gt_match: gt_node_id = gt_match.group(1) if pred_node_id == gt_node_id: return True # Node ID 匹配，判定为正确 return False # ===== 异步处理单个样本 (少量修改) ===== async def process_item(index, item, sem, stats): async with sem: image_paths = item["images"] prompt = item["messages"][0]["content"] prompt = prompt[:prompt.find("OBSERVATION:")] print(prompt) gt_json_str = item["messages"][-1]["content"] gt_answers_list = parse_ground_truth(gt_json_str) if not gt_answers_list: print(f"⚠️ 无法解析 Ground Truth: {gt_json_str}") return { "images": image_paths, "prompt": prompt, "ground_truth": "INVALID_GT_FORMAT", "prediction": None, "match": False, "raw_model_output": "Ground truth format is invalid." } image_contents = [] for path in image_paths: try: async with aiofiles.open(path, "rb") as f: content = await f.read() encoded_image = base64.b64encode(content).decode("utf-8") image_contents.append({ "type": "image_url", "image_url": {"url": f"data:image;base64,{encoded_image}"} }) except FileNotFoundError: error_msg = f"[ERROR] Image not found at {path}" print(error_msg) return { "images": image_paths, "prompt": prompt, "ground_truth": ";".join(gt_answers_list), "prediction": None, "match": False, "raw_model_output": error_msg } messages = [ { "role": "user", "content": image_contents + [ { "type": "text", "text": "Based on the provided image, task description,please output the element required to complete the task." + prompt.strip(), } ], }, ] try: response = await client.chat.completions.create( model=MODEL_NAME, messages=messages, temperature=0.1, top_p=0.95, max_tokens=2048, ) pred_text = response.choices[0].message.content.strip() except Exception as e: pred_text = f"[ERROR] {str(e)}" pred_answer = extract_action(pred_text) # <--- 关键修改 2: 使用新的智能比较函数 --- match = compare_actions(pred_answer, gt_answers_list) # 更新统计数据 stats["total"] += 1 stats["correct"] += int(match) if stats["total"] > 0 and stats["total"] % ACCURACY_PRINT_INTERVAL == 0: acc = stats["correct"] / stats["total"] * 100 print(f"\n📊 Step {stats['total']}: Accuracy = {acc:.2f}%\n") # 返回结果字典 return { "images": image_paths, "prompt": prompt, "ground_truth": ";".join(gt_answers_list), "prediction": pred_answer, "match": match, "raw_model_output": pred_text } # ===== 主函数 (无变化) ===== async def main(): with open(TEST_JSON_PATH, "r", encoding="utf-8") as f: test_data = json.load(f) if MAX_SAMPLE is not None and MAX_SAMPLE > 0: test_data = test_data[:MAX_SAMPLE] sem = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS) stats = {"total": 0, "correct": 0} tasks = [process_item(i, item, sem, stats) for i, item in enumerate(test_data)] print(f"\n🚀 Starting evaluation of {len(tasks)} samples with model '{MODEL_NAME}'...\n") results = await tqdm_asyncio.gather(*tasks) valid_results = [r for r in results if r is not None] if not valid_results: print("\n❌ No valid samples were processed. Evaluation cannot be completed.") return final_total = stats["total"] final_correct = stats["correct"] accuracy = (final_correct / final_total * 100) if final_total > 0 else 0 errors = [r for r in valid_results if not r["match"]] output = { "model_name": Model_name, "metrics": { "total_processed": final_total, "correct": final_correct, "accuracy": accuracy }, "results": valid_results, "errors": errors } 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"🎯 Accuracy: {accuracy:.2f}% ({final_correct}/{final_total})") print(f"📁 Results saved to: {OUTPUT_JSON_PATH}") if errors: print("\n❌ Sample Errors (up to 5):") for r in errors[:5]: print(f"- Images : {', '.join(r['images'])}") print(f" Ground Truth : {r['ground_truth']}") print(f" Prediction : {r['prediction']}") raw_output_snippet = r['raw_model_output'].replace('\n', ' ') if len(raw_output_snippet) > 200: raw_output_snippet = "..." + raw_output_snippet[-200:] print(f" Raw Output : {raw_output_snippet}\n") await client.aclose() # ===== 启动入口 (无变化) ===== if __name__ == "__main__": asyncio.run(main())