update at 2025-10-30 16:31:47
This commit is contained in:
douboer
446
scripts/benchmark_concurrent.py
Executable file
446
scripts/benchmark_concurrent.py
Executable file
@@ -0,0 +1,446 @@
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#!/usr/bin/env python3
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"""
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并发性能测试脚本 - 模拟多用户同时识别
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功能：
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- 模拟 n 个用户并发执行数字识别任务
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- 循环使用 valid 文件夹中的图片
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- 统计总体性能指标（吞吐量、响应时间、成功率）
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- 生成详细的性能报告
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使用方法：
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# 模拟10个并发用户，每个用户识别20张图片
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python scripts/benchmark_concurrent.py --users 10 --images-per-user 20
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# 使用不同的模型和配置
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python scripts/benchmark_concurrent.py --users 5 --images-per-user 10 --conf 0.15
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# 指定输出报告文件
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python scripts/benchmark_concurrent.py --users 20 --output results/benchmark_report.txt
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输出：
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- 实时进度显示
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- 详细的性能统计（总时间、平均响应时间、QPS等）
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- 每个用户的执行情况
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- 失败任务的详细信息
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作者: Gavin Chan
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日期: 2025-01-30
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"""
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import argparse
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import time
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import os
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from pathlib import Path
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from ultralytics import YOLO
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import cv2
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import numpy as np
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from datetime import datetime
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import json
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def parse_args():
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"""解析命令行参数"""
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parser = argparse.ArgumentParser(description='并发性能测试 - 模拟多用户识别')
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parser.add_argument('--users', type=int, default=10,
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help='并发用户数量（默认: 10）')
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parser.add_argument('--images-per-user', type=int, default=20,
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help='每个用户识别的图片数量（默认: 20）')
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parser.add_argument('--model', type=str,
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default='runs/digit_yolo/exp_preprocessed_color_150/weights/best.pt',
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help='模型路径')
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parser.add_argument('--source', type=str, default='valid',
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help='图片源文件夹（默认: valid）')
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parser.add_argument('--conf', type=float, default=0.2,
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help='置信度阈值（默认: 0.2）')
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parser.add_argument('--imgsz', type=int, default=320,
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help='输入图片大小（默认: 320）')
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parser.add_argument('--output', type=str, default='results/benchmark_report.txt',
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help='输出报告文件路径')
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parser.add_argument('--verbose', action='store_true',
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help='显示详细日志')
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return parser.parse_args()
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def load_image_paths(source_dir):
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"""
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加载所有图片路径
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Args:
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source_dir: 图片文件夹路径
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Returns:
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list: 图片路径列表
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"""
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source_path = Path(source_dir)
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if not source_path.exists():
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raise FileNotFoundError(f"图片文件夹不存在: {source_dir}")
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# 支持的图片格式
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extensions = ('.jpg', '.jpeg', '.png', '.bmp', '.webp')
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image_paths = [str(p) for p in source_path.iterdir()
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if p.suffix.lower() in extensions]
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if not image_paths:
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raise ValueError(f"文件夹中没有找到图片: {source_dir}")
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return sorted(image_paths)
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def extract_digits_from_predictions(results):
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"""
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从YOLO预测结果中提取数字
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Args:
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results: YOLO预测结果对象
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Returns:
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tuple: (识别的数字字符串, 平均置信度, 检测到的数字个数)
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"""
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if not results or len(results) == 0:
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return "", 0.0, 0
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result = results[0]
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if result.boxes is None or len(result.boxes) == 0:
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return "", 0.0, 0
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# 获取所有检测框
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boxes = result.boxes.cpu().numpy()
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# 提取类别、置信度和位置
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detections = []
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for box in boxes:
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cls = int(box.cls[0])
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conf = float(box.conf[0])
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x_center = float((box.xyxy[0][0] + box.xyxy[0][2]) / 2)
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detections.append((cls, conf, x_center))
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if not detections:
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return "", 0.0, 0
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# 按x坐标排序（从左到右）
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detections.sort(key=lambda x: x[2])
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# 提取数字和置信度
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digits = [str(d[0]) for d in detections]
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confidences = [d[1] for d in detections]
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result_str = ''.join(digits)
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avg_conf = sum(confidences) / len(confidences)
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return result_str, avg_conf, len(detections)
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def recognize_single_image(model, image_path, conf_threshold, img_size):
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"""
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识别单张图片
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Args:
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model: YOLO模型对象
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image_path: 图片路径
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conf_threshold: 置信度阈值
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img_size: 输入图片大小
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Returns:
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dict: 识别结果 {
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'filename': 文件名,
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'digits': 识别的数字,
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'confidence': 置信度,
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'count': 数字个数,
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'time': 耗时（秒）,
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'success': 是否成功
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}
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"""
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start_time = time.time()
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try:
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# 执行预测
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results = model.predict(
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source=image_path,
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conf=conf_threshold,
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imgsz=img_size,
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verbose=False
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)
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# 提取数字
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digits, confidence, count = extract_digits_from_predictions(results)
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elapsed = time.time() - start_time
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return {
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'filename': Path(image_path).name,
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'digits': digits,
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'confidence': confidence,
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'count': count,
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'time': elapsed,
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'success': True,
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'error': None
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}
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except Exception as e:
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elapsed = time.time() - start_time
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return {
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'filename': Path(image_path).name,
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'digits': '',
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'confidence': 0.0,
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'count': 0,
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'time': elapsed,
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'success': False,
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'error': str(e)
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}
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def user_task(user_id, model_path, image_paths, num_images, conf_threshold, img_size, verbose):
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"""
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单个用户的识别任务
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Args:
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user_id: 用户ID
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model_path: 模型路径
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image_paths: 所有可用图片路径
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num_images: 该用户要识别的图片数量
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conf_threshold: 置信度阈值
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img_size: 图片大小
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verbose: 是否显示详细日志
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Returns:
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dict: 用户任务结果 {
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'user_id': 用户ID,
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'results': 识别结果列表,
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'total_time': 总耗时,
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'success_count': 成功数量,
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'avg_time': 平均耗时
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}
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"""
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# 加载模型（每个线程独立加载）
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model = YOLO(model_path)
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# 循环使用图片
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user_images = []
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for i in range(num_images):
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img_idx = (user_id * num_images + i) % len(image_paths)
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user_images.append(image_paths[img_idx])
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start_time = time.time()
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results = []
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for img_path in user_images:
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result = recognize_single_image(model, img_path, conf_threshold, img_size)
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results.append(result)
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if verbose:
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status = "✓" if result['success'] else "✗"
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print(f" [{status}] 用户{user_id:2d} | {result['filename']:20s} | "
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f"{result['digits']:6s} | {result['time']:.3f}s")
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total_time = time.time() - start_time
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success_count = sum(1 for r in results if r['success'])
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avg_time = sum(r['time'] for r in results) / len(results) if results else 0
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return {
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'user_id': user_id,
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'results': results,
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'total_time': total_time,
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'success_count': success_count,
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'avg_time': avg_time
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}
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def generate_report(user_results, total_time, args):
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"""
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生成性能测试报告
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Args:
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user_results: 所有用户的结果列表
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total_time: 总执行时间
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args: 命令行参数
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Returns:
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str: 报告内容
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"""
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# 统计数据
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total_images = sum(len(ur['results']) for ur in user_results)
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total_success = sum(ur['success_count'] for ur in user_results)
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total_failed = total_images - total_success
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success_rate = (total_success / total_images * 100) if total_images > 0 else 0
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# 响应时间统计
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all_times = [r['time'] for ur in user_results for r in ur['results']]
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avg_response_time = sum(all_times) / len(all_times) if all_times else 0
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min_response_time = min(all_times) if all_times else 0
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max_response_time = max(all_times) if all_times else 0
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# 吞吐量
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qps = total_images / total_time if total_time > 0 else 0
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# 生成报告
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report = []
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report.append("=" * 80)
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report.append("YOLO 数字识别并发性能测试报告")
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report.append("=" * 80)
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report.append(f"\n测试时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
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report.append(f"\n配置参数:")
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report.append(f" - 并发用户数: {args.users}")
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report.append(f" - 每用户图片数: {args.images_per_user}")
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report.append(f" - 模型: {args.model}")
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report.append(f" - 置信度阈值: {args.conf}")
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report.append(f" - 图片大小: {args.imgsz}")
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report.append(f" - 图片源: {args.source}")
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report.append(f"\n总体性能:")
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report.append(f" - 总执行时间: {total_time:.2f} 秒")
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report.append(f" - 总识别图片数: {total_images}")
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report.append(f" - 成功: {total_success} ({success_rate:.1f}%)")
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report.append(f" - 失败: {total_failed}")
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report.append(f" - 吞吐量 (QPS): {qps:.2f} 图片/秒")
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report.append(f"\n响应时间统计:")
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report.append(f" - 平均响应时间: {avg_response_time:.3f} 秒")
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report.append(f" - 最小响应时间: {min_response_time:.3f} 秒")
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report.append(f" - 最大响应时间: {max_response_time:.3f} 秒")
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# 百分位数
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sorted_times = sorted(all_times)
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p50_idx = int(len(sorted_times) * 0.50)
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p90_idx = int(len(sorted_times) * 0.90)
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p95_idx = int(len(sorted_times) * 0.95)
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p99_idx = int(len(sorted_times) * 0.99)
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report.append(f" - P50 响应时间: {sorted_times[p50_idx]:.3f} 秒")
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report.append(f" - P90 响应时间: {sorted_times[p90_idx]:.3f} 秒")
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report.append(f" - P95 响应时间: {sorted_times[p95_idx]:.3f} 秒")
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report.append(f" - P99 响应时间: {sorted_times[p99_idx]:.3f} 秒")
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# 每个用户的统计
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report.append(f"\n各用户性能:")
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report.append(f" {'用户ID':>8} | {'图片数':>8} | {'成功':>8} | {'总耗时':>10} | {'平均耗时':>10}")
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report.append(f" {'-'*8}-+-{'-'*8}-+-{'-'*8}-+-{'-'*10}-+-{'-'*10}")
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for ur in sorted(user_results, key=lambda x: x['user_id']):
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report.append(f" {ur['user_id']:8d} | {len(ur['results']):8d} | "
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f"{ur['success_count']:8d} | {ur['total_time']:9.2f}s | "
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f"{ur['avg_time']:9.3f}s")
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# 失败任务详情
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failed_tasks = []
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for ur in user_results:
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for r in ur['results']:
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if not r['success']:
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failed_tasks.append((ur['user_id'], r))
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if failed_tasks:
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report.append(f"\n失败任务详情 (共 {len(failed_tasks)} 个):")
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for user_id, result in failed_tasks[:20]: # 最多显示20个
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report.append(f" 用户{user_id} | {result['filename']} | 错误: {result['error']}")
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if len(failed_tasks) > 20:
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report.append(f" ... 还有 {len(failed_tasks) - 20} 个失败任务")
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report.append("\n" + "=" * 80)
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return '\n'.join(report)
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def main():
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"""主函数"""
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args = parse_args()
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# 检查模型文件
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if not Path(args.model).exists():
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print(f"❌ 模型文件不存在: {args.model}")
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return
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# 加载图片路径
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print(f"\n📂 加载图片路径...")
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try:
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image_paths = load_image_paths(args.source)
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print(f"✓ 找到 {len(image_paths)} 张图片")
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except Exception as e:
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print(f"❌ 加载图片失败: {e}")
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return
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# 显示测试配置
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total_images = args.users * args.images_per_user
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print(f"\n⚙️ 测试配置:")
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print(f" - 并发用户数: {args.users}")
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print(f" - 每用户图片数: {args.images_per_user}")
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print(f" - 总图片数: {total_images}")
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print(f" - 模型: {Path(args.model).name}")
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print(f" - 置信度: {args.conf}")
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print(f" - 图片大小: {args.imgsz}")
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# 开始测试
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print(f"\n🚀 开始并发测试...\n")
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start_time = time.time()
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# 使用线程池并发执行
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user_results = []
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with ThreadPoolExecutor(max_workers=args.users) as executor:
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# 提交所有用户任务
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futures = []
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for user_id in range(args.users):
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future = executor.submit(
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user_task,
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user_id,
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args.model,
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image_paths,
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args.images_per_user,
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args.conf,
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args.imgsz,
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args.verbose
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)
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futures.append(future)
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# 收集结果并显示进度
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completed = 0
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for future in as_completed(futures):
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result = future.result()
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user_results.append(result)
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completed += 1
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if not args.verbose:
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print(f" 进度: {completed}/{args.users} 用户完成 "
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f"({completed/args.users*100:.0f}%)", end='\r')
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total_time = time.time() - start_time
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if not args.verbose:
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print() # 换行
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# 生成报告
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print(f"\n📊 生成性能报告...")
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report = generate_report(user_results, total_time, args)
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# 显示报告
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print(report)
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# 保存报告
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output_path = Path(args.output)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(report)
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print(f"\n✓ 报告已保存到: {args.output}")
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# 保存JSON格式的详细数据
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json_output = output_path.with_suffix('.json')
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json_data = {
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'config': vars(args),
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'summary': {
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'total_time': total_time,
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'total_images': total_images,
|
||||
'qps': total_images / total_time if total_time > 0 else 0,
|
||||
},
|
||||
'users': user_results
|
||||
}
|
||||
|
||||
with open(json_output, 'w', encoding='utf-8') as f:
|
||||
json.dump(json_data, f, indent=2, ensure_ascii=False)
|
||||
|
||||
print(f"✓ 详细数据已保存到: {json_output}")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
71
scripts/run_benchmark_quick.sh
Executable file
71
scripts/run_benchmark_quick.sh
Executable file
@@ -0,0 +1,71 @@
|
||||
#!/bin/bash
|
||||
# 快速并发性能测试（较小规模，适合快速验证）
|
||||
|
||||
echo "================================================================================"
|
||||
echo "YOLO 数字识别 - 快速并发测试"
|
||||
echo "================================================================================"
|
||||
echo ""
|
||||
|
||||
RESULT_DIR="results/benchmark_quick"
|
||||
mkdir -p "$RESULT_DIR"
|
||||
|
||||
IMAGES_PER_USER=10
|
||||
USER_COUNTS=(1 3 5 8)
|
||||
|
||||
echo "📋 测试配置:"
|
||||
echo " - 每用户图片数: $IMAGES_PER_USER"
|
||||
echo " - 测试并发级别: ${USER_COUNTS[@]}"
|
||||
echo ""
|
||||
|
||||
for users in "${USER_COUNTS[@]}"; do
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
echo "🚀 测试: ${users} 个并发用户 × ${IMAGES_PER_USER} 张图片"
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
|
||||
python scripts/benchmark_concurrent.py \
|
||||
--users "$users" \
|
||||
--images-per-user "$IMAGES_PER_USER" \
|
||||
--output "$RESULT_DIR/benchmark_${users}users.txt"
|
||||
|
||||
echo ""
|
||||
done
|
||||
|
||||
# 生成摘要
|
||||
SUMMARY_FILE="$RESULT_DIR/summary.txt"
|
||||
|
||||
{
|
||||
echo "================================================================================"
|
||||
echo "YOLO 数字识别 - 快速并发测试摘要"
|
||||
echo "================================================================================"
|
||||
echo ""
|
||||
echo "测试时间: $(date '+%Y-%m-%d %H:%M:%S')"
|
||||
echo ""
|
||||
echo "性能对比:"
|
||||
echo ""
|
||||
printf "%-12s | %-10s | %-12s | %-12s | %-15s\n" \
|
||||
"并发用户" "总图片" "总耗时(s)" "QPS" "平均响应(s)"
|
||||
echo "-------------+------------+--------------+--------------+----------------"
|
||||
} > "$SUMMARY_FILE"
|
||||
|
||||
for users in "${USER_COUNTS[@]}"; do
|
||||
json_file="$RESULT_DIR/benchmark_${users}users.json"
|
||||
|
||||
if [ -f "$json_file" ]; then
|
||||
total_images=$(python3 -c "import json; print(json.load(open('$json_file'))['summary']['total_images'])")
|
||||
total_time=$(python3 -c "import json; print(f\"{json.load(open('$json_file'))['summary']['total_time']:.2f}\")")
|
||||
qps=$(python3 -c "import json; print(f\"{json.load(open('$json_file'))['summary']['qps']:.2f}\")")
|
||||
avg_time=$(python3 -c "import json; d=json.load(open('$json_file')); times=[r['time'] for u in d['users'] for r in u['results']]; print(f\"{sum(times)/len(times):.3f}\")")
|
||||
|
||||
printf "%-12d | %-10d | %-12s | %-12s | %-15s\n" \
|
||||
"$users" "$total_images" "$total_time" "$qps" "$avg_time" >> "$SUMMARY_FILE"
|
||||
fi
|
||||
done
|
||||
|
||||
echo "" >> "$SUMMARY_FILE"
|
||||
echo "================================================================================" >> "$SUMMARY_FILE"
|
||||
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
cat "$SUMMARY_FILE"
|
||||
echo ""
|
||||
echo "✅ 快速测试完成！详细报告: $RESULT_DIR/"
|
||||
echo "================================================================================"
|
||||
104
scripts/run_benchmark_suite.sh
Executable file
104
scripts/run_benchmark_suite.sh
Executable file
@@ -0,0 +1,104 @@
|
||||
#!/bin/bash
|
||||
#
|
||||
# 并发性能测试套件 - 运行多组测试对比不同并发级别的性能
|
||||
#
|
||||
# 功能：
|
||||
# - 测试不同并发用户数下的系统性能
|
||||
# - 生成对比报告
|
||||
#
|
||||
# 使用：
|
||||
# ./scripts/run_benchmark_suite.sh
|
||||
#
|
||||
# 作者: Gavin Chan
|
||||
# 日期: 2025-01-30
|
||||
|
||||
echo "================================================================================"
|
||||
echo "YOLO 数字识别 - 并发性能测试套件"
|
||||
echo "================================================================================"
|
||||
echo ""
|
||||
|
||||
# 创建结果目录
|
||||
RESULT_DIR="results/benchmark_suite"
|
||||
mkdir -p "$RESULT_DIR"
|
||||
|
||||
# 测试配置
|
||||
IMAGES_PER_USER=20
|
||||
USER_COUNTS=(1 3 5 10 15 20)
|
||||
|
||||
echo "📋 测试配置:"
|
||||
echo " - 每用户图片数: $IMAGES_PER_USER"
|
||||
echo " - 测试并发级别: ${USER_COUNTS[@]}"
|
||||
echo ""
|
||||
|
||||
# 运行测试
|
||||
for users in "${USER_COUNTS[@]}"; do
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
echo "🚀 测试场景: ${users} 个并发用户"
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
|
||||
output_file="$RESULT_DIR/benchmark_${users}users.txt"
|
||||
|
||||
python scripts/benchmark_concurrent.py \
|
||||
--users "$users" \
|
||||
--images-per-user "$IMAGES_PER_USER" \
|
||||
--output "$output_file"
|
||||
|
||||
echo ""
|
||||
sleep 1 # 短暂休息，避免系统过载
|
||||
done
|
||||
|
||||
# 生成对比报告
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
echo "📊 生成对比报告..."
|
||||
echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
|
||||
|
||||
SUMMARY_FILE="$RESULT_DIR/summary.txt"
|
||||
|
||||
cat > "$SUMMARY_FILE" << 'EOF'
|
||||
================================================================================
|
||||
YOLO 数字识别 - 并发性能对比报告
|
||||
================================================================================
|
||||
|
||||
EOF
|
||||
|
||||
echo "测试时间: $(date '+%Y-%m-%d %H:%M:%S')" >> "$SUMMARY_FILE"
|
||||
echo "" >> "$SUMMARY_FILE"
|
||||
echo "各并发级别性能对比:" >> "$SUMMARY_FILE"
|
||||
echo "" >> "$SUMMARY_FILE"
|
||||
|
||||
printf "%-12s | %-10s | %-12s | %-12s | %-15s\n" \
|
||||
"并发用户" "总图片" "总耗时(s)" "QPS" "平均响应(s)" >> "$SUMMARY_FILE"
|
||||
echo "-------------+------------+--------------+--------------+----------------" >> "$SUMMARY_FILE"
|
||||
|
||||
# 从JSON文件中提取数据
|
||||
for users in "${USER_COUNTS[@]}"; do
|
||||
json_file="$RESULT_DIR/benchmark_${users}users.json"
|
||||
|
||||
if [ -f "$json_file" ]; then
|
||||
total_images=$(python3 -c "import json; print(json.load(open('$json_file'))['summary']['total_images'])")
|
||||
total_time=$(python3 -c "import json; print(f\"{json.load(open('$json_file'))['summary']['total_time']:.2f}\")")
|
||||
qps=$(python3 -c "import json; print(f\"{json.load(open('$json_file'))['summary']['qps']:.2f}\")")
|
||||
|
||||
# 计算平均响应时间
|
||||
avg_time=$(python3 -c "import json; d=json.load(open('$json_file')); times=[r['time'] for u in d['users'] for r in u['results']]; print(f\"{sum(times)/len(times):.3f}\")")
|
||||
|
||||
printf "%-12d | %-10d | %-12s | %-12s | %-15s\n" \
|
||||
"$users" "$total_images" "$total_time" "$qps" "$avg_time" >> "$SUMMARY_FILE"
|
||||
fi
|
||||
done
|
||||
|
||||
echo "" >> "$SUMMARY_FILE"
|
||||
echo "================================================================================" >> "$SUMMARY_FILE"
|
||||
|
||||
# 显示对比报告
|
||||
cat "$SUMMARY_FILE"
|
||||
|
||||
echo ""
|
||||
echo "✅ 测试完成！"
|
||||
echo ""
|
||||
echo "📁 详细报告位置:"
|
||||
echo " - 对比摘要: $SUMMARY_FILE"
|
||||
echo " - 各测试详情: $RESULT_DIR/benchmark_*users.txt"
|
||||
echo " - JSON数据: $RESULT_DIR/benchmark_*users.json"
|
||||
echo ""
|
||||
echo "================================================================================"
|
||||
Reference in New Issue
Block a user