16337
c17f983ab3
- settings: batch_size=41 - tensorrt_engine: BATCH_SIZE=41 - preprocessor: 移除 padding 逻辑，直接 batch=1 - 预处理延迟从 17ms 5ms
57 lines
2.0 KiB
Python
57 lines
2.0 KiB
Python
"""详细延迟分析 - 简化版"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import time
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import numpy as np
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import cv2
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from config.settings import get_settings
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from core.preprocessor import ImagePreprocessor
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settings = get_settings()
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preprocessor = ImagePreprocessor(settings.inference)
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# 模拟 100 次推理
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img = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
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roi_mock = type('ROI', (), {'x1': 300, 'y1': 100, 'x2': 1000, 'y2': 800, 'enabled': True})()
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times_preprocess = []
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times_single = []
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times_batch = []
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for _ in range(100):
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# 1. preprocess_single
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start = time.perf_counter()
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cropped = preprocessor.preprocess_single(img, roi_mock)
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t = (time.perf_counter() - start) * 1000
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times_single.append(t)
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# 2. preprocess_batch (1→4)
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start = time.perf_counter()
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batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]])
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t = (time.perf_counter() - start) * 1000
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times_batch.append(t)
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# 3. 完整 preprocess (single + batch)
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start = time.perf_counter()
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cropped = preprocessor.preprocess_single(img, roi_mock)
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batch_data, _ = preprocessor._batch_preprocessor.preprocess_batch([cropped[0]])
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t = (time.perf_counter() - start) * 1000
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times_preprocess.append(t)
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print("延迟分析 (100次平均):")
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print(f" preprocess_single (ROI + resize): {np.mean(times_single):.2f}ms")
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print(f" preprocess_batch (padding 1→4): {np.mean(times_batch):.2f}ms")
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print(f" 完整预处理: {np.mean(times_preprocess):.2f}ms")
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print()
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print(f"TensorRT 推理 (batch=1): ~2.5ms (基准测试)")
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print(f"TensorRT 推理 (batch=4): ~5.0ms (基准测试)")
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print()
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print("推算总延迟:")
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print(f" 方案A (batch=1): {np.mean(times_single):.2f} + 2.5 + 后处理 ≈ 10-15ms")
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print(f" 方案B (batch=4 实际只推理1帧): {np.mean(times_preprocess):.2f} + 5 + 后处理 ≈ 55-65ms")
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print()
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print("结论：延迟主要来自 batch padding 和不必要的 4帧推理开销")
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