tensorrt

name	tensorrt
description	OpenCV4 TensorRT 加速技能 - GPU 加速推理、INT8 量化、Engine 优化、YOLO 部署
user-invocable	true
argument-hint	tensorrt OR trt OR int8 OR fp16 OR gpu加速 OR cuda

OpenCV4 TensorRT Acceleration Skill

TensorRT 加速 OpenCV 推理完整指南

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何时使用

当需要以下帮助时使用此技能：

• TensorRT 安装和配置
• ONNX 模型转 TensorRT Engine
• INT8/FP16 量化优化
• OpenCV DNN TensorRT 后端
• YOLO TensorRT 部署
• 性能调优和基准测试

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快速参考

TensorRT 安装

# 下载 TensorRT tar 包（需登录 NVIDIA）
wget https://developer.nvidia.com/tensorrt/download

# 或使用 pip
pip install tensorrt

# 安装 CUDA 依赖
sudo apt install cuda-12-2  # 根据 CUDA 版本

# 验证安装
python -c "import tensorrt; print(tensorrt.__version__)"

ONNX 转 TensorRT Engine

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

# 创建 builder
logger = trt.Logger(trt.Logger.WARNING)
builder = trt.Builder(logger)
network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
config = builder.create_builder_config()

# 设置 FP16/INT8
config.set_flag(trt.BuilderFlag.FP16)
config.set_flag(trt.BuilderFlag.INT8)
config.int8_calibrator = calibrator  # INT8 需要校准器

# 解析 ONNX
parser = trt.OnnxParser(network, logger)
with open('model.onnx', 'rb') as f:
    parser.parse(f.read())

# 构建 engine
engine = builder.build_serialized_network(network, config)

# 保存
with open('model.engine', 'wb') as f:
    f.write(engine)

# 加载 engine
runtime = trt.Runtime(logger)
engine = runtime.deserialize_cuda_engine(engine)

OpenCV DNN TensorRT 后端

import cv2
import numpy as np

# 加载 TensorRT Engine
net = cv2.dnn.readNet('model.engine')

# 设置后端和目标
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)

# 推理
blob = cv2.dnn.blobFromImage(img, 1/255.0, (640, 640), swapRB=True)
net.setInput(blob)
output = net.forward()

INT8 校准

class INT8Calibrator(trt.IInt8Calibrator):
    def __init__(self, data_loader, cache_file='calibration.cache'):
        self.data_loader = data_loader
        self.cache_file = cache_file
        self.batch_size = 8
        
    def get_batch(self, names):
        data = next(self.data_loader)
        return [data]
    
    def get_batch_size(self):
        return self.batch_size
    
    def read_calibration_cache(self):
        if os.path.exists(self.cache_file):
            with open(self.cache_file, 'rb') as f:
                return f.read()
    
    def write_calibration_cache(self, cache):
        with open(self.cache_file, 'wb') as f:
            f.write(cache)

Ultralytics TensorRT 导出

from ultralytics import YOLO

# 导出为 TensorRT
model = YOLO('yolov8n.pt')
model.export(format='engine', half=True, int8=True, device=0)

# 加载和推理
model = YOLO('yolov8n.engine')
results = model.predict(source='image.jpg', device=0, half=True)

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ROS2 TensorRT 部署

import rclpy
from rclpy.node import Node
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import cv2
import numpy as np

class TensorRTNode(Node):
    def __init__(self):
        super().__init__('tensorrt_inference')
        self.bridge = CvBridge()
        
        # 加载 TensorRT Engine
        self.net = cv2.dnn.readNet('yolov8n.engine')
        self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
        self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
        
        self.sub = self.create_subscription(Image, '/image_raw', self.callback, 10)
        self.pub = self.create_publisher(Image, '/detections', 10)
    
    def callback(self, msg):
        img = self.bridge.imgmsg_to_cv2(msg, 'bgr8')
        blob = cv2.dnn.blobFromImage(img, 1/255.0, (640, 640), swapRB=True)
        self.net.setInput(blob)
        output = self.net.forward()
        

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性能对比

精度	相对 FP32 速度	精度损失
FP32	1x	无
FP16	2-3x	极小
INT8	3-4x	< 1% mAP

优化参数

config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30)  # 1GB
config.set_preview_feature(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0806, 1)
config.set_flag(trt.BuilderFlag.DIRECT_IO)  # 减少拷贝

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最佳实践

1. Engine 构建：

   • 使用实际部署硬件构建 Engine
   • 固定 batch size 通常更快
   • 设置合理的 workspace 大小

2. INT8 量化：

   • 需要代表性数据集（100-500 张图）
   • 避免极端值和异常样本
   • 量化后验证精度

3. 内存管理：

   • CUDA 流并行处理
   • 使用 Pinned Memory 加速传输
   • 批处理提高 GPU 利用率

4. 调试：

   • 使用 trtexec --verbose 测试
   • nvidia-smi dmon 监控 GPU
   • TensorRT profiler 分析瓶颈

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相关技能

• opencv-nvidia-jetson - Jetson 部署
• opencv-dnn-inference - DNN 推理基础