trtResnet

Tensorrt 推理 resnet 分类模型

使用tensorrt推理resnet模型流程

模型转换

trtexec --onnx=resnet.onnx --saveEngine=resnet.engine --fp16 --verbose

代码使用

1. 直接推理

   cv::Mat image = cv::imread("inference/car.jpg");
   auto resnet = resnet::load("resnet.engine");
   if (resnet == nullptr) return;
   auto attr = resnet->forward(cvimg(image));
   printf("score : %lf, label : %d\n", attr.confidence, attr.class_label);
   /*
   [infer.cu:393]: Infer 0x564a443b3440 [StaticShape]
   [infer.cu:405]: Inputs: 1
   [infer.cu:409]:     0.input.1 : shape {1x3x224x224}
   [infer.cu:412]: Outputs: 1
   [infer.cu:416]:     0.343 : shape {1x3}
   score : 0.997001, label : 2
   */
   

2. cpm模式

   cv::Mat image = cv::imread("inference/car.jpg");
   
   cpm::Instance<resnet::Attribute, resnet::Image, resnet::Infer> cpmi;
   bool ok = cpmi.start([] { return resnet::load("resnet.engine"); }, max_infer_batch);
   
   cpmi.commit(cvimg(image)).get();