c++调用tf.keras的模型

news2024/10/5 19:14:12

环境：

ubuntu 20.04
python 3.8
tensorflow-gpu 2.4.0
显卡 nvidia rtx A6000 驱动 495.29.05
cuda 11.5
cudnn 8.3.0
tensorRT 8.4

1.将keras保存的h5模型转成darknet的weight，然后用opencv加载

  cv::dnn::Net net = cv::dnn::readNetFromDarknet("load_model.weight");

卷积网络和全连接可以加载成功，lstm网络不支持，opencv源码中没有lstm网络：

opencv源码

2.将keras模型转成onnx，两种方式，keras2onnx和tensorflow2onnx：

keras-onnx
keras-onnx已经停止维护了
keras2onnx has been tested on Python 3.5 - 3.8, with tensorflow 1.x/2.0 - 2.2 (CI build). It does not support Python 2.x.

import keras2onnx
import onnx
import tensorflow as tf
from tensorflow.keras.models import load_model

if "__main__" == __name__:
    model = load_model('./model.h5')
    model.summary()
    onnx_model = keras2onnx.convert_keras(model, model.name)
    temp_model_file = 'model.onnx'
    onnx.save_model(onnx_model, temp_model_file)

tensorflow-onnx

安装

pip install -U tf2onnx

import tensorflow as tf
from tensorflow.keras.models import load_model

if "__main__" == __name__:
    model = load_model('./model.h5')
    tf.saved_model.save(model, './model')


# python -m tf2onnx.convert --saved-model ./model/ --output model.onnx

先load预训练的h5模型，用tf.saved_model.save转化成pb格式，再执行

python -m tf2onnx.convert --saved-model ./model/ --output model.onnx

转成onnx格式
可以在https://netron.app/查看你的模型架构：
在这里插入图片描述

3.将onnx转成tensorRT的格式：

在这里插入图片描述

在https://developer.nvidia.com/nvidia-tensorrt-8x-download下载并安装8.4版本：

sudo dpkg -i nv-tensorrt-repo-ubuntu2004-cuda11.6-trt8.4.0.6-ea-20220212_1-1_amd64.deb
sudo apt-key add /var/nv-tensorrt-repo-ubuntu2004-cuda11.6-trt8.4.0.6-ea-20220212/7fa2af80.pub
sudo apt-get update
sudo apt-get install tensorrt

测试tensorrt是否安装成功
准备MNIST的.PGM图像，将10张0～9的命名为0.PGM,1.PGM…的
MNIST图像拷贝至data/mnist文件夹

sudo cp *.pgm /usr/src/tensorrt/data/mnist
cd /usr/src/tensorrt/samples/sampleMNIST
sudo make
cd ../../bin/
./sample_mnist

在这里插入图片描述

查看tensorrt版本

dpkg -l | grep TensorRT

安装tensorRT如果报错
- libdvd-pkg: apt-get check failed, you may have broken packages. Aborting…
- sudo dpkg-reconfigure libdvd-pkg
- 参考https://blog.csdn.net/qq_22945165/article/details/87653208

将tensorRT加入环境变量

export PATH=$PATH:/usr/src/tensorrt/bin

trtexec --onnx=model.onnx --saveEngine=model.trt

也可参考https://github.com/onnx/onnx-tensorrt这种方式

onnx2trt my_model.onnx -o my_engine.trt

4.模型加载

参考https://github.com/shouxieai/tensorRT_Pro/

static void test_load_onnx(){
    /** 加载编译好的引擎 **/
    auto infer = TRT::load_infer("./model.trt");
    if(infer == nullptr){
        INFOE("Engine is nullptr");
        return;
    }

    /** 设置输入的值 **/
    infer->input(0)->set_to(1.0f);

    /** 引擎进行推理 **/
    infer->forward();

    /** 取出引擎的输出并打印 **/
    auto out = infer->output(0);
    INFO("out.shape = %s", out->shape_string());
    for(int i = 0; i < out->channel(); ++i)
        INFO("%f", out->at<float>(0, i));
}

static void test_load_onnx(){
    /** 加载编译好的引擎 **/
    auto infer = TRT::load_infer("/home/user/my_python_test/net_float16/float32-model-2400/actor_model_32.trt");
    if(infer == nullptr){
        INFOE("Engine is nullptr");
        return;
    }
    infer->print();

    double timeAll = 0;
    int inferTimes = 1000;
    for(int i = 0; i < inferTimes; i++){        
        /** 设置输入的值 **/
        // infer->input(0)->set_to(1.0f);
        std::vector<float> input_ = {0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    2., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    3., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    4., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    5., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    6., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    7., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    8., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
                                    9., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.};

        memcpy(infer->input(0)->cpu<float>(), input_.data(), sizeof(float) * input_.size());
        std::cout<<"############### input.data(): "<<input_.data()<<std::endl;
        std::cout<<"############### input.size(): "<<input_.size()<<std::endl;
        std::cout<<"############### infer->input(0): "<<infer->input(0)<<std::endl;
        std::cout<<"############### infer->input(0)->count(): "<<infer->input(0)->count()<<std::endl;
        std::cout<<"############### infer->input(0)->cpu<float>(0): "<<infer->input(0)->cpu<float>()<<std::endl;
        std::cout<<"############### infer->input(0)->at<float>(): "<<infer->input(0)->at<float>(0, 3)<<std::endl;
        
        clock_t start = clock();
        /** 引擎进行推理 **/
        infer->forward();
        clock_t end = clock();

        /** 取出引擎的输出并打印 **/
        auto out_0 = infer->output(0);
        auto out_1 = infer->output(1);
        auto out_2 = infer->output(2);
        auto out_3 = infer->output(3);

        INFO("out_0.shape = %s", out_0->shape_string());
        for(int i = 0; i < out_0->channel(); ++i)
            INFO("%f", out_0->at<float>(0, i));

        INFO("out_1.shape = %s", out_1->shape_string());
        for(int i = 0; i < out_1->channel(); ++i)
            INFO("%f", out_1->at<float>(0, i));

        INFO("out_2.shape = %s", out_2->shape_string());
        for(int i = 0; i < out_2->channel(); ++i)
            INFO("%f", out_2->at<float>(0, i));

        INFO("out_3.shape = %s", out_3->shape_string());
        for(int i = 0; i < out_3->channel(); ++i)
            INFO("%f", out_3->at<float>(0, i));
        
        double spend_time = (double)(end - start)/CLOCKS_PER_SEC*1000;
        std::cout << "############## Total inference time is " << spend_time << "ms" << std::endl;
        
        if(i>0)
            timeAll += spend_time;
    }
    std::cout << "############## average inference time is " << timeAll/inferTimes << "ms" << std::endl;


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