在TensorRT 2.1.2中提供了MNIST的model，這里拿來用Caffe的代碼調用實現，原始的mnist_mean.binaryproto文件調整為了純二進制文件mnist_tensorrt_mean.binary，測試結果與使用TensorRT調用(http://blog.csdn.net/fengbingchun/article/details/78552908)一致：

測試代碼如下：

#include "funset.hpp"
#include <memory>
#include <fstream>
#include <tuple>
#include "common.hpp"int mnist_tensorrt_predict()
{
#ifdef CPU_ONLYcaffe::Caffe::set_mode(caffe::Caffe::CPU);
#elsecaffe::Caffe::set_mode(caffe::Caffe::GPU);
#endifconst std::string deploy_file{ "E:/GitCode/Caffe_Test/test_data/model/mnist/mnist_tensorrt.prototxt" };const std::string model_filename{ "E:/GitCode/Caffe_Test/test_data/model/mnist/mnist_tensorrt.caffemodel" };const std::string mean_file{ "E:/GitCode/Caffe_Test/test_data/model/mnist/mnist_tensorrt_mean.binary" };const std::string image_path{ "E:/GitCode/Caffe_Test/test_data/images/handwritten_digits/" };caffe::Net<float> caffe_net(deploy_file, caffe::TEST);caffe_net.CopyTrainedLayersFrom(model_filename);// print net infofprintf(stdout, "input blob num: %d, output blob num: %d\n", caffe_net.num_inputs(), caffe_net.num_outputs());const boost::shared_ptr<caffe::Blob<float> > blob_by_name = caffe_net.blob_by_name("data");int image_num = blob_by_name->num();int image_channel = blob_by_name->channels();int image_height = blob_by_name->height();int image_width = blob_by_name->width();fprintf(stdout, "inpub blob shape(num, channels, height, width): %d, %d, %d, %d\n",image_num, image_channel, image_height, image_width);fprintf(stdout, "layer names: ");for (int i = 0; i < caffe_net.layer_names().size(); ++i) {fprintf(stdout, "  %s  ", caffe_net.layer_names()[i].c_str());}fprintf(stdout, "\nblob names: ");for (int i = 0; i < caffe_net.blob_names().size(); ++i) {fprintf(stdout, "  %s  ", caffe_net.blob_names()[i].c_str());}fprintf(stdout, "\nlayer types: ");for (int i = 0; i < caffe_net.layers().size(); ++i) {fprintf(stdout, "  %s  ", caffe_net.layers()[i]->type());}const std::vector<caffe::Blob<float>*> output_blobs = caffe_net.output_blobs();fprintf(stdout, "\noutput blobs num: %d, blob(num, channel, heihgt, width): %d, %d, %d, %d\n",output_blobs.size(), output_blobs[0]->num(), output_blobs[0]->channels(), output_blobs[0]->height(), output_blobs[0]->width());const int image_size{ image_num * image_channel * image_height * image_width };std::unique_ptr<float[]> mean_values(new float[image_size]);std::ifstream in(mean_file.c_str(), std::ios::in | std::ios::binary);if (!in.is_open()) {fprintf(stderr, "read mean file fail: %s\n", mean_file.c_str());return -1;}in.read((char*)mean_values.get(), image_size * sizeof(float));in.close();const std::vector<int> target{ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 };typedef std::tuple<int, float> result;std::vector<result> results;for (const auto& num : target) {std::string str = std::to_string(num);str += ".png";str = image_path + str;cv::Mat mat = cv::imread(str.c_str(), 0);if (!mat.data) {fprintf(stderr, "load image error: %s\n", str.c_str());return -1;}cv::resize(mat, mat, cv::Size(image_width, image_height));mat.convertTo(mat, CV_32FC1);float* p = (float*)mat.data;for (int i = 0; i < image_size; ++i) {p[i] -= mean_values.get()[i];}const std::vector<caffe::Blob<float>*>& blob_input = caffe_net.input_blobs();blob_input[0]->set_cpu_data((float*)mat.data);const std::vector<caffe::Blob<float>*>& output_blob_ = caffe_net.Forward(nullptr);const float* output = output_blob_[0]->cpu_data();float tmp{ -1.f };int pos{ -1 };for (int j = 0; j < output_blobs[0]->count(); j++) {if (tmp < output[j]) {pos = j;tmp = output[j];}}result ret = std::make_tuple(pos, tmp);results.push_back(ret);}for (auto i = 0; i < target.size(); i++)fprintf(stdout, "actual digit is: %d, result digit is: %d, probability: %f\n",target[i], std::get<0>(results[i]), std::get<1>(results[i]));fprintf(stdout, "predict finish\n");return 0;
}

測試圖像如下：

執行結果如下：

GitHub：?https://github.com/fengbingchun/Caffe_Test

總結

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