Question

如何调用caffe已经训练好的net

Answer 1

文件夹里面，然后写一个main函数调用这个类就可以了，比如：复制，保存到caffe/examples/myproject/net_operator.hpp，然后同目录下写一个main.cpp，在main函数里面#include "net_operator.hpp"，就可以使用这个类了：const string net_prototxt = "..."; // 你的网络的prototxt文件，用绝对路径，下面同理const string pre_trained_file = "..."; // 你训练好的.caffemodel文件const string img_path = "..."; // 你要测试的图片路径// 创建NetOperator对象NetOperator net_operator(net_prototxt, pre_trained_file);Blob<float> *blob = net_operator.processImage(img_path);// blob就得到了最后一层的输出结果，至于blob里面是怎么存放数据的，你需要去看看官网对它的定义写完main.cpp之后，到caffe目录下，make，然后它会编译你写的文件，对应生成的可执行文件。比如按我上面写的那样，make之后就会在caffe/build/examples/myproject文件夹里面生成一个main.bin，执行这个文件就可以了。因为生成的可执行文件并不是直接在代码目录下，所以前面我建议你写的路径用绝对路径另外如果你要获取的不是最后一层的输出，你需要修改一下processImage函数的返回值，通过NetOperator的成员变量net_来获取你需要的blob，比如有个blob名称为"label"，你想获取这个blob，可以通过net_->blob_by_name("label")来获取，当然获取到的是shared_ptr<Blob<float> >类型的，搜一下boost shared_ptr就知道跟普通指针有什么不同了好了，接下来是贴代码了：#include <caffe/caffe.hpp>#include <opencv2/core/core.hpp>#include <opencv2/highgui www.hbbz08.com /highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include <iosfwd>#include <memory>#include <string>#include <utility>#include <vector>using namespace caffe; // NOLINT(build/namespaces)using std::string;class NetOperator{public: NetOperator(const string& net_prototxt); NetOperator(const string& net_prototxt, const string& trained_file); ~NetOperator() { } int batch_size() { return batch_size_; } Blob<float>* processImage(const string &img_path, bool is_color = true); Blob<float>* processImages(const vector<string> &img_paths, bool is_color = true);private: void createNet(const string& net_prototxt); // read the image and store it in the idx position of images in the blob void readImageToBlob(const string &img_path, int idx = 0, bool is_color = true); shared_ptr<Net<float> > net_; cv::Size input_geometry_; int batch_size_; int num_channels_; Blob<float>* input_blob_; TransformationParameter transform_param_; shared_ptr<DataTransformer<float> > data_transformer_; Blob<float> transformed_data_;};NetOperator::NetOperator(const string& net_prototxt) { createNet(net_prototxt);}NetOperator::NetOperator(const string& net_prototxt, const string& trained_file) { createNet(net_prototxt); net_->CopyTrainedLayersFrom(trained_file);}void NetOperator::createNet(const string& net_prototxt) {#ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU);#else Caffe::set_mode(Caff www.hnnedu.com e::GPU);#endif net_.reset(new Net<float>(net_prototxt, TEST)); CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input."; CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output."; Blob<float>* input_layer = net_->input_blobs()[0]; batch_size_ = input_layer->num(); num_channels_ = input_layer->channels(); CHECK(num_channels_ == 3 || num_channels_ == 1) << "Input layer should have 1 or 3 channels."; input_geometry_ = cv::Size(input_layer->width(), input_layer->height()); // reshape the output shape of the DataTransformer vector<int> top_shape(4); top_shape[0] = 1; top_shape[1] = num_channels_; top_shape[2] = input_geometry_.height; top_shape[3] = input_geometry_.width; this->transformed_data_.Reshape(top_shape);}Blob<float>* NetOperator::processImage(const string &img_path, bool is_color) { // reshape the net for the input input_blob_ = net_->input_blobs()[0]; input_blob_->Reshape(1, num_channels_, input_geometry_.height, input_geometry_.width); net_->Reshape(); readImageToBlob(img_path, 0, is_color); net_->ForwardPrefilled(); return net_->output_blobs()[0];}Blob<float>* NetOperator::processImages(const vector<string> &img_paths, bool is_color) { int img_num = img_paths.size(); // reshape the net for the input input_blob_ = net_->input_blobs()[0]; input_blob_->Reshape(img_num, num_channels_, input_geometry_.height, input_geometry_.width); net_->Reshape(); for (int i=0; i<img_num; i++) { readImageToBlob(img_paths[i], i, is_color); } net_->ForwardPrefilled(); return net_->output_blobs()[0];}void NetOperator::readImageToBlob(const string &img_path, int idx, bool is_color) { // read the image and resize to the target size cv::Mat img; int cv_read_flag = (is_color ? CV_LOAD_IMAGE_COLOR : CV_LOAD_IMAGE_GRAYSCALE); cv::Mat cv_img_origin = cv::imread(img_path, cv_read_flag); if (!cv_img_origin.data) { LOG(ERROR) << "Could not open or find file " << img_path; return ; } if (input_geometry_.height > 0 && input_geometry_.width > 0) { cv::resize(cv_img_origin, img, input_geometry_); } else { img = cv_img_origin; } // transform the image to a blob using DataTransformer // create a DataTransformer using default TransformationParameter (no transformation) data_transformer_.reset( new DataTransformer<float>(transform_param_, TEST)); data_transformer_->InitRand(); // set the output of DataTransformer to the idx image of the input blob int offset = input_blob_->offset(idx); this->transformed_data_.set_cpu_data(input_blob_->mutable_cpu_data() + offset); // transform the input image data_transformer_->Transform(img, &(this->transformed_data_));