Caffe_manual/get_features.cpp

Caffe_manual/get_features.cpp

According to the feedback, specially wrote a CPP file, can be a forward, while completing multi-layer feature extraction, the final probability output and the top label output, see Get_features.cpp file:

/** * Usage:get_features.exe feat.prototxt H:\Models\Caffe\bvlc_reference_caffenet.caffemodel 6 Conv1,fc7,prob,argmax Conv1.dat,fc7.dat,prob.dat,argmax.dat GPU 0 for Feat.prototxt, see the following example:name: ' Caffenet ' state {PHA Se:test} layer {name: "Data" type: "ImageData" Top: "Data" Top: "label" Transform_param {m Irror:false crop_size:227 mean_file: "Imagenet_mean.binaryproto"} image_data_param {so Urce: "File_list.txt" Batch_size:1 new_height:256 new_width:256}} layer {name: "Conv 1 "type:" Convolution "bottom:" Data "Top:" Conv1 "Convolution_param {num_output:96 Kern El_size:11 Stride:4}} ################################################################################# # # # # # #some lines is ignored here for simplicity, complete them by yourself###### ######################################### ######################################## Layer {name: "Fc8" type: "Innerproduct" bottom: "fc7" Top: "Fc8" Inner_product_param {     num_output:1000}} layer {name: "Prob" type: "Softmax" bottom: "Fc8" Top: "prob"} layer { Name: "Argmax" type: "Argmax" bottom: "Prob" Top: "Argmax" Argmax_param {top_k:1}} for file _list.txt, see the following example:h:\data\ilsvrc2012\n01440764\n01440764_18.jpeg 0 H:\Data\ILSVRC2012\n0144076 4\n01440764_297.jpeg 0 H:\Data\ILSVRC2012\n01443537\n01443537_395.JPEG 1 H:\Data\ILSVRC2012\n01443537\n01443537_69 3.JPEG 1 H:\Data\ILSVRC2012\n01518878\n01518878_103.JPEG 9 H:\Data\ILSVRC2012\n01518878\n01518878_477.JPEG 9 How T o load features in Matlab? Use the following function, See:prob = sc_load (' Prob.dat '); function data = sc_load (filename, type) if ~exist (' type ', ' var ') | |     IsEmpty (type) type = ' single ';         End fid = fopen (filename, ' r '); rows = Fread (FID, 1, tYPE);     cols = Fread (FID, 1, type);         data = fread (FID, rows * cols, type);     Fclose (FID);     data = reshape (data, rows, cols);     Switch type case ' int32 ' Data = Int32 (data);     Case "single" data = single (data); End end***/#include<string>#include<vector>#include"boost/algorithm/string.hpp"#include"caffe/caffe.hpp"usingboost::shared_ptr;usingSTD::string;using namespaceCaffe;#defineMax_feat_num 16intMainintargcChar**argv) {    if(ARGC <6) {LOG (ERROR)<<"get_features proto_file model_file iterations blob_name1[,name2] save_name1[,name2]"<<"[Cpu/gpu] [Device ID]"; return 1; } Phase Phase=TEST; if(ARGC >=7&& strcmp (argv[6],"GPU") ==0) {Caffe::set_mode (CAFFE::GPU); intdevice_id =0; if(ARGC = =8) {device_id= Atoi (argv[7]);        } caffe::setdevice (device_id); LOG (ERROR)<<"Using GPU #"<<device_id; } Else{LOG (ERROR)<<"Using CPU";    Caffe::set_mode (CAFFE::CPU); } boost::shared_ptr<Net<float> >feature_net; Feature_net.reset (Newnet<float> (argv[1], phase)); Feature_net->copytrainedlayersfrom (argv[2]); intTotal_iter = Atoi (argv[3]); LOG (ERROR)<<"Running"<< Total_iter <<"iterations."; STD::stringFeature_blob_names (argv[4]); Std::vector&LT;STD::string>Blob_names; Boost::split (Blob_names, Feature_blob_names, boost::is_any_of (",")); STD::stringSave_file_names (argv[5]); Std::vector&LT;STD::string>File_names; Boost::split (File_names, Save_file_names, boost::is_any_of (",")); Check_eq (Blob_names.size (), File_names.size ())<<"The number of feature blob names and save file names must be equal"; size_t Num_features=blob_names.size ();  for(size_t i =0; i < num_features; i++) {CHECK (feature_net-Has_blob (Blob_names[i]))<<"Unknown feature blob name"<< Blob_names[i] <<"In the network"; } FILE*Fp[max_feat_num];  for(size_t i =0; i < num_features; i++) {Fp[i]= fopen (File_names[i].c_str (),"WB"); }     for(inti =0; i < Total_iter; ++i) {feature_net-forwardprefilled ();  for(intj =0; J < Num_features; ++j) {Constboost::shared_ptr<blob<float> > Feature_blob = feature_net->Blob_by_name (Blob_names[j]); floatNum_imgs = Feature_blob->num () *Total_iter; floatFeat_dim = Feature_blob->count ()/feature_blob->num (); Const float* Data_ptr = (Const float*) feature_blob->Cpu_data (); if(i = =0) {fwrite (&feat_dim,sizeof(float),1, Fp[j]); Fwrite (&num_imgs,sizeof(float),1, Fp[j]); } fwrite (Data_ptr,sizeof(float), feature_blob->count (), fp[j]); }    }     for(size_t i =0; i < num_features; i++) {fclose (fp[i]); }    return 0;}

Caffe_manual/get_features.cpp