(16)caffe总结之自定义数据输入层

以下的操作就是读入此图片，然后进行训练：

1.创建新定义的头文件include/caffe/layers/my_data_layer.hpp

   重新Layer名的方法：virtual inline const char* type() const {return "MyData";}

#ifndef CAFFE_MY_DATA_LAYER_HPP_#define CAFFE_MY_DATA_LAYER_HPP_#include <string>#include <utility>#include <vector>#include <algorithm>    // std::random_shuffle#include <vector>       // std::vector#include <ctime>        // std::time#include "caffe/blob.hpp"#include "caffe/data_transformer.hpp"#include "caffe/internal_thread.hpp"#include "caffe/layer.hpp"#include "caffe/layers/base_data_layer.hpp"#include "caffe/proto/caffe.pb.h"namespace caffe {template <typename Dtype>class MyDataLayer : public BasePrefetchingDataLayer<Dtype> {  public:    explicit MyDataLayer(const LayerParameter& param): BasePrefetchingDataLayer<Dtype>(param){}    virtual ~MyDataLayer();    virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top);    virtual inline const char* type() const { return "MyData";}    virtual inline int ExactNumBottomBlobs() const { return 0; }    virtual inline int ExactNumTopBlobs() const { return 2; }  protected:    shared_ptr<Caffe::RNG> prefetch_rng_;    virtual void ShuffleImages();    virtual void load_batch(Batch<Dtype>* batch);    inline cv::Mat get_one_sample(cv::Mat whole_img,int pos_x,int pos_y,int width,int height){        return source_image_(cv::Rect(pos_x,pos_y,width,height)).clone();    }    vector<std::pair<cv::Mat, int> > samples_;    int lines_id_;    string image_address_;    int start_col_;    int end_col_;    int sample_width_;    int sample_height_;    cv::Mat source_image_;};} // namespace caffe#endif // CAFFE_MY_DATA_LAYER_HPP_

2.重写LayerSetUp,ShuffleImage,load_batch(见代码my_data_layer.cpp)

#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#include <opencv2/highgui/highgui.hpp>#include <fstream>  // NOLINT(readability/streams)#include <iostream>  // NOLINT(readability/streams)#include <string>#include <utility>#include <vector>#include <stdio.h>#include "caffe/data_transformer.hpp"#include "caffe/layers/base_data_layer.hpp"#include "caffe/layers/my_data_layer.hpp"#include "caffe/util/benchmark.hpp"#include "caffe/util/io.hpp"#include "caffe/util/math_functions.hpp"#include "caffe/util/rng.hpp"namespace caffe {template <typename Dtype>MyDataLayer<Dtype>::~MyDataLayer<Dtype>() {  this->StopInternalThread();}template <typename Dtype>void MyDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top){    // Init private variables.    image_address_ = this->layer_param_.my_data_param().image_address();    start_col_= this->layer_param_.my_data_param().start_col();    end_col_  = this->layer_param_.my_data_param().end_col();    sample_width_ = this->layer_param_.my_data_param().sample_width();    sample_height_= this->layer_param_.my_data_param().sample_height();        lines_id_ = 0;    int label,x,y;    cv::Mat image;    source_image_ = cv::imread(image_address_);    for(int i=0 ; i< 50 ; i++){      label = (int)(i/5);      for(int j=start_col_ ; j<end_col_ ; j++){         y=20*i;         x=20*j;         image = get_one_sample(source_image_,x,y,20,20);         samples_.push_back(std::make_pair(image,label));      }    }    CHECK(!samples_.empty()) << "Data is empty";    // Shuffle images    if(this->layer_param_.my_data_param().shuffle()){    LOG(INFO) << "Shuffling data";    ShuffleImages();    }    // Save images    if(this->layer_param_.my_data_param().is_save()){LOG(INFO) << "Saving images ";        for(int i=0;i<samples_.size();i++){      string save_folder = this->layer_param_.my_data_param().save_folder();    char img_name[128];    sprintf(img_name,"%s/%d.jpg",save_folder.c_str(),i);    cv::imwrite(img_name,samples_[i].first);        }    }    // Read an image, and use it to initialize the top blob    cv::Mat cv_img = samples_[lines_id_].first;    CHECK(cv_img.data) << "Could not load first image";    // Use data_transformer to infer the expected blob shape from a cv_image    vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);    this->transformed_data_.Reshape(top_shape);    //Reshape prefetch_data and top[0] according to the batch_size.    const int batch_size = this->layer_param_.my_data_param().batch_size();    top_shape[0] = batch_size;   for (int i = 0; i < this->prefetch_.size(); ++i) {    this->prefetch_[i]->data_.Reshape(top_shape);   }    top[0]->Reshape(top_shape);    LOG(INFO) << "output data size: " << top[0]->num() << "," << top[0]->channels() << "," << top[0]->height() << "," << top[0]->width();    // label    vector<int> label_shape(1,batch_size);    top[1]->Reshape(label_shape);    for (int i = 0; i < this->prefetch_.size(); ++i) {      this->prefetch_[i]->label_.Reshape(label_shape);    }}template <typename Dtype>void MyDataLayer<Dtype>::ShuffleImages(){    std::srand ( unsigned ( std::time(0) ) );    std::random_shuffle(samples_.begin(),samples_.end());}template <typename Dtype>void MyDataLayer<Dtype>::load_batch(Batch<Dtype>* batch){    CPUTimer batch_timer;    batch_timer.Start();    double read_time = 0;    double trans_time = 0;    CPUTimer timer;    CHECK(batch->data_.count());    CHECK(this->transformed_data_.count());    MyDataParameter my_data_param = this-> layer_param_.my_data_param();    // Get batch size    const int batch_size = my_data_param.batch_size();    // Reshape according to the first image of each batch    // on single input batches allows for inputs of varying dimension    cv::Mat cv_img = samples_[lines_id_].first;    CHECK(cv_img.data) << "Could not load "<<lines_id_<<" sample";    // Use data_transformer to infer the expected blob shape from a cv_img    vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);    this->transformed_data_.Reshape(top_shape);    // Reshape batch according to the batch_size    top_shape[0] = batch_size;    batch->data_.Reshape(top_shape);        Dtype* prefetch_data = batch->data_.mutable_cpu_data();    Dtype* prefetch_label= batch->label_.mutable_cpu_data();    // datum scales    int samples_size = samples_.size();    for(int item_id=0;item_id<batch_size;++item_id){      // get a blob      timer.Start();      CHECK_GT(samples_size, lines_id_);      cv::Mat sample = samples_[lines_id_].first;      CHECK(sample.data) << "Could not load "<<lines_id_<<" sample";      read_time += timer.MicroSeconds();      timer.Start();      // apply transformations to the image      int offset = batch->data_.offset(item_id);      this->transformed_data_.set_cpu_data(prefetch_data + offset);      this->data_transformer_->Transform(sample,&(this->transformed_data_));      trans_time += timer.MicroSeconds();      prefetch_label[item_id] = samples_[lines_id_].second;      // got the the next iter      lines_id_++;      if(lines_id_>=samples_size){              // We have reached the end. restart from the first.      DLOG(INFO) << "Restarting data prefetching from start.";      lines_id_=0;      if(my_data_param.shuffle()){      ShuffleImages();      }      }    }    batch_timer.Stop();    DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";    DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";    DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";}INSTANTIATE_CLASS(MyDataLayer);REGISTER_LAYER_CLASS(MyData);} // namespaces caffe#endif  // USE_OPENCV

3.proto/caffe.proto注册新的Layer

message LayerParameter {...  // My new data layer for infer image data++optional MyDataParameter my_data_param = 150;}message V1LayerParameter {...  // My new data parameter  ++optional MyDataParameter my_data_param = 50;...}...// Message that stores parameters used to apply a new data layer// to infer image datamessage MyDataParameter{  // Image address  optional string image_address = 1;  // Start column for calculation  // Traning and testing use different columns  optional int32 start_col = 2;  // End column for computing  optional int32 end_col = 3;  // Height of each image  optional int32 sample_height = 4 [default = 0];  // Width of each image  optional int32 sample_width = 5 [default = 0];  // Shuffle or not  optional bool shuffle = 6;  // Batch size for each computing  optional int32 batch_size = 7;  // Gray or color (equals to single channel or 3 channels)  optional bool is_color = 8 [default = false];  // Save the image seperated from the image address or not  optional bool is_save = 9 [default = false];  // If save, use this save folder  optional string save_folder = 10;}

测试新定义的数据层，使用原来的LeNet网络，把输入层做修改，其他的不变化

name: "LeNet"layer {  name: "mnist"  type: "MyData"  top: "data"  top: "label"  include {    phase: TRAIN  }  transform_param {    scale: 0.00390625  }  my_data_param {    image_address: "/home/xyl/caffe/lesson2/lesson2-1/digits.png"    batch_size: 64    start_col:0    end_col:70    shuffle:true    sample_width:20    sample_height:20    is_save:true    save_folder: "/home/xyl/caffe/lesson2/lesson2-1/img"  }}layer {  name: "mnist"  type: "MyData"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    scale: 0.00390625  }  my_data_param {    image_address: "/home/xyl/caffe/lesson2/lesson2-1/digits.png"    batch_size:32    start_col:70    end_col:100    sample_width:20    sample_height:20    shuffle:true  }}

最后新建一个mydata.sh文件

#!/usr/bin/env shset -e./build/tools/caffe train --solver=lesson2/lesson2-1/mydata_lenet_solver.prototxt $@

运行此文件来做测试，结果如图：