Caffe源码解析4 Data_layer

转载请注明出处，楼燚(yì)航的blog， http://home.cnblogs.com/u/louyihang-loves-baiyan/

data_layer应该是网络的最底层，主要是将数据送给blob进入到net中，在data_layer中存在多个跟data_layer相关的类

• BaseDataLayer
• BasePrefetchingDataLayer
• DataLayer
• DummyDataLayer
• HDF5DataLayer
• HDF5OutputLayer
• ImageDataLayer
• MemoryDataLayer
• WindowDataLayer
• Batch

这里首先说明一下这几个类之间的区别。首先Layer是基类，这个之前就已经提到过了。其次看HDF5相关的类有两个，一个是HDF5DataLayer，另一个是HDF5OutputLayer，主要是基于HDF5数据格式的读取和存储

留意到这个data_layer的头文件还include了不少头文件

#include <string> #include <utility> #include <vector> #include "hdf5.h"  #include "caffe/blob.hpp" #include "caffe/common.hpp" #include "caffe/data_reader.hpp" #include "caffe/data_transformer.hpp" #include "caffe/filler.hpp" #include "caffe/internal_thread.hpp" #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" #include "caffe/util/blocking_queue.hpp" #include "caffe/util/db.hpp"

void Transform(const Datum& datum, Blob<Dtype>* transformed_blob); void Transform(const vector<Datum> & datum_vector, Blob<Dtype>* transformed_blob); void Transform(const vector<cv::Mat> & mat_vector, Blob<Dtype>* transformed_blob); void Transform(const cv::Mat& cv_img, Blob<Dtype>* transformed_blob); void Transform(Blob<Dtype>* input_blob, Blob<Dtype>* transformed_blob);

TransformationParameter是该类构造器中需要传入的一些变形参数，相关的操作定义在proto中，摘录如下,可以看到总共有sacle,mirror,crop_size,mean_file,mean_value,force_color,force_grey共7个相关操作

message TransformationParameter {   optional float scale = 1 [default = 1];   optional bool mirror = 2 [default = false];   optional uint32 crop_size = 3 [default = 0];   optional string mean_file = 4;   repeated float mean_value = 5;   optional bool force_color = 6 [default = false];   optional bool force_gray = 7 [default = false]; }

首先对于dat_layer，里面根据继承关系最后的几个子类分别是ImageDataLayer,DataLayer,WindowDataLayer,MemoryDataLayer,HDF5以及Dummy这里暂时先不做分析。其实最重要的就是类面的layerSetup.首先我们来看DataLayer的DataLayerSetUp

void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,       const vector<Blob<Dtype>*>& top) {   const int batch_size = this->layer_param_.data_param().batch_size();   //获得相应的datum，用来初始化top blob   Datum& datum = *(reader_.full().peek());   //使用data_transformer 来计算根据datum的期望blob的shape   vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);   this->transformed_data_.Reshape(top_shape);   //首先reshape top[0]，再根据batch的大小进行预取   top_shape[0] = batch_size;   top[0]->Reshape(top_shape);   for (int i = 0; i < this->PREFETCH_COUNT; ++i) {     this->prefetch_[i].data_.Reshape(top_shape);   }   LOG(INFO) << "output data size: " << top[0]->num() << ","       << top[0]->channels() << "," << top[0]->height() << ","       << top[0]->width();   // 同样reshape label的blob的shape   if (this->output_labels_) {     vector<int> label_shape(1, batch_size);     top[1]->Reshape(label_shape);     for (int i = 0; i < this->PREFETCH_COUNT; ++i) {       this->prefetch_[i].label_.Reshape(label_shape);     }   } }

MemoryDataLayer

void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {   //直接通过memory_data_param类设置layer的相关参数   batch_size_ = this->layer_param_.memory_data_param().batch_size();   channels_ = this->layer_param_.memory_data_param().channels();   height_ = this->layer_param_.memory_data_param().height();   width_ = this->layer_param_.memory_data_param().width();   size_ = channels_ * height_ * width_;   CHECK_GT(batch_size_ * size_, 0) <<       "batch_size, channels, height, and width must be specified and"       " positive in memory_data_param";   //这里跟datalayer一样都是先设置top[0]，然后对label进行reshape   vector<int> label_shape(1, batch_size_);   top[0]->Reshape(batch_size_, channels_, height_, width_);   top[1]->Reshape(label_shape);   added_data_.Reshape(batch_size_, channels_, height_, width_);   added_label_.Reshape(label_shape);   data_ = NULL;   labels_ = NULL;   added_data_.cpu_data();   added_label_.cpu_data(); }

ImageDataLayer,它的DataLayerSetUP函数

void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,       const vector<Blob<Dtype>*>& top) {   const int new_height = this->layer_param_.image_data_param().new_height();   const int new_width  = this->layer_param_.image_data_param().new_width();   const bool is_color  = this->layer_param_.image_data_param().is_color();   string root_folder = this->layer_param_.image_data_param().root_folder();    CHECK((new_height == 0 && new_width == 0) ||       (new_height > 0 && new_width > 0)) << "Current implementation requires "       "new_height and new_width to be set at the same time.";   //读取图像文件和相应的label   const string& source = this->layer_param_.image_data_param().source();   LOG(INFO) << "Opening file " << source;   std::ifstream infile(source.c_str());   string filename;   int label;   while (infile >> filename >> label) {     lines_.push_back(std::make_pair(filename, label));   }    if (this->layer_param_.image_data_param().shuffle()) {     // randomly shuffle data     LOG(INFO) << "Shuffling data";     const unsigned int prefetch_rng_seed = caffe_rng_rand();     prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));     ShuffleImages();   }   LOG(INFO) << "A total of " << lines_.size() << " images.";    lines_id_ = 0;   //check是否需要随机跳过一些图像   if (this->layer_param_.image_data_param().rand_skip()) {     unsigned int skip = caffe_rng_rand() %         this->layer_param_.image_data_param().rand_skip();     LOG(INFO) << "Skipping first " << skip << " data points.";     CHECK_GT(lines_.size(), skip) << "Not enough points to skip";     lines_id_ = skip;   }   //使用Opencv来读进图像，然后使用它初始化相应的top blob   cv::Mat cv_img = ReadImageToCVMat(root_folder + lines_[lines_id_].first,                                     new_height, new_width, is_color);   CHECK(cv_img.data) << "Could not load " << lines_[lines_id_].first;   //这里的步骤和上面相同，使用transformer来做reshape   vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);   this->transformed_data_.Reshape(top_shape);   //之后部分跟前面差不多，初始化top[0]   const int batch_size = this->layer_param_.image_data_param().batch_size();   CHECK_GT(batch_size, 0) << "Positive batch size required";   top_shape[0] = batch_size;   for (int i = 0; i < this->PREFETCH_COUNT; ++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();   //reshape label   vector<int> label_shape(1, batch_size);   top[1]->Reshape(label_shape);   for (int i = 0; i < this->PREFETCH_COUNT; ++i) {     this->prefetch_[i].label_.Reshape(label_shape);   } }

WindowDataLayer的DataLayerSetUp，这个函数标比较长，我只列出了其中主要的部分，之前的Image相当于是已经剪裁过的一个图像，也就是说你的目标基本上是充棉了整个画面，而Window File是用于原始图的，也就是说有background和object，这个window file 的格式如下

window_file format repeated:    # image_index    img_path (abs path)    channels    height    width    num_windows    class_index overlap x1 y1 x2 y2

//读取每一个box int num_windows; infile >> num_windows; const float fg_threshold =     this->layer_param_.window_data_param().fg_threshold(); const float bg_threshold =     this->layer_param_.window_data_param().bg_threshold(); for (int i = 0; i < num_windows; ++i) {   int label, x1, y1, x2, y2;   float overlap;   infile >> label >> overlap >> x1 >> y1 >> x2 >> y2;    vector<float> window(WindowDataLayer::NUM);   window[WindowDataLayer::IMAGE_INDEX] = image_index;   window[WindowDataLayer::LABEL] = label;   window[WindowDataLayer::OVERLAP] = overlap;   window[WindowDataLayer::X1] = x1;   window[WindowDataLayer::Y1] = y1;   window[WindowDataLayer::X2] = x2;   window[WindowDataLayer::Y2] = y2;    // add window to foreground list or background list// read each box int num_windows; infile >> num_windows; const float fg_threshold =     this->layer_param_.window_data_param().fg_threshold(); const float bg_threshold =     this->layer_param_.window_data_param().bg_threshold(); for (int i = 0; i < num_windows; ++i) {   int label, x1, y1, x2, y2;   float overlap;   infile >> label >> overlap >> x1 >> y1 >> x2 >> y2;    vector<float> window(WindowDataLayer::NUM);   window[WindowDataLayer::IMAGE_INDEX] = image_index;   window[WindowDataLayer::LABEL] = label;   window[WindowDataLayer::OVERLAP] = overlap;   window[WindowDataLayer::X1] = x1;   window[WindowDataLayer::Y1] = y1;   window[WindowDataLayer::X2] = x2;   window[WindowDataLayer::Y2] = y2;    //首先计算得到overlap,根据Overlap与fg_threshold的比较载添加到fg的list中   if (overlap >= fg_threshold) {     int label = window[WindowDataLayer::LABEL];     CHECK_GT(label, 0);     fg_windows_.push_back(window);     label_hist.insert(std::make_pair(label, 0));     label_hist[label]++;   } else if (overlap < bg_threshold) {     // background window, force label and overlap to 0     window[WindowDataLayer::LABEL] = 0;     window[WindowDataLayer::OVERLAP] = 0;     bg_windows_.push_back(window);     label_hist[0]++;   } } =-   if (overlap >= fg_threshold) {     int label = window[WindowDataLayer::LABEL];     CHECK_GT(label, 0);     fg_windows_.push_back(window);     label_hist.insert(std::make_pair(label, 0));     label_hist[label]++;   } else if (overlap < bg_threshold) {     //background的label和overlap都是0     window[WindowDataLayer::LABEL] = 0;     window[WindowDataLayer::OVERLAP] = 0;     bg_windows_.push_back(window);     label_hist[0]++;   } }   .............. for (map<int, int>::iterator it = label_hist.begin();       it != label_hist.end(); ++it) {     LOG(INFO) << "class " << it->first << " has " << label_hist[it->first]               << " samples";   }    LOG(INFO) << "Amount of context padding: "       << this->layer_param_.window_data_param().context_pad();    LOG(INFO) << "Crop mode: "       << this->layer_param_.window_data_param().crop_mode();    //这里之后的步骤就差不多了，同样是对transform的一些操作   const int crop_size = this->transform_param_.crop_size();   CHECK_GT(crop_size, 0);   const int batch_size = this->layer_param_.window_data_param().batch_size();   top[0]->Reshape(batch_size, channels, crop_size, crop_size);   for (int i = 0; i < this->PREFETCH_COUNT; ++i)     this->prefetch_[i].data_.Reshape(         batch_size, channels, crop_size, crop_size);    LOG(INFO) << "output data size: " << top[0]->num() << ","       << top[0]->channels() << "," << top[0]->height() << ","       << top[0]->width();   // 对label进行reshape   vector<int> label_shape(1, batch_size);   top[1]->Reshape(label_shape);   for (int i = 0; i < this->PREFETCH_COUNT; ++i) {     this->prefetch_[i].label_.Reshape(label_shape);   }    //做减均值的操作   has_mean_file_ = this->transform_param_.has_mean_file();   has_mean_values_ = this->transform_param_.mean_value_size() > 0;   if (has_mean_file_) {     const string& mean_file =           this->transform_param_.mean_file();     LOG(INFO) << "Loading mean file from: " << mean_file;     BlobProto blob_proto;     ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);     data_mean_.FromProto(blob_proto);   }   if (has_mean_values_) {     CHECK(has_mean_file_ == false) <<       "Cannot specify mean_file and mean_value at the same time";     for (int c = 0; c < this->transform_param_.mean_value_size(); ++c) {       mean_values_.push_back(this->transform_param_.mean_value(c));     }     CHECK(mean_values_.size() == 1 || mean_values_.size() == channels) <<      "Specify either 1 mean_value or as many as channels: " << channels;     if (channels > 1 && mean_values_.size() == 1) {       // Replicate the mean_value for simplicity       for (int c = 1; c < channels; ++c) {         mean_values_.push_back(mean_values_[0]);       }     }   }