Caffe源码解析4： Data_layer

原文地址：http://www.cnblogs.com/louyihang-loves-baiyan/p/5153155.html

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"

hdf5就是之前说到的一种主要用于科学数据记录、能自我描述的数据格式。
还有几个跟data相关的头文件比如data_read.hpp,data_transformer.hpp
其中data_reader主要是负责数据的读取，传送到data layer中。并且对于每一个source，都会开一一起独立的reading thread读取线程，几十有多个solver在并行的跑。比如在多GPU训练的时候，可以保证对于数据库的读取是顺序的

data_transformer.hpp里面的DataTransformer这个类，这个类我们要关注一下，这个类主要能对input data 执一些预处理操作，比如缩放、镜像、减去均值。同时还支持一些随机的操作。
其核心的函数如下，这里总共有5个常在的Transform函数，其中所有函数的第二部分是相同的，都是一个目标blob，而输入根据输入的情况可以有所选择，可以是blob,也可以是opencv的mat 结构，或者proto中定义的datum结构。

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

//读取每一个boxint 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 boxint 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]);      }    }  }