caffe代码阅读8: Data_layers的实现细节（各个数据读取层的实现细节） 2016.3.25-28

一、Data_layers.hpp文件的作用简介

Data_layers.hpp在目前caffe的master分支中已经不能存在了，分散到各个文件中去了。

而之前是存在于cafferoot\include\caffe中。现在已经变成了各个类的名称的头文件了。这里做个提醒

首先给出这个文件中所包含的几个与数据读取有关的类。

分别为：

BaseDataLayer

数据层的基类，继承自通用的类Layer

Batch

Batch实际上就是一个data_和label_类标

BasePrefetchingDataLayer

是预取层的基类，继承自BaseDataLayer和InternalThread，包含能够读取一批数据的能力

DataLayer

DataLayer才是主角，继承自BasePrefetchingDataLayer

使用DataReader来进行数据共享，从而实现并行化

DummyDataLayer

该类是继承自Layer,通过Filler产生数据

HDF5DataLayer

从HDF5中读取，继承自Layer

HDF5OutputLayer

将数据写入到HDF5文件，继承自Layer

ImageDataLayer

从图像文件中读取数据，这个应该比较常用，继承自BasePrefetchingDataLayer

MemoryDataLayer

从内存中读取数据，这里指已经从数据文件或者图像文件中读取到了数据，然后输入到该层，继承自BaseDataLayer

WindowDataLayer

从图像文件的窗口获取数据，需要指定窗口数据文件，继承自BasePrefetchingDataLayer

二、Data_layers文件的的详细介绍

上述类虽然在同一个头文件中进行的定义，但是却都是在不同的cpp文件进行的实现。

下面给出类的实现文件

BaseDataLayer和BasePrefetchingDataLayer

对应于：

base_data_layer.cpp

base_data_layer.cu

DataLayer

对应于：

data_layer.cpp

DummyDataLayer

对应于：

dummy_data_layer.cpp

HDF5DataLayer

HDF5OutputLayer

对应于：

hdf5_data_layer.cpp

hdf5_data_layer.cu

以及

hdf5_output_layer.cpp

hdf5_output_layer.cu

ImageDataLayer

对应于：

image_data_layer.cpp

MemoryDataLayer

对应于：

memory_data_layer.cpp

WindowDataLayer

对应于

window_data_layer.cpp

接下来对这些类进行详细阐述：

（1）BaseDataLayer的类定义以及实现如下：

/** * @brief Provides base for data layers that feed blobs to the Net. * * TODO(dox): thorough documentation for Forward and proto params. * 数据层的基类 */template <typename Dtype>class BaseDataLayer : public Layer<Dtype> { public:  // 显式构造函数  explicit BaseDataLayer(const LayerParameter& param);  // LayerSetUp: implements common data layer setup functionality, and calls  // DataLayerSetUp to do special data layer setup for individual layer types.  // This method may not be overridden except by the BasePrefetchingDataLayer.  // 该函数只能被BasePrefetchingDataLayer层进行重载  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // Data layers should be shared by multiple solvers in parallel  // 数据是否需要给多个并行solver进行共享  virtual inline bool ShareInParallel() const { return true; }  // 数据层的初始化  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {}  // 数据层是没有输入的(即bottoms)，所以reshape只是形式  // Data layers have no bottoms, so reshaping is trivial.  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {}  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {} protected:  // 对输入的数据进行变换的参数，这其中包括是否需要mirror，是否需要crop  // 是否需要减去meanfile，是否需要scale  TransformationParameter transform_param_;  // 实际执行数据变换类的指针(一个Transform函数加上参数即可完成对数据的变换，参数是数据哈)  shared_ptr<DataTransformer<Dtype> > data_transformer_;  bool output_labels_;};

具体的实现：

// 构造函数就是初始化数据变换参数template <typename Dtype>BaseDataLayer<Dtype>::BaseDataLayer(const LayerParameter& param)    : Layer<Dtype>(param),      transform_param_(param.transform_param()) {}// 初始化的时候根据top的大小来确定，如果是1表明只输出数据，而不输出类标template <typename Dtype>void BaseDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  if (top.size() == 1) {    output_labels_ = false;  } else {    output_labels_ = true;  }  // 初始化一个DataTransformer实例，便于对数据进行预处理  data_transformer_.reset(      new DataTransformer<Dtype>(transform_param_, this->phase_));  // 初始化种子  data_transformer_->InitRand();  // The subclasses should setup the size of bottom and top  // 执行数据层的初始化  DataLayerSetUp(bottom, top);}

（2）BasePrefetchingDataLayer类的定义以及实现如下：

BasePrefetchingDataLayer类的定义如下：

// BasePrefetchingDataLayer层是继承于BaseDataLayer的// 是预取层的基类template <typename Dtype>class BasePrefetchingDataLayer :    public BaseDataLayer<Dtype>, public InternalThread { public:  explicit BasePrefetchingDataLayer(const LayerParameter& param);  // LayerSetUp: implements common data layer setup functionality, and calls  // DataLayerSetUp to do special data layer setup for individual layer types.  // This method may not be overridden.  void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // Prefetches batches (asynchronously if to GPU memory)  static const int PREFETCH_COUNT = 3; protected:  virtual void InternalThreadEntry();  // 多了load_batch函数，该函数是纯虚函数，继承该函数的类都需要实现的  virtual void load_batch(Batch<Dtype>* batch) = 0;  // 还有prefetch数组,prefetch_free_,prefetch_full_  Batch<Dtype> prefetch_[PREFETCH_COUNT];  BlockingQueue<Batch<Dtype>*> prefetch_free_;  BlockingQueue<Batch<Dtype>*> prefetch_full_;  Blob<Dtype> transformed_data_;};BasePrefetchingDataLayer类的具体实现如下：// 构造函数，初始化预取的队列,free和fulltemplate <typename Dtype>BasePrefetchingDataLayer<Dtype>::BasePrefetchingDataLayer(    const LayerParameter& param)    : BaseDataLayer<Dtype>(param),      prefetch_free_(), prefetch_full_() {  for (int i = 0; i < PREFETCH_COUNT; ++i) {    prefetch_free_.push(&prefetch_[i]);  }}// 进行层的初始化template <typename Dtype>void BasePrefetchingDataLayer<Dtype>::LayerSetUp(    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {    // 首先执行基类BaseDataLayer的层初始化  BaseDataLayer<Dtype>::LayerSetUp(bottom, top);  // Before starting the prefetch thread, we make cpu_data and gpu_data  // calls so that the prefetch thread does not accidentally make simultaneous  // cudaMalloc calls when the main thread is running. In some GPUs this  // seems to cause failures if we do not so.  // 在开启预取线程的时候，需要让cpu数据和gpu数据分配空间  // 这样才能够避免在某些GPU上出现问题  // 首先是CPU  for (int i = 0; i < PREFETCH_COUNT; ++i) {    prefetch_[i].data_.mutable_cpu_data();    if (this->output_labels_) {      prefetch_[i].label_.mutable_cpu_data();    }  }#ifndef CPU_ONLY  // 然后是GPU  if (Caffe::mode() == Caffe::GPU) {    for (int i = 0; i < PREFETCH_COUNT; ++i) {      prefetch_[i].data_.mutable_gpu_data();      if (this->output_labels_) {        prefetch_[i].label_.mutable_gpu_data();      }    }  }#endif  DLOG(INFO) << "Initializing prefetch";  // 初始化随机数种子  this->data_transformer_->InitRand();  // 开启线程  StartInternalThread();  DLOG(INFO) << "Prefetch initialized.";}// 在StartInternalThread开启线程后就会执行下面自己定义的函数// 这个就是自己定义的函数，让线程去执行的template <typename Dtype>void BasePrefetchingDataLayer<Dtype>::InternalThreadEntry() {#ifndef CPU_ONLY  cudaStream_t stream;  if (Caffe::mode() == Caffe::GPU) {      // 创建非阻塞流    CUDA_CHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));  }#endif  try {    while (!must_stop()) {        // 弹出一个batch      Batch<Dtype>* batch = prefetch_free_.pop();        // 装载batch      load_batch(batch);#ifndef CPU_ONLY      if (Caffe::mode() == Caffe::GPU) {          // 如果GPU模式开始，则推送到GPU        batch->data_.data().get()->async_gpu_push(stream);        // 检查是否成功        CUDA_CHECK(cudaStreamSynchronize(stream));      }#endif      // 将装好的batch压入full队列      prefetch_full_.push(batch);    }  } catch (boost::thread_interrupted&) {    // Interrupted exception is expected on shutdown  }#ifndef CPU_ONLY  if (Caffe::mode() == Caffe::GPU) {      // 销毁流    CUDA_CHECK(cudaStreamDestroy(stream));  }#endif}template <typename Dtype>void BasePrefetchingDataLayer<Dtype>::Forward_cpu(    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {    // 传递的时候是从full队列中弹出一个数据  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");  // Reshape to loaded data.  // 根据batch的形状改变数据形状  top[0]->ReshapeLike(batch->data_);  // Copy the data  // 将batch数据复制到top[0]  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),             top[0]->mutable_cpu_data());  DLOG(INFO) << "Prefetch copied";  if (this->output_labels_) {      // 输出类标的话    // Reshape to loaded labels.    // 根据batch中类标的形状改变top[1]的形状    top[1]->ReshapeLike(batch->label_);    // Copy the labels.    // 复制类标到top[1]    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),        top[1]->mutable_cpu_data());  }  // 将该batch压入free队列  prefetch_free_.push(batch);}// 如果没有GPU的话则在BasePrefetchingDataLayer类中生成一个Forward函数// 该函数并不前传，而是直接报错#ifdef CPU_ONLYSTUB_GPU_FORWARD(BasePrefetchingDataLayer, Forward);#endif// 初始化层INSTANTIATE_CLASS(BaseDataLayer);INSTANTIATE_CLASS(BasePrefetchingDataLayer);

（3）DataLayer类的定义以及实现如下：

数据层的主要功能是：

首先给出类的定义

// DataLayer才是主角，继承自BasePrefetchingDataLayertemplate <typename Dtype>class DataLayer : public BasePrefetchingDataLayer<Dtype> { public:  explicit DataLayer(const LayerParameter& param);  virtual ~DataLayer();  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // DataLayer uses DataReader instead for sharing for parallelism  // 多了下面几个  virtual inline bool ShareInParallel() const { return false; }  virtual inline const char* type() const { return "Data"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int MinTopBlobs() const { return 1; }  virtual inline int MaxTopBlobs() const { return 2; } protected:  virtual void load_batch(Batch<Dtype>* batch);  DataReader reader_;};

具体的实现如下：

#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#endif  // USE_OPENCV#include <stdint.h>#include <string>#include <vector>#include "caffe/common.hpp"#include "caffe/data_layers.hpp"#include "caffe/layer.hpp"#include "caffe/proto/caffe.pb.h"#include "caffe/util/benchmark.hpp"#include "caffe/util/io.hpp"namespace caffe {// 初始化DataReader，层参数template <typename Dtype>DataLayer<Dtype>::DataLayer(const LayerParameter& param)  : BasePrefetchingDataLayer<Dtype>(param),    reader_(param) {}// 析构函数停止内部线程template <typename Dtype>DataLayer<Dtype>::~DataLayer() {  this->StopInternalThread();}// 数据层的初始化template <typename Dtype>void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  // 从层参数中读取batch_size  const int batch_size = this->layer_param_.data_param().batch_size();  // Read a data point, and use it to initialize the top blob.  // 从reader_中获取一个数据  Datum& datum = *(reader_.full().peek());  // Use data_transformer to infer the expected blob shape from datum.  // 用数据来推断blob的形状存放到top_shape  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);  this->transformed_data_.Reshape(top_shape);  // Reshape top[0] and prefetch_data according to the batch_size.  // 既然获取了数据的形状(channel,height,width)，那么这里再设置一下batch_size  // top_shape[0]=batch_size  // top_shape[1]=channel  // top_shape[2]=height  // top_shape[3]=width  top_shape[0] = batch_size;  // 根据形状设置top[0]的形状  top[0]->Reshape(top_shape);  // 设置预取数据的形状  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();  // label  // 如果输出类标的话则把top[1]的形状也弄一下  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);    }  }}// This function is called on prefetch thread// 这个函数是在自己定义的线程执行函数内部执行的template<typename Dtype>void DataLayer<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());  // Reshape according to the first datum of each batch  // on single input batches allows for inputs of varying dimension.  // 意思是像以下这种做法这样的话，每个batch的数据的维度可以不一样  // 从参数文件获取batch_size  const int batch_size = this->layer_param_.data_param().batch_size();  // 获取第一个数据  Datum& datum = *(reader_.full().peek());  // Use data_transformer to infer the expected blob shape from datum.  // 使用第一个数据推断blob的形状  vector<int> top_shape = this->data_transformer_->InferBlobShape(datum);  this->transformed_data_.Reshape(top_shape);  // Reshape batch according to the batch_size.  top_shape[0] = batch_size;  batch->data_.Reshape(top_shape);  // top_data存数据  Dtype* top_data = batch->data_.mutable_cpu_data();  Dtype* top_label = NULL;  // suppress warnings about uninitialized variables  // top_label存类标  if (this->output_labels_) {    top_label = batch->label_.mutable_cpu_data();  }  // 对这批数据进行处理  for (int item_id = 0; item_id < batch_size; ++item_id) {    timer.Start();    // get a datum    Datum& datum = *(reader_.full().pop("Waiting for data"));    read_time += timer.MicroSeconds();    timer.Start();    // Apply data transformations (mirror, scale, crop...)    // 对于给定批的数据获取offset，这里调用的是给定batchid，然后获取offset    int offset = batch->data_.offset(item_id);    this->transformed_data_.set_cpu_data(top_data + offset);    this->data_transformer_->Transform(datum, &(this->transformed_data_));    // Copy label.    // 复制类标    if (this->output_labels_) {      top_label[item_id] = datum.label();    }    // 数据传输时间    trans_time += timer.MicroSeconds();    // 将数据指针压到free队列    reader_.free().push(const_cast<Datum*>(&datum));  }  timer.Stop();  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(DataLayer);REGISTER_LAYER_CLASS(Data);}  // namespace caffe

（4）DummyDataLayer类的定义与实现介绍：

Dummy数据层的主要功能就是根据所给定的Filler产生数据，然后前向传

首先给出定义

/** * @brief Provides data to the Net generated by a Filler. * * TODO(dox): thorough documentation for Forward and proto params. * 该类是继承自Layer,通过Filler产生数据 */template <typename Dtype>class DummyDataLayer : public Layer<Dtype> { public:  explicit DummyDataLayer(const LayerParameter& param)      : Layer<Dtype>(param) {}  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // Data layers should be shared by multiple solvers in parallel  virtual inline bool ShareInParallel() const { return true; }  // Data layers have no bottoms, so reshaping is trivial.  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {}  virtual inline const char* type() const { return "DummyData"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int MinTopBlobs() const { return 1; } protected:  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}  vector<shared_ptr<Filler<Dtype> > > fillers_;  vector<bool> refill_;};

接下来给出详细的定义：

首先给出FillerParameter的定义，里面指定了值的类型，值是啥，最小是啥，最大是啥，平均值、方差是啥、是否稀疏、以及将扇入个数还是扇出个数还是所有的加起来求均值作为分母

message FillerParameter {  // The filler type.  optional string type = 1 [default = 'constant'];  optional float value = 2 [default = 0]; // the value in constant filler  optional float min = 3 [default = 0]; // the min value in uniform filler  optional float max = 4 [default = 1]; // the max value in uniform filler  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler  optional float std = 6 [default = 1]; // the std value in Gaussian filler  // The expected number of non-zero output weights for a given input in  // Gaussian filler -- the default -1 means don't perform sparsification.  optional int32 sparse = 7 [default = -1];  // Normalize the filler variance by fan_in, fan_out, or their average.  // Applies to 'xavier' and 'msra' fillers.  enum VarianceNorm {    FAN_IN = 0;    FAN_OUT = 1;    AVERAGE = 2;  }  optional VarianceNorm variance_norm = 8 [default = FAN_IN];}

再看看该类的参数

</pre><pre name="code" class="plain">// DummyDataLayer fills any number of arbitrarily shaped blobs with random// (or constant) data generated by "Fillers" (see "message FillerParameter").message DummyDataParameter {  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N  // shape fields, and 0, 1 or N data_fillers.  //  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.  // If 1 data_filler is specified, it is applied to all top blobs.  If N are  // specified, the ith is applied to the ith top blob.  repeated FillerParameter data_filler = 1;  repeated BlobShape shape = 6;  // 4D dimensions -- deprecated.  Use "shape" instead.  repeated uint32 num = 2;  repeated uint32 channels = 3;  repeated uint32 height = 4;  repeated uint32 width = 5;}

接下来给出具体的实现

#include <vector>#include "caffe/filler.hpp"#include "caffe/layer.hpp"#include "caffe/vision_layers.hpp"namespace caffe {template <typename Dtype>void DummyDataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  // 输出有几个  const int num_top = top.size();  // 获取该层的参数  const DummyDataParameter& param = this->layer_param_.dummy_data_param();  // 有几个filler  const int num_data_filler = param.data_filler_size();  // 检查filler的个数，要么为0、1、或者等于输出的个数  CHECK(num_data_filler == 0 || num_data_filler == 1 ||        num_data_filler == num_top)      << "Number of data fillers must be 0, 1 or equal to the number of tops: "      << num_top << "; you specified " << num_data_filler << " data fillers.";  // 判断是否全部为0  const bool legacy_dims = param.num_size() || param.channels_size() ||                           param.height_size() || param.width_size();  // 下面就是检查参数是不是满足要求，1或者0或者等于num_top  if (legacy_dims) {// 如果不是全部为0    CHECK_EQ(0, param.shape_size())        << "Both shape and legacy fields were specified";    // Using deprecated 4D output dim specifiers.    CHECK(param.num_size() == 1 || param.num_size() == num_top)        << "Must specify 'num' once, or once per top blob "        << "(" << num_top << "); specified " << param.num_size() << ".";    CHECK(param.channels_size() == 1 || param.channels_size() == num_top)        << "Must specify 'channels' once, or once per top blob "        << "(" << num_top << "); specified " << param.channels_size() << ".";    CHECK(param.height_size() == 1 || param.height_size() == num_top)        << "Must specify 'height' once, or once per top blob "        << "(" << num_top << "); specified " << param.height_size() << ".";    CHECK(param.width_size() == 1 || param.width_size() == num_top)        << "Must specify 'width' once, or once per top blob "        << "(" << num_top << "); specified " << param.width_size() << ".";  } else {    CHECK(param.shape_size() == 1 || param.shape_size() == num_top)        << "Must specify 'shape' once, or once per top blob "        << "(" << num_top << "); specified " << param.shape_size() << ".";  }  // refill_[i] tells Forward i whether or not to actually refill top Blob i.  // If refill_[i] is false, Forward does nothing for Blob i. We use this to  // avoid wastefully refilling "constant" Blobs in every forward pass.  // We first fill refill_ in with the INVERSE of its final values.  // The first time we run Forward from the LayerSetUp method, we'll fill only  // Blobs for which refill_ is normally false.  These Blobs will never be  // filled again.  // refill_表明是不是需要填充Blob，如果refill_[i]=false，那么就不会Blob i做任何事  //  refill_.clear();  fillers_.clear();  // 要么是0，要么是1  if (num_data_filler <= 1) {      // 定义了生成数据的参数      // 比如均值、方差等，详细请看其定义    FillerParameter filler_param;    if (num_data_filler == 0) {      // 如果没有指定，那么就是常数值填充      filler_param.set_type("constant");      filler_param.set_value(0);    } else {      // 否则复制filler到filler_param      filler_param.CopyFrom(param.data_filler(0));    }    // Refill on each iteration iff not using a constant filler,    // but use the inverse of this rule for the first run.    // 如果    refill_.resize(1);    refill_[0] = (strcmp(filler_param.type().c_str(), "constant") == 0);    fillers_.resize(1);    // 实例化填充器    fillers_[0].reset(GetFiller<Dtype>(filler_param));  } else {// 如果等于=num_top    refill_.resize(num_top);    fillers_.resize(num_top);    for (int i = 0; i < num_top; ++i) {      fillers_[i].reset(GetFiller<Dtype>(param.data_filler(i)));      // Refill on each iteration iff not using a constant filler,      // but use the inverse of this rule for the first run.      refill_[i] =          (strcmp(param.data_filler(i).type().c_str(), "constant") == 0);    }  }  // 改变形状  for (int i = 0; i < num_top; ++i) {    if (legacy_dims) {      const int num = (param.num_size() == 1) ? param.num(0) : param.num(i);      const int channels =          (param.channels_size() == 1) ? param.channels(0) : param.channels(i);      const int height =          (param.height_size() == 1) ? param.height(0) : param.height(i);      const int width =          (param.width_size() == 1) ? param.width(0) : param.width(i);      top[i]->Reshape(num, channels, height, width);    } else {      const int shape_index = (param.shape_size() == 1) ? 0 : i;      top[i]->Reshape(param.shape(shape_index));    }  }  // Run Forward once, with refill_ inverted, to fill the constant Blobs.  // 执行forward_cpu  this->Forward(bottom, top);  // Invert the inverted refill_ values to refill the desired (non-constant)  // Blobs in every usual forward pass.  for (int i = 0; i < refill_.size(); ++i) {    refill_[i] = !refill_[i];  }}// Forward里调用了该函数template <typename Dtype>void DummyDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {      // 调用fillers_来进行錐ill  for (int i = 0; i < top.size(); ++i) {    const int filler_id = (fillers_.size() > 1) ? i : 0;    if (refill_[filler_id]) {      fillers_[filler_id]->Fill(top[i]);    }  }}// 初始化类// 注册类INSTANTIATE_CLASS(DummyDataLayer);REGISTER_LAYER_CLASS(DummyData);}  // namespace caffe

（5）HDF5DataLayer类的定义以及实现如下：

HDF5数据层的主要功能是从给定的HDF5文件列表读取数据，然后设置top，即向前传播的数据。

首先给出类的定义：

template <typename Dtype>class HDF5DataLayer : public Layer<Dtype> { public:  explicit HDF5DataLayer(const LayerParameter& param)      : Layer<Dtype>(param) {}  virtual ~HDF5DataLayer();  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // Data layers should be shared by multiple solvers in parallel  virtual inline bool ShareInParallel() const { return true; }  // Data layers have no bottoms, so reshaping is trivial.  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {}  virtual inline const char* type() const { return "HDF5Data"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int MinTopBlobs() const { return 1; } protected:  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}  // 从HDF5文件读取数据  virtual void LoadHDF5FileData(const char* filename);  std::vector<std::string> hdf_filenames_;  unsigned int num_files_;  unsigned int current_file_;  hsize_t current_row_;  std::vector<shared_ptr<Blob<Dtype> > > hdf_blobs_;  // 存放的是数据的索引,可以对索引进行shuffle  std::vector<unsigned int> data_permutation_;  // 存放的是文件名字的索引，可以对索引进行shuffle  std::vector<unsigned int> file_permutation_;};

接下来给出类的具体实现：

给出实现之前先给出HDF5的操作

头文件：

#ifndef CAFFE_UTIL_HDF5_H_#define CAFFE_UTIL_HDF5_H_#include <string>#include "hdf5.h"#include "hdf5_hl.h"#include "caffe/blob.hpp"namespace caffe {// 获取HDF5文件的信息以及数据的维度template <typename Dtype>void hdf5_load_nd_dataset_helper(    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,    Blob<Dtype>* blob);// float类型的获取数据维度和信息的包裹函数template <typename Dtype>void hdf5_load_nd_dataset(    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,    Blob<Dtype>* blob);// double类型的获取数据维度和信息的包裹函数template <typename Dtype>void hdf5_save_nd_dataset(    const hid_t file_id, const string& dataset_name, const Blob<Dtype>& blob,    bool write_diff = false);// 读取int和存储int，读取字符串和存储字符串到文件int hdf5_load_int(hid_t loc_id, const string& dataset_name);void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i);string hdf5_load_string(hid_t loc_id, const string& dataset_name);void hdf5_save_string(hid_t loc_id, const string& dataset_name,                      const string& s);// 获取链接数int hdf5_get_num_links(hid_t loc_id);// 根据名字找到索引string hdf5_get_name_by_idx(hid_t loc_id, int idx);}  // namespace caffe#endif   // CAFFE_UTIL_HDF5_H_

cpp文件：

#include "caffe/util/hdf5.hpp"#include <string>#include <vector>namespace caffe {// Verifies format of data stored in HDF5 file and reshapes blob accordingly.// 获取HDF5文件的信息以及数据的维度template <typename Dtype>void hdf5_load_nd_dataset_helper(    hid_t file_id, const char* dataset_name_, int min_dim, int max_dim,    Blob<Dtype>* blob) {  // Verify that the dataset exists.  // 检查是否存在  CHECK(H5LTfind_dataset(file_id, dataset_name_))      << "Failed to find HDF5 dataset " << dataset_name_;  // Verify that the number of dimensions is in the accepted range.  herr_t status;  int ndims;  // 获取数据维度  status = H5LTget_dataset_ndims(file_id, dataset_name_, &ndims);  CHECK_GE(status, 0) << "Failed to get dataset ndims for " << dataset_name_;  CHECK_GE(ndims, min_dim);  CHECK_LE(ndims, max_dim);  // Verify that the data format is what we expect: float or double.  std::vector<hsize_t> dims(ndims);  H5T_class_t class_;  // 获取数据信息  status = H5LTget_dataset_info(      file_id, dataset_name_, dims.data(), &class_, NULL);  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name_;  switch (class_) {  case H5T_FLOAT:    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_FLOAT";    break;  case H5T_INTEGER:    LOG_FIRST_N(INFO, 1) << "Datatype class: H5T_INTEGER";    break;  case H5T_TIME:    LOG(FATAL) << "Unsupported datatype class: H5T_TIME";  case H5T_STRING:    LOG(FATAL) << "Unsupported datatype class: H5T_STRING";  case H5T_BITFIELD:    LOG(FATAL) << "Unsupported datatype class: H5T_BITFIELD";  case H5T_OPAQUE:    LOG(FATAL) << "Unsupported datatype class: H5T_OPAQUE";  case H5T_COMPOUND:    LOG(FATAL) << "Unsupported datatype class: H5T_COMPOUND";  case H5T_REFERENCE:    LOG(FATAL) << "Unsupported datatype class: H5T_REFERENCE";  case H5T_ENUM:    LOG(FATAL) << "Unsupported datatype class: H5T_ENUM";  case H5T_VLEN:    LOG(FATAL) << "Unsupported datatype class: H5T_VLEN";  case H5T_ARRAY:    LOG(FATAL) << "Unsupported datatype class: H5T_ARRAY";  default:    LOG(FATAL) << "Datatype class unknown";  }  // 设置blob的维度  vector<int> blob_dims(dims.size());  for (int i = 0; i < dims.size(); ++i) {    blob_dims[i] = dims[i];  }  blob->Reshape(blob_dims);}// float类型的获取数据维度和信息的包裹函数template <>void hdf5_load_nd_dataset<float>(hid_t file_id, const char* dataset_name_,        int min_dim, int max_dim, Blob<float>* blob) {  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);  herr_t status = H5LTread_dataset_float(    file_id, dataset_name_, blob->mutable_cpu_data());  CHECK_GE(status, 0) << "Failed to read float dataset " << dataset_name_;}// double类型的获取数据维度和信息的包裹函数template <>void hdf5_load_nd_dataset<double>(hid_t file_id, const char* dataset_name_,        int min_dim, int max_dim, Blob<double>* blob) {  hdf5_load_nd_dataset_helper(file_id, dataset_name_, min_dim, max_dim, blob);  herr_t status = H5LTread_dataset_double(    file_id, dataset_name_, blob->mutable_cpu_data());  CHECK_GE(status, 0) << "Failed to read double dataset " << dataset_name_;}// 存放float类型到hdf5文件template <>void hdf5_save_nd_dataset<float>(    const hid_t file_id, const string& dataset_name, const Blob<float>& blob,    bool write_diff) {  // blob信息放到dims  int num_axes = blob.num_axes();  hsize_t *dims = new hsize_t[num_axes];  for (int i = 0; i < num_axes; ++i) {    dims[i] = blob.shape(i);  }  // 获取数据指针  const float* data;  if (write_diff) {    data = blob.cpu_diff();  } else {    data = blob.cpu_data();  }  // 存放数据到hdf5  herr_t status = H5LTmake_dataset_float(      file_id, dataset_name.c_str(), num_axes, dims, data);  CHECK_GE(status, 0) << "Failed to make float dataset " << dataset_name;  delete[] dims;}// 存放double类型到hdf5文件template <>void hdf5_save_nd_dataset<double>(    hid_t file_id, const string& dataset_name, const Blob<double>& blob,    bool write_diff) {  int num_axes = blob.num_axes();  hsize_t *dims = new hsize_t[num_axes];  for (int i = 0; i < num_axes; ++i) {    dims[i] = blob.shape(i);  }  const double* data;  if (write_diff) {    data = blob.cpu_diff();  } else {    data = blob.cpu_data();  }  herr_t status = H5LTmake_dataset_double(      file_id, dataset_name.c_str(), num_axes, dims, data);  CHECK_GE(status, 0) << "Failed to make double dataset " << dataset_name;  delete[] dims;}// 读取string到字符串string hdf5_load_string(hid_t loc_id, const string& dataset_name) {  // Get size of dataset  size_t size;  H5T_class_t class_;  herr_t status = \    H5LTget_dataset_info(loc_id, dataset_name.c_str(), NULL, &class_, &size);  CHECK_GE(status, 0) << "Failed to get dataset info for " << dataset_name;  char *buf = new char[size];  status = H5LTread_dataset_string(loc_id, dataset_name.c_str(), buf);  CHECK_GE(status, 0)    << "Failed to load int dataset with name " << dataset_name;  string val(buf);  delete[] buf;  return val;}// 保存string到字符串void hdf5_save_string(hid_t loc_id, const string& dataset_name,                      const string& s) {  herr_t status = \    H5LTmake_dataset_string(loc_id, dataset_name.c_str(), s.c_str());  CHECK_GE(status, 0)    << "Failed to save string dataset with name " << dataset_name;}// 载入int类型int hdf5_load_int(hid_t loc_id, const string& dataset_name) {  int val;  herr_t status = H5LTread_dataset_int(loc_id, dataset_name.c_str(), &val);  CHECK_GE(status, 0)    << "Failed to load int dataset with name " << dataset_name;  return val;}// 存储int类型void hdf5_save_int(hid_t loc_id, const string& dataset_name, int i) {  hsize_t one = 1;  herr_t status = \    H5LTmake_dataset_int(loc_id, dataset_name.c_str(), 1, &one, &i);  CHECK_GE(status, 0)    << "Failed to save int dataset with name " << dataset_name;}// 获取链接数int hdf5_get_num_links(hid_t loc_id) {  H5G_info_t info;  herr_t status = H5Gget_info(loc_id, &info);  CHECK_GE(status, 0) << "Error while counting HDF5 links.";  return info.nlinks;}// 通过名字找到索引string hdf5_get_name_by_idx(hid_t loc_id, int idx) {  ssize_t str_size = H5Lget_name_by_idx(      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, NULL, 0, H5P_DEFAULT);  CHECK_GE(str_size, 0) << "Error retrieving HDF5 dataset at index " << idx;  char *c_str = new char[str_size+1];  ssize_t status = H5Lget_name_by_idx(      loc_id, ".", H5_INDEX_NAME, H5_ITER_NATIVE, idx, c_str, str_size+1,      H5P_DEFAULT);  CHECK_GE(status, 0) << "Error retrieving HDF5 dataset at index " << idx;  string result(c_str);  delete[] c_str;  return result;}}  // namespace caffe给出具体实现：/*TODO:- load file in a separate thread ("prefetch")- can be smarter about the memcpy call instead of doing it row-by-row  :: use util functions caffe_copy, and Blob->offset()  :: don't forget to update hdf5_daa_layer.cu accordingly- add ability to shuffle filenames if flag is set*/#include <fstream>  // NOLINT(readability/streams)#include <string>#include <vector>#include "hdf5.h"#include "hdf5_hl.h"#include "stdint.h"#include "caffe/data_layers.hpp"#include "caffe/layer.hpp"#include "caffe/util/hdf5.hpp"namespace caffe {template <typename Dtype>HDF5DataLayer<Dtype>::~HDF5DataLayer<Dtype>() { }// Load data and label from HDF5 filename into the class property blobs.// 读取HDF5文件数据到hdf_blobstemplate <typename Dtype>void HDF5DataLayer<Dtype>::LoadHDF5FileData(const char* filename) {  DLOG(INFO) << "Loading HDF5 file: " << filename;  // 打开文件  hid_t file_id = H5Fopen(filename, H5F_ACC_RDONLY, H5P_DEFAULT);  if (file_id < 0) {    LOG(FATAL) << "Failed opening HDF5 file: " << filename;  }  int top_size = this->layer_param_.top_size();  hdf_blobs_.resize(top_size);  const int MIN_DATA_DIM = 1;  const int MAX_DATA_DIM = INT_MAX;  for (int i = 0; i < top_size; ++i) {    hdf_blobs_[i] = shared_ptr<Blob<Dtype> >(new Blob<Dtype>());    // message LayerParameter {    // optional string name = 1; // the layer name    // optional string type = 2; // the layer type    // repeated string bottom = 3; // the name of each bottom blob    // repeated string top = 4; // the name of each top blob    hdf5_load_nd_dataset(file_id, this->layer_param_.top(i).c_str(),        MIN_DATA_DIM, MAX_DATA_DIM, hdf_blobs_[i].get());  }  herr_t status = H5Fclose(file_id);  CHECK_GE(status, 0) << "Failed to close HDF5 file: " << filename;  // MinTopBlobs==1 guarantees at least one top blob  CHECK_GE(hdf_blobs_[0]->num_axes(), 1) << "Input must have at least 1 axis.";  const int num = hdf_blobs_[0]->shape(0);  for (int i = 1; i < top_size; ++i) {    CHECK_EQ(hdf_blobs_[i]->shape(0), num);  }  // Default to identity permutation.  data_permutation_.clear();  data_permutation_.resize(hdf_blobs_[0]->shape(0));  for (int i = 0; i < hdf_blobs_[0]->shape(0); i++)    data_permutation_[i] = i;  // Shuffle if needed.  // 将数据索引映射表进行shuffle  if (this->layer_param_.hdf5_data_param().shuffle()) {    std::random_shuffle(data_permutation_.begin(), data_permutation_.end());    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0)               << " rows (shuffled)";  } else {    DLOG(INFO) << "Successully loaded " << hdf_blobs_[0]->shape(0) << " rows";  }}// 主要的功能就是读取HDF5文件，并且设置top blob的形状template <typename Dtype>void HDF5DataLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  // Refuse transformation parameters since HDF5 is totally generic.  CHECK(!this->layer_param_.has_transform_param()) <<      this->type() << " does not transform data.";  // Read the source to parse the filenames.  // 读取HDF列表文件  const string& source = this->layer_param_.hdf5_data_param().source();  LOG(INFO) << "Loading list of HDF5 filenames from: " << source;  hdf_filenames_.clear();  std::ifstream source_file(source.c_str());  if (source_file.is_open()) {    std::string line;    while (source_file >> line) {      hdf_filenames_.push_back(line);    }  } else {    LOG(FATAL) << "Failed to open source file: " << source;  }  source_file.close();  num_files_ = hdf_filenames_.size();  current_file_ = 0;  LOG(INFO) << "Number of HDF5 files: " << num_files_;  CHECK_GE(num_files_, 1) << "Must have at least 1 HDF5 filename listed in "    << source;  file_permutation_.clear();  file_permutation_.resize(num_files_);  // 文件名字是否shuffle  // Default to identity permutation.  for (int i = 0; i < num_files_; i++) {    file_permutation_[i] = i;  }  // Shuffle if needed.  if (this->layer_param_.hdf5_data_param().shuffle()) {    std::random_shuffle(file_permutation_.begin(), file_permutation_.end());  }  // Load the first HDF5 file and initialize the line counter.  // 从给定的文件名列表中的第一个文件名读取数据到hdf_blobs  LoadHDF5FileData(hdf_filenames_[file_permutation_[current_file_]].c_str());  // 设置行指针  current_row_ = 0;  // Reshape blobs.  // 根据读取的hdf_blobs形状改变top的形状  const int batch_size = this->layer_param_.hdf5_data_param().batch_size();  const int top_size = this->layer_param_.top_size();  vector<int> top_shape;  for (int i = 0; i < top_size; ++i) {    top_shape.resize(hdf_blobs_[i]->num_axes());    top_shape[0] = batch_size;    for (int j = 1; j < top_shape.size(); ++j) {      top_shape[j] = hdf_blobs_[i]->shape(j);    }    top[i]->Reshape(top_shape);  }}template <typename Dtype>void HDF5DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  const int batch_size = this->layer_param_.hdf5_data_param().batch_size();  for (int i = 0; i < batch_size; ++i, ++current_row_) {      // 因为SetUp里面已经读取了第一个文件的数据了    if (current_row_ == hdf_blobs_[0]->shape(0)) {      if (num_files_ > 1) {// 如果文件数目大于1        ++current_file_;        // 如果current_file是最后一个文件的索引编号则        if (current_file_ == num_files_) {          current_file_ = 0;// 重置          // 打乱文件索引，再来一遍          if (this->layer_param_.hdf5_data_param().shuffle()) {            std::random_shuffle(file_permutation_.begin(),                                file_permutation_.end());          }          DLOG(INFO) << "Looping around to first file.";        }        // 读取数据到hdf_blobs        LoadHDF5FileData(            hdf_filenames_[file_permutation_[current_file_]].c_str());      }// end of if (current_row_      current_row_ = 0;      // 打乱数据顺序索引      if (this->layer_param_.hdf5_data_param().shuffle())        std::random_shuffle(data_permutation_.begin(), data_permutation_.end());    }    // 复制数据到top    for (int j = 0; j < this->layer_param_.top_size(); ++j) {      int data_dim = top[j]->count() / top[j]->shape(0);      caffe_copy(data_dim,          &hdf_blobs_[j]->cpu_data()[data_permutation_[current_row_]            * data_dim], &top[j]->mutable_cpu_data()[i * data_dim]);    }  }}#ifdef CPU_ONLYSTUB_GPU_FORWARD(HDF5DataLayer, Forward);#endifINSTANTIATE_CLASS(HDF5DataLayer);REGISTER_LAYER_CLASS(HDF5Data);}  // namespace caffe

（6）HDF5OutputLayer类的定义以及实现如下：

HDF5输出层主要就是将传递过来的数据存储到HDF5文件，并没有向前传播数据啥的，也没有反传，仅仅是将前一层传输过来的bottom存储到文件。

HDF5输出层的定义：

/** * @brief Write blobs to disk as HDF5 files. * * TODO(dox): thorough documentation for Forward and proto params. * 将数据写入到HDF5文件 */template <typename Dtype>class HDF5OutputLayer : public Layer<Dtype> { public:  explicit HDF5OutputLayer(const LayerParameter& param)      : Layer<Dtype>(param), file_opened_(false) {}  virtual ~HDF5OutputLayer();  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  // Data layers should be shared by multiple solvers in parallel  virtual inline bool ShareInParallel() const { return true; }  // Data layers have no bottoms, so reshaping is trivial.  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {}  virtual inline const char* type() const { return "HDF5Output"; }  // TODO: no limit on the number of blobs  virtual inline int ExactNumBottomBlobs() const { return 2; }  virtual inline int ExactNumTopBlobs() const { return 0; }  inline std::string file_name() const { return file_name_; } protected:  // HDF5输出层不前向传也不反向传，只是将前一层传递过来的数据写入HDF5文件  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);  // 将bottom的数据存储到文件  virtual void SaveBlobs();  bool file_opened_;  std::string file_name_;  hid_t file_id_;  Blob<Dtype> data_blob_;  Blob<Dtype> label_blob_;};

HDF5输出层的实现如下：

#include <vector>#include "hdf5.h"#include "hdf5_hl.h"#include "caffe/blob.hpp"#include "caffe/common.hpp"#include "caffe/layer.hpp"#include "caffe/util/hdf5.hpp"#include "caffe/vision_layers.hpp"namespace caffe {template <typename Dtype>void HDF5OutputLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,    const vector<Blob<Dtype>*>& top) {  // 参数文件中的文件名  file_name_ = this->layer_param_.hdf5_output_param().file_name();  // 打开文件  file_id_ = H5Fcreate(file_name_.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,                       H5P_DEFAULT);  CHECK_GE(file_id_, 0) << "Failed to open HDF5 file" << file_name_;  file_opened_ = true;// 设置文件打开标志}template <typename Dtype>HDF5OutputLayer<Dtype>::~HDF5OutputLayer<Dtype>() {  if (file_opened_) {    herr_t status = H5Fclose(file_id_);    CHECK_GE(status, 0) << "Failed to close HDF5 file " << file_name_;  }}// 将blob存放到hdf5文件// 数据和类标template <typename Dtype>void HDF5OutputLayer<Dtype>::SaveBlobs() {  // TODO: no limit on the number of blobs  LOG(INFO) << "Saving HDF5 file " << file_name_;  CHECK_EQ(data_blob_.num(), label_blob_.num()) <<      "data blob and label blob must have the same batch size";  hdf5_save_nd_dataset(file_id_, HDF5_DATA_DATASET_NAME, data_blob_);  hdf5_save_nd_dataset(file_id_, HDF5_DATA_LABEL_NAME, label_blob_);  LOG(INFO) << "Successfully saved " << data_blob_.num() << " rows";}// 实际上就是从bottom将输入过来的数据存放到hdf5文件template <typename Dtype>void HDF5OutputLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  CHECK_GE(bottom.size(), 2);  CHECK_EQ(bottom[0]->num(), bottom[1]->num());  // 改变data_blob_的形状以及label_blob_的形状  data_blob_.Reshape(bottom[0]->num(), bottom[0]->channels(),                     bottom[0]->height(), bottom[0]->width());  label_blob_.Reshape(bottom[1]->num(), bottom[1]->channels(),                     bottom[1]->height(), bottom[1]->width());  const int data_datum_dim = bottom[0]->count() / bottom[0]->num();  const int label_datum_dim = bottom[1]->count() / bottom[1]->num();  // 从bottom[0]和[1]复制到data_blob_和label_blob_  for (int i = 0; i < bottom[0]->num(); ++i) {    caffe_copy(data_datum_dim, &bottom[0]->cpu_data()[i * data_datum_dim],        &data_blob_.mutable_cpu_data()[i * data_datum_dim]);    caffe_copy(label_datum_dim, &bottom[1]->cpu_data()[i * label_datum_dim],        &label_blob_.mutable_cpu_data()[i * label_datum_dim]);  }  // 存放到文件  SaveBlobs();}// 不反传template <typename Dtype>void HDF5OutputLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {  return;}#ifdef CPU_ONLYSTUB_GPU(HDF5OutputLayer);#endifINSTANTIATE_CLASS(HDF5OutputLayer);REGISTER_LAYER_CLASS(HDF5Output);}  // namespace caffe

（7）ImageDataLayer类的定义以及实现如下：

该层主要的功能是，从参数中给定的列表文件读取图像列表以及类标，读取图像的时候会进行预处理，然后前向传。

首先给出该层的参数的定义：

message ImageDataParameter {  // Specify the data source.  // 列表文件包含图像的路径和对应的类标，以空格隔开  optional string source = 1;  // Specify the batch size.  // 批大小  optional uint32 batch_size = 4 [default = 1];  // The rand_skip variable is for the data layer to skip a few data points  // to avoid all asynchronous sgd clients to start at the same point. The skip  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not  // be larger than the number of keys in the database.  // 随机调过一些数据  optional uint32 rand_skip = 7 [default = 0];  // 是否需要打乱数据顺序  // Whether or not ImageLayer should shuffle the list of files at every epoch.  optional bool shuffle = 8 [default = false];  // It will also resize images if new_height or new_width are not zero.  // 将图像resize到新的高度的宽度  optional uint32 new_height = 9 [default = 0];  optional uint32 new_width = 10 [default = 0];  // Specify if the images are color or gray  // 图像是否是彩色的  optional bool is_color = 11 [default = true];  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do  // simple scaling and subtracting the data mean, if provided. Note that the  // mean subtraction is always carried out before scaling.  // 是否需要对图像进行缩放  optional float scale = 2 [default = 1];  // 均值文件  optional string mean_file = 3;  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly  // crop an image.  // crop的大小  optional uint32 crop_size = 5 [default = 0];  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror  // data.  // 是否需要对图像进行镜像，所谓镜像就是左边复制到右边  optional bool mirror = 6 [default = false];  // 图像的根目录  optional string root_folder = 12 [default = ""];}

首先给出类的定义：

/** * @brief Provides data to the Net from image files. * * TODO(dox): thorough documentation for Forward and proto params. * 从图像文件中读取数据，这个应该比较常用 * 从一个列表文件读取图像的路径和类标，列表文件的路径在层参数的配置文件中指定 */template <typename Dtype>class ImageDataLayer : public BasePrefetchingDataLayer<Dtype> { public:  explicit ImageDataLayer(const LayerParameter& param)      : BasePrefetchingDataLayer<Dtype>(param) {}  virtual ~ImageDataLayer();  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual inline const char* type() const { return "ImageData"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int ExactNumTopBlobs() const { return 2; } protected:  shared_ptr<Caffe::RNG> prefetch_rng_;  // 对图像索引进行打乱  virtual void ShuffleImages();  virtual void load_batch(Batch<Dtype>* batch);  // 图像路径和类标的vector  vector<std::pair<std::string, int> > lines_;  // 随机跳过的图像的个数，也就是调过之后的一开始的图像的id  int lines_id_;};

下面给出具体的实现细节：

#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#include <fstream>  // NOLINT(readability/streams)#include <iostream>  // NOLINT(readability/streams)#include <string>#include <utility>#include <vector>#include "caffe/data_layers.hpp"#include "caffe/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>ImageDataLayer<Dtype>::~ImageDataLayer<Dtype>() {  this->StopInternalThread();}template <typename Dtype>void ImageDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  // 根据参数文件设置参数  // 图像的高度、宽度、是否彩色图像、图像目录  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();  // 当前只支持读取高度和宽度同样大小的图像  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.";  // Read the file with filenames and labels  // 读取存放图像文件名和类标的列表文件  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;  // lines_存放文件名和类标的pair  while (infile >> filename >> label) {    lines_.push_back(std::make_pair(filename, label));  }  // 是否需要打乱文件的顺序  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.";  // 随机跳过的图像，调过的图像个数在[0, rand_skip-1]之间  lines_id_ = 0;  // Check if we would need to randomly skip a few data points  // 如果参数中的rand_skip大于1，则随机跳过[0,rand_skip-1]个图片  //  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;  }  // Read an image, and use it to initialize the top blob.  // 读取文件名到Mat  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;  // Use data_transformer to infer the expected blob shape from a cv_image.  // 对数据的形状进行推断  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);  // 设置transformed_data_的形状  this->transformed_data_.Reshape(top_shape);  // Reshape prefetch_data and top[0] according to the batch_size.  // 设置batch_size  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;  // 设置预取数组中数据的形状  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {    this->prefetch_[i].data_.Reshape(top_shape);  }  // 设置输出的数据的形状  top[0]->Reshape(top_shape);  LOG(INFO) << "output data size: " << top[0]->num() << ","      << top[0]->channels() << "," << top[0]->height() << ","      << top[0]->width();  // label  // 设置输出的类标的形状  vector<int> label_shape(1, batch_size);  top[1]->Reshape(label_shape);  // 设置预取数组中类标的形状  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {    this->prefetch_[i].label_.Reshape(label_shape);  }}// 产生打乱图像顺序的数组template <typename Dtype>void ImageDataLayer<Dtype>::ShuffleImages() {  caffe::rng_t* prefetch_rng =      static_cast<caffe::rng_t*>(prefetch_rng_->generator());  shuffle(lines_.begin(), lines_.end(), prefetch_rng);}// This function is called on prefetch thread// 该函数会被内部的线程调用template <typename Dtype>void ImageDataLayer<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());  // 获取层参数，具体参见层参数的定义的解释  ImageDataParameter image_data_param = this->layer_param_.image_data_param();  const int batch_size = image_data_param.batch_size();  const int new_height = image_data_param.new_height();  const int new_width = image_data_param.new_width();  const bool is_color = image_data_param.is_color();  string root_folder = image_data_param.root_folder();  // Reshape according to the first image of each batch  // on single input batches allows for inputs of varying dimension.  // 读取跳过之后的第一幅图像，然后根据该图像设置相撞  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;  // Use data_transformer to infer the expected blob shape from a cv_img.  // 推断图像形状  vector<int> top_shape = this->data_transformer_->InferBlobShape(cv_img);  // 设置transformed_data_形状  this->transformed_data_.Reshape(top_shape);  // Reshape batch according to the batch_size.  // 设置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  // 读取一批图像，并进行预处理  const int lines_size = lines_.size();  for (int item_id = 0; item_id < batch_size; ++item_id) {    // get a blob    timer.Start();    CHECK_GT(lines_size, lines_id_);    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;    read_time += timer.MicroSeconds();    timer.Start();    // Apply transformations (mirror, crop...) to the image    // 进行预处理    // 根据图像的批次获得图像数据的偏移量    int offset = batch->data_.offset(item_id);    // 设置图像数据的指针到transformed_data_    this->transformed_data_.set_cpu_data(prefetch_data + offset);    // 进行预处理    this->data_transformer_->Transform(cv_img, &(this->transformed_data_));    trans_time += timer.MicroSeconds();//统计预处理时间    // 复制类标到prefetch_label    prefetch_label[item_id] = lines_[lines_id_].second;    // go to the next iter    lines_id_++;    // 是否是图像目录中的最后一个图像    if (lines_id_ >= lines_size) {      // We have reached the end. Restart from the first.      DLOG(INFO) << "Restarting data prefetching from start.";      lines_id_ = 0;      // 打乱图像索引的顺序      if (this->layer_param_.image_data_param().shuffle()) {        ShuffleImages();      }    }  }  batch_timer.Stop();  DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";  DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";  // 预处理时间  DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";}INSTANTIATE_CLASS(ImageDataLayer);REGISTER_LAYER_CLASS(ImageData);}  // namespace caffe#endif  // USE_OPENCV

（8）MemoryDataLayer 类的定义以及实现如下：

该类主要就是对于读取好的Datum或者OpenCV读取的Mat的Vector进行预处理（图像的crop、scale等），然后前传。

首先给出该类的定义

/** * @brief Provides data to the Net from memory. * 从内存中读取数据，这里指已经从数据文件或者图像文件中读取到了数据，然后输入到该层 * TODO(dox): thorough documentation for Forward and proto params. */template <typename Dtype>class MemoryDataLayer : public BaseDataLayer<Dtype> { public:  explicit MemoryDataLayer(const LayerParameter& param)      : BaseDataLayer<Dtype>(param), has_new_data_(false) {}  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual inline const char* type() const { return "MemoryData"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int ExactNumTopBlobs() const { return 2; }  // 将内存中的数据加入added_data_和added_label_(数据和类标)  virtual void AddDatumVector(const vector<Datum>& datum_vector);#ifdef USE_OPENCV  // 如果有opencv则将opencv读取到的Mat,并且将labels加入added_data_和added_label_(数据和类标)  virtual void AddMatVector(const vector<cv::Mat>& mat_vector,      const vector<int>& labels);#endif  // USE_OPENCV  // Reset should accept const pointers, but can't, because the memory  //  will be given to Blob, which is mutable  // Reset函数实际上是将data、label、以及batchsize(n)设置到内部的变量里面去  void Reset(Dtype* data, Dtype* label, int n);  void set_batch_size(int new_size);  int batch_size() { return batch_size_; }  int channels() { return channels_; }  int height() { return height_; }  int width() { return width_; } protected:  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  int batch_size_, channels_, height_, width_, size_;  Dtype* data_;  Dtype* labels_;  // batch_size  int n_;  size_t pos_;  // 内部的数据和类标  Blob<Dtype> added_data_;  Blob<Dtype> added_label_;  // 是否有新的数据  bool has_new_data_;};

下面给出具体的实现细节：

#ifdef USE_OPENCV#include <opencv2/core/core.hpp>#endif  // USE_OPENCV#include <vector>#include "caffe/data_layers.hpp"#include "caffe/layer.hpp"#include "caffe/util/io.hpp"namespace caffe {template <typename Dtype>void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,     const vector<Blob<Dtype>*>& top) {  // 从参数文件获取参数  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";  // 设置top的形状  vector<int> label_shape(1, batch_size_);  top[0]->Reshape(batch_size_, channels_, height_, width_);  top[1]->Reshape(label_shape);  // 设置内部变量added_data_和added_label_的形状  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();}// 将Datum的vector放入到added_data_和added_label_// 并进行预处理template <typename Dtype>void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {  CHECK(!has_new_data_) <<      "Can't add data until current data has been consumed.";  size_t num = datum_vector.size();  CHECK_GT(num, 0) << "There is no datum to add.";  CHECK_EQ(num % batch_size_, 0) <<      "The added data must be a multiple of the batch size.";  // 改变形状  added_data_.Reshape(num, channels_, height_, width_);  added_label_.Reshape(num, 1, 1, 1);  // Apply data transformations (mirror, scale, crop...)  // 对数据进行预处理  this->data_transformer_->Transform(datum_vector, &added_data_);  // Copy Labels  // 复制类标到top_label  Dtype* top_label = added_label_.mutable_cpu_data();  for (int item_id = 0; item_id < num; ++item_id) {    top_label[item_id] = datum_vector[item_id].label();  }  // num_images == batch_size_  Dtype* top_data = added_data_.mutable_cpu_data();  // 将数据、类标以及数据个数设置到该类的内部变量  Reset(top_data, top_label, num);  // 设置标记为true  has_new_data_ = true;}// 如果定义OPENCV，则对数据进行处理存放到added_data_和added_label_#ifdef USE_OPENCVtemplate <typename Dtype>void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,    const vector<int>& labels) {  size_t num = mat_vector.size();  CHECK(!has_new_data_) <<      "Can't add mat until current data has been consumed.";  CHECK_GT(num, 0) << "There is no mat to add";  CHECK_EQ(num % batch_size_, 0) <<      "The added data must be a multiple of the batch size.";  added_data_.Reshape(num, channels_, height_, width_);  added_label_.Reshape(num, 1, 1, 1);  // Apply data transformations (mirror, scale, crop...)  // 预处理  this->data_transformer_->Transform(mat_vector, &added_data_);  // Copy Labels  Dtype* top_label = added_label_.mutable_cpu_data();  for (int item_id = 0; item_id < num; ++item_id) {    top_label[item_id] = labels[item_id];  }  // num_images == batch_size_  Dtype* top_data = added_data_.mutable_cpu_data();  Reset(top_data, top_label, num);  has_new_data_ = true;}#endif  // USE_OPENCV// 将数据和类标设置到内部的变量// data_、labels_、n_// 并且设置位置pos_=0template <typename Dtype>void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {  CHECK(data);  CHECK(labels);  CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";  // Warn with transformation parameters since a memory array is meant to  // be generic and no transformations are done with Reset().  if (this->layer_param_.has_transform_param()) {    LOG(WARNING) << this->type() << " does not transform array data on Reset()";  }  data_ = data;  labels_ = labels;  n_ = n;// batch_size  pos_ = 0;}// 设置内内部变量added_data_和added_label_的批数template <typename Dtype>void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {  CHECK(!has_new_data_) <<      "Can't change batch_size until current data has been consumed.";  batch_size_ = new_size;  added_data_.Reshape(batch_size_, channels_, height_, width_);  added_label_.Reshape(batch_size_, 1, 1, 1);}// 将内部变量added_data_和added_label_复制到top传递给下一层template <typename Dtype>void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";  // 这里直接使用内部变量将数据复制到top[0]、将类标复制到top[1]  top[0]->Reshape(batch_size_, channels_, height_, width_);  top[1]->Reshape(batch_size_, 1, 1, 1);  top[0]->set_cpu_data(data_ + pos_ * size_);  top[1]->set_cpu_data(labels_ + pos_);  pos_ = (pos_ + batch_size_) % n_;  if (pos_ == 0)    has_new_data_ = false;// 传过一次之后，就没有新数据啦}INSTANTIATE_CLASS(MemoryDataLayer);REGISTER_LAYER_CLASS(MemoryData);}  // namespace caffe

（9）WindowDataLayer类的定义以及实现如下：

该类主要就是对于读取好的Datum或者OpenCV读取的Mat的Vector进行预处理（图像的crop、scale等），然后前传。

首先给出窗口数据文件的格式，便于自己训练

窗口文件的格式如下:

# 图像索引(举例:# 1就表示第一个图像,注意#号与数字之间有空格)

图像的路径

图像通道数

图像高度

图像宽度

窗口数目

类标,与前景目标的重叠率,x1,y1,x2,y2

注:x1,y1,x2,y2是窗口的左上和右下的坐标

为了理解的更清楚我这里举个例子：

# 1 /1.jpg 3 720 480 100 1 1 0 0 100 100 2 30 100 1500 1500

上述的例子表示一个编号为1的图像相对路径为/1.jpg，通道为3，高度为720

宽度为480，窗口数目为100，类标为1，与前景目标的重叠率为0.8，类标为1窗口的左上坐标为(0,0),右下坐标为(100,100)

类标为2的窗口的左上角坐标为(30,100)，右下角的坐标为(1500,1500)。有多少窗口后面就这么继续写下去

接下来给出该层的参数：

message WindowDataParameter {  // Specify the data source.  // 装有窗口数据的列表文件  optional string source = 1;  // For data pre-processing, we can do simple scaling and subtracting the  // data mean, if provided. Note that the mean subtraction is always carried  // out before scaling.  // 是否需要缩放图像中的像素值，注意哈这不是缩放图像的大小，是拿图像的像素值乘以这个  optional float scale = 2 [default = 1];  // 平均值文件路径  optional string mean_file = 3;  // Specify the batch size.  // 批大小  optional uint32 batch_size = 4;  // Specify if we would like to randomly crop an image.  // 随机crop的图像块的大小  optional uint32 crop_size = 5 [default = 0];  // Specify if we want to randomly mirror data.  // 是否随机镜像图像  optional bool mirror = 6 [default = false];  // Foreground (object) overlap threshold  // 前景重叠阈值  optional float fg_threshold = 7 [default = 0.5];  // Background (non-object) overlap threshold  // 背景重叠阈值  optional float bg_threshold = 8 [default = 0.5];  // Fraction of batch that should be foreground objects  // 每一批中有多少比例应该是前景(也就是是你要检测的物体)  optional float fg_fraction = 9 [default = 0.25];  // Amount of contextual padding to add around a window  // (used only by the window_data_layer)  // 是否需要在窗口周围padding  optional uint32 context_pad = 10 [default = 0];  // Mode for cropping out a detection window  // warp: cropped window is warped to a fixed size and aspect ratio  // square: the tightest square around the window is cropped  // crop的模式,square还是warp  optional string crop_mode = 11 [default = "warp"];  // cache_images: will load all images in memory for faster access  // 是否将文件缓冲到内存  optional bool cache_images = 12 [default = false];  // append root_folder to locate images  // 图像文件根目录  optional string root_folder = 13 [default = ""];}

我们给出该类的定义：

/** * @brief Provides data to the Net from windows of images files, specified *        by a window data file. *  从图像文件的窗口获取数据，需要指定窗口数据文件 * TODO(dox): thorough documentation for Forward and proto params. */template <typename Dtype>class WindowDataLayer : public BasePrefetchingDataLayer<Dtype> { public:  explicit WindowDataLayer(const LayerParameter& param)      : BasePrefetchingDataLayer<Dtype>(param) {}  virtual ~WindowDataLayer();  virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);  virtual inline const char* type() const { return "WindowData"; }  virtual inline int ExactNumBottomBlobs() const { return 0; }  virtual inline int ExactNumTopBlobs() const { return 2; } protected:  virtual unsigned int PrefetchRand();  virtual void load_batch(Batch<Dtype>* batch);  shared_ptr<Caffe::RNG> prefetch_rng_;  vector<std::pair<std::string, vector<int> > > image_database_;  // 窗口类中所使用的窗口数据的枚举  // 就是定义个vector<float>，然后里面按顺序存放下面这些类型的数据  enum WindowField { IMAGE_INDEX, LABEL, OVERLAP, X1, Y1, X2, Y2, NUM };  vector<vector<float> > fg_windows_;  vector<vector<float> > bg_windows_;  Blob<Dtype> data_mean_;  vector<Dtype> mean_values_;  bool has_mean_file_;  bool has_mean_values_;  bool cache_images_;  vector<std::pair<std::string, Datum > > image_database_cache_;};

然后给出该类的实现

#ifdef USE_OPENCV#include <opencv2/highgui/highgui_c.h>#include <stdint.h>#include <algorithm>#include <map>#include <string>#include <utility>#include <vector>#include "opencv2/core/core.hpp"#include "opencv2/highgui/highgui.hpp"#include "opencv2/imgproc/imgproc.hpp"#include "caffe/common.hpp"#include "caffe/data_layers.hpp"#include "caffe/layer.hpp"#include "caffe/util/benchmark.hpp"#include "caffe/util/io.hpp"#include "caffe/util/math_functions.hpp"#include "caffe/util/rng.hpp"// caffe.proto > LayerParameter > WindowDataParameter//   'source' field specifies the window_file//   'crop_size' indicates the desired warped sizenamespace caffe {template <typename Dtype>WindowDataLayer<Dtype>::~WindowDataLayer<Dtype>() {  this->StopInternalThread();}// 读取窗口数据文件的信息,并设置各个数据结构的形状template <typename Dtype>void WindowDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  // LayerSetUp runs through the window_file and creates two structures  // that hold windows: one for foreground (object) windows and one  // for background (non-object) windows. We use an overlap threshold  // to decide which is which.  // window_file format  // repeated:  //    # image_index  //    img_path (abs path)  //    channels  //    height  //    width  //    num_windows  //    class_index overlap x1 y1 x2 y2  // 窗口文件的格式如下:  // # 图像索引(举例:# 1就表示第一个图像,注意#号与数字之间有空格)  // 图像的路径  // 图像通道数  // 图像高度  // 图像宽度  // 窗口数目  // 类标,overlap,x1,y1,x2,y2  // 注:x1,y1,x2,y2是窗口的左上和右下的坐标  // 我这里举个例子  // # 1 /1.jpg 3 720 480 100 1 1 0 0 100 100  // 上述的例子即使表示一个编号为1的图像相对路径为/1.jpg，通道为3，高度为720  // 宽度为480，窗口数目为100，类标为1，overlap为1，窗口的左上坐标为(0,0),右下坐标为(100,100)  LOG(INFO) << "Window data layer:" << std::endl      << "  foreground (object) overlap threshold: "      << this->layer_param_.window_data_param().fg_threshold() << std::endl      << "  background (non-object) overlap threshold: "      << this->layer_param_.window_data_param().bg_threshold() << std::endl      << "  foreground sampling fraction: "      << this->layer_param_.window_data_param().fg_fraction() << std::endl      << "  cache_images: "      << this->layer_param_.window_data_param().cache_images() << std::endl      << "  root_folder: "      << this->layer_param_.window_data_param().root_folder();  cache_images_ = this->layer_param_.window_data_param().cache_images();  string root_folder = this->layer_param_.window_data_param().root_folder();  // 根据参数文件中是否需要进行左右mirror，或者是否进行crop，  // 来判断是否需要初始化随机数种子  const bool prefetch_needs_rand =      this->transform_param_.mirror() ||      this->transform_param_.crop_size();  if (prefetch_needs_rand) {    const unsigned int prefetch_rng_seed = caffe_rng_rand();    prefetch_rng_.reset(new Caffe::RNG(prefetch_rng_seed));  } else {    prefetch_rng_.reset();  }  // 打开窗口文件  std::ifstream infile(this->layer_param_.window_data_param().source().c_str());  CHECK(infile.good()) << "Failed to open window file "      << this->layer_param_.window_data_param().source() << std::endl;  // 这个是类标与类标出现的次数之间的映射  // 这里称之为类标直方图  map<int, int> label_hist;  label_hist.insert(std::make_pair(0, 0));  string hashtag;  int image_index, channels;  // 先从窗口文件中读取一个图像索引测试一下是否为空  if (!(infile >> hashtag >> image_index)) {    LOG(FATAL) << "Window file is empty";  }  do {      // 检查是否# 开头    CHECK_EQ(hashtag, "#");    // read image path    string image_path;    // 接下来读取图像的相对路径    // 将该路径与根目录路径拼接    infile >> image_path;    image_path = root_folder + image_path;    // read image dimensions    vector<int> image_size(3);    // 读取图像的维度信息，分别为channel，height , width    infile >> image_size[0] >> image_size[1] >> image_size[2];    channels = image_size[0];    // 将图像路径和图像大小压入到image_database_中    image_database_.push_back(std::make_pair(image_path, image_size));    // 如果需要缓存图像到内存的话，则用image_database_cache_进行存储    if (cache_images_) {      Datum datum;      // 将图像数据读取到Datum这个结构      if (!ReadFileToDatum(image_path, &datum)) {        LOG(ERROR) << "Could not open or find file " << image_path;        return;      }      // 将Datum结构的图像缓存到到image_database_cache_      image_database_cache_.push_back(std::make_pair(image_path, datum));    }    // read each box    int num_windows;    // 读取窗口个数    infile >> num_windows;    // 从参数文件获取前景和背景阈值    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;      // 读取  类标,与前景目标的重叠率,x1,y1,x2,y2      infile >> label >> overlap >> x1 >> y1 >> x2 >> y2;      // 按照顺序放在window这个数据结构里头      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      // 下面是将窗口的前景和背景都装入到fg_windows_和bg_windows_中去      // 如果重叠的比例大于前景阈值，那么就认为是前景      if (overlap >= fg_threshold) {        int label = window[WindowDataLayer::LABEL];        // 类标必须大于0，因为重叠区域已经大于前景阈值了        // 此时如果类标不大于0，表明数据有误!        CHECK_GT(label, 0);        fg_windows_.push_back(window);        // 该类的直方图+1        label_hist.insert(std::make_pair(label, 0));        label_hist[label]++;      } else if (overlap < bg_threshold) {      // 如果重叠阈值小于背景阈值则认为是背景        // background window, force label and overlap to 0        window[WindowDataLayer::LABEL] = 0;        window[WindowDataLayer::OVERLAP] = 0;        bg_windows_.push_back(window);        // 0类的直方图(也就是背景的直方图)+1        label_hist[0]++;      }    }    // 每处理100个就显示一瞎    if (image_index % 100 == 0) {      LOG(INFO) << "num: " << image_index << " "          << image_path << " "          << image_size[0] << " "          << image_size[1] << " "          << image_size[2] << " "          << "windows to process: " << num_windows;    }  } while (infile >> hashtag >> image_index);  // 读取完毕后输出图像的个数  LOG(INFO) << "Number of images: " << image_index+1;  // 输出统计的每个类别的个数  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();  // image  // 获取crop_size  const int crop_size = this->transform_param_.crop_size();  CHECK_GT(crop_size, 0);  // 获取batch_size  const int batch_size = this->layer_param_.window_data_param().batch_size();  // 将top[0]设置为batch_size,channels, crop_size, crop_size大小的  top[0]->Reshape(batch_size, channels, crop_size, crop_size);  // 将prefetch_中的数据形状也这么设置  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();  // label  // 将top[1]设置为类标大小  vector<int> label_shape(1, batch_size);  top[1]->Reshape(label_shape);  // 将prefetch_中的类标形状也这么设置  for (int i = 0; i < this->PREFETCH_COUNT; ++i) {    this->prefetch_[i].label_.Reshape(label_shape);  }  // data mean  // 是否有均值文件或者有均值  has_mean_file_ = this->transform_param_.has_mean_file();  has_mean_values_ = this->transform_param_.mean_value_size() > 0;  if (has_mean_file_) {// 有均值文件就读    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_) {// 有均值就直接从参数中获取    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));    }    // 检查均值是不是等于1，或者等于图像的通道数    // 也就是要么所有通道都使用同一个均值    // 要么每个通道用一个均值    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]);      }    }  }}// 随机数生成器进行初始化并生成随机数template <typename Dtype>unsigned int WindowDataLayer<Dtype>::PrefetchRand() {  CHECK(prefetch_rng_);  caffe::rng_t* prefetch_rng =      static_cast<caffe::rng_t*>(prefetch_rng_->generator());  return (*prefetch_rng)();}// 因为继承BasePrefetchingDataLayer所以要实现load_batch// 以供线程调用// This function is called on prefetch threadtemplate <typename Dtype>void WindowDataLayer<Dtype>::load_batch(Batch<Dtype>* batch) {  // At each iteration, sample N windows where N*p are foreground (object)  // windows and N*(1-p) are background (non-object) windows  CPUTimer batch_timer;  batch_timer.Start();  double read_time = 0;  double trans_time = 0;  CPUTimer timer;  // top数据和类标  Dtype* top_data = batch->data_.mutable_cpu_data();  Dtype* top_label = batch->label_.mutable_cpu_data();  // 缩放尺度  const Dtype scale = this->layer_param_.window_data_param().scale();  // batch_size  const int batch_size = this->layer_param_.window_data_param().batch_size();  // 上下文填充  const int context_pad = this->layer_param_.window_data_param().context_pad();  // crop_size  const int crop_size = this->transform_param_.crop_size();  // 是否镜像  const bool mirror = this->transform_param_.mirror();  // 前景比例  const float fg_fraction =      this->layer_param_.window_data_param().fg_fraction();  Dtype* mean = NULL;  int mean_off = 0;  int mean_width = 0;  int mean_height = 0;  // 如果有平均值文件则  if (this->has_mean_file_) {    mean = this->data_mean_.mutable_cpu_data();    // 经过crop之后的平均值图像的中心    mean_off = (this->data_mean_.width() - crop_size) / 2;    mean_width = this->data_mean_.width();    mean_height = this->data_mean_.height();  }  cv::Size cv_crop_size(crop_size, crop_size);  // 获取crop的模式，是warp还是square  const string& crop_mode = this->layer_param_.window_data_param().crop_mode();  bool use_square = (crop_mode == "square") ? true : false;  // zero out batch  caffe_set(batch->data_.count(), Dtype(0), top_data);  // 根据前景比例获得前景图像的数目  const int num_fg = static_cast<int>(static_cast<float>(batch_size)      * fg_fraction);  // 样本数量，是前景还是背景?[0]是背景[1]是前景  const int num_samples[2] = { batch_size - num_fg, num_fg };  int item_id = 0;  // sample from bg set then fg set  // 先对背景进行采样  // 再对前景进行采样  for (int is_fg = 0; is_fg < 2; ++is_fg) {    for (int dummy = 0; dummy < num_samples[is_fg]; ++dummy) {      // sample a window      timer.Start();      // 生成一个随机数      const unsigned int rand_index = PrefetchRand();      // fg_windows_和bg_windows_存储的是对应的窗口信息      // 在SetUp中读取的窗口数据文件的时候获得的      // 从该图像的若干窗口中去随机选择一个窗口      vector<float> window = (is_fg) ?          fg_windows_[rand_index % fg_windows_.size()] :          bg_windows_[rand_index % bg_windows_.size()];      // 随机选择是否需要镜像      bool do_mirror = mirror && PrefetchRand() % 2;      // load the image containing the window      // 载入图像的路径以及类标      pair<std::string, vector<int> > image =          image_database_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];      // 读取图像      cv::Mat cv_img;      if (this->cache_images_) {          // 如果图像缓冲到内存则获得对应图像的Datum        pair<std::string, Datum> image_cached =          image_database_cache_[window[WindowDataLayer<Dtype>::IMAGE_INDEX]];        // 将图像的Datum解码为OpenCV的Mat        cv_img = DecodeDatumToCVMat(image_cached.second, true);      } else {        // 否则直接读取        cv_img = cv::imread(image.first, CV_LOAD_IMAGE_COLOR);        if (!cv_img.data) {          LOG(ERROR) << "Could not open or find file " << image.first;          return;        }      }      read_time += timer.MicroSeconds();      timer.Start();      const int channels = cv_img.channels();      // crop window out of image and warp it      // 窗口坐标      int x1 = window[WindowDataLayer<Dtype>::X1];      int y1 = window[WindowDataLayer<Dtype>::Y1];      int x2 = window[WindowDataLayer<Dtype>::X2];      int y2 = window[WindowDataLayer<Dtype>::Y2];      int pad_w = 0;      int pad_h = 0;      // context_pad也是个大小，具体什么含义，我没有具体研究      // 毕竟不是搞检测的      // context_scale = crop_size / (crop_size - 2*context_pad)      if (context_pad > 0 || use_square) {        // scale factor by which to expand the original region        // such that after warping the expanded region to crop_size x crop_size        // there's exactly context_pad amount of padding on each side        Dtype context_scale = static_cast<Dtype>(crop_size) /            static_cast<Dtype>(crop_size - 2*context_pad);        // compute the expanded region        // 高度的一半        Dtype half_height = static_cast<Dtype>(y2-y1+1)/2.0;        // 宽度的一半        Dtype half_width = static_cast<Dtype>(x2-x1+1)/2.0;        // x中心        Dtype center_x = static_cast<Dtype>(x1) + half_width;        // y中心        Dtype center_y = static_cast<Dtype>(y1) + half_height;        if (use_square) {// 如果使用正方形形状则将较大的那个赋值给小的          if (half_height > half_width) {            half_width = half_height;          } else {            half_height = half_width;          }        }        // 获取经过处理之后的x1,y1,x2,y2        x1 = static_cast<int>(round(center_x - half_width*context_scale));        x2 = static_cast<int>(round(center_x + half_width*context_scale));        y1 = static_cast<int>(round(center_y - half_height*context_scale));        y2 = static_cast<int>(round(center_y + half_height*context_scale));        // the expanded region may go outside of the image        // so we compute the clipped (expanded) region and keep track of        // the extent beyond the image        // 经过处理之后的窗口如果不在图像内部是有问题的        // 这里对窗口的坐标进行处理        // 使得窗口的左上角不超过图像的左上角        // 窗口的右下角不超过图像的右下角        // 所以这里叫clip bounds嘛        int unclipped_height = y2-y1+1;        int unclipped_width = x2-x1+1;        int pad_x1 = std::max(0, -x1);        int pad_y1 = std::max(0, -y1);        int pad_x2 = std::max(0, x2 - cv_img.cols + 1);        int pad_y2 = std::max(0, y2 - cv_img.rows + 1);        // clip bounds        x1 = x1 + pad_x1;        x2 = x2 - pad_x2;        y1 = y1 + pad_y1;        y2 = y2 - pad_y2;        CHECK_GT(x1, -1);        CHECK_GT(y1, -1);        CHECK_LT(x2, cv_img.cols);        CHECK_LT(y2, cv_img.rows);        // 经过clip之后的高度和宽度        int clipped_height = y2-y1+1;        int clipped_width = x2-x1+1;        // scale factors that would be used to warp the unclipped        // expanded region        // scale_x/scale_y=crop_size除以未经clip之后的宽度/高度        Dtype scale_x =            static_cast<Dtype>(crop_size)/static_cast<Dtype>(unclipped_width);        Dtype scale_y =            static_cast<Dtype>(crop_size)/static_cast<Dtype>(unclipped_height);        // size to warp the clipped expanded region to        // 用clip的宽度和高度乘以scale_x或者scale_y得到crop_size中的宽度和高度        cv_crop_size.width =            static_cast<int>(round(static_cast<Dtype>(clipped_width)*scale_x));        cv_crop_size.height =            static_cast<int>(round(static_cast<Dtype>(clipped_height)*scale_y));        // 再对pad的边界进行处理        pad_x1 = static_cast<int>(round(static_cast<Dtype>(pad_x1)*scale_x));        pad_x2 = static_cast<int>(round(static_cast<Dtype>(pad_x2)*scale_x));        pad_y1 = static_cast<int>(round(static_cast<Dtype>(pad_y1)*scale_y));        pad_y2 = static_cast<int>(round(static_cast<Dtype>(pad_y2)*scale_y));        pad_h = pad_y1;        // if we're mirroring, we mirror the padding too (to be pedantic)        // 如果需要镜像填充的部分也要镜像        if (do_mirror) {          pad_w = pad_x2;        } else {          pad_w = pad_x1;        }        // ensure that the warped, clipped region plus the padding fits in the        // crop_size x crop_size image (it might not due to rounding)        // 确保大小是在crop_size x crop_size以内的        if (pad_h + cv_crop_size.height > crop_size) {          cv_crop_size.height = crop_size - pad_h;        }        if (pad_w + cv_crop_size.width > crop_size) {          cv_crop_size.width = crop_size - pad_w;        }      }      cv::Rect roi(x1, y1, x2-x1+1, y2-y1+1);      // 进行crop      cv::Mat cv_cropped_img = cv_img(roi);      // 使用线性插值进行缩放，缩放到cv_crop_size      cv::resize(cv_cropped_img, cv_cropped_img,          cv_crop_size, 0, 0, cv::INTER_LINEAR);      // horizontal flip at random      if (do_mirror) {          // 对图像进行镜像        cv::flip(cv_cropped_img, cv_cropped_img, 1);      }      // copy the warped window into top_data      for (int h = 0; h < cv_cropped_img.rows; ++h) {        const uchar* ptr = cv_cropped_img.ptr<uchar>(h);        int img_index = 0;        for (int w = 0; w < cv_cropped_img.cols; ++w) {          for (int c = 0; c < channels; ++c) {            int top_index = ((item_id * channels + c) * crop_size + h + pad_h)                     * crop_size + w + pad_w;            // int top_index = (c * height + h) * width + w;            Dtype pixel = static_cast<Dtype>(ptr[img_index++]);            if (this->has_mean_file_) {// 有均值文件减去均值文件中对应的数值              int mean_index = (c * mean_height + h + mean_off + pad_h)                           * mean_width + w + mean_off + pad_w;              top_data[top_index] = (pixel - mean[mean_index]) * scale;            } else {              if (this->has_mean_values_) {// 有均值则减去均值                top_data[top_index] = (pixel - this->mean_values_[c]) * scale;              } else {                top_data[top_index] = pixel * scale;// 像素值进行缩放              }            }          }        }      }      trans_time += timer.MicroSeconds();      // get window label      top_label[item_id] = window[WindowDataLayer<Dtype>::LABEL];      #if 0      // useful debugging code for dumping transformed windows to disk      string file_id;      std::stringstream ss;      ss << PrefetchRand();      ss >> file_id;      std::ofstream inf((string("dump/") + file_id +          string("_info.txt")).c_str(), std::ofstream::out);      inf << image.first << std::endl          << window[WindowDataLayer<Dtype>::X1]+1 << std::endl          << window[WindowDataLayer<Dtype>::Y1]+1 << std::endl          << window[WindowDataLayer<Dtype>::X2]+1 << std::endl          << window[WindowDataLayer<Dtype>::Y2]+1 << std::endl          << do_mirror << std::endl          << top_label[item_id] << std::endl          << is_fg << std::endl;      inf.close();      std::ofstream top_data_file((string("dump/") + file_id +          string("_data.txt")).c_str(),          std::ofstream::out | std::ofstream::binary);      for (int c = 0; c < channels; ++c) {        for (int h = 0; h < crop_size; ++h) {          for (int w = 0; w < crop_size; ++w) {            top_data_file.write(reinterpret_cast<char*>(                &top_data[((item_id * channels + c) * crop_size + h)                          * crop_size + w]),                sizeof(Dtype));          }        }      }      top_data_file.close();      #endif      item_id++;    }  }  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(WindowDataLayer);REGISTER_LAYER_CLASS(WindowData);}  // namespace caffe#endif  // USE_OPENCV

最后提醒一下该类并没有重载前传函数，而是调用了基类的前传，我把对应的代码贴出来便于你整体进行理解

template <typename Dtype>void BasePrefetchingDataLayer<Dtype>::Forward_cpu(    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {    // 传递的时候是从full队列中弹出一个数据  Batch<Dtype>* batch = prefetch_full_.pop("Data layer prefetch queue empty");  // Reshape to loaded data.  // 根据batch的形状改变数据形状  top[0]->ReshapeLike(batch->data_);  // Copy the data  // 将batch数据复制到top[0]  caffe_copy(batch->data_.count(), batch->data_.cpu_data(),             top[0]->mutable_cpu_data());  DLOG(INFO) << "Prefetch copied";  if (this->output_labels_) {      // 输出类标的话    // Reshape to loaded labels.    // 根据batch中类标的形状改变top[1]的形状    top[1]->ReshapeLike(batch->label_);    // Copy the labels.    // 复制类标到top[1]    caffe_copy(batch->label_.count(), batch->label_.cpu_data(),        top[1]->mutable_cpu_data());  }  // 将该batch压入free队列  prefetch_free_.push(batch);}

三、总结

首先理顺类与类之间的关系：

Layer类是所有神经网络层的基类，BaseDataLayer继承自该类，BasePrefetchingDataLayer继承自BaseDataLayer，DataLayer继承自BasePrefetchingDataLayer。

有了上述几个基础的类之后，其他的类都是从这几个类进行派生。

比如DummyDataLayer，HDF5Layer和HDF5OutputLayer都是直接继承自Layer。

MemoryDataLayer则是继承自BaseDataLayer

凡是涉及到直接读取数据文件的一般都是继承自BasePrefetchingDataLayer，这样可以有效地读数据进行预取。

比如：ImageDataLayer、WindowDataLayer

继承自BasePrefetchingDataLayer需要实现load_batch函数以供内部的线程进行调用，实现数据预取。

此外每一个网络层的类（因为所有的网络层都继承自Layer类嘛）都需要实现SetUp，这个是必须的。

这一次的量还真有点大。。。

注释的代码可以从以下位置下载：

http://download.csdn.net/detail/xizero00/9474806

参考：

[1]HDF5格式的介绍

http://malagis.com/about-hdf.html

http://www.hdfgroup.org/HDF5/Tutor/h5lite.html