（Caffe）基本类Blob，Layer，Net（一）

本文地址：http://blog.csdn.net/mounty_fsc/article/details/51085654

Caffe中，Blob。Layer，Net，Solver是最为核心的类，下面介绍这几个类，Solver将在下一节介绍。

1 Blob

1.1 简单介绍

Blob是：

对待处理数据带一层封装，用于在Caffe中通信传递。
也为CPU和GPU间提供同步能力
数学上，是一个N维的C风格的存储数组
总的来说。Caffe使用Blob来交流数据，其是Caffe中标准的数组与统一的内存接口，它是多功能的。在不同的应用场景具有不同的含义，如能够是：batches of images, model parameters, and derivatives for optimization等。

1.2 源码

/** 
 * @brief A wrapper around SyncedMemory holders serving as the basic 
 *        computational unit through which Layer%s, Net%s, and Solver%s 
 *        interact. 
 * 
 * TODO(dox): more thorough description. 
 */  
template <typename Dtype>  
class Blob {  
 public:  
  Blob()  
       : data_(), diff_(), count_(0), capacity_(0) {}  

  /// @brief Deprecated; use <code>Blob(const vector<int>& shape)</code>.  
  explicit Blob(const int num, const int channels, const int height,  
      const int width);  
  explicit Blob(const vector<int>& shape);  

  .....  

 protected:  
  shared_ptr<SyncedMemory> data_;  
  shared_ptr<SyncedMemory> diff_;  
  shared_ptr<SyncedMemory> shape_data_;  
  vector<int> shape_;  
  int count_;  
  int capacity_;  

  DISABLE_COPY_AND_ASSIGN(Blob);  
};  // class Blob

注：此处仅仅保留了构造函数与成员变量。

说明：

Blob在实现上是对SyncedMemory（见1.5部分）进行了一层封装。
shape_为blob维度，见1.3部分
data_为原始数据
diff_为梯度信息
count_为该blob的总容量（即数据的size）。函数count(x,y)（或count(x)）返回某个切片[x,y]（[x,end]）内容量，本质上就是shape[x]shape[x+1]….*shape[y]的值

1.3 Blob的shape

由源码中能够注意到Blob有个成员变量：vector shape_
其作用：

对于图像数据，shape能够定义为4维的数组(Num, Channels, Height, Width)或(n, k, h, w)。所以Blob数据维度为n*k*h*w。Blob是row-major保存的，因此在(n, k, h, w)位置的值物理位置为((n * K + k) * H + h) * W + w。当中Number是数据的batch size，对于256张图片为一个training batch的ImageNet来说n = 256;Channel是特征维度，如RGB图像k = 3
对于全连接网络，使用2D blobs (shape (N, D))。然后调用InnerProductLayer
对于參数，维度依据该层的类型和配置来确定。对于有3个输入96个输出的卷积层，Filter核 11 x 11，则blob为96 x 3 x 11 x 11. 对于全连接层，1000个输出。1024个输入。则blob为1000 x 1024.

1.4 Blob的行优先的存储方式

以Blob中二维矩阵为例（如全连接网络shape (N, D)）。如图所看到的。同样的存储方式能够推广到多维。

（Caffe）基本类Blob，Layer，Net（一）

1.5 SyncedMemory

由1.2知。Blob本质是对SyncedMemory的再封装。

其核心代码例如以下：

/** 
 * @brief Manages memory allocation and synchronization between the host (CPU) 
 *        and device (GPU). 
 * 
 * TODO(dox): more thorough description. 
 */  
class SyncedMemory {  
 public:  
...  
 const void* cpu_data();  
  const void* gpu_data();  
  void* mutable_cpu_data();  
  void* mutable_gpu_data();  
...  
 private:  
...  
  void* cpu_ptr_;  
  void* gpu_ptr_;  
...  
};  // class SyncedMemory

Blob同一时候保存了data_和diff_,其类型为SyncedMemory的指针。
对于data_(diff_同样),事实上际值要么存储在CPU(cpu_ptr_)要么存储在GPU(gpu_ptr_),有两种方式訪问CPU数据(GPU同样)：

常量方式,void* cpu_data()，其不改变cpu_ptr_指向存储区域的值。

可变方式，void* mutable_cpu_data()，其可改变cpu_ptr_指向存储区值。
以mutable_cpu_data()为例

void* SyncedMemory::mutable_cpu_data() {
  to_cpu();
  head_ = HEAD_AT_CPU;
  return cpu_ptr_;
}

inline void SyncedMemory::to_cpu() {
  switch (head_) {
  case UNINITIALIZED:
    CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
    caffe_memset(size_, 0, cpu_ptr_);
    head_ = HEAD_AT_CPU;
    own_cpu_data_ = true;
    break;
  case HEAD_AT_GPU:

#ifndef CPU_ONLY

    if (cpu_ptr_ == NULL) {
      CaffeMallocHost(&cpu_ptr_, size_, &cpu_malloc_use_cuda_);
      own_cpu_data_ = true;
    }
    caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
    head_ = SYNCED;

#else

    NO_GPU;

#endif

    break;
  case HEAD_AT_CPU:
  case SYNCED:
    break;
  }
}

说明：

经验上来说，假设不须要改变其值，则使用常量调用的方式，而且，不要在你对象中保存其指针。为何要这样设计呢。由于这样涉及能够隐藏CPU到GPU的同步细节，以及降低数据传递从而提高效率。当你调用它们的时候。SyncedMem会决定何时去复制数据，通常情况是仅当gnu或cpu改动后有复制操作，引用1官方文档中有一个样例说明何时进行复制操作。
调用mutable_cpu_data()能够让head转移到cpu上
第一次调用mutable_cpu_data()是UNINITIALIZED将运行9到14行。将为cpu_ptr_分配host内存
若head从gpu转移到cpu。将把数据从gpu拷贝到cpu中

2 Layer

2.1 简单介绍

Layer是Caffe的基础以及基本计算单元。Caffe十分强调网络的层次性，能够说。一个网络的大部分功能都是以Layer的形式去展开的，如convolute,pooling,loss等等。
在创建一个Caffe模型的时候，也是以Layer为基础进行的，需依照src/caffe/proto/caffe.proto中定义的网络及參数格式定义网络 prototxt文件（需了解google protocol buffer）

2.2 Layer与Blob的关系

如图，名为conv1的Layer 的输入是名为data的bottom blob，其输出是名为conv1的top blob。

其protobuff定义例如以下，一个layer有一个到多个的top和bottom，其相应于blob

layer {  
      name: "conv1"  
      type: "Convolution"  
      bottom: "data"  
      top: "conv1"  
     ....  
    }

2.3 源码

 /** 
     * Layer%s must implement a Forward function, in which they take their input 
     * (bottom) Blob%s (if any) and compute their output Blob%s (if any). 
     * They may also implement a Backward function, in which they compute the error 
     * gradients with respect to their input Blob%s, given the error gradients with 
     * their output Blob%s. 
     */  
    template <typename Dtype>  
    class Layer {  
     public:  
      /** 
       * You should not implement your own constructor. Any set up code should go 
       * to SetUp(), where the dimensions of the bottom blobs are provided to the 
       * layer. 
       */  
      explicit Layer(const LayerParameter& param)  
        : layer_param_(param), is_shared_(false) {  
    ...  
        }  
      virtual ~Layer() {}  

      /** 
       * @brief Implements common layer setup functionality. 
       * @param bottom the preshaped input blobs 
       * @param top 
       *     the allocated but unshaped output blobs, to be shaped by Reshape 
       */  
      void SetUp(const vector<Blob<Dtype>*>& bottom,  
          const vector<Blob<Dtype>*>& top) {  
    ...  
      }  

      ...  

      /** 
       * @brief Given the bottom blobs, compute the top blobs and the loss. 
       * \return The total loss from the layer. 
       * 
       * The Forward wrapper calls the relevant device wrapper function 
       * (Forward_cpu or Forward_gpu) to compute the top blob values given the 
       * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper 
       * then computes and returns the loss.
       * 
       * Your layer should implement Forward_cpu and (optionally) Forward_gpu. 
       */  
      inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,  
          const vector<Blob<Dtype>*>& top);  

      /** 
       * @brief Given the top blob error gradients, compute the bottom blob error 
       *        gradients. 
       * 
       * @param top 
       *     the output blobs, whose diff fields store the gradient of the error 
       *     with respect to themselves 
       * @param propagate_down 
       *     a vector with equal length to bottom, with each index indicating 
       *     whether to propagate the error gradients down to the bottom blob at 
       *     the corresponding index 
       * @param bottom 
       *     the input blobs, whose diff fields will store the gradient of the error 
       *     with respect to themselves after Backward is run 
       * 
       * The Backward wrapper calls the relevant device wrapper function 
       * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the 
       * top blob diffs. 
       * 
       * Your layer should implement Backward_cpu and (optionally) Backward_gpu. 
       */  
      inline void Backward(const vector<Blob<Dtype>*>& top,  
          const vector<bool>& propagate_down,  
          const vector<Blob<Dtype>*>& bottom);  

     ...  

     protected:  
      /** The protobuf that stores the layer parameters */  
      LayerParameter layer_param_;  
      /** The phase: TRAIN or TEST */  
      Phase phase_;  
      /** The vector that stores the learnable parameters as a set of blobs. */  
      vector<shared_ptr<Blob<Dtype> > > blobs_;  
      /** Vector indicating whether to compute the diff of each param blob. */  
      vector<bool> param_propagate_down_;  

      /** The vector that indicates whether each top blob has a non-zero weight in 
       *  the objective function. */  
      vector<Dtype> loss_;  

      virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,  
          const vector<Blob<Dtype>*>& top) = 0;  

      virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,  
          const vector<Blob<Dtype>*>& top) {  
        // LOG(WARNING) << "Using CPU code as backup.";  
        return Forward_cpu(bottom, top);  
      }  

      virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,  
          const vector<bool>& propagate_down,  
          const vector<Blob<Dtype>*>& bottom) = 0;  

      virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,  
          const vector<bool>& propagate_down,  
          const vector<Blob<Dtype>*>& bottom) {  
        // LOG(WARNING) << "Using CPU code as backup.";  
        Backward_cpu(top, propagate_down, bottom);  
      }  

    ...  

     };  // class Layer

说明：每一层定义了三种操作

Setup：Layer的初始化
Forward：前向传导计算。依据bottom计算top，调用了Forward_cpu（必须实现）和Forward_gpu（可选，若未实现，则调用cpu的）
Backward：反向传导计算。依据top计算bottom的梯度。其它同上

2.4 派生类分类

在Layer的派生类中，主要能够分为Vision Layers

Vision Layers
Vison 层主要用于处理视觉图像相关的层。以图像作为输入，产生其它的图像。其主要特点是具有空间结构。
包括Convolution(conv_layer.hpp)、Pooling(pooling_layer.hpp)、Local Response Normalization(LRN)(lrn_layer.hpp)、im2col等。注：老版本号的Caffe有头文件include/caffe/vision_layers.hpp，新版本号中用include/caffe/layer/conv_layer.hpp等代替
Loss Layers
这些层产生loss，如Softmax(SoftmaxWithLoss)、Sum-of-Squares / Euclidean(EuclideanLoss)、Hinge / Margin(HingeLoss)、Sigmoid Cross-Entropy(SigmoidCrossEntropyLoss)、Infogain(InfogainLoss)、Accuracy and Top-k等
Activation / Neuron Layers
元素级别的运算，运算均为同址计算（in-place computation。返回值覆盖原值而占用新的内存）。如：ReLU / Rectified-Linear and Leaky-ReLU(ReLU)、Sigmoid(Sigmoid)、TanH / Hyperbolic Tangent(TanH)、Absolute Value(AbsVal)、Power(Power)、BNLL(BNLL)等
Data Layers
网络的最底层，主要实现数据格式的转换，如：Database(Data)、In-Memory(MemoryData)、HDF5 Input(HDF5Data)、HDF5 Output(HDF5Output)、Images(ImageData)、Windows(WindowData)、Dummy(DummyData)等
Common Layers
Caffe提供了单个层与多个层的连接。

如：Inner Product(InnerProduct)、Splitting(Split)、Flattening(Flatten)、Reshape(Reshape)、Concatenation(Concat)、Slicing(Slice)、Elementwise(Eltwise)、Argmax(ArgMax)、Softmax(Softmax)、Mean-Variance Normalization(MVN)等

注，括号内为Layer Type,没有括号暂缺信息。具体咱见引用2

3 Net

3.1 简单介绍

一个Net由多个Layer组成。

一个典型的网络从data layer（从磁盘中加载数据）出发到loss layer结束。如图是一个简单的逻辑回归分类器。

例如以下定义：

name: "LogReg"
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  data_param {
    source: "input_leveldb"
    batch_size: 64
  }
}
layer {
  name: "ip"
  type: "InnerProduct"
  bottom: "data"
  top: "ip"
  inner_product_param {
    num_output: 2
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip"
  bottom: "label"
  top: "loss"
}

3.2 源码

/**
 * @brief Connects Layer%s together into a directed acyclic graph (DAG)
 *        specified by a NetParameter.
 *
 * TODO(dox): more thorough description.
 */
template <typename Dtype>
class Net {
 public:
...
  /// @brief Initialize a network with a NetParameter.
  void Init(const NetParameter& param);
...

  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
      Dtype* loss = NULL);
...
  /**
   * The network backward should take no input and output, since it solely
   * computes the gradient w.r.t the parameters, and the data has already been
   * provided during the forward pass.
   */
  void Backward();
  ...
  Dtype ForwardBackward(const vector<Blob<Dtype>* > & bottom) {
    Dtype loss;
    Forward(bottom, &loss);
    Backward();
    return loss;
  }
...

 protected:
  ...
  /// @brief The network name
  string name_;
  /// @brief The phase: TRAIN or TEST
  Phase phase_;
  /// @brief Individual layers in the net
  vector<shared_ptr<Layer<Dtype> > > layers_;
  /// @brief the blobs storing intermediate results between the layer.
  vector<shared_ptr<Blob<Dtype> > > blobs_;
  vector<vector<Blob<Dtype>*> > bottom_vecs_;
  vector<vector<Blob<Dtype>*> > top_vecs_;
  ...
  /// The root net that actually holds the shared layers in data parallelism
  const Net* const root_net_;
};
}  // namespace caffe