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:

  1. 对待处理数据带一层封装,用于在Caffe中通信传递。
  2. 也为CPU和GPU间提供同步能力
  3. 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:

  1. Blob在实现上是对SyncedMemory(见1.5部分)进行了一层封装。

  2. shape_为blob维度,见1.3部分
  3. data_为原始数据
  4. diff_为梯度信息
  5. 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:

  1. 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
  2. 对于全连接网络,使用2D blobs (shape (N, D))。然后调用InnerProductLayer
  3. 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):

  1. 常量方式,void* cpu_data(),其不改变cpu_ptr_指向存储区域的值。
  2. 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

  1. 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。
  2. 调用mutable_cpu_data()能够让head转移到cpu上
  3. 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
  4. 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。

(Caffe)基本类Blob,Layer,Net(一)

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

  1. Setup:Layer的初始化
  2. 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)
  3. 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。

(Caffe)基本类Blob,Layer,Net(一)

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

说明:

  1. Init中,通过创建blob和layer搭建了整个网络框架,以及调用各层的SetUp函数。
  2. blobs_存放这每一层产生的blobls的中间结果。bottom_vecs_存放每一层的bottom blobs,top_vecs_存放每一层的top blobs

參考文献:
[1].http://caffe.berkeleyvision.org/tutorial/net_layer_blob.html
[2].http://caffe.berkeleyvision.org/tutorial/layers.html
[3].https://yufeigan.github.io
[4].https://www.zhihu.com/question/27982282