Caffe 代码解读之全连接层concat layer

今天，我们看一下caffe的拼接层，即将两个或多个layer进行拼接。
首先，看一下caffe官方文档。

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同其他layer一样，分为setup、reshape、Forward_cpu、Backward_cpu。

//concat_layer 用来实现两个或者多个blob的链接，即多输入一输出//支持在num 维度上的链接（concat_dim = 0 : (n1+n2+...+nk)∗c∗h∗w ）//和channel维度上的链接（concat_dim = 1 : n∗(c1+c2+...+ck)∗h∗w）。//axis ，dim ：0 为 num 维度链接，1 为 channel 维度链接//这里需要给出axis或concat_dimtemplate <typename Dtype>void ConcatLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  const ConcatParameter& concat_param = this->layer_param_.concat_param();  CHECK(!(concat_param.has_axis() && concat_param.has_concat_dim()))      << "Either axis or concat_dim should be specified; not both.";}template <typename Dtype>void ConcatLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  //获取axis，确定拼接哪一维度  const int num_axes = bottom[0]->num_axes();  const ConcatParameter& concat_param = this->layer_param_.concat_param();  //以下都在获取、判断axis的维度  if (concat_param.has_concat_dim()) {    concat_axis_ = static_cast<int>(concat_param.concat_dim());    // Don't allow negative indexing for concat_dim, a uint32 -- almost    // certainly unintended.    CHECK_GE(concat_axis_, 0) << "casting concat_dim from uint32 to int32 "        << "produced negative result; concat_dim must satisfy "        << "0 <= concat_dim < " << kMaxBlobAxes;    CHECK_LT(concat_axis_, num_axes) << "concat_dim out of range.";  } else {    concat_axis_ = bottom[0]->CanonicalAxisIndex(concat_param.axis());  }  // Initialize with the first blob.  //这里有一点需要解释，可以看到，bottom类型为 vector<Blob<Dtype>*>，这里只需要使用bottom[0]  //给shape赋值就好，其实botom本身就是一个Blob的vector  //比如：我要将两个layer拼接，那么久有bottom[0]以及bottom[1]  vector<int> top_shape = bottom[0]->shape();  //concat_axis_ = 0 : num_concats_=num;concat_axis_ = 1 : num_concats_=num x channel;  num_concats_ = bottom[0]->count(0, concat_axis_);  //concat_axis_ = 0 : concat_input_size_=channel x height x width;  //concat_axis_ = 1 : concat_input_size_=height x width;  concat_input_size_ = bottom[0]->count(concat_axis_ + 1);  int bottom_count_sum = bottom[0]->count();  //检测num_axes拼接的层是否相同，num_axes为维度信息  for (int i = 1; i < bottom.size(); ++i) {    CHECK_EQ(num_axes, bottom[i]->num_axes())        << "All inputs must have the same #axes.";    for (int j = 0; j < num_axes; ++j) {      if (j == concat_axis_) { continue; }      CHECK_EQ(top_shape[j], bottom[i]->shape(j))          << "All inputs must have the same shape, except at concat_axis.";    }    bottom_count_sum += bottom[i]->count();    top_shape[concat_axis_] += bottom[i]->shape(concat_axis_);  }  top[0]->Reshape(top_shape);  CHECK_EQ(bottom_count_sum, top[0]->count());}

1、这里有一点需要解释，可以看到，bottom类型为 vector blob，这里只需要使用bottom[0]给shape赋值就好，其实bottom本身就是一个Blob的vector。
2、CHECK_**，这里给小白们解释一下，就是判断是否相等、小于、大于

3、 count，这看到有好多的count函数，这些函数在blob层实现，解释如下：

inline int count() const { return count_; }  /**   * @brief Compute the volume of a slice; i.e., the product of dimensions   *        among a range of axes.   *   * @param start_axis The first axis to include in the slice.   *   * @param end_axis The first axis to exclude from the slice.   */  inline int count(int start_axis, int end_axis) const {    CHECK_LE(start_axis, end_axis);    CHECK_GE(start_axis, 0);    CHECK_GE(end_axis, 0);    CHECK_LE(start_axis, num_axes());    CHECK_LE(end_axis, num_axes());    int count = 1;    for (int i = start_axis; i < end_axis; ++i) {      count *= shape(i);    }    return count;  }  /**   * @brief Compute the volume of a slice spanning from a particular first   *        axis to the final axis.   *   * @param start_axis The first axis to include in the slice.   */  inline int count(int start_axis) const {    return count(start_axis, num_axes());  }

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前向传播就是layer的拼接

template <typename Dtype>void ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top) {  Dtype* top_data = top[0]->mutable_cpu_data();  int offset_concat_axis = 0;  const int top_concat_axis = top[0]->shape(concat_axis_);  //遍历所有输入bottom  for (int i = 0; i < bottom.size(); ++i) {    const Dtype* bottom_data = bottom[i]->cpu_data();    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);    //把 各个bottom data 拷贝到输出 top data 的对应位置    for (int n = 0; n < num_concats_; ++n) {      //case 0：num x channel x h x w;case 1: channel x h x w      //case 0：bottom_data + n x num x channel x h x w ;      //case 1：bottom_data + n x channel x h x w ;      caffe_copy(bottom_concat_axis * concat_input_size_,          bottom_data + n * bottom_concat_axis * concat_input_size_,          top_data + (n * top_concat_axis + offset_concat_axis)              * concat_input_size_);    }    offset_concat_axis += bottom_concat_axis;  }}

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反向传播，就是layer层之间diff和data的传播

//反向传播就是对每一个bottom的 diff 做和 data 相同的链接template <typename Dtype>void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {  const Dtype* top_diff = top[0]->cpu_diff();  int offset_concat_axis = 0;  const int top_concat_axis = top[0]->shape(concat_axis_);  for (int i = 0; i < bottom.size(); ++i) {    if (!propagate_down[i]) { continue; }    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();    const int bottom_concat_axis = bottom[i]->shape(concat_axis_);    for (int n = 0; n < num_concats_; ++n) {      caffe_copy(bottom_concat_axis * concat_input_size_, top_diff +          (n * top_concat_axis + offset_concat_axis) * concat_input_size_,          bottom_diff + n * bottom_concat_axis * concat_input_size_);    }    offset_concat_axis += bottom_concat_axis;  }}