SoftmaxLayer and SoftmaxwithLossLayer 代码解读
Wang Xiao
先来看看 SoftmaxWithLoss 在prototext文件中的定义：
1 layer {
2 name: "loss"
3 type: "SoftmaxWithLoss"
4 bottom: "fc8"
5 bottom: "label"
6 top: "loss"
7 }
再看SoftmaxWithLossLayer的.cpp文件：
1 #include <algorithm> 2 #include <cfloat> 3 #include <vector> 4 5 #include "caffe/layers/softmax_loss_layer.hpp" 6 #include "caffe/util/math_functions.hpp" 7 8 namespace caffe { 9 10 template <typename Dtype> 11 void SoftmaxWithLossLayer<Dtype>::LayerSetUp( 12 const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { 13 LossLayer<Dtype>::LayerSetUp(bottom, top); 14 LayerParameter softmax_param(this->layer_param_); 15 softmax_param.set_type("Softmax"); 16 softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param); 17 softmax_bottom_vec_.clear(); 18 softmax_bottom_vec_.push_back(bottom[0]); // 将bottom[0]存入softmax_bottom_vec_; 19 softmax_top_vec_.clear(); 20 softmax_top_vec_.push_back(&prob_); // 将 prob_ 存入 softmax_top_vec_;
21 softmax_layer_->SetUp(softmax_bottom_vec_, softmax_top_vec_);
22
23 has_ignore_label_ = // draw the parameter from layer
24 this->layer_param_.loss_param().has_ignore_label();
25 if (has_ignore_label_) {
26 ignore_label_ = this->layer_param_.loss_param().ignore_label();
27 }
28 if (!this->layer_param_.loss_param().has_normalization() &&
29 this->layer_param_.loss_param().has_normalize()) {
30 normalization_ = this->layer_param_.loss_param().normalize() ?
31 LossParameter_NormalizationMode_VALID :
32 LossParameter_NormalizationMode_BATCH_SIZE;
33 } else {
34 normalization_ = this->layer_param_.loss_param().normalization();
35 }
36 }
接下来是对输入数据进行 reshape 操作：
1 template <typename Dtype> 2 void SoftmaxWithLossLayer<Dtype>::Reshape( 3 const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { 4 LossLayer<Dtype>::Reshape(bottom, top); 5 softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_); 6 softmax_axis_ = 7 bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis()); 8 outer_num_ = bottom[0]->count(0, softmax_axis_); 9 inner_num_ = bottom[0]->count(softmax_axis_ + 1); 10 CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count()) 11 << "Number of labels must match number of predictions; " 12 << "e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), " 13 << "label count (number of labels) must be N*H*W, " 14 << "with integer values in {0, 1, ..., C-1}."; 15 if (top.size() >= 2) { 16 // softmax output 17 top[1]->ReshapeLike(*bottom[0]); 18 } 19 }