accuracy_layer

#include <functional>#include <utility>#include <vector>#include "caffe/layers/accuracy_layer.hpp"#include "caffe/util/math_functions.hpp"namespace caffe {template <typename Dtype>void AccuracyLayer<Dtype>::LayerSetUp(  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  top_k_ = this->layer_param_.accuracy_param().top_k();//获得k，也就是正确类别排前k名算个入acc  has_ignore_label_ =    this->layer_param_.accuracy_param().has_ignore_label();//有没有要忽略的标签  if (has_ignore_label_) {    ignore_label_ = this->layer_param_.accuracy_param().ignore_label();  }}/*定义中关于axis的说明：axis指出在预测blob中，哪一维是label轴，如(N x C x H x W)的blob，axis=0，则N为label对应的维度。axis=1,则C为label对应的维度，而剩下的N为outer样本数量， H x W为inner样本数量。由代码可知，当axis=k时outer_num_=blob.shape[0,..,k)，inner_num_=blob.shape[k+1,..,shape.size)。一般的，label blob的维度为(N x C)，N为样本数量，C为标签数量（即类别个数）。axis=1,outer_num_=N,inner_num_=shape[2,2)=1(即没有inner)*/template <typename Dtype>void AccuracyLayer<Dtype>::Reshape(  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  CHECK_LE(top_k_, bottom[0]->count() / bottom[1]->count())//要取的k不能比总类别数大      << "top_k must be less than or equal to the number of classes.";  label_axis_ =      bottom[0]->CanonicalAxisIndex(this->layer_param_.accuracy_param().axis());//label的坐标轴  outer_num_ = bottom[0]->count(0, label_axis_);//基本可以理解为batch中的样本数  inner_num_ = bottom[0]->count(label_axis_ + 1);//1  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())      << "Number of labels must match number of predictions; "      << "e.g., if label axis == 1 and prediction shape is (N, C, H, W), "      << "label count (number of labels) must be N*H*W, "      << "with integer values in {0, 1, ..., C-1}.";  vector<int> top_shape(0);  // Accuracy is a scalar; 0 axes.  top[0]->Reshape(top_shape);//top[0]是总体样本正确率，标量top[1]为每个类别的正确率，向量  if (top.size() > 1) {    // Per-class accuracy is a vector; 1 axes.    vector<int> top_shape_per_class(1);    top_shape_per_class[0] = bottom[0]->shape(label_axis_);    top[1]->Reshape(top_shape_per_class);    nums_buffer_.Reshape(top_shape_per_class);  }}template <typename Dtype>void AccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,    const vector<Blob<Dtype>*>& top) {  Dtype accuracy = 0;  const Dtype* bottom_data = bottom[0]->cpu_data();//样本数*标签个数（也就是最后一个全链接的输出层节点个数）  const Dtype* bottom_label = bottom[1]->cpu_data();  const int dim = bottom[0]->count() / outer_num_;  const int num_labels = bottom[0]->shape(label_axis_);  vector<Dtype> maxval(top_k_+1);  vector<int> max_id(top_k_+1);  if (top.size() > 1) {    caffe_set(nums_buffer_.count(), Dtype(0), nums_buffer_.mutable_cpu_data());    caffe_set(top[1]->count(), Dtype(0), top[1]->mutable_cpu_data());  }  int count = 0;  for (int i = 0; i < outer_num_; ++i) {//对于每个样本    for (int j = 0; j < inner_num_; ++j) {      const int label_value =          static_cast<int>(bottom_label[i * inner_num_ + j]);//第i个样本的label      if (has_ignore_label_ && label_value == ignore_label_) {//如果这个类别被忽略就计算下一个。        continue;      }      if (top.size() > 1) ++nums_buffer_.mutable_cpu_data()[label_value];//batch中每个类别的总样本数，为了计算类内正确率      DCHECK_GE(label_value, 0);      DCHECK_LT(label_value, num_labels);      // Top-k accuracy      std::vector<std::pair<Dtype, int> > bottom_data_vector;      for (int k = 0; k < num_labels; ++k) {        bottom_data_vector.push_back(std::make_pair(            bottom_data[i * dim + k * inner_num_ + j], k));//完成带序号的排序      }      std::partial_sort(          bottom_data_vector.begin(), bottom_data_vector.begin() + top_k_,          bottom_data_vector.end(), std::greater<std::pair<Dtype, int> >());      // check if true label is in top k predictions      for (int k = 0; k < top_k_; k++) {        if (bottom_data_vector[k].second == label_value) { //如果标定的label在预测的前k个label中          ++accuracy;          if (top.size() > 1) ++top[1]->mutable_cpu_data()[label_value];          break;        }      }      ++count;    }  }  // LOG(INFO) << "Accuracy: " << accuracy;  top[0]->mutable_cpu_data()[0] = accuracy / count;  if (top.size() > 1) {    for (int i = 0; i < top[1]->count(); ++i) {      top[1]->mutable_cpu_data()[i] =          nums_buffer_.cpu_data()[i] == 0 ? 0 //batch中没有某一类样本就把这类样本的正确率设置为0，不然的话就正常计算          : top[1]->cpu_data()[i] / nums_buffer_.cpu_data()[i];    }  }  // Accuracy layer should not be used as a loss function.}INSTANTIATE_CLASS(AccuracyLayer);REGISTER_LAYER_CLASS(Accuracy);}  // namespace caffe