seg_accuracy_layer.cpp

#include <algorithm>#include <functional>#include <utility>#include <vector>#include "caffe/layer.hpp"#include "caffe/util/io.hpp"#include "caffe/util/math_functions.hpp"#include "caffe/layers/seg_accuracy_layer.hpp"namespace caffe {template <typename Dtype>void SegAccuracyLayer<Dtype>::LayerSetUp(  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  confusion_matrix_.clear();  confusion_matrix_.resize(bottom[0]->channels());  SegAccuracyParameter seg_accuracy_param = this->layer_param_.seg_accuracy_param();  for (int c = 0; c < seg_accuracy_param.ignore_label_size(); ++c){    ignore_label_.insert(seg_accuracy_param.ignore_label(c));  }}template <typename Dtype>void SegAccuracyLayer<Dtype>::Reshape(  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {  CHECK_LE(1, bottom[0]->channels())      << "top_k must be less than or equal to the number of channels (classes).";  CHECK_EQ(bottom[0]->num(), bottom[1]->num())    << "The data and label should have the same number.";  CHECK_EQ(bottom[1]->channels(), 1)    << "The label should have one channel.";  CHECK_EQ(bottom[0]->height(), bottom[1]->height())    << "The data should have the same height as label.";  CHECK_EQ(bottom[0]->width(), bottom[1]->width())    << "The data should have the same width as label.";  //confusion_matrix_.clear();   top[0]->Reshape(1, 1, 1, 3);}template <typename Dtype>void SegAccuracyLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,    const vector<Blob<Dtype>*>& top) {#cpu前向计算输入数据和标签  const Dtype* bottom_data = bottom[0]->cpu_data();  const Dtype* bottom_label = bottom[1]->cpu_data();  int num = bottom[0]->num();  int channels = bottom[0]->channels();  int height = bottom[0]->height();  int width = bottom[0]->width();  int data_index, label_index;  int top_k = 1;  // only support for top_k = 1  // remove old predictions if reset() flag is true  if (this->layer_param_.seg_accuracy_param().reset()) {    confusion_matrix_.clear();  }  for (int i = 0; i < num; ++i) {    for (int h = 0; h < height; ++h) {      for (int w = 0; w < width; ++w) {    // Top-k accuracy    std::vector<std::pair<Dtype, int> > bottom_data_vector;    for (int c = 0; c < channels; ++c) {      data_index = (c * height + h) * width + w;      bottom_data_vector.push_back(std::make_pair(bottom_data[data_index], c));    }    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    label_index = h * width + w;    const int gt_label = static_cast<int>(bottom_label[label_index]);    if (ignore_label_.count(gt_label) != 0) {      // ignore the pixel with this gt_label      continue;    } else if (gt_label >= 0 && gt_label < channels) {      // current position is not "255", indicating ambiguous position      confusion_matrix_.accumulate(gt_label, bottom_data_vector[0].second);    } else {      LOG(FATAL) << "Unexpected label " << gt_label << ". num: " << i               << ". row: " << h << ". col: " << w;      }      }    }    bottom_data  += bottom[0]->offset(1);    bottom_label += bottom[1]->offset(1);  }  /* for debug  LOG(INFO) << "confusion matrix info:" << confusion_matrix_.numRows() << "," << confusion_matrix_.numCols();  confusion_matrix_.printCounts();  */  // we report all the resuls  top[0]->mutable_cpu_data()[0] = (Dtype)confusion_matrix_.accuracy();  top[0]->mutable_cpu_data()[1] = (Dtype)confusion_matrix_.avgRecall(false);  top[0]->mutable_cpu_data()[2] = (Dtype)confusion_matrix_.avgJaccard();}INSTANTIATE_CLASS(SegAccuracyLayer);REGISTER_LAYER_CLASS(SegAccuracy);}  // namespace caffe