text: #include <algorithm>
  #include <vector>
  
  #include "caffe/layers/sigmoid_cross_entropy_loss_layer.hpp"
  #include "caffe/util/math_functions.hpp"
  
  namespace caffe {
  
  template <typename Dtype>
  void SigmoidCrossEntropyLossLayer<Dtype>::LayerSetUp(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    LossLayer<Dtype>::LayerSetUp(bottom, top);
    sigmoid_bottom_vec_.clear();
    sigmoid_bottom_vec_.push_back(bottom[0]);
    sigmoid_top_vec_.clear();
    sigmoid_top_vec_.push_back(sigmoid_output_.get());
    sigmoid_layer_->SetUp(sigmoid_bottom_vec_, sigmoid_top_vec_);
  
    has_ignore_label_ =
      this->layer_param_.loss_param().has_ignore_label();
    if (has_ignore_label_) {
      ignore_label_ = this->layer_param_.loss_param().ignore_label();
    }
    has_positive_weight = this->layer_param_.loss_param().has_positive_weight();
    if(has_positive_weight)
    {
        positive_weight = this->layer_param_.loss_param().positive_weight();
    }
    has_neg_focal_ = this->layer_param_.loss_param().has_neg_focal();
    if(has_neg_focal_)
    {
        neg_focal_ = this->layer_param_.loss_param().neg_focal();
    }
    if (this->layer_param_.loss_param().has_normalization()) {
      normalization_ = this->layer_param_.loss_param().normalization();
    } else if (this->layer_param_.loss_param().has_normalize()) {
      normalization_ = this->layer_param_.loss_param().normalize() ?
                       LossParameter_NormalizationMode_VALID :
                       LossParameter_NormalizationMode_BATCH_SIZE;
    } else {
      normalization_ = LossParameter_NormalizationMode_BATCH_SIZE;
    }
  }
  
  template <typename Dtype>
  void SigmoidCrossEntropyLossLayer<Dtype>::Reshape(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    LossLayer<Dtype>::Reshape(bottom, top);
    outer_num_ = bottom[0]->shape(0);  // batch size
    inner_num_ = bottom[0]->count(1);  // instance size: |output| == |target|
    CHECK_EQ(bottom[0]->count(), bottom[1]->count()) <<
        "SIGMOID_CROSS_ENTROPY_LOSS layer inputs must have the same count.";
    sigmoid_layer_->Reshape(sigmoid_bottom_vec_, sigmoid_top_vec_);
  }
  
  // TODO(shelhamer) loss normalization should be pulled up into LossLayer,
  // instead of duplicated here and in SoftMaxWithLossLayer
  template <typename Dtype>
  Dtype SigmoidCrossEntropyLossLayer<Dtype>::get_normalizer(
      LossParameter_NormalizationMode normalization_mode, int valid_count) {
    Dtype normalizer;
    switch (normalization_mode) {
      case LossParameter_NormalizationMode_FULL:
        normalizer = Dtype(outer_num_ * inner_num_);
        break;
      case LossParameter_NormalizationMode_VALID:
        if (valid_count == -1) {
          normalizer = Dtype(outer_num_ * inner_num_);
        } else {
          normalizer = Dtype(valid_count);
        }
        break;
      case LossParameter_NormalizationMode_BATCH_SIZE:
        normalizer = Dtype(outer_num_);
        break;
      case LossParameter_NormalizationMode_NONE:
        normalizer = Dtype(1);
        break;
      default:
        LOG(FATAL) << "Unknown normalization mode: "
            << LossParameter_NormalizationMode_Name(normalization_mode);
    }
    // Some users will have no labels for some examples in order to 'turn off' a
    // particular loss in a multi-task setup. The max prevents NaNs in that case.
    return std::max(Dtype(1.0), normalizer);
  }
  
  template <typename Dtype>
  void SigmoidCrossEntropyLossLayer<Dtype>::Forward_cpu(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    // The forward pass computes the sigmoid outputs.
    sigmoid_bottom_vec_[0] = bottom[0];
    sigmoid_layer_->Forward(sigmoid_bottom_vec_, sigmoid_top_vec_);
    // Compute the loss (negative log likelihood)
    // Stable version of loss computation from input data
    const Dtype* input_data = bottom[0]->cpu_data();
    const Dtype* target = bottom[1]->cpu_data();
    int valid_count = 0;
    Dtype loss = 0;
    for (int i = 0; i < bottom[0]->count(); ++i) {
      const int target_value = static_cast<int>(target[i]);
      if (has_ignore_label_ && target_value == ignore_label_) {
        continue;
      }
      loss -= input_data[i] * (target[i] - (input_data[i] >= 0)) -
          log(1 + exp(input_data[i] - 2 * input_data[i] * (input_data[i] >= 0)));
      ++valid_count;
    }
    normalizer_ = get_normalizer(normalization_, valid_count);
    top[0]->mutable_cpu_data()[0] = loss / normalizer_;
  }
  
  template <typename Dtype>
  void SigmoidCrossEntropyLossLayer<Dtype>::Backward_cpu(
      const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom) {
    if (propagate_down[1]) {
      LOG(FATAL) << this->type()
                 << " Layer cannot backpropagate to label inputs.";
    }
    if (propagate_down[0]) {
      // First, compute the diff
      const int count = bottom[0]->count();
      const Dtype* sigmoid_output_data = sigmoid_output_->cpu_data();
      const Dtype* target = bottom[1]->cpu_data();
      Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
      caffe_sub(count, sigmoid_output_data, target, bottom_diff);
      // Zero out gradient of ignored targets.
      if (has_ignore_label_) {
        for (int i = 0; i < count; ++i) {
          const int target_value = static_cast<int>(target[i]);
          if (target_value == ignore_label_) {
            bottom_diff[i] = 0;
          }
        }
      }
      // Scale down gradient
      Dtype loss_weight = top[0]->cpu_diff()[0] / normalizer_;
      caffe_scal(count, loss_weight, bottom_diff);
    }
  }
  
  #ifdef CPU_ONLY
  STUB_GPU(SigmoidCrossEntropyLossLayer);
  #endif
  
  INSTANTIATE_CLASS(SigmoidCrossEntropyLossLayer);
  REGISTER_LAYER_CLASS(SigmoidCrossEntropyLoss);
  
  }  // namespace caffe
  