text: #include <algorithm>
  #include <cfloat>
  #include <vector>
  
  #include "caffe/layers/softmax_loss_layer.hpp"
  #include "caffe/util/math_functions.hpp"
  
  namespace caffe {
  
  template <typename Dtype>
  void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  
    LossLayer<Dtype>::LayerSetUp(bottom, top);
    
    LayerParameter softmax_param(this->layer_param_);
    softmax_param.set_type("Softmax");
    softmax_layer_ = LayerRegistry<Dtype>::CreateLayer(softmax_param);
    softmax_bottom_vec_.clear();
    softmax_bottom_vec_.push_back(bottom[0]);
    softmax_top_vec_.clear();
    softmax_top_vec_.push_back(&prob_);
    softmax_layer_->SetUp(softmax_bottom_vec_, softmax_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();
    }
    if (!this->layer_param_.loss_param().has_normalization() &&
        this->layer_param_.loss_param().has_normalize()) {
      normalization_ = this->layer_param_.loss_param().normalize() ?
                       LossParameter_NormalizationMode_VALID :
                       LossParameter_NormalizationMode_BATCH_SIZE;
    } else {
      normalization_ = this->layer_param_.loss_param().normalization();
    }
  }
  
  template <typename Dtype>
  void SoftmaxWithLossLayer<Dtype>::Reshape(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    LossLayer<Dtype>::Reshape(bottom, top);
    softmax_layer_->Reshape(softmax_bottom_vec_, softmax_top_vec_);
    softmax_axis_ =
        bottom[0]->CanonicalAxisIndex(this->layer_param_.softmax_param().axis());
  
    outer_num_ = bottom[0]->count(0, softmax_axis_);
    inner_num_ = bottom[0]->count(softmax_axis_ + 1);
    
    CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
        << "Number of labels must match number of predictions; "
        << "e.g., if softmax 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}.";
    if (top.size() >= 2) {
      // softmax output
      top[1]->ReshapeLike(*bottom[0]);
    }
  }
  
  template <typename Dtype>
  Dtype SoftmaxWithLossLayer<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 SoftmaxWithLossLayer<Dtype>::Forward_cpu(
      const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
    // The forward pass computes the softmax prob values.
    softmax_layer_->Forward(softmax_bottom_vec_, softmax_top_vec_);
  
    const Dtype* prob_data = prob_.cpu_data();
    const Dtype* label = bottom[1]->cpu_data();
    
    int dim = prob_.count() / outer_num_;
    int count = 0;
    Dtype loss = 0;
    
    for (int i = 0; i < outer_num_; ++i) {
      for (int j = 0; j < inner_num_; j++) {
        const int label_value = static_cast<int>(label[i * inner_num_ + j]);
  
        if (has_ignore_label_ && label_value == ignore_label_) {
          continue;
        }
        DCHECK_GE(label_value, 0);
        DCHECK_LT(label_value, prob_.shape(softmax_axis_));
        
        loss -= log(std::max(prob_data[i * dim + label_value * inner_num_ + j],
                             Dtype(FLT_MIN)));
        ++count;
      }
    }
  
    top[0]->mutable_cpu_data()[0] = loss / get_normalizer(normalization_, count);
    LOG(INFO) << "valid_count: " << get_normalizer(normalization_, count) ;
  
    if (top.size() == 2) {
      top[1]->ShareData(prob_);
    }
  }
  
  template <typename Dtype>
  void SoftmaxWithLossLayer<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]) {
      Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();
      const Dtype* prob_data = prob_.cpu_data();
    
      caffe_copy(prob_.count(), prob_data, bottom_diff);
    
      const Dtype* label = bottom[1]->cpu_data();
    
      int dim = prob_.count() / outer_num_;
      int count = 0;
    
      for (int i = 0; i < outer_num_; ++i) {
        for (int j = 0; j < inner_num_; ++j) {
          const int label_value = static_cast<int>(label[i * inner_num_ + j]);
  
          if (has_ignore_label_ && label_value == ignore_label_) {
            for (int c = 0; c < bottom[0]->shape(softmax_axis_); ++c) {
              bottom_diff[i * dim + c * inner_num_ + j] = 0;
            }
          } 
          else {
            bottom_diff[i * dim + label_value * inner_num_ + j] -= 1;
            ++count;
          }
        }
      }
      // Scale gradient
      Dtype loss_weight = top[0]->cpu_diff()[0] /
                          get_normalizer(normalization_, count);
      caffe_scal(prob_.count(), loss_weight, bottom_diff);
    }
  }
  
  #ifdef CPU_ONLY
  STUB_GPU(SoftmaxWithLossLayer);
  #endif
  
  INSTANTIATE_CLASS(SoftmaxWithLossLayer);
  REGISTER_LAYER_CLASS(SoftmaxWithLoss);
  
  }  // namespace caffe