#include <algorithm>
#include <cfloat>
#include <vector>

#include "caffe/layer.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/vision_layers.hpp"

namespace caffe {

template <typename Dtype>
void SoftmaxWithLossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
  LossLayer<Dtype>::LayerSetUp(bottom, top);
  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_);
}

template <typename Dtype>
void SoftmaxWithLossLayer<Dtype>::Reshape(
    const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  softmax_layer_->Reshape(softmax_bottom_vec_, &softmax_top_vec_);
  if (top->size() >= 2) {
    // softmax output
    (*top)[1]->ReshapeLike(*bottom[0]);
  }
}

template <typename Dtype>
void SoftmaxWithLossLayer<Dtype>::Forward_cpu(
    const vector<Blob<Dtype>*>& bottom, 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 num = prob_.num();
  int dim = prob_.count() / num;
  int spatial_dim = prob_.height() * prob_.width();
  Dtype loss = 0;
  for (int i = 0; i < num; ++i) {
      for (int j = 0; j < spatial_dim; j++) {
        loss -= log(std::max(prob_data[i * dim +
            static_cast<int>(label[i * spatial_dim + j]) * spatial_dim + j],
                             Dtype(FLT_MIN)));
        		  }
    }
  //LOG(INFO) << "dim:" << dim <<", spatial_dim:" << spatial_dim;
  if (this->layer_param_.has_recording()) {
       		  if (this->layer_param_.recording()) {
       			std::ofstream file;
       			file.open("./loss_softmax.txt", ios::out|ios::app);
  for (int i = 0; i < num; ++i) {
	  file << i << "\t";
    for (int j=0; j<dim; ++j)
    {
    	file << prob_data[i*dim+j] << "\t";
    }
    file << "\n";
  }
  file.close();
    }
    /*if (i<10)
    {
    	int maxprob=0;
    	Dtype maxvalue=0;
    	for (int k=0;k<10575;k++)
    	{
    		if (prob_data[i*dim+k]>maxvalue)
    		{
    			maxvalue=prob_data[i*dim+k];
    			maxprob=k;
    		}
    	}
    	LOG(INFO) << "label:" << label[i] << ", prob[" << maxprob << "]=" << maxvalue;
    	//LOG(INFO) << i << ", label:" << static_cast<int>(label[i]) << ", prob:" << prob_data[i * dim + static_cast<int>(label[i])] << ", min:" << Dtype(FLT_MIN);
    }*/
  }
  (*top)[0]->mutable_cpu_data()[0] = loss / num / spatial_dim;
  //LOG(INFO) << "softmax_loss:" << loss / num / spatial_dim;
  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,
    vector<Blob<Dtype>*>* bottom) {
  if (propagate_down[1]) {
    LOG(FATAL) << this->type_name()
               << " 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 num = prob_.num();
    int dim = prob_.count() / num;
    int spatial_dim = prob_.height() * prob_.width();
    for (int i = 0; i < num; ++i) {
      for (int j = 0; j < spatial_dim; ++j) {
        bottom_diff[i * dim + static_cast<int>(label[i * spatial_dim + j])
            * spatial_dim + j] -= 1;
      }
    }
    // Scale gradient
    const Dtype loss_weight = top[0]->cpu_diff()[0];
    caffe_scal(prob_.count(), loss_weight / num / spatial_dim, bottom_diff);
  }
}


#ifdef CPU_ONLY
STUB_GPU(SoftmaxWithLossLayer);
#endif

INSTANTIATE_CLASS(SoftmaxWithLossLayer);


}  // namespace caffe
