Modify euclidean_loss_layer.cpp and euclidean_loss_layer.cu

[Jobs Bill]

Hi everyone,

I need to use special loss layer which can do some judgment to exclude some data. For example, when label is (-1,-1), I set the predict results to (-1,-1) so that (-1,-1) label will not be contribute to loss. Since I use GPU, I need modify both euclidean_loss_layer.cpp and euclidean_loss_layer.cu.

BUT, after debug, I simply add some code in Reshape of euclidean_loss_layer.cpp like:

template <typename Dtype>
void EuclideanLossLayer<Dtype>::Reshape(
  const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {
  LossLayer<Dtype>::Reshape(bottom, top);
  CHECK_EQ(bottom[0]->count(1), bottom[1]->count(1))
      << "Inputs must have the same dimension.";
  diff_.ReshapeLike(*bottom[0]);
  ///////////////////////////////////////////////
  ////////////////@201603////////////////////////
  int outer_num_ = bottom[0]->count(0,1);                                   // set iter number.
  int inner_num_ = bottom[0]->count(1);
  CHECK_EQ(outer_num_ * inner_num_, bottom[1]->count())
      <<"(N,C,H,W)";
  const Dtype* label = bottom[1]->cpu_data();                              // get label
  int label_value[2];
  for (int i=0; i< outer_num_; ++i){
    for(int j =0; j < inner_num_; j++){
      label_value[j]= static_cast<int>(label[i * inner_num_ + j]);
    }
    if ((label_value[0] == -1) && (label_value[1] == -1)){          // if label is (-1,-1), set bottom[0] to (-1,-1)
      bottom[0]->mutable_cpu_data()[i * inner_num_] = -1.0;         // I want to change bottom[0]'s value
      bottom[0]->mutable_cpu_data()[i * inner_num_ + 1] = -1.0;   // maybe wrong here.
    }
  }
   ///////////////////add above/////////////////////
}

bottom[0] is regression_layer's output(256 * 2), and bottom[1] is the label(256 * 2 * 1 * 1).

I train the net with GPU mode, and I think every loop it will do Reshape first and then run the .cu file.

So it seems correct to just modify Reshape in .cpp.

Am I wrong? Any suggestions will be appreciated.