null pointer defaults for forward loss outputs

diff --git a/include/caffe/net.hpp b/include/caffe/net.hpp
index d6c892f..a30491f 100644 (file)
--- a/include/caffe/net.hpp
+++ b/include/caffe/net.hpp
@@ -31,15 +31,13 @@ class Net {
 
   // Run forward with the input blobs already fed separately. You can get the
   // input blobs using input_blobs().
-  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss);
-  const vector<Blob<Dtype>*>& ForwardPrefilled();
+  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL);
   // Run forward using a set of bottom blobs, and return the result.
   const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
-      Dtype* loss);
-  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom);
+      Dtype* loss = NULL);
   // Run forward using a serialized BlobProtoVector and return the result
   // as a serialized BlobProtoVector
-  string Forward(const string& input_blob_protos, Dtype* loss);
+  string Forward(const string& input_blob_protos, Dtype* loss = NULL);
 
   // The network backward should take no input and output, since it solely
   // computes the gradient w.r.t the parameters, and the data has already

diff --git a/src/caffe/net.cpp b/src/caffe/net.cpp
index 397ee02..f3429b2 100644 (file)
--- a/src/caffe/net.cpp
+++ b/src/caffe/net.cpp
@@ -207,30 +207,22 @@ void Net<Dtype>::GetLearningRateAndWeightDecay() {
 }
 
 template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled() {
-  Dtype ignored_loss;
-  return ForwardPrefilled(&ignored_loss);
-}
-
-template <typename Dtype>
 const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss) {
-  *loss = Dtype(0.);
+  if (loss != NULL) {
+    *loss = Dtype(0.);
+  }
   for (int i = 0; i < layers_.size(); ++i) {
     // LOG(ERROR) << "Forwarding " << layer_names_[i];
-    *loss += layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
+    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
+    if (loss != NULL) {
+      *loss += layer_loss;
+    }
   }
   return net_output_blobs_;
 }
 
 template <typename Dtype>
 const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
-    const vector<Blob<Dtype>*> & bottom) {
-  Dtype ignored_loss;
-  return Forward(bottom, &ignored_loss);
-}
-
-template <typename Dtype>
-const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
     const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
   // Copy bottom to internal bottom
   for (int i = 0; i < bottom.size(); ++i) {