Generalize loss by allowing any top blob to be used as a loss in which

its elements are summed with a scalar coefficient.

Forward for layers no longer returns a loss; instead all loss layers must have
top blobs.  Existing loss layers are given a top blob automatically by
Net::Init, with an associated top_loss_weight of 1 (set in
LossLayer::FurtherSetUp).  Due to the increased amount of common SetUp logic,
the SetUp interface is modified such that all subclasses should normally
override FurtherSetUp only, which is called by SetUp.

1 files changed, 5 insertions, 6 deletions

diff --git a/src/caffe/layers/loss_layer.cpp b/src/caffe/layers/loss_layer.cpp
index 48665221..89d8c91e 100644
--- a/src/caffe/layers/loss_layer.cpp
+++ b/src/caffe/layers/loss_layer.cpp
@@ -11,16 +11,15 @@
 namespace caffe {
 
 template <typename Dtype>
-void LossLayer<Dtype>::SetUp(
+void LossLayer<Dtype>::LayerSetUp(
     const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
-  Layer<Dtype>::SetUp(bottom, top);
   CHECK_EQ(bottom[0]->num(), bottom[1]->num())
       << "The data and label should have the same number.";
-  if (top->size() == 1) {
-  // Layers should copy the loss in the top blob
-    (*top)[0]->Reshape(1, 1, 1, 1);
+  (*top)[0]->Reshape(1, 1, 1, 1);
+  // LossLayers have a non-zero (1) loss by default.
+  if (this->layer_param_.loss_weight_size() == 0) {
+    this->layer_param_.add_loss_weight(Dtype(1));
   }
-  FurtherSetUp(bottom, top);
 }
 
 INSTANTIATE_CLASS(LossLayer);