Added a per node loss interface for the structural_graph_labeling_trainer.

dlib/svm/structural_graph_labeling_trainer.h

@@ -167,20 +167,23 @@ namespace dlib
             >
         const graph_labeler<vector_type> train (  
             const dlib::array<graph_type>& samples,
-            const std::vector<label_type>& labels
+            const std::vector<label_type>& labels,
+            const std::vector<std::vector<double> >& losses
         ) const
         {
-            // make sure requires clause is not broken
-            DLIB_ASSERT(is_graph_labeling_problem(samples, labels),
-                "\t void structural_graph_labeling_trainer::train()"
-                << "\n\t Invalid inputs were given to this function."
-                << "\n\t samples.size(): " << samples.size() 
-                << "\n\t labels.size():  " << labels.size() 
-                << "\n\t this: " << this
-                );
+            DLIB_ASSERT(is_graph_labeling_problem(samples, labels) == true &&
+                        (losses.size() == 0 || sizes_match(labels, losses) == true) &&
+                        all_values_are_nonnegative(losses) == true,
+                    "\t void structural_graph_labeling_trainer::train()"
+                    << "\n\t Invalid inputs were given to this function."
+                    << "\n\t samples.size(): " << samples.size() 
+                    << "\n\t labels.size():  " << labels.size() 
+                    << "\n\t losses.size():  " << losses.size() 
+                    << "\n\t sizes_match(labels,losses): " << sizes_match(labels,losses) 
+                    << "\n\t all_values_are_nonnegative(losses): " << all_values_are_nonnegative(losses) 
+                    << "\n\t this: " << this );
 
 
-            std::vector<std::vector<double> > losses;
             structural_svm_graph_labeling_problem<graph_type> prob(samples, labels, losses, num_threads);
 
             if (verbose)
@@ -189,8 +192,11 @@ namespace dlib
             prob.set_c(C);
             prob.set_epsilon(eps);
             prob.set_max_cache_size(max_cache_size);
-            prob.set_loss_on_positive_class(loss_pos);
-            prob.set_loss_on_negative_class(loss_neg);
+            if (prob.get_losses().size() == 0)
+            {
+                prob.set_loss_on_positive_class(loss_pos);
+                prob.set_loss_on_negative_class(loss_neg);
+            }
 
             matrix<double,0,1> w;
             solver(prob, w, prob.get_num_edge_weights());
@@ -201,6 +207,18 @@ namespace dlib
             return graph_labeler<vector_type>(edge_weights, node_weights);
         }
 
+        template <
+            typename graph_type
+            >
+        const graph_labeler<vector_type> train (  
+            const dlib::array<graph_type>& samples,
+            const std::vector<label_type>& labels
+        ) const
+        {
+            std::vector<std::vector<double> > losses;
+            return train(samples, labels, losses);
+        }
+
     private:
 
         template <typename T>

dlib/svm/structural_graph_labeling_trainer_abstract.h

@@ -212,14 +212,49 @@ namespace dlib
             requires
                 - is_graph_labeling_problem(samples,labels) == true
             ensures
-                - Uses the structural_svm_graph_labeling_problem to train a 
-                  graph_labeler on the given samples/labels training pairs.  
-                  The idea is to learn to predict a label given an input sample.
+                - Uses the structural_svm_graph_labeling_problem to train a graph_labeler
+                  on the given samples/labels training pairs.  The idea is to learn to
+                  predict a label given an input sample.
+                - The values of get_loss_on_positive_class() and get_loss_on_negative_class() 
+                  are used to determine how to value mistakes on each node during training.
                 - returns a function F with the following properties:
-                    - F(new_sample) == The predicted labels for the nodes in the
-                      graph new_sample.
+                    - F(new_sample) == The predicted labels for the nodes in the graph
+                      new_sample.
         !*/
 
+        template <
+            typename graph_type
+            >
+        const graph_labeler<vector_type> train (  
+            const dlib::array<graph_type>& samples,
+            const std::vector<label_type>& labels,
+            const std::vector<std::vector<double> >& losses
+        ) const;
+        /*!
+            requires
+                - is_graph_labeling_problem(samples,labels) == true
+                - if (losses.size() != 0) then
+                    - sizes_match(labels, losses) == true
+                    - all_values_are_nonnegative(losses) == true
+            ensures
+                - Uses the structural_svm_graph_labeling_problem to train a graph_labeler
+                  on the given samples/labels training pairs.  The idea is to learn to
+                  predict a label given an input sample.
+                - returns a function F with the following properties:
+                    - F(new_sample) == The predicted labels for the nodes in the graph
+                      new_sample.
+                - if (losses.size() == 0) then
+                    - The values of get_loss_on_positive_class() and get_loss_on_negative_class() 
+                      are used to determine how to value mistakes on each node during training.
+                    - The losses argument is effectively ignored if its size is zero.
+                - else
+                    - Each node in the training data has its own loss value defined by the
+                      corresponding entry of losses.  In particular, this means that the
+                      node with label labels[i][j] incurs a loss of losses[i][j] if it is
+                      incorrectly labeled.
+                    - The get_loss_on_positive_class() and get_loss_on_negative_class()
+                      parameters are ignored.  Only losses is used in this case.
+        !*/
     };
 
 // ----------------------------------------------------------------------------------------