add cardinality penalty term to baseline val metric

kraken/lib/train/segmentation.py

@@ -271,8 +271,15 @@ def validation_step(self, batch, batch_idx):
             if pred_curves:
                 pred_curves = torch.stack(pred_curves)
                 cost_curves = torch.cdist(pred_curves, y_curves[line_cls][0], p=1).cpu()
-                row_ind, col_ind = linear_sum_assignment(cost_curves)
-                self.val_line_dist.update(cost_curves[row_ind, col_ind]/8.0)
+                costs = cost_curves = [linear_sum_assignment(cost_curves)]
+                # num of predictions differs from target -> take n best
+                # predictions and add error penalty term for the rest.
+                if diff := abs(len(pred_curves) - len(y_curves[line_cls][0])):
+                    costs = np.sort(costs)[:len(y_curves[line_cls][0])]
+                    penalty = np.full(diff, 8.0)
+                    costs = np.concatenate([costs, penalty])
+                self.val_line_dist.update(costs/8.0)
+            # no line output
             else:
                 self.val_line_dist.update(torch.ones(len(y_curves[line_cls][0])))