Add NERL inference evaluation

Author: Donglai Wei

connectomics/config/schema/inference.py

@@ -194,6 +194,18 @@ class EvaluationConfig:
     metrics: Optional[List[str]] = None  # e.g., ['dice', 'jaccard', 'accuracy']
     prediction_threshold: float = 0.5  # Probability/logit threshold for binary metrics
     instance_iou_threshold: float = 0.5  # IoU threshold for instance matching metrics
+    # Neurite ERL evaluation via lib/em_erl. nerl_graph accepts an ERLGraph
+    # .npz or a BANIS/NISB-style NetworkX skeleton.pkl.
+    nerl_graph: Any = None
+    nerl_mask: Any = None
+    nerl_resolution: Optional[List[float]] = None
+    nerl_merge_threshold: int = 1
+    nerl_chunk_num: int = 1
+    nerl_skeleton_id_attribute: str = "id"
+    nerl_skeleton_position_attribute: str = "index_position"
+    nerl_skeleton_edge_length_attribute: str = "edge_length"
+    nerl_skeleton_position_order: str = "xyz"
+    nerl_prediction_position_order: Optional[str] = None
 
 
 @dataclass

connectomics/training/lightning/model.py

@@ -850,6 +850,24 @@ def _save_metrics_to_file(self, metrics_dict: Dict[str, Any]):
                         f.write(f"  Accuracy:                     {metrics_dict['accuracy']:.6f}\n")
                     f.write("\n")
 
+                if "nerl" in metrics_dict:
+                    f.write("Neurite ERL Metrics:\n")
+                    f.write("-" * 80 + "\n")
+                    f.write(f"  NERL:                         {metrics_dict['nerl']:.6f}\n")
+                    if "nerl_erl" in metrics_dict:
+                        f.write(f"  ERL:                          {metrics_dict['nerl_erl']:.6f}\n")
+                    if "nerl_max_erl" in metrics_dict:
+                        f.write(
+                            f"  Max ERL:                      {metrics_dict['nerl_max_erl']:.6f}\n"
+                        )
+                    if "nerl_num_skeletons" in metrics_dict:
+                        f.write(
+                            f"  Skeletons:                    {metrics_dict['nerl_num_skeletons']}\n"
+                        )
+                    if "nerl_graph" in metrics_dict:
+                        f.write(f"  Graph:                        {metrics_dict['nerl_graph']}\n")
+                    f.write("\n")
+
                 f.write("=" * 80 + "\n")
 
             logger.info(f"Metrics saved to: {metrics_file}")
@@ -925,6 +943,10 @@ def _log_decode_experiment(
             "instance_precision_detail",
             "instance_recall_detail",
             "instance_f1_detail",
+            "nerl",
+            "nerl_erl",
+            "nerl_max_erl",
+            "nerl_num_skeletons",
         ]
 
         header_cols = (