diff --git a/rfa_toolbox/encodings/pytorch/ingest_architecture.py b/rfa_toolbox/encodings/pytorch/ingest_architecture.py
index d261cea..1e67cd3 100644
--- a/rfa_toolbox/encodings/pytorch/ingest_architecture.py
+++ b/rfa_toolbox/encodings/pytorch/ingest_architecture.py
@@ -1,4 +1,4 @@
-from typing import Tuple
+from typing import List, Optional, Tuple
 
 from rfa_toolbox.graphs import EnrichedNetworkNode
 
@@ -19,6 +19,7 @@ def make_graph(
     input_preds=None,
     parent_dot=None,
     ref_mod=None,
+    classes_to_not_visit=None,
 ):
     """
     This code was adapted from this blog article:
@@ -145,20 +146,38 @@ def is_relevant_type(t):
                     elem = "[" + elem + "]."
                 pr += elem
 
-            if classes_found is not None:
-                classes_found.add(fq_submodule_name)
-            if (
-                classes_to_visit is None
-                and (
-                    not fq_submodule_name.startswith("torch.nn")
-                    or fq_submodule_name.startswith("torch.nn.modules.container")
+            def _check_white_list(submodule_type, fq_submodule_name, classes_to_visit):
+                return (
+                    classes_to_visit is None
+                    and (
+                        not fq_submodule_name.startswith("torch.nn")
+                        or fq_submodule_name.startswith("torch.nn.modules.container")
+                    )
+                ) or (
+                    classes_to_visit is not None
+                    and (
+                        submodule_type in classes_to_visit
+                        or fq_submodule_name in classes_to_visit
+                    )
                 )
-            ) or (
-                classes_to_visit is not None
-                and (
-                    submodule_type in classes_to_visit
-                    or fq_submodule_name in classes_to_visit
+
+            def _check_black_list(
+                submodule_type, fq_submodule_name, classes_to_not_visit
+            ):
+                return (classes_to_not_visit is None) or (
+                    classes_to_not_visit is not None
+                    and (
+                        submodule_type not in classes_to_not_visit
+                        or fq_submodule_name not in classes_to_not_visit
+                    )
                 )
+
+            if classes_found is not None:
+                classes_found.add(fq_submodule_name)
+            if _check_white_list(
+                submodule_type, fq_submodule_name, classes_to_visit
+            ) and _check_black_list(
+                submodule_type, fq_submodule_name, classes_to_not_visit
             ):
                 # go into subgraph
                 sub_prefix = prefix + submodule_name + "."
@@ -177,6 +196,7 @@ def is_relevant_type(t):
                     parent_dot=dot,
                     classes_to_visit=classes_to_visit,
                     classes_found=classes_found,
+                    classes_to_not_visit=classes_to_not_visit,
                 )
                 # creating a mapping from the c-values
                 # to the output of the respective subgraph
@@ -272,7 +292,9 @@ def is_relevant_type(t):
 
 
 def create_graph_from_model(
-    model: torch.nn.Module, input_res: Tuple[int, int, int, int] = (1, 3, 399, 399)
+    model: torch.nn.Module,
+    input_res: Tuple[int, int, int, int] = (1, 3, 399, 399),
+    custom_layers: Optional[List[str]] = None,
 ) -> EnrichedNetworkNode:
     """Create a graph of enriched network nodes from a PyTorch-Model.
 
@@ -283,9 +305,15 @@ def create_graph_from_model(
                         color_channels, height, width) for CNNs.
                         Needs to be a 2-Tuple of shape (batch_size,
                         num_features) for fully connected networks.
+        custom_layers:  Class-names of custom layers, like DropPath
+        or Involutions, which are not part of
+                        torch.nn. Keep in mind that unknown layers
+                        will defaulted to have no effect on the
+                        receptive field size. You may need to
+                        implement some additional layer handlers.
 
     Returns:
         The EnrichedNetworkNodeGraph
     """
     tm = torch.jit.trace(model, (torch.randn(*input_res),))
-    return make_graph(tm, ref_mod=model).to_graph()
+    return make_graph(tm, ref_mod=model, classes_to_not_visit=custom_layers).to_graph()
diff --git a/rfa_toolbox/encodings/pytorch/intermediate_graph.py b/rfa_toolbox/encodings/pytorch/intermediate_graph.py
index 6cc5328..9caa371 100644
--- a/rfa_toolbox/encodings/pytorch/intermediate_graph.py
+++ b/rfa_toolbox/encodings/pytorch/intermediate_graph.py
@@ -160,10 +160,11 @@ def _check_for_lone_node(self, resolved_nodes: Dict[str, EnrichedNetworkNode]):
         for name, node in resolved_nodes.items():
             if len(node.predecessors) == 0 and len(node.succecessors) == 0:
                 warnings.warn(
-                    f"Found a node with no predecessors and no successors: {name},"
-                    f"this may be caused by some control-flow in "
-                    f"this node disabling any processing"
-                    f"within the node."
+                    f"Found a node with no predecessors and no successors: "
+                    f"'{node.layer_info.name}',"
+                    f" this may be caused by some control-flow in "
+                    f" this node disabling any processing"
+                    f" within the node."
                 )
 
     def to_graph(self) -> EnrichedNetworkNode: