remove need for backwards compatibility operation in feature nodes (l…

…anl#65)

* remove need for backwards compatibility operation in feature nodes

eliminates unneeded warning when creating new networks

examples/feature_extraction.py

@@ -23,10 +23,6 @@
 # Here we get nodes associated with the features from each block of a HIP-NN model.
 # Note: the first set is typically just a one-hot species representation, and does not reflect the atom's environment.
 
-# Note!!!: For backwards compatibility, you may need to run this function on the network:
-hippynn.graphs.networks._make_feature_nodes(network_node)
-# If your network is created with this version of hippynn or later, it is not necessary to run this function.
-
 feature_nodes = network_node.feature_nodes  # list of feature nodes
 feature_node_dict = {node.name: node for node in feature_nodes}  # dictionary of feature nodes
 

hippynn/graphs/nodes/networks.py

@@ -53,7 +53,37 @@ def expansion1(self, pidxer, pairfinder, **kwargs):
         return pidxer.indexed_features, pairfinder
 
 
-class Hipnn(DefaultNetworkExpansion, AutoKw, Network, SingleNode):
+class _FeatureNodesMixin:
+    @property
+    def feature_nodes(self):
+        if not hasattr(self, "_feature_nodes"):
+            self._make_feature_nodes()
+        return self._feature_nodes
+
+    def _make_feature_nodes(self):
+        """
+        This function can be used on a network to make nodes that refer to the individual feature blocks.
+        We use this function/class to provide backwards compatibility with models that did not have this
+        attribute when created.
+        :param self: the input network, which is modified in-place
+        :return: None
+        """
+
+        net_module = self.torch_module
+        n_interactions = net_module.ni
+
+        feature_nodes = []
+
+        index_state = IdxType.Atoms
+        parents = (self,)
+        for i in range(n_interactions + 1):
+            name = f"{self.name}_features_{i}"
+            fnode = IndexNode(name=name, parents=parents, index=i, index_state=index_state)
+            feature_nodes.append(fnode)
+        self._feature_nodes = feature_nodes
+
+
+class Hipnn(DefaultNetworkExpansion, AutoKw, Network, SingleNode, _FeatureNodesMixin):
     """
     Node for HIP-NN neural networks
     """
@@ -81,11 +111,8 @@ def __init__(self, name, parents, periodic=False, module="auto", module_kwargs=N
         )
         super().__init__(name, parents, module=net_module)
 
-        _make_feature_nodes(self)
-
 
-
-class HipnnVec(DefaultNetworkExpansion, AutoKw, Network, SingleNode):
+class HipnnVec(DefaultNetworkExpansion, AutoKw, Network, SingleNode, _FeatureNodesMixin):
     """
     Node for HIP-NN-TS neural networks, l=1
     """
@@ -114,41 +141,10 @@ def __init__(self, name, parents, periodic=False, module="auto", module_kwargs=N
 
         super().__init__(name, parents, module=net_module)
 
-        _make_feature_nodes(self)
-
 
 class HipnnQuad(HipnnVec):
     """
     Node for HIP-NN-TS neural networks, l=2
     """
 
     _auto_module_class = network_modules.hipnn.HipnnQuad
-
-
-def _make_feature_nodes(network_node):
-    """
-    This function can be used on a network to make nodes that refer to the individual feature blocks.
-    :param network_node: the input network, which is modified in-place
-    :return: None
-    """
-    import warnings
-    warnings.warn("This function is included for backwards compatibility and may be removed in a future release. "
-                  "The preferred way to access these nodes is through `network.feature_nodes`, which is available on "
-                  "networks created with this version of hippynn or later.")
-
-    if hasattr(network_node, "feature_nodes"):
-        return network_node.feature_nodes
-
-    net_module = network_node.torch_module
-    n_interactions = net_module.ni
-
-    feature_nodes = []
-
-    index_state = IdxType.Atoms
-    parents = (network_node,)
-    for i in range(n_interactions + 1):
-        name = f"{network_node.name}_features_{i}"
-        fnode = IndexNode(name=name, parents=parents, index=i, index_state=index_state)
-        feature_nodes.append(fnode)
-    network_node.feature_nodes = feature_nodes
-