Fix for non-leaf .grad warning.

src/icosagon/input.py

@@ -60,11 +60,11 @@ class OneHotInputLayer(torch.nn.Module):
         self.build()
 
     def build(self) -> None:
-        self.node_reps = []
+        self.node_reps = torch.nn.ParameterList()
         for i, nt in enumerate(self.data.node_types):
             reps = torch.eye(nt.count).to_sparse()
-            reps = torch.nn.Parameter(reps)
-            self.register_parameter('node_reps[%d]' % i, reps)
+            reps = torch.nn.Parameter(reps, requires_grad=False)
+            # self.register_parameter('node_reps[%d]' % i, reps)
             self.node_reps.append(reps)
 
     def forward(self, x) -> List[torch.nn.Parameter]:

tests/icosagon/test_trainloop.py

@@ -37,10 +37,16 @@ def test_train_loop_02():
 
     m = Model(prep_d)
 
+    for prm in m.parameters():
+        print(prm.shape, prm.is_leaf, prm.requires_grad)
+
     loop = TrainLoop(m)
 
     loop.run_epoch()
 
+    for prm in m.parameters():
+        print(prm.shape, prm.is_leaf, prm.requires_grad)
+
 
 def test_train_loop_03():
     # pdb.set_trace()