outsource spline conv

torch_spline_conv/functions/spline_conv.py

@@ -5,15 +5,13 @@ from .degree import node_degree
 from .utils import spline_basis, spline_weighting
 
 
-def spline_conv(x,
+def _spline_conv(x,
                  edge_index,
                  pseudo,
                  weight,
                  kernel_size,
                  is_open_spline,
-                root_weight=None,
-                degree=1,
-                bias=None):
+                 degree=1):
 
     n, e = x.size(0), edge_index.size(1)
     K, m_in, m_out = weight.size()
@@ -34,7 +32,22 @@ def spline_conv(x,
     row = edge_index[0].unsqueeze(-1).expand(e, m_out)
     row = row if torch.is_tensor(x) else Var(row)
     zero = x.new(n, m_out) if torch.is_tensor(x) else Var(x.data.new(n, m_out))
-    output = zero.fill_(0).scatter_add_(0, row, output)
+    return zero.fill_(0).scatter_add_(0, row, output)
+
+
+def spline_conv(x,
+                edge_index,
+                pseudo,
+                weight,
+                kernel_size,
+                is_open_spline,
+                root_weight=None,
+                degree=1,
+                bias=None):
+
+    n = x.size(0)
+    output = _spline_conv(x, edge_index, pseudo, weight, kernel_size,
+                          is_open_spline, degree)
 
     # Normalize output by node degree.
     degree = x.new() if torch.is_tensor(x) else x.data.new()