Prefix dense versions of convolution classes with Dense.

src/decagon_pytorch/convolve.py

@@ -240,7 +240,7 @@ class SparseMultiDGCA(torch.nn.Module):
         return out
 
 
-class GraphConv(torch.nn.Module):
+class DenseGraphConv(torch.nn.Module):
     def __init__(self, in_channels: int, out_channels: int,
         adjacency_matrix: torch.Tensor, **kwargs) -> None:
         super().__init__(**kwargs)
@@ -255,13 +255,13 @@ class GraphConv(torch.nn.Module):
         return x
 
 
-class DropoutGraphConvActivation(torch.nn.Module):
+class DenseDropoutGraphConvActivation(torch.nn.Module):
     def __init__(self, input_dim: int, output_dim: int,
         adjacency_matrix: torch.Tensor, keep_prob: float=1.,
         activation: Callable[[torch.Tensor], torch.Tensor]=torch.nn.functional.relu,
         **kwargs) -> None:
         super().__init__(**kwargs)
-        self.graph_conv = GraphConv(input_dim, output_dim, adjacency_matrix)
+        self.graph_conv = DenseGraphConv(input_dim, output_dim, adjacency_matrix)
         self.keep_prob = keep_prob
         self.activation = activation
 
@@ -272,7 +272,7 @@ class DropoutGraphConvActivation(torch.nn.Module):
         return x
 
 
-class MultiDGCA(torch.nn.Module):
+class DenseMultiDGCA(torch.nn.Module):
     def __init__(self, input_dim: List[int], output_dim: int,
         adjacency_matrices: List[torch.Tensor], keep_prob: float=1.,
         activation: Callable[[torch.Tensor], torch.Tensor]=torch.nn.functional.relu,
@@ -291,7 +291,7 @@ class MultiDGCA(torch.nn.Module):
             raise ValueError('input_dim must have the same length as adjacency_matrices')
         self.dgca = []
         for input_dim, adj_mat in zip(self.input_dim, self.adjacency_matrices):
-            self.dgca.append(DropoutGraphConvActivation(input_dim, self.output_dim, adj_mat, self.keep_prob, self.activation))
+            self.dgca.append(DenseDropoutGraphConvActivation(input_dim, self.output_dim, adj_mat, self.keep_prob, self.activation))
 
     def forward(self, x: List[torch.Tensor]) -> List[torch.Tensor]:
         if not isinstance(x, list):

tests/decagon_pytorch/test_convolve.py

@@ -41,25 +41,25 @@ def dropout_sparse_tf(x, keep_prob, num_nonzero_elems):
     return pre_out * (1./keep_prob)
 
 
-def graph_conv_torch():
+def dense_graph_conv_torch():
     torch.random.manual_seed(0)
     latent, adjacency_matrices = prepare_data()
     latent = torch.tensor(latent)
     adj_mat = adjacency_matrices[0]
     adj_mat = torch.tensor(adj_mat)
-    conv = decagon_pytorch.convolve.GraphConv(10, 10,
+    conv = decagon_pytorch.convolve.DenseGraphConv(10, 10,
         adj_mat)
     latent = conv(latent)
     return latent
 
 
-def dropout_graph_conv_activation_torch(keep_prob=1.):
+def dense_dropout_graph_conv_activation_torch(keep_prob=1.):
     torch.random.manual_seed(0)
     latent, adjacency_matrices = prepare_data()
     latent = torch.tensor(latent)
     adj_mat = adjacency_matrices[0]
     adj_mat = torch.tensor(adj_mat)
-    conv = decagon_pytorch.convolve.DropoutGraphConvActivation(10, 10,
+    conv = decagon_pytorch.convolve.DenseDropoutGraphConvActivation(10, 10,
         adj_mat, keep_prob=keep_prob)
     latent = conv(latent)
     return latent
@@ -173,7 +173,7 @@ def test_sparse_multi_dgca():
 
 
 def test_graph_conv():
-    latent_dense = graph_conv_torch()
+    latent_dense = dense_graph_conv_torch()
     latent_sparse = sparse_graph_conv_torch()
 
     assert np.all(latent_dense.detach().numpy() == latent_sparse.detach().numpy())
@@ -206,7 +206,7 @@ def test_dropout_graph_conv_activation():
             keep_prob += np.finfo(np.float32).eps
         print('keep_prob:', keep_prob)
 
-        latent_dense = dropout_graph_conv_activation_torch(keep_prob)
+        latent_dense = dense_dropout_graph_conv_activation_torch(keep_prob)
         latent_dense = latent_dense.detach().numpy()
         print('latent_dense:', latent_dense)
 
@@ -239,7 +239,7 @@ def test_multi_dgca():
     multi_sparse = decagon_pytorch.convolve.SparseMultiDGCA([10,] * len(adjacency_matrices), 10, adjacency_matrices_sparse, keep_prob=keep_prob)
 
     torch.random.manual_seed(0)
-    multi = decagon_pytorch.convolve.MultiDGCA([10,] * len(adjacency_matrices), 10, adjacency_matrices, keep_prob=keep_prob)
+    multi = decagon_pytorch.convolve.DenseMultiDGCA([10,] * len(adjacency_matrices), 10, adjacency_matrices, keep_prob=keep_prob)
 
     print('len(adjacency_matrices):', len(adjacency_matrices))
     print('len(multi_sparse.sparse_dgca):', len(multi_sparse.sparse_dgca))