Need to de-bug test_multi_dgca().

src/decagon_pytorch/convolve.py

@@ -44,6 +44,7 @@ class SparseMultiDGCA(torch.nn.Module):
         activation=torch.nn.functional.relu,
         **kwargs):
         super().__init__(**kwargs)
+        self.output_dim = output_dim
         self.sparse_dgca =  [ SparseDropoutGraphConvActivation(input_dim, output_dim, adj_mat, keep_prob, activation) for adj_mat in adjacency_matrices ]
 
     def forward(self, x):
@@ -92,6 +93,7 @@ class MultiDGCA(torch.nn.Module):
         activation=torch.nn.functional.relu,
         **kwargs):
         super().__init__(**kwargs)
+        self.output_dim = output_dim
         self.dgca =  [ DropoutGraphConvActivation(input_dim, output_dim, adj_mat, keep_prob, activation) for adj_mat in adjacency_matrices ]
 
     def forward(self, x):

tests/decagon_pytorch/test_convolve.py

@@ -180,7 +180,9 @@ def test_graph_conv():
 
 
 def setup_function(fun):
-    if fun == test_dropout_graph_conv_activation:
+    if fun == test_dropout_graph_conv_activation or \
+        fun == test_multi_dgca:
+        print('Disabling dropout for testing...')
         setup_function.old_dropout = decagon_pytorch.convolve.dropout, \
             decagon_pytorch.convolve.dropout_sparse
 
@@ -189,7 +191,9 @@ def setup_function(fun):
 
 
 def teardown_function(fun):
-    if fun == test_dropout_graph_conv_activation:
+    print('Re-enabling dropout...')
+    if fun == test_dropout_graph_conv_activation or \
+        fun == test_multi_dgca:
         decagon_pytorch.convolve.dropout, \
             decagon_pytorch.convolve.dropout_sparse = \
             setup_function.old_dropout
@@ -213,3 +217,26 @@ def test_dropout_graph_conv_activation():
         nonzero = (latent_dense != 0) & (latent_sparse != 0)
 
         assert np.all(latent_dense[nonzero] == latent_sparse[nonzero])
+
+
+def test_multi_dgca():
+    keep_prob = .5
+
+    torch.random.manual_seed(0)
+    latent, adjacency_matrices = prepare_data()
+
+    latent_sparse = torch.tensor(latent).to_sparse()
+    latent = torch.tensor(latent)
+
+    adjacency_matrices_sparse = [ torch.tensor(a).to_sparse() for a in adjacency_matrices ]
+    adjacency_matrices = [ torch.tensor(a) for a in adjacency_matrices ]
+
+    multi_sparse = decagon_pytorch.convolve.SparseMultiDGCA(10, 10, adjacency_matrices_sparse, keep_prob=keep_prob)
+    multi = decagon_pytorch.convolve.MultiDGCA(10, 10, adjacency_matrices, keep_prob=keep_prob)
+
+    # torch.random.manual_seed(0)
+    latent_sparse = multi_sparse(latent_sparse)
+    # torch.random.manual_seed(0)
+    latent = multi(latent)
+
+    assert np.all(latent_sparse.detach().numpy() - latent.detach().numpy() < .000001)