def test_evolve_gcn_h_layer():
"""
Testing the Evolve GCN-H Layer.
"""
number_of_nodes = 100
edge_per_node = 10
in_channels = 8
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
X, edge_index = create_mock_data(number_of_nodes, edge_per_node,
in_channels)
X = X.to(device)
edge_index = edge_index.to(device)
edge_weight = create_mock_edge_weight(edge_index).to(device)
layer = EvolveGCNH(in_channels=in_channels,
num_of_nodes=number_of_nodes).to(device)
X = layer(X, edge_index)
assert X.shape == (number_of_nodes, in_channels)
X = layer(X, edge_index, edge_weight)
assert X.shape == (number_of_nodes, in_channels)
def test_evolve_gcn_h_layer():
"""
Testing the Evolve GCN-H Layer.
"""
number_of_nodes = 100
edge_per_node = 10
in_channels = 8
X, edge_index = create_mock_data(number_of_nodes, edge_per_node,
in_channels)
edge_weight = create_mock_edge_weight(edge_index)
layer = EvolveGCNH(in_channels=in_channels, num_of_nodes=number_of_nodes)
X = layer(X, edge_index)
assert X.shape == (number_of_nodes, in_channels)
X = layer(X, edge_index, edge_weight)
assert X.shape == (number_of_nodes, in_channels)
def __init__(self, node_count, node_features, num_classes):
super(RecurrentGCN, self).__init__()
self.recurrent_1 = EvolveGCNH(node_count, node_features)
self.recurrent_2 = EvolveGCNH(node_count, node_features)
self.linear = torch.nn.Linear(node_features, num_classes)