def __init__(self):
super(Net, self).__init__()
self.lin1 = torch.nn.Linear(dataset.num_features, 16)
self.prop1 = AGNNConv(requires_grad=False)
self.prop2 = AGNNConv(requires_grad=True)
self.lin2 = torch.nn.Linear(16, dataset.num_classes)
def __init__(self, in_dim, out_dim):
super(Breadth, self).__init__()
self.gatconv = AGNNConv(requires_grad=True)
def __init__(self, in_dim, out_dim):
super(Breadth, self).__init__()
self.lin1 = torch.nn.Linear(in_dim, 16)
self.prop1 = AGNNConv(requires_grad=False)
self.prop2 = AGNNConv(requires_grad=True)
self.lin2 = torch.nn.Linear(16, out_dim)
def __init__(self,
in_dim,
hidden_dim,
out_dim,
dropout=0.5,
name='gat',
heads=8,
residual=True):
super(GNNModelPYG, self).__init__()
self.dropout = dropout
self.name = name
self.residual = None
if residual:
if in_dim == out_dim:
self.residual = Identity()
else:
self.residual = Linear(in_dim, out_dim)
if name == 'gat':
self.conv1 = GATConv(in_dim,
hidden_dim,
heads=heads,
dropout=dropout)
self.conv2 = GATConv(hidden_dim * heads,
out_dim,
heads=1,
concat=False,
dropout=dropout)
elif name == 'gcn':
self.conv1 = GCNConv(in_dim,
hidden_dim,
cached=True,
normalize=True,
add_self_loops=False)
self.conv2 = GCNConv(hidden_dim,
out_dim,
cached=True,
normalize=True,
add_self_loops=False)
elif name == 'cheb':
self.conv1 = ChebConv(in_dim, hidden_dim, K=2)
self.conv2 = ChebConv(hidden_dim, out_dim, K=2)
elif name == 'spline':
self.conv1 = SplineConv(in_dim, hidden_dim, dim=1, kernel_size=2)
self.conv2 = SplineConv(hidden_dim, out_dim, dim=1, kernel_size=2)
elif name == 'gin':
self.conv1 = GINConv(
Sequential(Linear(in_dim, hidden_dim), ReLU(),
Linear(hidden_dim, hidden_dim)))
self.conv2 = GINConv(
Sequential(Linear(hidden_dim, hidden_dim), ReLU(),
Linear(hidden_dim, out_dim)))
elif name == 'unet':
self.conv1 = GraphUNet(in_dim, hidden_dim, out_dim, depth=3)
elif name == 'agnn':
self.lin1 = Linear(in_dim, hidden_dim)
self.conv1 = AGNNConv(requires_grad=False)
self.conv2 = AGNNConv(requires_grad=True)
self.lin2 = Linear(hidden_dim, out_dim)
else:
raise NotImplemented("""
Unknown model name. Choose from gat, gcn, cheb, spline, gin, unet, agnn."""
)