def __init__(self, out_channels=64, num_edge_type=4, ch_list=None,
n_atom_types=MAX_ATOMIC_NUM, input_type='int',
scale_adj=False):
super(RelGCN, self).__init__()
if ch_list is None:
ch_list = [16, 128, 64]
with self.init_scope():
if input_type == 'int':
self.embed = EmbedAtomID(out_size=ch_list[0],
in_size=n_atom_types)
elif input_type == 'float':
self.embed = GraphLinear(None, ch_list[0])
else:
raise ValueError("[ERROR] Unexpected value input_type={}"
.format(input_type))
self.rgcn_convs = chainer.ChainList(*[
RelGCNUpdate(ch_list[i], ch_list[i+1], num_edge_type)
for i in range(len(ch_list)-1)])
self.rgcn_readout = GGNNReadout(
out_dim=out_channels, hidden_dim=ch_list[-1],
nobias=True, activation=functions.tanh)
# self.num_relations = num_edge_type
self.input_type = input_type
self.scale_adj = scale_adj
def __init__(self,
out_dim=64,
hidden_channels=None,
n_update_layers=None,
n_atom_types=MAX_ATOMIC_NUM,
n_edge_types=4,
input_type='int',
scale_adj=False):
super(RelGCN, self).__init__()
if hidden_channels is None:
hidden_channels = [16, 128, 64]
elif isinstance(hidden_channels, int):
if not isinstance(n_update_layers, int):
raise ValueError(
'Must specify n_update_layers when hidden_channels is int')
hidden_channels = [hidden_channels] * n_update_layers
with self.init_scope():
if input_type == 'int':
self.embed = EmbedAtomID(out_size=hidden_channels[0],
in_size=n_atom_types)
elif input_type == 'float':
self.embed = GraphLinear(None, hidden_channels[0])
else:
raise ValueError(
"[ERROR] Unexpected value input_type={}".format(
input_type))
self.rgcn_convs = chainer.ChainList(*[
RelGCNUpdate(hidden_channels[i], hidden_channels[i + 1],
n_edge_types)
for i in range(len(hidden_channels) - 1)
])
self.rgcn_readout = GGNNReadout(out_dim=out_dim,
in_channels=hidden_channels[-1],
nobias=True,
activation=functions.tanh)
# self.num_relations = num_edge_type
self.input_type = input_type
self.scale_adj = scale_adj
def update():
return RelGCNUpdate(in_channels=in_channels,
out_channels=hidden_dim,
num_edge_type=num_edge_type)