def __init__(self,
in_n,
out_message,
out_update,
l_target,
type='regression'):
super(MpnnIntNet, self).__init__()
n_layers = len(out_update)
# Define message 1 & 2
self.m = nn.ModuleList([
MessageFunction('intnet',
args={
'in': 2 * in_n[0] + in_n[1],
'out': out_message[i]
}) if i == 0 else MessageFunction(
'intnet',
args={
'in': 2 * out_update[i - 1] + in_n[1],
'out': out_message[i]
}) for i in range(n_layers)
])
# Define Update 1 & 2
self.u = nn.ModuleList([
UpdateFunction('intnet',
args={
'in': in_n[0] + out_message[i],
'out': out_update[i]
}) if i == 0 else UpdateFunction(
'intnet',
args={
'in': out_update[i - 1] + out_message[i],
'out': out_update[i]
}) for i in range(n_layers)
])
# Define Readout
self.r = ReadoutFunction('intnet',
args={
'in': out_update[-1],
'target': l_target
})
self.type = type
def __init__(self,
d,
in_n,
out_update,
hidden_state_readout,
l_target,
type='regression'):
super(MpnnDuvenaud, self).__init__()
n_layers = len(out_update)
# Define message 1 & 2
self.m = nn.ModuleList(
[MessageFunction('duvenaud') for _ in range(n_layers)])
# Define Update 1 & 2
self.u = nn.ModuleList([
UpdateFunction('duvenaud',
args={
'deg': d,
'in': self.m[i].get_out_size(in_n[0], in_n[1]),
'out': out_update[0]
})
if i == 0 else UpdateFunction(
'duvenaud',
args={
'deg': d,
'in': self.m[i].get_out_size(out_update[i - 1], in_n[1]),
'out': out_update[i]
}) for i in range(n_layers)
])
# Define Readout
self.r = ReadoutFunction('duvenaud',
args={
'layers':
len(self.m) + 1,
'in': [
in_n[0] if i == 0 else out_update[i -
1]
for i in range(n_layers + 1)
],
'out':
hidden_state_readout,
'target':
l_target
})
self.type = type
def __init__(self,
e,
hidden_state_size,
message_size,
n_layers,
l_target,
type='regression'):
super(MpnnGGNN, self).__init__()
# Define message
self.m = nn.ModuleList([
MessageFunction('ggnn',
args={
'e_label': e,
'in': hidden_state_size,
'out': message_size
})
])
# Define Update
self.u = nn.ModuleList([
UpdateFunction('ggnn',
args={
'in_m': message_size,
'out': hidden_state_size
})
])
# Define Readout
self.r = ReadoutFunction('ggnn',
args={
'in': hidden_state_size,
'target': l_target
})
self.type = type
self.args = {}
self.args['out'] = hidden_state_size
self.n_layers = n_layers
idx = np.random.permutation(len(files))
idx = idx.tolist()
valid_ids = [files[i] for i in idx[0:10000]]
test_ids = [files[i] for i in idx[10000:20000]]
train_ids = [files[i] for i in idx[20000:]]
data_train = datasets.Qm9(root, train_ids)
data_valid = datasets.Qm9(root, valid_ids)
data_test = datasets.Qm9(root, test_ids)
# d = datasets.utils.get_graph_stats(data_train, 'degrees')
d = [1, 2, 3, 4]
## Define message
m = MessageFunction('duvenaud')
## Parameters for the update function
# Select one graph
g_tuple, l = data_train[0]
g, h_t, e = g_tuple
m_v = m.forward(h_t[0], h_t[1], e[list(e.keys())[0]])
in_n = len(m_v)
out_n = 30
## Define Update
u = UpdateFunction('duvenaud', args={'deg': d, 'in': in_n, 'out': out_n})
in_n = len(h_t[0])