def __init__(self, net_params):
super().__init__()
num_atom_type = net_params['num_atom_type']
hidden_dim = net_params['hidden_dim']
out_dim = net_params['out_dim']
kernel = net_params['kernel'] # for MoNet
dim = net_params['pseudo_dim_MoNet'] # for MoNet
dropout = net_params['dropout']
n_layers = net_params['L']
self.readout = net_params['readout']
graph_norm = net_params['graph_norm']
batch_norm = net_params['batch_norm']
residual = net_params['residual']
self.device = net_params['device']
aggr_type = "sum" # default for MoNet
self.embedding_h = nn.Embedding(num_atom_type, hidden_dim)
self.layers = nn.ModuleList()
self.pseudo_proj = nn.ModuleList()
# Hidden layer
for _ in range(n_layers-1):
self.layers.append(GMMLayer(hidden_dim, hidden_dim, F.relu, dim, kernel, aggr_type, dropout, graph_norm, batch_norm, residual))
self.pseudo_proj.append(nn.Sequential(nn.Linear(2, dim), nn.Tanh()))
# Output layer
self.layers.append(GMMLayer(hidden_dim, out_dim, None, dim, kernel, aggr_type, dropout, graph_norm, batch_norm, residual))
self.pseudo_proj.append(nn.Sequential(nn.Linear(2, dim), nn.Tanh()))
self.MLP_layer = MLPReadout(out_dim, 1) # out dim 1 since regression
def __init__(self, net_params):
super().__init__()
self.name = 'MoNet'
in_dim = net_params['in_dim']
hidden_dim = net_params['hidden_dim']
out_dim = net_params['out_dim']
kernel = net_params['kernel'] # for MoNet
dim = net_params['pseudo_dim_MoNet'] # for MoNet
n_classes = net_params['n_classes']
dropout = net_params['dropout']
n_layers = net_params['L']
self.readout = net_params['readout']
batch_norm = net_params['batch_norm']
residual = net_params['residual']
self.device = net_params['device']
aggr_type = "sum" # default for MoNet
self.embedding_h = nn.Linear(in_dim, hidden_dim)
self.layers = nn.ModuleList()
self.pseudo_proj = nn.ModuleList()
# Hidden layer
for _ in range(n_layers-1):
self.layers.append(GMMLayer(hidden_dim, hidden_dim, dim, kernel, aggr_type,
dropout, batch_norm, residual))
self.pseudo_proj.append(nn.Sequential(nn.Linear(2, dim), nn.Tanh()))
# Output layer
self.layers.append(GMMLayer(hidden_dim, out_dim, dim, kernel, aggr_type,
dropout, batch_norm, residual))
self.pseudo_proj.append(nn.Sequential(nn.Linear(2, dim), nn.Tanh()))
self.MLP_layer = MLPReadout(out_dim, n_classes)
def __init__(self, net_params):
super().__init__()
self.readout = net_params.readout
self.device = net_params.device
self.aggr_type = "sum" # default for MoNet
self.embedding_h = nn.Linear(net_params.in_dim, net_params.hidden_dim)
self.layers = nn.ModuleList()
self.pseudo_proj = nn.ModuleList()
# Hidden layer
for _ in range(net_params.L - 1):
self.layers.append(
GMMLayer(net_params.hidden_dim, net_params.hidden_dim,
net_params.pseudo_dim_MoNet, net_params.kernel,
self.aggr_type, net_params.dropout,
net_params.graph_norm, net_params.batch_norm,
net_params.residual))
self.pseudo_proj.append(
nn.Sequential(nn.Linear(2, net_params.pseudo_dim_MoNet),
nn.Tanh()))
pass
# Output layer
self.layers.append(
GMMLayer(net_params.hidden_dim, net_params.out_dim,
net_params.pseudo_dim_MoNet, net_params.kernel,
self.aggr_type, net_params.dropout, net_params.graph_norm,
net_params.batch_norm, net_params.residual))
self.pseudo_proj.append(
nn.Sequential(nn.Linear(2, net_params.pseudo_dim_MoNet),
nn.Tanh()))
self.readout_mlp = MLPReadout(net_params.out_dim, net_params.n_classes)
pass
