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from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_add_pool
import torch.nn.functional as F
from torch.nn import ModuleList, Linear, BatchNorm1d
import torch
class MolecularGCN(torch.nn.Module):
def __init__(self, dim, n_conv_hidden, n_mlp_hidden, dropout):
super(MolecularGCN, self).__init__()
self.n_features = 75 # This is the mol2graph.py-specific value
self.n_conv_hidden = n_conv_hidden
self.n_mlp_hidden = n_mlp_hidden
self.dim = dim
self.dropout = dropout
self.graphconv1 = GCNConv(self.n_features, self.dim, cached=False)
self.bn1 = BatchNorm1d(self.dim)
self.graphconv_hidden = ModuleList(
[GCNConv(self.dim, self.dim, cached=False) for _ in range(self.n_conv_hidden)]
)
self.bn_conv = ModuleList(
[BatchNorm1d(self.dim) for _ in range(self.n_conv_hidden)]
)
self.mlp_hidden = ModuleList(
[Linear(self.dim, self.dim) for _ in range(self.n_mlp_hidden)]
)
self.bn_mlp = ModuleList(
[BatchNorm1d(self.dim) for _ in range(self.n_mlp_hidden)]
)
self.mlp_out = Linear(self.dim, 1000) # classification of 1000 templates.
def forward(self, x, edge_index, batch, edge_weight=None):
x = F.relu(self.graphconv1(x, edge_index, edge_weight))
x = self.bn1(x)
for graphconv, bn_conv in zip(self.graphconv_hidden, self.bn_conv):
x = graphconv(x, edge_index, edge_weight)
x = bn_conv(x)
x = global_add_pool(x, batch)
for fc_mlp, bn_mlp in zip(self.mlp_hidden, self.bn_mlp):
x = F.relu(fc_mlp(x))
x = bn_mlp(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = F.log_softmax(self.mlp_out(x), dim=-1)
return x |