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import torch
import torch_geometric as pyg
from torch_geometric.nn import SAGEConv, GCNConv, GATConv, Sequential, global_max_pool, global_mean_pool, BatchNorm
from torch_geometric.nn import SAGPooling, Set2Set
class PolymerGNN_Tg(torch.nn.Module):
def __init__(self, input_feat, hidden_channels, num_additional = 0):
super(PolymerGNN_Tg, self).__init__()
self.hidden_channels = hidden_channels
self.Asage = Sequential('x, edge_index, batch', [
(GATConv(input_feat, hidden_channels, aggr = 'max'), 'x, edge_index -> x'),
BatchNorm(hidden_channels, track_running_stats=False),
torch.nn.PReLU(),
(SAGEConv(hidden_channels, hidden_channels, aggr = 'max'), 'x, edge_index -> x'),
BatchNorm(hidden_channels, track_running_stats=False),
torch.nn.PReLU(),
(SAGPooling(hidden_channels), 'x, edge_index, batch=batch -> x'),
])
self.Gsage = Sequential('x, edge_index, batch', [
(GATConv(input_feat, hidden_channels, aggr = 'max'), 'x, edge_index -> x'),
BatchNorm(hidden_channels, track_running_stats=False),
torch.nn.PReLU(),
(SAGEConv(hidden_channels, hidden_channels, aggr = 'max'), 'x, edge_index -> x'),
BatchNorm(hidden_channels, track_running_stats=False),
torch.nn.PReLU(),
(SAGPooling(hidden_channels), 'x, edge_index, batch=batch -> x'),
])
self.fc1 = torch.nn.Linear(hidden_channels * 2 + num_additional, hidden_channels)
self.leaky1 = torch.nn.PReLU()
self.fc2 = torch.nn.Linear(hidden_channels, 1)
self.mult_factor = torch.nn.Linear(hidden_channels, 1)
def forward(self, Abatch: torch.Tensor, Gbatch: torch.Tensor, add_features: torch.Tensor):
'''
'''
# Decompose X into acid and glycol
Aembeddings = self.Asage(Abatch.x, Abatch.edge_index, Abatch.batch)[0]
Gembeddings = self.Gsage(Gbatch.x, Gbatch.edge_index, Gbatch.batch)[0]
Aembed, _ = torch.max(Aembeddings, dim=0)
Gembed, _ = torch.max(Gembeddings, dim=0)
# Aggregate pooled vectors
if add_features is not None:
poolAgg = torch.cat([Aembed, Gembed, add_features])
else:
poolAgg = torch.cat([Aembed, Gembed])
x = self.leaky1(self.fc1(poolAgg))
pred = self.fc2(x)
factor = self.mult_factor(x).tanh()
# Because we're predicting log:
return torch.exp(pred) * factor |