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import torch
from torch_geometric.nn import SAGEConv, GATConv, Sequential, BatchNorm
from torch_geometric.nn import SAGPooling
class PolymerGNN_Joint(torch.nn.Module):
def __init__(self, input_feat, hidden_channels, num_additional = 0):
super(PolymerGNN_Joint, 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.Sequential( # IV model
torch.nn.Linear(hidden_channels, hidden_channels),
torch.nn.PReLU(),
torch.nn.Linear(hidden_channels, 1)
)
self.fc3 = torch.nn.Linear(hidden_channels, 1) # Tg
self.mult_factor = torch.nn.Sequential(
torch.nn.Linear(hidden_channels, 1),
torch.nn.Tanh(),
)
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.Asage(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))
# Run IV:
x_IV = self.fc2(x)
# Run Tg (+ multiplying factor):
x_Tg = torch.exp(self.fc3(x))
m = self.mult_factor(x)
x_Tg = x_Tg * m
return {'IV': x_IV, 'Tg': x_Tg} |