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import torch
import torch_geometric
from torch_geometric.nn import SAGEConv, GATConv, Sequential, BatchNorm
from torch_geometric.nn import SAGPooling
class PolymerGNN_IV(torch.nn.Module):
'''
Args:
input_feat (int): Number of input features on each node.
hidden_channels (int): Number of neurons in hidden layers throughout
the neural network.
num_additional (int, optional): Number of additional resin properties
to be used during the training/prediction.
'''
def __init__(self, input_feat, hidden_channels, num_additional = 0):
super(PolymerGNN_IV, 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)
def forward(self, Abatch: torch_geometric.data.Batch, Gbatch: torch_geometric.data.Batch,
add_features: torch.Tensor):
'''
Args:
Abatch (torch_geometric.data.Batch): Batch object representing all acids in
the input. See make_like_batch for transforming to this.
Gbatch (torch_geometric.data.Batch): Batch object representing all glycols in
the input. See make_like_batch for transforming to this.
add_features (torch.Tensor): Additional features for this sample.
'''
# 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))
x = self.fc2(x)
# Because we're predicting log:
return torch.exp(x) |