# Copyright (c) 2021. Lucas G. S. Jeub
#
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# of this software and associated documentation files (the "Software"), to deal
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# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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import torch
import torch_geometric as tg
from torch.nn import functional as F
[docs]
class VGAEconv(torch.nn.Module):
"""
implements the convolution operator for use with :class:`torch_geometric.nn.VGAE`
"""
[docs]
def __init__(self, dim, num_node_features, hidden_dim=32, cached=True, bias=True, add_self_loops=True, normalize=True):
super().__init__()
self.conv1 = tg.nn.GCNConv(num_node_features, hidden_dim, cached=cached, bias=bias, add_self_loops=add_self_loops,
normalize=normalize)
self.mean_conv2 = tg.nn.GCNConv(hidden_dim, dim, cached=cached, bias=bias, add_self_loops=add_self_loops,
normalize=normalize)
self.var_conv2 = tg.nn.GCNConv(hidden_dim, dim, cached=cached, bias=bias, add_self_loops=add_self_loops,
normalize=normalize)
[docs]
def forward(self, data: tg.data.Data):
"""
compute mean and variance given data
Args:
data: input data
Returns:
mu, sigma
"""
x = data.x
edge_index = data.edge_index
x = self.conv1(x, edge_index)
x = F.relu(x)
mu = self.mean_conv2(x, edge_index)
sigma = self.var_conv2(x, edge_index)
return mu, sigma