# Copyright (c) 2021. Lucas G. S. Jeub
#
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import torch
import torch_geometric as tg
from torch.nn import functional as F
[docs]
class GAEconv(torch.nn.Module):
"""
implements the convolution operator for use with :class:`tg.nn.GAE`
"""
[docs]
def __init__(self, dim, num_node_features, hidden_dim=32, cached=True, bias=True, add_self_loops=True, normalize=True):
"""
Initialise parameters
Args:
dim: output dimension
num_node_features: input dimension
hidden_dim: hidden dimension
cached: if ``True``, cache the normalised adjacency matrix after first call
bias: if ``True``, include bias terms
add_self_loops: if ``True``, add self loops before normalising
normalize: if ``True``, normalise adjacency matrix
"""
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.conv2 = tg.nn.GCNConv(hidden_dim, dim, cached=cached, bias=bias, add_self_loops=add_self_loops,
normalize=normalize)
[docs]
def forward(self, data):
"""compute coordinates given data"""
edge_index = data.edge_index
x = F.relu(self.conv1(data.x, edge_index))
return self.conv2(x, edge_index)