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models/gnn.py

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+# Copyright 2023 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Implementation of Graph Convolutional Neural Networks."""
+
+import copy
+import math
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+def clones(module, n):
+  return nn.ModuleList([copy.deepcopy(module) for _ in range(n)])
+
+
+class GraphConvolution(nn.Module):
+  """Simple GCN layer, similar to https://arxiv.org/abs/1609.02907."""
+
+  def __init__(self, in_features, out_features, bias=True):
+    super().__init__()
+    self.in_features = in_features
+    self.out_features = out_features
+    self.weight = nn.Parameter(torch.FloatTensor(in_features, out_features))
+    if bias:
+      self.bias = nn.Parameter(torch.FloatTensor(out_features))
+    else:
+      self.register_parameter('bias', None)
+    self.reset_parameters()
+
+  def reset_parameters(self):
+    stdv = 1.0 / math.sqrt(self.weight.size(1))
+    self.weight.data.uniform_(-stdv, stdv)
+    if self.bias is not None:
+      self.bias.data.uniform_(-stdv, stdv)
+
+  def forward(self, inp, adj):
+    support = torch.matmul(inp, self.weight)
+    output = torch.matmul(adj.to_dense() if adj.is_sparse else adj, support)
+    if self.bias is not None:
+      return output + self.bias
+    else:
+      return output
+
+  def __repr__(self):
+    return (
+        self.__class__.__name__
+        + ' ('
+        + str(self.in_features)
+        + ' -> '
+        + str(self.out_features)
+        + ')'
+    )
+
+
+class GCN(nn.Module):
+  """Graph Convolutional Neural Network class."""
+
+  def __init__(self, nfeat, nhid, nout, dropout, num_hidden):
+    super().__init__()
+
+    self.gc0 = GraphConvolution(nfeat, nhid)
+    self.gc_layers = clones(GraphConvolution(nhid, nhid), num_hidden)
+    self.out = nn.Linear(nhid, nout)
+    self.dropout = dropout
+
+  def forward(self, x, adj):
+    x = F.relu(self.gc0(x, adj))
+
+    for i, _ in enumerate(self.gc_layers):
+      x = F.relu(self.gc_layers[i](x, adj))
+    return self.out(x)