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import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.data import Batch
from torch_geometric.nn import (
GraphConv,
JumpingKnowledge,
global_mean_pool,
graclus,
max_pool,
)
class Graclus(torch.nn.Module):
def __init__(self, dataset, num_layers, hidden):
super().__init__()
self.conv1 = GraphConv(dataset.num_features, hidden, aggr='mean')
self.convs = torch.nn.ModuleList()
for i in range(num_layers - 1):
self.convs.append(GraphConv(hidden, hidden, aggr='mean'))
self.jump = JumpingKnowledge(mode='cat')
self.lin1 = Linear(num_layers * hidden, hidden)
self.lin2 = Linear(hidden, dataset.num_classes)
def reset_parameters(self):
self.conv1.reset_parameters()
for conv in self.convs:
conv.reset_parameters()
self.jump.reset_parameters()
self.lin1.reset_parameters()
self.lin2.reset_parameters()
def forward(self, data):
x, edge_index, batch = data.x, data.edge_index, data.batch
x = F.relu(self.conv1(x, edge_index))
xs = [global_mean_pool(x, batch)]
for i, conv in enumerate(self.convs):
x = F.relu(conv(x, edge_index))
xs += [global_mean_pool(x, batch)]
if i % 2 == 0 and i < len(self.convs) - 1:
cluster = graclus(edge_index, num_nodes=x.size(0))
data = Batch(x=x, edge_index=edge_index, batch=batch)
data = max_pool(cluster, data)
x, edge_index, batch = data.x, data.edge_index, data.batch
x = self.jump(xs)
x = F.relu(self.lin1(x))
x = F.dropout(x, p=0.5, training=self.training)
x = self.lin2(x)
return F.log_softmax(x, dim=-1)
def __repr__(self):
return self.__class__.__name__
|
