sort_pool.py

import torch
import torch.nn.functional as F
from torch.nn import Conv1d, Linear

from torch_geometric.nn import SAGEConv, SortAggregation


class SortPool(torch.nn.Module):
    def __init__(self, dataset, num_layers, hidden):
        super().__init__()
        self.conv1 = SAGEConv(dataset.num_features, hidden)
        self.convs = torch.nn.ModuleList()
        for i in range(num_layers - 1):
            self.convs.append(SAGEConv(hidden, hidden))
        self.pool = SortAggregation(k=30)
        self.conv1d = Conv1d(hidden, 32, 5)
        self.lin1 = Linear(32 * (30 - 5 + 1), 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.conv1d.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))
        for conv in self.convs:
            x = F.relu(conv(x, edge_index))
        x = self.pool(x, batch)
        x = x.view(len(x), self.k, -1).permute(0, 2, 1)
        x = F.relu(self.conv1d(x))
        x = x.view(len(x), -1)
        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__