edge_pool.py

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

from torch_geometric.nn import (
    EdgePooling,
    GraphConv,
    JumpingKnowledge,
    global_mean_pool,
)


class EdgePool(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()
        self.pools = torch.nn.ModuleList()
        self.convs.extend([
            GraphConv(hidden, hidden, aggr='mean')
            for i in range(num_layers - 1)
        ])
        self.pools.extend(
            [EdgePooling(hidden) for i in range((num_layers) // 2)])
        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()
        for pool in self.pools:
            pool.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:
                pool = self.pools[i // 2]
                x, edge_index, batch, _ = pool(x, edge_index, batch=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__