cluster_gcn_reddit.py

import torch
import torch.nn.functional as F
from torch.nn import ModuleList
from tqdm import tqdm

from torch_geometric.datasets import Reddit
from torch_geometric.loader import ClusterData, ClusterLoader, NeighborSampler
from torch_geometric.nn import SAGEConv

dataset = Reddit('../data/Reddit')
data = dataset[0]

cluster_data = ClusterData(data, num_parts=1500, recursive=False,
                           save_dir=dataset.processed_dir)
train_loader = ClusterLoader(cluster_data, batch_size=20, shuffle=True,
                             num_workers=12)

subgraph_loader = NeighborSampler(data.edge_index, sizes=[-1], batch_size=1024,
                                  shuffle=False, num_workers=12)


class Net(torch.nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.convs = ModuleList(
            [SAGEConv(in_channels, 128),
             SAGEConv(128, out_channels)])

    def forward(self, x, edge_index):
        for i, conv in enumerate(self.convs):
            x = conv(x, edge_index)
            if i != len(self.convs) - 1:
                x = F.relu(x)
                x = F.dropout(x, p=0.5, training=self.training)
        return F.log_softmax(x, dim=-1)

    def inference(self, x_all):
        pbar = tqdm(total=x_all.size(0) * len(self.convs))
        pbar.set_description('Evaluating')

        # Compute representations of nodes layer by layer, using *all*
        # available edges. This leads to faster computation in contrast to
        # immediately computing the final representations of each batch.
        for i, conv in enumerate(self.convs):
            xs = []
            for batch_size, n_id, adj in subgraph_loader:
                edge_index, _, size = adj.to(device)
                x = x_all[n_id].to(device)
                x_target = x[:size[1]]
                x = conv((x, x_target), edge_index)
                if i != len(self.convs) - 1:
                    x = F.relu(x)
                xs.append(x.cpu())

                pbar.update(batch_size)

            x_all = torch.cat(xs, dim=0)

        pbar.close()

        return x_all


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(dataset.num_features, dataset.num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.005)


def train():
    model.train()

    total_loss = total_nodes = 0
    for batch in train_loader:
        batch = batch.to(device)
        optimizer.zero_grad()
        out = model(batch.x, batch.edge_index)
        loss = F.nll_loss(out[batch.train_mask], batch.y[batch.train_mask])
        loss.backward()
        optimizer.step()

        nodes = batch.train_mask.sum().item()
        total_loss += loss.item() * nodes
        total_nodes += nodes

    return total_loss / total_nodes


@torch.no_grad()
def test():  # Inference should be performed on the full graph.
    model.eval()

    out = model.inference(data.x)
    y_pred = out.argmax(dim=-1)

    accs = []
    for mask in [data.train_mask, data.val_mask, data.test_mask]:
        correct = y_pred[mask].eq(data.y[mask]).sum().item()
        accs.append(correct / mask.sum().item())
    return accs


for epoch in range(1, 31):
    loss = train()
    if epoch % 5 == 0:
        train_acc, val_acc, test_acc = test()
        print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, '
              f'Val: {val_acc:.4f}, test: {test_acc:.4f}')
    else:
        print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}')