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# Reaches around 0.7870 ± 0.0036 test accuracy.
import argparse
import os.path as osp
import torch
import torch.nn.functional as F
from ogb.nodeproppred import PygNodePropPredDataset
from tqdm import tqdm
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import SAGEConv
from torch_geometric.utils import to_undirected
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda')
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--num_layers', type=int, default=3)
parser.add_argument('--batch_size', type=int, default=1024)
parser.add_argument('--num_neighbors', type=int, default=10)
parser.add_argument('--channels', type=int, default=256)
parser.add_argument('--lr', type=float, default=0.003)
parser.add_argument('--dropout', type=float, default=0.5)
parser.add_argument('--num_workers', type=int, default=12)
args = parser.parse_args()
root = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'papers100')
dataset = PygNodePropPredDataset('ogbn-papers100M', root)
split_idx = dataset.get_idx_split()
data = dataset[0]
data.edge_index = to_undirected(data.edge_index, reduce="mean")
train_loader = NeighborLoader(
data,
input_nodes=split_idx['train'],
num_neighbors=[args.num_neighbors] * args.num_layers,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
persistent_workers=args.num_workers > 0,
)
val_loader = NeighborLoader(
data,
input_nodes=split_idx['valid'],
num_neighbors=[args.num_neighbors] * args.num_layers,
batch_size=args.batch_size,
num_workers=args.num_workers,
persistent_workers=args.num_workers > 0,
)
test_loader = NeighborLoader(
data,
input_nodes=split_idx['test'],
num_neighbors=[args.num_neighbors] * args.num_layers,
batch_size=args.batch_size,
num_workers=args.num_workers,
persistent_workers=args.num_workers > 0,
)
class SAGE(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.convs = torch.nn.ModuleList()
self.convs.append(SAGEConv(in_channels, args.channels))
for _ in range(args.num_layers - 2):
self.convs.append(SAGEConv(args.channels, args.channels))
self.convs.append(SAGEConv(args.channels, out_channels))
def forward(self, x, edge_index):
for i, conv in enumerate(self.convs):
x = conv(x, edge_index)
if i != args.num_layers - 1:
x = x.relu()
x = F.dropout(x, p=args.dropout, training=self.training)
return x
model = SAGE(dataset.num_features, dataset.num_classes).to(args.device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
def train():
model.train()
total_loss = total_correct = total_examples = 0
for batch in tqdm(train_loader):
batch = batch.to(args.device)
optimizer.zero_grad()
out = model(batch.x, batch.edge_index)[:batch.batch_size]
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
loss = F.cross_entropy(out, y)
loss.backward()
optimizer.step()
total_loss += float(loss) * y.size(0)
total_correct += int(out.argmax(dim=-1).eq(y).sum())
total_examples += y.size(0)
return total_loss / total_examples, total_correct / total_examples
@torch.no_grad()
def test(loader):
model.eval()
total_correct = total_examples = 0
for batch in tqdm(loader):
batch = batch.to(args.device)
out = model(batch.x, batch.edge_index)[:batch.batch_size]
y = batch.y[:batch.batch_size].view(-1).to(torch.long)
total_correct += int(out.argmax(dim=-1).eq(y).sum())
total_examples += y.size(0)
return total_correct / total_examples
for epoch in range(1, args.epochs + 1):
loss, train_acc = train()
print(f'Epoch {epoch:02d}, Loss: {loss:.4f}, Train Acc: {train_acc:.4f}')
val_acc = test(val_loader)
print(f'Epoch {epoch:02d}, Val Acc: {val_acc:.4f}')
test_acc = test(test_loader)
print(f'Epoch {epoch:02d}, Test Acc: {test_acc:.4f}')
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