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# Reaches around 0.7945 ± 0.0059 test accuracy.
import os.path as osp
import torch
import torch.nn.functional as F
from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
from torch.nn import Linear as Lin
from tqdm import tqdm
from torch_geometric.loader import NeighborLoader
from torch_geometric.nn import GATConv
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
root = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'products')
dataset = PygNodePropPredDataset('ogbn-products', root)
split_idx = dataset.get_idx_split()
evaluator = Evaluator(name='ogbn-products')
data = dataset[0].to(device, 'x', 'y')
train_loader = NeighborLoader(
data,
input_nodes=split_idx['train'],
num_neighbors=[10, 10, 5],
batch_size=512,
shuffle=True,
num_workers=12,
persistent_workers=True,
)
subgraph_loader = NeighborLoader(
data,
input_nodes=None,
num_neighbors=[-1],
batch_size=2048,
num_workers=12,
persistent_workers=True,
)
class GAT(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
heads):
super().__init__()
self.num_layers = num_layers
self.convs = torch.nn.ModuleList()
self.convs.append(GATConv(dataset.num_features, hidden_channels,
heads))
for _ in range(num_layers - 2):
self.convs.append(
GATConv(heads * hidden_channels, hidden_channels, heads))
self.convs.append(
GATConv(heads * hidden_channels, out_channels, heads,
concat=False))
self.skips = torch.nn.ModuleList()
self.skips.append(Lin(dataset.num_features, hidden_channels * heads))
for _ in range(num_layers - 2):
self.skips.append(
Lin(hidden_channels * heads, hidden_channels * heads))
self.skips.append(Lin(hidden_channels * heads, out_channels))
def reset_parameters(self):
for conv in self.convs:
conv.reset_parameters()
for skip in self.skips:
skip.reset_parameters()
def forward(self, x, edge_index):
for i, (conv, skip) in enumerate(zip(self.convs, self.skips)):
x = conv(x, edge_index) + skip(x)
if i != self.num_layers - 1:
x = F.elu(x)
x = F.dropout(x, p=0.5, training=self.training)
return x
def inference(self, x_all):
pbar = tqdm(total=x_all.size(0) * self.num_layers)
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 in range(self.num_layers):
xs = []
for batch in subgraph_loader:
x = x_all[batch.n_id].to(device)
edge_index = batch.edge_index.to(device)
x = self.convs[i](x, edge_index) + self.skips[i](x)
x = x[:batch.batch_size]
if i != self.num_layers - 1:
x = F.elu(x)
xs.append(x.cpu())
pbar.update(batch.batch_size)
x_all = torch.cat(xs, dim=0)
pbar.close()
return x_all
model = GAT(dataset.num_features, 128, dataset.num_classes, num_layers=3,
heads=4).to(device)
def train(epoch):
model.train()
pbar = tqdm(total=split_idx['train'].size(0))
pbar.set_description(f'Epoch {epoch:02d}')
total_loss = total_correct = 0
for batch in train_loader:
optimizer.zero_grad()
out = model(batch.x, batch.edge_index.to(device))[:batch.batch_size]
y = batch.y[:batch.batch_size].squeeze()
loss = F.cross_entropy(out, y)
loss.backward()
optimizer.step()
total_loss += float(loss)
total_correct += int(out.argmax(dim=-1).eq(y).sum())
pbar.update(batch.batch_size)
pbar.close()
loss = total_loss / len(train_loader)
approx_acc = total_correct / split_idx['train'].size(0)
return loss, approx_acc
@torch.no_grad()
def test():
model.eval()
out = model.inference(data.x)
y_true = data.y.cpu()
y_pred = out.argmax(dim=-1, keepdim=True)
train_acc = evaluator.eval({
'y_true': y_true[split_idx['train']],
'y_pred': y_pred[split_idx['train']],
})['acc']
val_acc = evaluator.eval({
'y_true': y_true[split_idx['valid']],
'y_pred': y_pred[split_idx['valid']],
})['acc']
test_acc = evaluator.eval({
'y_true': y_true[split_idx['test']],
'y_pred': y_pred[split_idx['test']],
})['acc']
return train_acc, val_acc, test_acc
test_accs = []
for run in range(1, 11):
print(f'\nRun {run:02d}:\n')
model.reset_parameters()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
best_val_acc = final_test_acc = 0.0
for epoch in range(1, 101):
loss, acc = train(epoch)
print(f'Epoch {epoch:02d}, Loss: {loss:.4f}, Approx. Train: {acc:.4f}')
if epoch > 50 and epoch % 10 == 0:
train_acc, val_acc, test_acc = test()
print(f'Train: {train_acc:.4f}, Val: {val_acc:.4f}, '
f'Test: {test_acc:.4f}')
if val_acc > best_val_acc:
best_val_acc = val_acc
final_test_acc = test_acc
test_accs.append(final_test_acc)
test_acc = torch.tensor(test_accs)
print('============================')
print(f'Final Test: {test_acc.mean():.4f} ± {test_acc.std():.4f}')
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