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import time
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, NeighborLoader
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 = NeighborLoader(data, num_neighbors=[-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 in subgraph_loader:
edge_index = batch.edge_index.to(device)
x = x_all[batch.n_id].to(device)
x_target = x[:batch.batch_size]
x = conv((x, x_target), edge_index)
if i != len(self.convs) - 1:
x = F.relu(x)
xs.append(x.cpu())
pbar.update(batch.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
times = []
for epoch in range(1, 31):
start = time.time()
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}')
times.append(time.time() - start)
print(f"Median time per epoch: {torch.tensor(times).median():.4f}s")
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