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import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Flickr
from torch_geometric.loader import ShaDowKHopSampler
from torch_geometric.nn import SAGEConv, global_mean_pool
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'Flickr')
dataset = Flickr(path)
data = dataset[0]
kwargs = {'batch_size': 1024, 'num_workers': 6, 'persistent_workers': True}
train_loader = ShaDowKHopSampler(data, depth=2, num_neighbors=5,
node_idx=data.train_mask, **kwargs)
val_loader = ShaDowKHopSampler(data, depth=2, num_neighbors=5,
node_idx=data.val_mask, **kwargs)
test_loader = ShaDowKHopSampler(data, depth=2, num_neighbors=5,
node_idx=data.test_mask, **kwargs)
class GNN(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels):
super().__init__()
self.conv1 = SAGEConv(in_channels, hidden_channels)
self.conv2 = SAGEConv(hidden_channels, hidden_channels)
self.conv3 = SAGEConv(hidden_channels, hidden_channels)
self.lin = torch.nn.Linear(2 * hidden_channels, out_channels)
def forward(self, x, edge_index, batch, root_n_id):
x = self.conv1(x, edge_index).relu()
x = F.dropout(x, p=0.3)
x = self.conv2(x, edge_index).relu()
x = F.dropout(x, p=0.3, training=self.training)
x = self.conv3(x, edge_index).relu()
x = F.dropout(x, p=0.3, training=self.training)
# We merge both central node embeddings and subgraph embeddings:
x = torch.cat([x[root_n_id], global_mean_pool(x, batch)], dim=-1)
x = self.lin(x)
return x
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = GNN(dataset.num_features, 256, dataset.num_classes).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
def train():
model.train()
total_loss = total_examples = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out = model(data.x, data.edge_index, data.batch, data.root_n_id)
loss = F.cross_entropy(out, data.y)
loss.backward()
optimizer.step()
total_loss += float(loss) * data.num_graphs
total_examples += data.num_graphs
return total_loss / total_examples
@torch.no_grad()
def test(loader):
model.eval()
total_correct = total_examples = 0
for data in loader:
data = data.to(device)
out = model(data.x, data.edge_index, data.batch, data.root_n_id)
total_correct += int((out.argmax(dim=-1) == data.y).sum())
total_examples += data.num_graphs
return total_correct / total_examples
for epoch in range(1, 51):
loss = train()
val_acc = test(val_loader)
test_acc = test(test_loader)
print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, ',
f'Val: {val_acc:.4f} Test: {test_acc:.4f}')
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