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import os.path as osp
import torch
import torch.nn.functional as F
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GraphUNet
from torch_geometric.utils import dropout_edge
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
dataset = Planetoid(path, dataset)
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
pool_ratios = [2000 / data.num_nodes, 0.5]
self.unet = GraphUNet(dataset.num_features, 32, dataset.num_classes,
depth=3, pool_ratios=pool_ratios)
def forward(self):
edge_index, _ = dropout_edge(data.edge_index, p=0.2,
force_undirected=True,
training=self.training)
x = F.dropout(data.x, p=0.92, training=self.training)
x = self.unet(x, edge_index)
return F.log_softmax(x, dim=1)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model, data = Net().to(device), data.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.01, weight_decay=0.001)
def train():
model.train()
optimizer.zero_grad()
F.nll_loss(model()[data.train_mask], data.y[data.train_mask]).backward()
optimizer.step()
@torch.no_grad()
def test():
model.eval()
out, accs = model(), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
pred = out[mask].argmax(1)
acc = pred.eq(data.y[mask]).sum().item() / mask.sum().item()
accs.append(acc)
return accs
best_val_acc = test_acc = 0
for epoch in range(1, 201):
train()
train_acc, val_acc, tmp_test_acc = test()
if val_acc > best_val_acc:
best_val_acc = val_acc
test_acc = tmp_test_acc
print(f'Epoch: {epoch:03d}, Train: {train_acc:.4f}, '
f'Val: {best_val_acc:.4f}, Test: {test_acc:.4f}')
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