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import os.path as osp
import time
import torch
import torch.nn.functional as F
from torch.nn import Linear
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid
from torch_geometric.nn import GCN2Conv
dataset = 'Cora'
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', dataset)
transform = T.Compose([T.NormalizeFeatures(), T.GCNNorm(), T.ToSparseTensor()])
dataset = Planetoid(path, dataset, transform=transform)
data = dataset[0]
class Net(torch.nn.Module):
def __init__(self, hidden_channels, num_layers, alpha, theta,
shared_weights=True, dropout=0.0):
super().__init__()
self.lins = torch.nn.ModuleList()
self.lins.append(Linear(dataset.num_features, hidden_channels))
self.lins.append(Linear(hidden_channels, dataset.num_classes))
self.convs = torch.nn.ModuleList()
for layer in range(num_layers):
self.convs.append(
GCN2Conv(hidden_channels, alpha, theta, layer + 1,
shared_weights, normalize=False))
self.dropout = dropout
def forward(self, x, adj_t):
x = F.dropout(x, self.dropout, training=self.training)
x = x_0 = self.lins[0](x).relu()
for conv in self.convs:
x = F.dropout(x, self.dropout, training=self.training)
x = conv(x, x_0, adj_t)
x = x.relu()
x = F.dropout(x, self.dropout, training=self.training)
x = self.lins[1](x)
return x.log_softmax(dim=-1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net(hidden_channels=64, num_layers=64, alpha=0.1, theta=0.5,
shared_weights=True, dropout=0.6).to(device)
data = data.to(device)
optimizer = torch.optim.Adam([
dict(params=model.convs.parameters(), weight_decay=0.01),
dict(params=model.lins.parameters(), weight_decay=5e-4)
], lr=0.01)
def train():
model.train()
optimizer.zero_grad()
out = model(data.x, data.adj_t)
loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])
loss.backward()
optimizer.step()
return float(loss)
@torch.no_grad()
def test():
model.eval()
pred, accs = model(data.x, data.adj_t).argmax(dim=-1), []
for _, mask in data('train_mask', 'val_mask', 'test_mask'):
accs.append(int((pred[mask] == data.y[mask]).sum()) / int(mask.sum()))
return accs
best_val_acc = test_acc = 0
times = []
for epoch in range(1, 1001):
start = time.time()
loss = 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:04d}, Loss: {loss:.4f} Train: {train_acc:.4f}, '
f'Val: {val_acc:.4f}, Test: {tmp_test_acc:.4f}, '
f'Final Test: {test_acc:.4f}')
times.append(time.time() - start)
print(f"Median time per epoch: {torch.tensor(times).median():.4f}s")
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