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import os.path as osp
import time
from math import ceil
import torch
import torch.nn.functional as F
from torch.nn import Linear
from torch_geometric.datasets import TUDataset
from torch_geometric.loader import DataLoader
from torch_geometric.nn import DenseGraphConv, DMoNPooling, GCNConv
from torch_geometric.utils import to_dense_adj, to_dense_batch
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'PROTEINS')
dataset = TUDataset(path, name='PROTEINS').shuffle()
avg_num_nodes = int(dataset._data.x.size(0) / len(dataset))
n = (len(dataset) + 9) // 10
test_dataset = dataset[:n]
val_dataset = dataset[n:2 * n]
train_dataset = dataset[2 * n:]
test_loader = DataLoader(test_dataset, batch_size=20)
val_loader = DataLoader(val_dataset, batch_size=20)
train_loader = DataLoader(train_dataset, batch_size=20)
class Net(torch.nn.Module):
def __init__(self, in_channels, out_channels, hidden_channels=32):
super().__init__()
self.conv1 = GCNConv(in_channels, hidden_channels)
num_nodes = ceil(0.5 * avg_num_nodes)
self.pool1 = DMoNPooling([hidden_channels, hidden_channels], num_nodes)
self.conv2 = DenseGraphConv(hidden_channels, hidden_channels)
num_nodes = ceil(0.5 * num_nodes)
self.pool2 = DMoNPooling([hidden_channels, hidden_channels], num_nodes)
self.conv3 = DenseGraphConv(hidden_channels, hidden_channels)
self.lin1 = Linear(hidden_channels, hidden_channels)
self.lin2 = Linear(hidden_channels, out_channels)
def forward(self, x, edge_index, batch):
x = self.conv1(x, edge_index).relu()
x, mask = to_dense_batch(x, batch)
adj = to_dense_adj(edge_index, batch)
_, x, adj, sp1, _, c1 = self.pool1(x, adj, mask)
x = self.conv2(x, adj).relu()
_, x, adj, sp2, _, c2 = self.pool2(x, adj)
x = self.conv3(x, adj)
x = x.mean(dim=1)
x = self.lin1(x).relu()
x = self.lin2(x)
return F.log_softmax(x, dim=-1), sp1 + sp2 + c1 + c2
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=0.001)
def train(train_loader):
model.train()
loss_all = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
out, tot_loss = model(data.x, data.edge_index, data.batch)
loss = F.nll_loss(out, data.y.view(-1)) + tot_loss
loss.backward()
loss_all += data.y.size(0) * float(loss)
optimizer.step()
return loss_all / len(train_dataset)
@torch.no_grad()
def test(loader):
model.eval()
correct = 0
loss_all = 0
for data in loader:
data = data.to(device)
pred, tot_loss = model(data.x, data.edge_index, data.batch)
loss = F.nll_loss(pred, data.y.view(-1)) + tot_loss
loss_all += data.y.size(0) * float(loss)
correct += int(pred.max(dim=1)[1].eq(data.y.view(-1)).sum())
return loss_all / len(loader.dataset), correct / len(loader.dataset)
times = []
for epoch in range(1, 101):
start = time.time()
train_loss = train(train_loader)
_, train_acc = test(train_loader)
val_loss, val_acc = test(val_loader)
test_loss, test_acc = test(test_loader)
print(f'Epoch: {epoch:03d}, Train Loss: {train_loss:.3f}, '
f'Train Acc: {train_acc:.3f}, Val Loss: {val_loss:.3f}, '
f'Val Acc: {val_acc:.3f}, Test Loss: {test_loss:.3f}, '
f'Test Acc: {test_acc:.3f}')
times.append(time.time() - start)
print(f"Median time per epoch: {torch.tensor(times).median():.4f}s")
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