pna.py

import os.path as osp

import torch
import torch.nn.functional as F
from torch.nn import Embedding, Linear, ModuleList, ReLU, Sequential
from torch.optim.lr_scheduler import ReduceLROnPlateau

import torch_geometric
from torch_geometric.datasets import ZINC
from torch_geometric.loader import DataLoader
from torch_geometric.nn import BatchNorm, PNAConv, global_add_pool
from torch_geometric.utils import degree

path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ZINC')
train_dataset = ZINC(path, subset=True, split='train')
val_dataset = ZINC(path, subset=True, split='val')
test_dataset = ZINC(path, subset=True, split='test')

train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=128)
test_loader = DataLoader(test_dataset, batch_size=128)

# Compute the maximum in-degree in the training data.
max_degree = -1
for data in train_dataset:
    d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
    max_degree = max(max_degree, int(d.max()))

# Compute the in-degree histogram tensor
deg = torch.zeros(max_degree + 1, dtype=torch.long)
for data in train_dataset:
    d = degree(data.edge_index[1], num_nodes=data.num_nodes, dtype=torch.long)
    deg += torch.bincount(d, minlength=deg.numel())


class Net(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.node_emb = Embedding(21, 75)
        self.edge_emb = Embedding(4, 50)

        aggregators = ['mean', 'min', 'max', 'std']
        scalers = ['identity', 'amplification', 'attenuation']

        self.convs = ModuleList()
        self.batch_norms = ModuleList()
        for _ in range(4):
            conv = PNAConv(in_channels=75, out_channels=75,
                           aggregators=aggregators, scalers=scalers, deg=deg,
                           edge_dim=50, towers=5, pre_layers=1, post_layers=1,
                           divide_input=False)
            self.convs.append(conv)
            self.batch_norms.append(BatchNorm(75))

        self.mlp = Sequential(Linear(75, 50), ReLU(), Linear(50, 25), ReLU(),
                              Linear(25, 1))

    def forward(self, x, edge_index, edge_attr, batch):
        x = self.node_emb(x.squeeze())
        edge_attr = self.edge_emb(edge_attr)

        for conv, batch_norm in zip(self.convs, self.batch_norms):
            x = F.relu(batch_norm(conv(x, edge_index, edge_attr)))

        x = global_add_pool(x, batch)
        return self.mlp(x)


if torch.cuda.is_available():
    device = torch.device('cuda')
elif torch_geometric.is_xpu_available():
    device = torch.device('xpu')
else:
    device = torch.device('cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20,
                              min_lr=0.00001)


def train(epoch):
    model.train()

    total_loss = 0
    for data in train_loader:
        data = data.to(device)
        optimizer.zero_grad()
        out = model(data.x, data.edge_index, data.edge_attr, data.batch)
        loss = (out.squeeze() - data.y).abs().mean()
        loss.backward()
        total_loss += loss.item() * data.num_graphs
        optimizer.step()
    return total_loss / len(train_loader.dataset)


@torch.no_grad()
def test(loader):
    model.eval()

    total_error = 0
    for data in loader:
        data = data.to(device)
        out = model(data.x, data.edge_index, data.edge_attr, data.batch)
        total_error += (out.squeeze() - data.y).abs().sum().item()
    return total_error / len(loader.dataset)


for epoch in range(1, 301):
    loss = train(epoch)
    val_mae = test(val_loader)
    test_mae = test(test_loader)
    scheduler.step(val_mae)
    print(f'Epoch: {epoch:02d}, Loss: {loss:.4f}, Val: {val_mae:.4f}, '
          f'Test: {test_mae:.4f}')