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import argparse
import os.path as osp
from typing import Any, Dict, Optional
import torch
from torch.nn import (
BatchNorm1d,
Embedding,
Linear,
ModuleList,
ReLU,
Sequential,
)
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch_geometric.transforms as T
from torch_geometric.datasets import ZINC
from torch_geometric.loader import DataLoader
from torch_geometric.nn import GINEConv, GPSConv, global_add_pool
from torch_geometric.nn.attention import PerformerAttention
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'ZINC-PE')
transform = T.AddRandomWalkPE(walk_length=20, attr_name='pe')
train_dataset = ZINC(path, subset=True, split='train', pre_transform=transform)
val_dataset = ZINC(path, subset=True, split='val', pre_transform=transform)
test_dataset = ZINC(path, subset=True, split='test', pre_transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=64)
test_loader = DataLoader(test_dataset, batch_size=64)
parser = argparse.ArgumentParser()
parser.add_argument(
'--attn_type', default='multihead',
help="Global attention type such as 'multihead' or 'performer'.")
args = parser.parse_args()
class GPS(torch.nn.Module):
def __init__(self, channels: int, pe_dim: int, num_layers: int,
attn_type: str, attn_kwargs: Dict[str, Any]):
super().__init__()
self.node_emb = Embedding(28, channels - pe_dim)
self.pe_lin = Linear(20, pe_dim)
self.pe_norm = BatchNorm1d(20)
self.edge_emb = Embedding(4, channels)
self.convs = ModuleList()
for _ in range(num_layers):
nn = Sequential(
Linear(channels, channels),
ReLU(),
Linear(channels, channels),
)
conv = GPSConv(channels, GINEConv(nn), heads=4,
attn_type=attn_type, attn_kwargs=attn_kwargs)
self.convs.append(conv)
self.mlp = Sequential(
Linear(channels, channels // 2),
ReLU(),
Linear(channels // 2, channels // 4),
ReLU(),
Linear(channels // 4, 1),
)
self.redraw_projection = RedrawProjection(
self.convs,
redraw_interval=1000 if attn_type == 'performer' else None)
def forward(self, x, pe, edge_index, edge_attr, batch):
x_pe = self.pe_norm(pe)
x = torch.cat((self.node_emb(x.squeeze(-1)), self.pe_lin(x_pe)), 1)
edge_attr = self.edge_emb(edge_attr)
for conv in self.convs:
x = conv(x, edge_index, batch, edge_attr=edge_attr)
x = global_add_pool(x, batch)
return self.mlp(x)
class RedrawProjection:
def __init__(self, model: torch.nn.Module,
redraw_interval: Optional[int] = None):
self.model = model
self.redraw_interval = redraw_interval
self.num_last_redraw = 0
def redraw_projections(self):
if not self.model.training or self.redraw_interval is None:
return
if self.num_last_redraw >= self.redraw_interval:
fast_attentions = [
module for module in self.model.modules()
if isinstance(module, PerformerAttention)
]
for fast_attention in fast_attentions:
fast_attention.redraw_projection_matrix()
self.num_last_redraw = 0
return
self.num_last_redraw += 1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
attn_kwargs = {'dropout': 0.5}
model = GPS(channels=64, pe_dim=8, num_layers=10, attn_type=args.attn_type,
attn_kwargs=attn_kwargs).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)
scheduler = ReduceLROnPlateau(optimizer, mode='min', factor=0.5, patience=20,
min_lr=0.00001)
def train():
model.train()
total_loss = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
model.redraw_projection.redraw_projections()
out = model(data.x, data.pe, 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.pe, 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, 101):
loss = train()
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}')
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