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qm9_nn_conv.py
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qm9_nn_conv.py
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import copy
import os.path as osp
import torch
import torch.nn.functional as F
from torch.nn import GRU, Linear, ReLU, Sequential
import torch_geometric.transforms as T
from torch_geometric.datasets import QM9
from torch_geometric.loader import DataLoader
from torch_geometric.nn import NNConv, Set2Set
from torch_geometric.utils import remove_self_loops
target = 0
dim = 64
class MyTransform:
def __call__(self, data):
data = copy.copy(data)
data.y = data.y[:, target] # Specify target.
return data
class Complete:
def __call__(self, data):
data = copy.copy(data)
device = data.edge_index.device
row = torch.arange(data.num_nodes, dtype=torch.long, device=device)
col = torch.arange(data.num_nodes, dtype=torch.long, device=device)
row = row.view(-1, 1).repeat(1, data.num_nodes).view(-1)
col = col.repeat(data.num_nodes)
edge_index = torch.stack([row, col], dim=0)
edge_attr = None
if data.edge_attr is not None:
idx = data.edge_index[0] * data.num_nodes + data.edge_index[1]
size = list(data.edge_attr.size())
size[0] = data.num_nodes * data.num_nodes
edge_attr = data.edge_attr.new_zeros(size)
edge_attr[idx] = data.edge_attr
edge_index, edge_attr = remove_self_loops(edge_index, edge_attr)
data.edge_attr = edge_attr
data.edge_index = edge_index
return data
path = osp.join(osp.dirname(osp.realpath(__file__)), '..', 'data', 'QM9')
transform = T.Compose([MyTransform(), Complete(), T.Distance(norm=False)])
dataset = QM9(path, transform=transform).shuffle()
# Normalize targets to mean = 0 and std = 1.
mean = dataset.data.y.mean(dim=0, keepdim=True)
std = dataset.data.y.std(dim=0, keepdim=True)
dataset.data.y = (dataset.data.y - mean) / std
mean, std = mean[:, target].item(), std[:, target].item()
# Split datasets.
test_dataset = dataset[:10000]
val_dataset = dataset[10000:20000]
train_dataset = dataset[20000:]
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
val_loader = DataLoader(val_dataset, batch_size=128, shuffle=False)
train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True)
class Net(torch.nn.Module):
def __init__(self):
super().__init__()
self.lin0 = torch.nn.Linear(dataset.num_features, dim)
nn = Sequential(Linear(5, 128), ReLU(), Linear(128, dim * dim))
self.conv = NNConv(dim, dim, nn, aggr='mean')
self.gru = GRU(dim, dim)
self.set2set = Set2Set(dim, processing_steps=3)
self.lin1 = torch.nn.Linear(2 * dim, dim)
self.lin2 = torch.nn.Linear(dim, 1)
def forward(self, data):
out = F.relu(self.lin0(data.x))
h = out.unsqueeze(0)
for i in range(3):
m = F.relu(self.conv(out, data.edge_index, data.edge_attr))
out, h = self.gru(m.unsqueeze(0), h)
out = out.squeeze(0)
out = self.set2set(out, data.batch)
out = F.relu(self.lin1(out))
out = self.lin2(out)
return out.view(-1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min',
factor=0.7, patience=5,
min_lr=0.00001)
def train(epoch):
model.train()
loss_all = 0
for data in train_loader:
data = data.to(device)
optimizer.zero_grad()
loss = F.mse_loss(model(data), data.y)
loss.backward()
loss_all += loss.item() * data.num_graphs
optimizer.step()
return loss_all / len(train_loader.dataset)
def test(loader):
model.eval()
error = 0
for data in loader:
data = data.to(device)
error += (model(data) * std - data.y * std).abs().sum().item() # MAE
return error / len(loader.dataset)
best_val_error = None
for epoch in range(1, 301):
lr = scheduler.optimizer.param_groups[0]['lr']
loss = train(epoch)
val_error = test(val_loader)
scheduler.step(val_error)
if best_val_error is None or val_error <= best_val_error:
test_error = test(test_loader)
best_val_error = val_error
print(f'Epoch: {epoch:03d}, LR: {lr:7f}, Loss: {loss:.7f}, '
f'Val MAE: {val_error:.7f}, Test MAE: {test_error:.7f}')