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mlp.py
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mlp.py
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import argparse
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
from logger import Logger
class LinkPredictor(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout):
super(LinkPredictor, self).__init__()
self.lins = torch.nn.ModuleList()
self.lins.append(torch.nn.Linear(in_channels, hidden_channels))
for _ in range(num_layers - 2):
self.lins.append(torch.nn.Linear(hidden_channels, hidden_channels))
self.lins.append(torch.nn.Linear(hidden_channels, out_channels))
self.dropout = dropout
def reset_parameters(self):
for lin in self.lins:
lin.reset_parameters()
def forward(self, x_i, x_j):
x = x_i * x_j
for lin in self.lins[:-1]:
x = lin(x)
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.lins[-1](x)
return torch.sigmoid(x)
def train(predictor, x, split_edge, optimizer, batch_size):
predictor.train()
pos_train_edge = split_edge['train']['edge'].to(x.device)
neg_train_edge = split_edge['train']['edge_neg'].to(x.device) # modified
total_loss = total_examples = 0
for perm in DataLoader(range(pos_train_edge.size(0)), batch_size,
shuffle=True):
optimizer.zero_grad()
edge = pos_train_edge[perm].t()
pos_out = predictor(x[edge[0]], x[edge[1]])
pos_loss = -torch.log(pos_out + 1e-15).mean()
# random element of previously sampled negative edges
# negative samples are obtained by using spatial sampling criteria
edge = neg_train_edge[perm].t()
neg_out = predictor(x[edge[0]], x[edge[1]])
neg_loss = -torch.log(1 - neg_out + 1e-15).mean()
loss = pos_loss + neg_loss
loss.backward()
optimizer.step()
num_examples = pos_out.size(0)
total_loss += loss.item() * num_examples
total_examples += num_examples
return total_loss / total_examples
@torch.no_grad()
def test(predictor, x, split_edge, evaluator, batch_size):
predictor.eval()
pos_train_edge = split_edge['train']['edge'].to(x.device)
neg_train_edge = split_edge['train']['edge_neg'].to(x.device)
pos_valid_edge = split_edge['valid']['edge'].to(x.device)
neg_valid_edge = split_edge['valid']['edge_neg'].to(x.device)
pos_test_edge = split_edge['test']['edge'].to(x.device)
neg_test_edge = split_edge['test']['edge_neg'].to(x.device)
pos_train_preds = []
for perm in DataLoader(range(pos_train_edge.size(0)), batch_size):
edge = pos_train_edge[perm].t()
pos_train_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
pos_train_pred = torch.cat(pos_train_preds, dim=0)
neg_train_preds = []
for perm in DataLoader(range(neg_train_edge.size(0)), batch_size):
edge = neg_train_edge[perm].t()
neg_train_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
neg_train_pred = torch.cat(neg_train_preds, dim=0)
pos_valid_preds = []
for perm in DataLoader(range(pos_valid_edge.size(0)), batch_size):
edge = pos_valid_edge[perm].t()
pos_valid_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
pos_valid_pred = torch.cat(pos_valid_preds, dim=0)
neg_valid_preds = []
for perm in DataLoader(range(neg_valid_edge.size(0)), batch_size):
edge = neg_valid_edge[perm].t()
neg_valid_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
neg_valid_pred = torch.cat(neg_valid_preds, dim=0)
pos_test_preds = []
for perm in DataLoader(range(pos_test_edge.size(0)), batch_size):
edge = pos_test_edge[perm].t()
pos_test_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
pos_test_pred = torch.cat(pos_test_preds, dim=0)
neg_test_preds = []
for perm in DataLoader(range(neg_test_edge.size(0)), batch_size):
edge = neg_test_edge[perm].t()
neg_test_preds += [predictor(x[edge[0]], x[edge[1]]).squeeze().cpu()]
neg_test_pred = torch.cat(neg_test_preds, dim=0)
train_rocauc = evaluator.eval({
'y_pred_pos': pos_train_pred,
'y_pred_neg': neg_train_pred,
})[f'rocauc']
valid_rocauc = evaluator.eval({
'y_pred_pos': pos_valid_pred,
'y_pred_neg': neg_valid_pred,
})[f'rocauc']
test_rocauc = evaluator.eval({
'y_pred_pos': pos_test_pred,
'y_pred_neg': neg_test_pred,
})[f'rocauc']
return train_rocauc, valid_rocauc, test_rocauc
def main():
parser = argparse.ArgumentParser(description='OGBL-VESSEL (MLP)')
parser.add_argument('--device', type=int, default=0)
parser.add_argument('--log_steps', type=int, default=1)
parser.add_argument('--num_layers', type=int, default=4)
parser.add_argument('--hidden_channels', type=int, default=256)
parser.add_argument('--dropout', type=float, default=0.0)
parser.add_argument('--batch_size', type=int, default=64 * 1024)
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--eval_steps', type=int, default=1)
parser.add_argument('--runs', type=int, default=10)
args = parser.parse_args()
print(args)
device = f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu'
device = torch.device(device)
dataset = PygLinkPropPredDataset(name='ogbl-vessel')
split_edge = dataset.get_edge_split()
data = dataset[0]
# normalize x,y,z coordinates of bifurcation points (nodes)
data.x[:, 0] = torch.nn.functional.normalize(data.x[:, 0], dim=0)
data.x[:, 1] = torch.nn.functional.normalize(data.x[:, 1], dim=0)
data.x[:, 2] = torch.nn.functional.normalize(data.x[:, 2], dim=0)
# use embeddings
x = data.x.to(torch.float)
embedding = torch.load('embedding.pt', map_location='cpu')
x = torch.cat([x, embedding], dim=-1)
x = x.to(device)
predictor = LinkPredictor(x.size(-1), args.hidden_channels, 1,
args.num_layers, args.dropout).to(device)
optimizer = torch.optim.Adam(predictor.parameters(), lr=args.lr)
evaluator = Evaluator(name='ogbl-vessel')
logger = Logger(args.runs, args)
for run in range(args.runs):
predictor.reset_parameters()
for epoch in range(1, 1 + args.epochs):
loss = train(predictor, x, split_edge, optimizer, args.batch_size)
if epoch % args.eval_steps == 0:
result = test(predictor, x, split_edge, evaluator,
args.batch_size)
logger.add_result(run, result)
train_roc_auc, valid_roc_auc, test_roc_auc = result
print(f'Run: {run + 1:02d}, '
f'Epoch: {epoch:02d}, '
f'Loss: {loss:.4f}, '
f'Train: {train_roc_auc:.4f}, '
f'Valid: {valid_roc_auc:.4f}, '
f'Test: {test_roc_auc:.4f}')
print('MLP')
logger.print_statistics(run)
print('MLP')
logger.print_statistics()
if __name__ == "__main__":
main()