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sign_training.py
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sign_training.py
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# Copyright 2020 Twitter, Inc.
# SPDX-License-Identifier: Apache-2.0
import argparse
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from ogb.nodeproppred import Evaluator
from logger import Logger
class SimpleDataset(Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
assert self.x.size(0) == self.y.size(0)
def __len__(self):
return self.x.size(0)
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
class MLP(torch.nn.Module):
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout):
super(MLP, self).__init__()
self.lins = torch.nn.ModuleList()
self.lins.append(torch.nn.Linear(in_channels, hidden_channels))
self.bns = torch.nn.ModuleList()
self.bns.append(torch.nn.BatchNorm1d(hidden_channels))
for _ in range(num_layers - 2):
self.lins.append(torch.nn.Linear(hidden_channels, hidden_channels))
self.bns.append(torch.nn.BatchNorm1d(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()
for bn in self.bns:
bn.reset_parameters()
def forward(self, x):
for i, lin in enumerate(self.lins[:-1]):
x = lin(x)
x = self.bns[i](x)
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.lins[-1](x)
return torch.log_softmax(x, dim=-1)
def train(model, device, train_loader, optimizer):
model.train()
total_loss = 0
for x, y in tqdm(train_loader):
x, y = x.to(device), y.to(device)
optimizer.zero_grad()
out = model(x)
loss = F.nll_loss(out, y.squeeze(1))
loss.backward()
optimizer.step()
total_loss += loss.item() * x.size(0)
return total_loss / len(train_loader.dataset)
@torch.no_grad()
def test(model, device, loader, evaluator):
model.eval()
y_pred, y_true = [], []
for x, y in tqdm(loader):
x = x.to(device)
out = model(x)
y_pred.append(torch.argmax(out, dim=1, keepdim=True).cpu())
y_true.append(y)
return evaluator.eval({
"y_true": torch.cat(y_true, dim=0),
"y_pred": torch.cat(y_pred, dim=0),
})['acc']
# run sign
# python3 sign_training.py --device 0 --dropout 0.3 --lr 0.00005 --hidden_channels 512 --num_layers 3 --embeddings_file_name sign_333_embeddings.pt --result_file_name sign_results.txt
# run sign-xl
# python3 sign_training.py --device 1 --dropout 0.5 --lr 0.00005 --hidden_channels 2048 --num_layers 3 --embeddings_file_name sign_333_embeddings.pt --result_file_name sign-xl_results.txt
def main():
parser = argparse.ArgumentParser(description='OGBN-papers100M (SIGN)')
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=3)
parser.add_argument('--hidden_channels', type=int, default=256)
parser.add_argument('--dropout', type=float, default=0)
parser.add_argument('--lr', type=float, default=0.01)
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--epochs', type=int, default=45)
parser.add_argument('--runs', type=int, default=10)
parser.add_argument('--embeddings_file_name', type=str, default='op_dict.pt')
parser.add_argument('--result_file_name', type=str, default='results.txt')
args = parser.parse_args()
print(args)
device = f'cuda:{args.device}' if torch.cuda.is_available() else 'cpu'
device = torch.device(device)
try:
op_dict = torch.load(args.embeddings_file_name)
except:
raise RuntimeError('File {} not found. Need to run python preprocessing.py first'.format(args.embeddings_file_name))
split_idx = op_dict['split_idx']
x = torch.cat(op_dict['op_embedding'], dim=1)
y = op_dict['label'].to(torch.long)
num_classes = 172
print('Input feature dimension: {}'.format(x.shape[-1]))
print('Total number of nodes: {}'.format(x.shape[0]))
train_dataset = SimpleDataset(x[split_idx['train']], y[split_idx['train']])
valid_dataset = SimpleDataset(x[split_idx['valid']], y[split_idx['valid']])
test_dataset = SimpleDataset(x[split_idx['test']], y[split_idx['test']])
train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True)
valid_loader = DataLoader(valid_dataset, batch_size=128, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False)
model = MLP(
x.size(-1),
args.hidden_channels,
num_classes,
args.num_layers,
args.dropout).to(device)
num_trainable_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad)
print('Total number of parameters: {}.'.format(num_trainable_parameters))
evaluator = Evaluator(name='ogbn-papers100M')
logger = Logger(args.runs, info=args, file_name=args.result_file_name)
for run in range(args.runs):
model.reset_parameters()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
for epoch in range(1, 1 + args.epochs):
train(model, device, train_loader, optimizer)
train_acc = test(model, device, train_loader, evaluator)
valid_acc = test(model, device, valid_loader, evaluator)
test_acc = test(model, device, test_loader, evaluator)
logger.add_result(run, (train_acc, valid_acc, test_acc))
if epoch % args.log_steps == 0:
print(f'Run: {run + 1:02d}, '
f'Epoch: {epoch:02d}, '
f'Train: {100 * train_acc:.2f}%, '
f'Valid: {100 * valid_acc:.2f}%, '
f'Test: {100 * test_acc:.2f}%')
logger.print_statistics(run)
logger.print_statistics()
if __name__=='__main__':
main()