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train.py
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train.py
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import argparse
import logging
import os
import torch
import torch.nn.functional as F
from torch.utils.data import random_split
from torch_geometric import utils
from torch_geometric.loader import DataLoader
from torch_geometric.datasets import TUDataset
from networks import Net
from utils import EarlyStopping
def arg_parse(args=None):
parser = argparse.ArgumentParser(description='MAGPool')
parser.add_argument('--dataset', type=str, default='FRANKENSTEIN',
help='DD/PROTEINS/NCI1/NCI109/FRANKENSTEIN')
parser.add_argument('--epochs', type=int, default=500,
help='maximum number of epochs')
parser.add_argument('--seed', type=int, default=777, help='seed')
parser.add_argument('--batch_size', type=int,
default=32, help='batch size')
parser.add_argument('--lr', type=float, default=0.000005, help='learning rate')
parser.add_argument('--weight_decay', type=float,
default=0.0001, help='weight decay')
parser.add_argument('--hidden', type=int, default=512, help='hidden size')
parser.add_argument('--pooling_ratio', type=float,
default=0.35, help='pooling ratio')
parser.add_argument('--dropout_ratio', type=float,
default=0.2, help='dropout ratio')
parser.add_argument('--num_heads', type=int, default=8,
help="number of hidden attention heads")
parser.add_argument('--alpha', type=float, default=.3, help='alpha')
parser.add_argument("--hop_num", type=int, default=3, help="hop number")
parser.add_argument("--p_norm", type=int, default=0.0, help="p_norm")
parser.add_argument('--early_stop', default=False,
help="indicates whether to use early stop or not")
parser.add_argument('--patience', type=int, default=100,
help='patience for earlystopping')
args = parser.parse_args(args)
return args
def test(model, loader, args):
'''
Evaluation and test function
Inputs:
- model: PyTorch model
- loader: dataloader corresponding to evaluation
- args: specified arguments
Outputs:
- accuracy, loss
'''
model.eval()
correct = 0
loss = 0
loss_fcn = torch.nn.CrossEntropyLoss()
for data in loader:
data = data.to(args.device)
out = model(data)
pred = out.max(dim=1)[1]
correct += pred.eq(data.y).sum().item()
loss += loss_fcn(out, data.y).item()
return correct / len(loader.dataset), loss / len(loader.dataset)
def main(args):
args.device = 'cpu'
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(args.seed)
args.device = 'cuda:0'
# loading the dataset
dataset = TUDataset(os.path.join('data', args.dataset), name=args.dataset, use_node_attr=True)
args.num_classes = dataset.num_classes
args.num_features = dataset.num_features
args.num_graphs = len(dataset)
# data spliting
num_training = int(len(dataset)*0.8)
num_val = int(len(dataset)*0.1)
num_test = len(dataset) - (num_training+num_val)
training_set, validation_set, test_set = random_split(
dataset, [num_training, num_val, num_test])
# dataloader
train_loader = DataLoader(
training_set, batch_size=args.batch_size, shuffle=True)
val_loader = DataLoader(
validation_set, batch_size=args.batch_size, shuffle=False)
test_loader = DataLoader(test_set, batch_size=1, shuffle=False)
# logger
print("""----Data Statistics----
Dataset: %s
# of graphs: %d
# of classes: %d
# of features: %d
---------------------
Train samples: %d
Validation samples: %d
Test samples: %d""" % (args.dataset, args.num_graphs, args.num_classes, args.num_features, len(training_set), len(validation_set), len(test_set)))
model = Net(args).to(args.device)
print("----Model Configuration and Parameters----\n", model)
print(model.alpha)
optimizer = torch.optim.Adam(
model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
if args.early_stop:
stopper = EarlyStopping(args.patience)
loss_fcn = torch.nn.CrossEntropyLoss()
min_loss = 10e9
patience = 0
train_losses = list()
val_losses = list()
train_accs = list()
val_accs = list()
# Training the model
for epoch in range(args.epochs):
model.train()
correct = 0
loss_all = 0
for i, data in enumerate(train_loader):
data = data.to(args.device)
out = model(data)
loss = loss_fcn(out, data.y)
pred = out.max(dim=1)[1]
correct += pred.eq(data.y).sum().item()
loss_all += data.y.size(0) * loss.item()
train_acc = correct / len(train_loader.dataset)
print('Training Loss: {0:.4f} | Training Acc: {1:.4f}'.format(
loss, train_acc))
loss.backward()
optimizer.step()
optimizer.zero_grad
val_acc, val_loss = test(model, val_loader, args)
train_loss = loss_all / len(train_loader)
print('Epoch: {0} | Train Loss: {1:.4f} | Val Loss: {2:.4f} | Val Acc: {3:.4f}'.format(
epoch, train_loss, val_loss, val_acc))
train_losses.append(train_loss)
val_losses.append(val_loss)
train_accs.append(train_acc)
val_accs.append(val_acc)
if val_loss < min_loss:
torch.save(model.state_dict(), 'latest.pth')
print('Model saved at epoch {}'.format(epoch))
min_loss = val_loss
patience = 0
else:
patience += 1
if patience > args.patience:
print(
'Maximum patience reached at epoch {} and val loss had no change'.format(epoch))
break
# Testing the model
model.load_state_dict(torch.load('latest.pth'))
test_acc, test_loss = test(model, test_loader, args)
print('---------------Test----------------')
print('Test loss: {0:.4f} | Test Acc: {1:.4f}'.format(test_loss, test_acc))
# Plotting the necessary metrics
losses = [train_losses, val_losses]
accs = [train_accs, val_accs]
plotter(losses, accs)
if __name__ == '__main__':
main(arg_parse())