parser.add_argument('--num-workers', type=int, default=0, metavar='W',
help='How many subprocesses to use for data loading (default: 0)')
parser.add_argument('--epochs', type=int, default=100, metavar='N',
help='Number of epochs to train (default: 100)')
parser.add_argument('--patience', type=int, default=10, metavar='P',
help='Number of epochs with no improvement after which training will be stopped (default: 10)')
parser.add_argument('--lr', type=float, default=0.0001, metavar='LR',
help='Learning rate (default: 0.0001)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='Random seed (default: 1)')
parser.add_argument('--checkpoint', type=str, default='model.pt', metavar='M',
help='checkpoint file name (default: model.pt)')
args = parser.parse_args()
torch.manual_seed(args.seed)
# Data Initialization and Loading
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
preprocess(args.data)
train_loader, valid_loader = get_train_loaders(
args.data, device, args.batch_size, args.num_workers, args.class_count)
# Neural Network and Optimizer
model = TrafficSignNet().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
# Training and Validation
fit(args.epochs, model, criterion, optimizer,
train_loader, valid_loader, args.patience, args.checkpoint)
console.setFormatter(formatter)
logger.addHandler(console)
logger.propagate = False
# load predefined vocabulary and pretrained word embeddings if applicable
vocab = pickle.load(open(os.path.join(opt.data_path, 'vocab.pkl'), 'rb'))
opt.vocab_size = len(vocab)
if opt.init_embeddings:
opt.vocab_init_embeddings = os.path.join(
opt.data_path, f'vocab.pkl.{opt.init_embeddings_key}_embeddings.npy'
)
# Load data loaders
train_loader, val_loader = data.get_train_loaders(
opt.data_path, vocab, opt.batch_size, opt.workers
)
# construct the model
model = VGNSL(opt)
best_rsum = 0
for epoch in range(opt.num_epochs):
adjust_learning_rate(opt, model.optimizer, epoch)
# train for one epoch
train(opt, train_loader, model, epoch, val_loader, vocab)
# evaluate on validation set using VSE metrics
rsum = validate(opt, val_loader, model, vocab)