def run(args):
print("Arguments in args:\n{}".format(pprint.pformat(vars(args))),
flush=True)
# load configurations
with open(args.conf, "r") as f:
conf = yaml.load(f, Loader=yaml.FullLoader)
print("Arguments in yaml:\n{}".format(pprint.pformat(conf)), flush=True)
checkpoint_dir = Path(conf['train']['checkpoint'])
checkpoint_dir.mkdir(exist_ok=True, parents=True)
random.seed(conf['train']['seed'])
np.random.seed(conf['train']['seed'])
th.cuda.manual_seed_all(conf['train']['seed'])
# if exist, resume training
last_checkpoint = checkpoint_dir / "last.pt.tar"
if last_checkpoint.exists():
print(f"Found old checkpoint: {last_checkpoint}", flush=True)
conf['train']['resume'] = last_checkpoint.as_posix()
# dump configurations
with open(checkpoint_dir / "train.yaml", "w") as f:
yaml.dump(conf, f)
#build nnet
nnet = model(**conf["nnet_conf"])
# build optimizer
optimizer = make_optimizer(nnet.parameters(), conf)
# build dataloader
train_loader, valid_loader = make_dataloader(conf)
# build scheduler
scheduler = ReduceLROnPlateau(optimizer,
mode="min",
factor=conf['scheduler']['factor'],
patience=conf['scheduler']['patience'],
min_lr=conf['scheduler']['min_lr'],
verbose=True)
device = th.device('cuda' if conf['train']['use_cuda']
and th.cuda.is_available() else 'cpu')
trainer = Trainer(nnet, optimizer, scheduler, device, conf)
trainer.run(
train_loader,
valid_loader,
num_epoches=conf['train']['epoch'],
)
def train(config):
fields = rstdt_fields()
dataset = Dataset(config, fields)
train_iter, valid_iter = dataset.load_train_valid()
model = SpanBasedParser.build_model(config, fields)
print(model)
optimizer = {
'adam': optim.Adam,
'sgd': optim.SGD
}[config.optimizer](model.parameters(),
lr=config.lr,
weight_decay=config.weight_decay)
scheduler = optim.lr_scheduler.ExponentialLR(optimizer, config.lr_decay)
trainer = Trainer(config, model, optimizer, scheduler, train_iter,
valid_iter, fields)
trainer.run()
return
