constant.args.continue_from)
start_epoch = epoch # index starts from zero
verbose = constant.args.verbose
if loaded_args != None:
# Unwrap nn.DataParallel
if loaded_args.parallel:
logging.info("unwrap from DataParallel")
model = model.module
# Parallelize the batch
if args.parallel:
model = nn.DataParallel(model, device_ids=args.device_ids)
else:
if constant.args.model == "TRFS":
model = init_transformer_model(constant.args, label2id, id2label)
opt = init_optimizer(constant.args, model, "noam")
else:
logging.info("The model is not supported, check args --h")
loss_type = args.loss
if constant.USE_CUDA:
model = model.cuda(0)
logging.info(model)
num_epochs = constant.args.epochs
trainer = Trainer()
trainer.train(model, train_loader, train_sampler, valid_loader_list, opt, loss_type, start_epoch, num_epochs, label2id, id2label, metrics)
num_workers=args.num_workers)
valid_loader_list.append(valid_loader)
start_epoch = 0
metrics = None
loaded_args = None
if args.continue_from != "":
logging.info("Continue from checkpoint:" + args.continue_from)
model, vocab, opt, epoch, metrics, loaded_args = load_joint_model(
args.continue_from)
start_epoch = (epoch) # index starts from zero
verbose = args.verbose
else:
if args.model == "TRFS":
model = init_transformer_model(args,
vocab,
is_factorized=args.is_factorized,
r=args.r)
else:
logging.info("The model is not supported, check args --h")
loss_type = args.loss
if USE_CUDA:
model = model.cuda()
logging.info(model)
num_epochs = args.epochs
print("Parameters: {}(trainable), {}(non-trainable)".format(
compute_num_params(model)[0],
compute_num_params(model)[1]))
