def train_single_ext(args, device_id):
init_logger(args.log_file)
device = "cpu" if args.visible_gpus == '-1' else "cuda"
logger.info('Device ID %d' % device_id)
logger.info('Device %s' % device)
torch.manual_seed(args.seed)
random.seed(args.seed)
torch.backends.cudnn.deterministic = True
if device_id >= 0:
torch.cuda.set_device(device_id)
torch.cuda.manual_seed(args.seed)
torch.manual_seed(args.seed)
random.seed(args.seed)
torch.backends.cudnn.deterministic = True
if args.train_from != '':
logger.info('Loading checkpoint from %s' % args.train_from)
checkpoint = torch.load(args.train_from,
map_location=lambda storage, loc: storage)
opt = vars(checkpoint['opt'])
for k in opt.keys():
if (k in model_flags):
setattr(args, k, opt[k])
else:
checkpoint = None
def train_iter_fct():
return data_loader.Dataloader(args, load_dataset(args, 'train', shuffle=True), args.batch_size, device,
shuffle=True, is_test=False)
model = ExtSummarizer(args, device, checkpoint)
optim = model_builder.build_optim(args, model, checkpoint)
logger.info(model)
trainer = build_trainer_ext(args, device_id, model, optim)
trainer.train(train_iter_fct, args.train_steps)
def validate(args, device_id, pt, step):
device = "cpu" if args.visible_gpus == '-1' else "cuda"
if (pt != ''):
test_from = pt
else:
test_from = args.test_from
logger.info('Loading checkpoint from %s' % test_from)
checkpoint = torch.load(test_from, map_location=lambda storage, loc: storage)
opt = vars(checkpoint['opt'])
for k in opt.keys():
if (k in model_flags):
setattr(args, k, opt[k])
print(args)
model = ExtSummarizer(args, device, checkpoint)
model.eval()
valid_iter = data_loader.Dataloader(args, load_dataset(args, 'valid', shuffle=False),
args.batch_size, device,
shuffle=False, is_test=False)
trainer = build_trainer_ext(args, device_id, model, None)
stats = trainer.validate(valid_iter, step)
return stats.xent()