def train_iter_fct():
return DataLoader.Dataloader(args,
load_dataset(args, 'train', shuffle=True),
args.batch_size,
device,
shuffle=True,
is_test=False)
def validate(args, device_id, pt, step):
if pt != '':
test_from = pt
else:
test_from = args.test_from
print('Loading checkpoint from %s' % 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 = Summarizer(args, device, checkpoint)
valid_iter = DataLoader.Dataloader(args,
load_dataset(args,
'valid',
shuffle=False),
args.batch_size,
device,
shuffle=False,
is_test=False)
trainer = build_trainer(args, device_id, model, None)
stats = trainer.validate(valid_iter, step)
return stats.xent()
def test_ext(args, device_id, pt, step):
if pt != '':
test_from = pt
else:
test_from = args.test_from
print('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 = Summarizer(args, device, checkpoint)
test_iter = DataLoader.Dataloader(args,
load_dataset(args, 'test',
shuffle=False),
args.test_batch_size,
device,
shuffle=False,
is_test=True)
trainer = build_trainer(args, device_id, model, None)
trainer.test(test_iter, step)
