def test_text_abs(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 = AbsSummarizer(args, device, checkpoint)
model.eval()
test_iter = data_loader.Dataloader(args, load_dataset(args, 'test', shuffle=False),
args.test_batch_size, device,
shuffle=False, is_test=True)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, cache_dir=args.temp_dir)
symbols = {'BOS': tokenizer.vocab['[unused0]'], 'EOS': tokenizer.vocab['[unused1]'],
'PAD': tokenizer.vocab['[PAD]'], 'EOQ': tokenizer.vocab['[unused2]']}
predictor = build_predictor(args, tokenizer, symbols, model, logger)
predictor.translate(test_iter, step)
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 = AbsSummarizer(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)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True, cache_dir=args.temp_dir)
symbols = {'BOS': tokenizer.vocab['[unused0]'], 'EOS': tokenizer.vocab['[unused1]'],
'PAD': tokenizer.vocab['[PAD]'], 'EOQ': tokenizer.vocab['[unused2]']}
valid_loss = abs_loss(model.generator, symbols, model.vocab_size, train=False, device=device)
trainer = build_trainer(args, device_id, model, None, valid_loss)
stats = trainer.validate(valid_iter, step)
return stats.xent()
def test_ext(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()
test_iter = data_loader.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)
def train_iter_fct():
return data_loader.Dataloader(args,
load_dataset(args, 'train',
shuffle=True),
args.batch_size,
device,
shuffle=True,
is_test=False)
def baseline(args, cal_lead=False, cal_oracle=False):
test_iter = data_loader.Dataloader(args, load_dataset(args, 'test', shuffle=False),
args.batch_size, 'cpu',
shuffle=False, is_test=True)
trainer = build_trainer(args, '-1', None, None, None)
#
if (cal_lead):
trainer.test(test_iter, 0, cal_lead=True)
elif (cal_oracle):
trainer.test(test_iter, 0, cal_oracle=True)