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)
spm = sentencepiece.SentencePieceProcessor()
spm.Load(args.vocab_path)
word_padding_idx = spm.PieceToId('<PAD>')
symbols = {'BOS': spm.PieceToId('<S>'), 'EOS': spm.PieceToId('</S>'), 'PAD': word_padding_idx,
'EOT': spm.PieceToId('<T>'), 'EOP': spm.PieceToId('<P>'), 'EOQ': spm.PieceToId('<Q>')}
vocab_size = len(spm)
model = Summarizer(args, word_padding_idx, vocab_size, device, checkpoint)
model.eval()
valid_iter = data_loader.AbstractiveDataloader(args, load_dataset(args, 'valid', shuffle=False), symbols,
args.batch_size, device, shuffle=False, is_test=False)
trainer = build_trainer(args, device_id, model, symbols, vocab_size, None)
stats = trainer.validate(valid_iter)
trainer._report_step(0, step, valid_stats=stats)
return stats.ppl()
def train(args, device_id):
init_logger(args.log_file)
logger.info(str(args))
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)
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])
ckpt_step = args.train_from.strip('.pt').split('_')[-1]
else:
checkpoint = None
spm = sentencepiece.SentencePieceProcessor()
spm.Load(args.vocab_path)
word_padding_idx = spm.PieceToId('<PAD>')
symbols = {
'BOS': spm.PieceToId('<S>'),
'EOS': spm.PieceToId('</S>'),
'PAD': word_padding_idx,
'EOT': spm.PieceToId('<T>'),
'EOP': spm.PieceToId('<P>'),
'EOQ': spm.PieceToId('<Q>')
}
print(symbols)
vocab_size = len(spm)
def train_iter_fct():
return data_loader.AbstractiveDataloader(args,
load_dataset(args,
'train',
shuffle=True),
symbols,
args.batch_size,
device,
shuffle=True,
is_test=False)
model = Summarizer(args, word_padding_idx, vocab_size, device, checkpoint)
optim = model_builder.build_optim(args, model, checkpoint)
if args.train_from != '':
optim._step = int(ckpt_step)
logger.info(model)
trainer = build_trainer(args, device_id, model, symbols, vocab_size, optim)
trainer.train(train_iter_fct, args.train_steps)