def __init__(self,
args=None,
vocab=None,
emb_matrix=None,
model_file=None,
use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load from file
self.load(model_file, use_cuda)
else:
self.args = args
self.model = None if args['dict_only'] else Seq2SeqModel(
args, emb_matrix=emb_matrix)
self.vocab = vocab
self.expansion_dict = dict()
if not self.args['dict_only']:
self.crit = loss.SequenceLoss(self.vocab.size)
self.parameters = [
p for p in self.model.parameters() if p.requires_grad
]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'],
self.parameters,
self.args['lr'])
def __init__(self, args=None, vocab=None, emb_matrix=None, model_file=None, use_cuda=False):
self.use_cuda = use_cuda
if model_file is not None:
# load everything from file
self.load(model_file, use_cuda)
else:
# build model from scratch
self.args = args
self.model = None if args['dict_only'] else Seq2SeqModel(args, emb_matrix=emb_matrix, use_cuda=use_cuda)
self.vocab = vocab
# dict-based components
self.word_dict = dict()
self.composite_dict = dict()
if not self.args['dict_only']:
if self.args.get('edit', False):
self.crit = loss.MixLoss(self.vocab['char'].size, self.args['alpha'])
logger.debug("Running seq2seq lemmatizer with edit classifier...")
else:
self.crit = loss.SequenceLoss(self.vocab['char'].size)
self.parameters = [p for p in self.model.parameters() if p.requires_grad]
if use_cuda:
self.model.cuda()
self.crit.cuda()
else:
self.model.cpu()
self.crit.cpu()
self.optimizer = utils.get_optimizer(self.args['optim'], self.parameters, self.args['lr'])
def load(self, filename, use_cuda=False):
try:
checkpoint = torch.load(filename, lambda storage, loc: storage)
except BaseException:
logger.exception("Cannot load model from {}".format(filename))
sys.exit(1)
self.args = checkpoint['config']
self.expansion_dict = checkpoint['dict']
if not self.args['dict_only']:
self.model = Seq2SeqModel(self.args, use_cuda=use_cuda)
self.model.load_state_dict(checkpoint['model'])
else:
self.model = None
self.vocab = Vocab.load_state_dict(checkpoint['vocab'])
def load(self, filename, use_cuda=False):
try:
checkpoint = torch.load(filename, lambda storage, loc: storage)
except BaseException:
logger.error("Cannot load model from {}".format(filename))
raise
self.args = checkpoint['config']
self.word_dict, self.composite_dict = checkpoint['dicts']
if not self.args['dict_only']:
self.model = Seq2SeqModel(self.args, use_cuda=use_cuda)
self.model.load_state_dict(checkpoint['model'])
else:
self.model = None
self.vocab = MultiVocab.load_state_dict(checkpoint['vocab'])
