def build_criterion(args, dataset):
padding_idx = dataset.dst_dict.pad()
if args.label_smoothing > 0:
return criterions.LabelSmoothedCrossEntropyCriterion(
args.label_smoothing, padding_idx)
else:
return criterions.CrossEntropyCriterion(padding_idx)
def __init__(self, opt, shared=None):
# initialize defaults first
super().__init__(opt, shared)
if not shared:
# this is not a shared instance of this class, so do full
# initialization. if shared is set, only set up shared members.
saved_state = None
if opt.get('model_file') and os.path.isfile(opt['model_file']):
# load model parameters if available
print('Loading existing model params from ' +
opt['model_file'])
new_opt, saved_state = self.load(opt['model_file'])
# override options with stored ones
opt = self._override_opt(new_opt)
self.args = OptWrapper(opt)
self.fairseq_dict = _make_fairseq_dict(DictionaryAgent(opt))
self.id = 'Fairseq'
self.truncate = opt['truncate'] if opt['truncate'] > 0 else None
self.EOS = self.fairseq_dict[self.fairseq_dict.eos()]
self.EOS_TENSOR = (torch.LongTensor(1, 1).fill_(
self.fairseq_dict.eos()))
self.NULL_IDX = self.fairseq_dict.pad()
encoder = fconv.Encoder(self.fairseq_dict,
embed_dim=self.args.encoder_embed_dim,
convolutions=eval(
self.args.encoder_layers),
dropout=self.args.dropout,
max_positions=self.args.max_positions)
decoder = fconv.Decoder(
self.fairseq_dict,
embed_dim=self.args.decoder_embed_dim,
convolutions=eval(self.args.decoder_layers),
out_embed_dim=self.args.decoder_out_embed_dim,
attention=eval(self.args.decoder_attention),
dropout=self.args.dropout,
max_positions=self.args.max_positions)
self.model = fconv.FConvModel(encoder, decoder)
# from fairseq's build_criterion()
if self.args.label_smoothing > 0:
self.criterion = criterions.LabelSmoothedCrossEntropyCriterion(
self.args.label_smoothing, self.NULL_IDX)
else:
self.criterion = criterions.CrossEntropyCriterion(
self.NULL_IDX)
self.trainer = MultiprocessingTrainer(self.args, self.model,
self.criterion)
if saved_state is not None:
self.set_states(saved_state)
self.reset()
def build_criterion(args, src_dict, dst_dict):
if args.label_smoothing > 0:
return criterions.LabelSmoothedCrossEntropyCriterion(args, dst_dict)
else:
return criterions.CrossEntropyCriterion(args, dst_dict)
def build_token_criterion(args, padding_idx):
# token-level training
if args.label_smoothing > 0:
return criterions.LabelSmoothedCrossEntropyCriterion(args.label_smoothing, padding_idx)
else:
return criterions.CrossEntropyCriterion(padding_idx)
