def decode_output(self,
output: Union[torch.Tensor, Dict[str, torch.Tensor], Iterable[torch.Tensor], Any],
mask: torch.BoolTensor,
batch: Dict[str, Any],
decoder, **kwargs) -> Union[Dict[str, Any], Any]:
(arc_scores, rel_scores), mask, punct_mask = output
return BiaffineSemanticDependencyParser.decode(self, arc_scores, rel_scores, mask, batch)
def decode(self,
arc_scores,
rel_scores,
mask,
batch=None,
predicting=None):
output_1st, output_2nd = batch.get('outputs', (None, None))
if output_1st is None:
arc_scores_1st, arc_scores_2nd, rel_scores_1st, rel_scores_2nd = self.unpack_scores(
arc_scores, rel_scores)
output_1st = super().decode(arc_scores_1st, rel_scores_1st, mask)
mask = self.compute_mask(arc_scores_2nd, batch, mask)
# noinspection PyCallByClass
output_2nd = BiaffineSemanticDependencyParser.decode(
self, arc_scores_2nd, rel_scores_2nd, mask, batch)
if self.config.get('no_cycle'):
assert predicting, 'No cycle constraint for evaluation is not implemented yet. If you are ' \
'interested, welcome to submit a pull request.'
root_rel_idx = self.vocabs['rel'].token_to_idx.get(
self.config.get('root', None), None)
arc_pred_1st, rel_pred_1st, arc_pred_2nd, rel_pred_2nd = *output_1st, *output_2nd
arc_scores_2nd = arc_scores_2nd.transpose(
1, 2).cpu().detach().numpy()
arc_pred_2nd = arc_pred_2nd.cpu().detach().numpy()
rel_pred_2nd = rel_pred_2nd.cpu().detach().numpy()
trees = arc_pred_1st.cpu().detach().numpy()
graphs = []
for i, (arc_scores, arc_preds, rel_preds, tree,
tokens) in enumerate(
zip(arc_scores_2nd, arc_pred_2nd, rel_pred_2nd,
trees, batch['token'])):
sent_len = len(tokens)
graph = add_secondary_arcs_by_preds(
arc_scores, arc_preds[:sent_len, :sent_len], rel_preds,
tree[:sent_len], root_rel_idx)
graphs.append(graph[1:]) # Remove root
# if not predicting:
# # Write back to torch Tensor
# for d, hr in zip(graph):
# pass
output_2nd = None, graphs
return tuple(zip(output_1st, output_2nd))