def get_ccm_labels(
self,
output_dict: Dict[str, torch.Tensor],
partial_labels: Optional[List[List[Tuple[int, int]]]] = None
) -> List[List[int]]:
_start_transitions = self.crf.start_transitions \
if hasattr(self.crf, "start_transitions") else None
_end_transitions = self.crf.end_transitions \
if hasattr(self.crf, "end_transitions") else None
logits, mask, transitions, start_transitions, end_transitions = [
(x.numpy() if isinstance(x, torch.Tensor) else x)
for x in Metric.unwrap_to_tensors(
output_dict["logits"], output_dict["mask"],
self.crf.transitions, _start_transitions, _end_transitions)
]
return self._ccm_decoder.ccm_tags(
logits=logits,
mask=mask,
transitions=transitions,
start_transitions=start_transitions,
end_transitions=end_transitions,
partial_labels=partial_labels,
sentence_boundaries=output_dict["sentence_markers"])
def update_confusion_matrices(self, predictions, gold_labels):
mask = gold_labels > 0
predictions, gold_labels, mask = Metric.unwrap_to_tensors(
predictions, gold_labels, mask)
num_classes = predictions.size(-1)
if gold_labels.dim() != predictions.dim() - 1:
raise ConfigurationError(
"gold_labels must have dimension == predictions.size() - 1 but "
"found tensor of shape: {}".format(predictions.size()))
if (gold_labels >= num_classes).any():
raise ConfigurationError(
"A gold label passed to Categorical Accuracy contains an id >= {}, "
"the number of classes.".format(num_classes))
predictions = predictions.view((-1, num_classes))
gold_labels = gold_labels.view(-1).long()
top_k = predictions.max(-1)[1].unsqueeze(-1)
gold_labels = gold_labels.unsqueeze(-1)
vocab = self.model.vocab
for i, gold_label in enumerate(gold_labels):
if gold_label == 0:
continue
pred = top_k[i]
gold_label, pred = gold_label.item(), pred.item()
self.chord_cm[gold_label][pred] += 1
gold_token = vocab.get_token_from_index(gold_label)
pred_token = vocab.get_token_from_index(pred)
gold_key, gold_form, gold_figbass = parse_chord_name_core(
gold_token)
pred_key, pred_form, pred_figbass = parse_chord_name_core(
pred_token)
if gold_key is None and gold_token == "@end@":
gold_key = "@end@"
if pred_key is None and pred_token == "@end@":
pred_key = "@end@"
if gold_key in self.key_list and pred_key in self.key_list:
gold_key_idx = self.key_list.index(gold_key)
pred_key_idx = self.key_list.index(pred_key)
self.key_cm[gold_key_idx][pred_key_idx] += 1
else:
print((gold_token, gold_key), (pred_token, pred_key))
if gold_key != "@end@":
form = gold_form if gold_form is not None else ""
figbass = gold_figbass if gold_figbass is not None else ""
gold_type = form + figbass
else:
gold_type = "@end@"
if pred_key != "@end@":
form = pred_form if pred_form is not None else ""
figbass = pred_figbass if pred_figbass is not None else ""
pred_type = form + figbass
else:
pred_type = "@end@"
if gold_type in self.type_list and pred_type in self.type_list:
gold_type_idx = self.type_list.index(gold_type)
pred_type_idx = self.type_list.index(pred_type)
self.type_cm[gold_type_idx][pred_type_idx] += 1
else:
print((gold_token, gold_type), (pred_token, pred_type))
