def update_metrics(self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor],
Iterable[torch.Tensor], Any],
prediction: Dict[str, Any], metric: Union[MetricDict,
Metric]):
return CRFConstituencyParser.update_metrics(self, metric, batch,
prediction)
def compute_loss(
self, batch: Dict[str, Any],
output: Union[torch.Tensor, Dict[str, torch.Tensor],
Iterable[torch.Tensor], Any], criterion
) -> Union[torch.FloatTensor, Dict[str, torch.FloatTensor]]:
out, mask = output['output'], output['mask']
loss, span_probs = CRFConstituencyParser.compute_loss(
self, out, batch['chart_id'], mask, crf_decoder=criterion)
output['span_probs'] = span_probs
return loss
def feed_batch(self, h: torch.FloatTensor, batch: Dict[str, torch.Tensor],
mask: torch.BoolTensor, decoder: torch.nn.Module):
return {
'output':
decoder(h),
'mask':
CRFConstituencyParser.compute_mask(
self,
batch,
offset=1 if 'constituency' in batch
or batch['token'][0][-1] == EOS else -1)
}
def decode_output(self, output: Union[torch.Tensor, Dict[str,
torch.Tensor],
Iterable[torch.Tensor],
Any], mask: torch.BoolTensor,
batch: Dict[str, Any], decoder: torch.nn.Module,
**kwargs) -> Union[Dict[str, Any], Any]:
out, mask = output['output'], output['mask']
tokens = []
for sent in batch['token']:
if sent[0] == BOS:
sent = sent[1:]
if sent[-1] == EOS:
sent = sent[:-1]
tokens.append(sent)
return CRFConstituencyParser.decode_output(self,
out,
mask,
batch,
output.get(
'span_probs', None),
decoder=decoder,
tokens=tokens)
def build_samples(self, inputs):
return CRFConstituencyParser.build_samples(self, inputs)
def input_is_flat(self, data) -> bool:
return CRFConstituencyParser.input_is_flat(self, data)
def build_metric(self, **kwargs):
return CRFConstituencyParser.build_metric(self)
def build_samples(self, inputs, cls_is_bos=False, sep_is_eos=False):
return CRFConstituencyParser.build_samples(self, inputs)