def _predict(self, loader):
self.model.eval()
preds = {}
charts, probs = [], []
for words, *feats in progress_bar(loader):
mask = words.ne(self.WORD.pad_index)
mask = mask.unsqueeze(1) & mask.unsqueeze(2)
mask[:, 0] = 0
lens = mask[:, 1].sum(-1).tolist()
s_edge, s_label = self.model(words, feats)
edge_preds, label_preds = self.model.decode(s_edge, s_label)
chart_preds = label_preds.masked_fill(~(edge_preds.gt(0) & mask),
-1)
charts.extend(chart[1:i, :i].tolist()
for i, chart in zip(lens, chart_preds.unbind()))
if self.args.prob:
probs.extend([
prob[1:i, :i].cpu()
for i, prob in zip(lens,
s_edge.softmax(-1).unbind())
])
charts = [
CoNLL.build_relations(
[[self.LABEL.vocab[i] if i >= 0 else None for i in row]
for row in chart]) for chart in charts
]
preds = {'labels': charts}
if self.args.prob:
preds['probs'] = probs
return preds
def _predict(self, loader):
self.model.eval()
preds = {'labels': [], 'probs': [] if self.args.prob else None}
for words, *feats in progress_bar(loader):
word_mask = words.ne(self.args.pad_index)
mask = word_mask if len(words.shape) < 3 else word_mask.any(-1)
mask = mask.unsqueeze(1) & mask.unsqueeze(2)
mask[:, 0] = 0
lens = mask[:, 1].sum(-1).tolist()
s_edge, s_sib, s_cop, s_grd, s_label = self.model(words, feats)
s_edge = self.model.inference((s_edge, s_sib, s_cop, s_grd), mask)
label_preds = self.model.decode(s_edge,
s_label).masked_fill(~mask, -1)
preds['labels'].extend(chart[1:i, :i].tolist()
for i, chart in zip(lens, label_preds))
if self.args.prob:
preds['probs'].extend([
prob[1:i, :i].cpu()
for i, prob in zip(lens, s_edge.unbind())
])
preds['labels'] = [
CoNLL.build_relations(
[[self.LABEL.vocab[i] if i >= 0 else None for i in row]
for row in chart]) for chart in preds['labels']
]
return preds
def _predict(self, loader):
self.model.eval()
preds = {}
charts, probs = [], []
for words, feats in progress_bar(loader):
mask = words.ne(self.WORD.pad_index)
mask = mask.unsqueeze(1) & mask.unsqueeze(2)
lens = mask[:, 0].sum(-1).tolist()
s_edge, s_label = self.model(words, feats)
charts.extend(self.model.decode(s_edge, s_label, mask))
if self.args.prob:
edge_probs = s_edge.softmax(-1)
probs.extend([
prob[:i, :i].cpu()
for i, prob in zip(lens, edge_probs.unbind())
])
charts = [[[self.LABEL.vocab[i] if i >= 0 else None for i in row]
for row in chart] for chart in charts]
preds = {'labels': [CoNLL.build_relations(chart) for chart in charts]}
if self.args.prob:
preds['probs'] = probs
return preds
