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 _train(self, loader):
self.model.train()
bar = progress_bar(loader)
if self.args.em_alg:
self.model.zero_cache()
for words, _ in bar:
mask = words.ne(self.WORD.pad_index)
# ignore the first token of each sentence
emit_probs, trans_probs = self.model(words, mask)
if self.args.em_alg:
logP = self.model.baum_welch(words, mask, emit_probs,
trans_probs)
else:
self.optimizer.zero_grad()
logP = self.model.get_logP(emit_probs, trans_probs, mask)
loss = -logP.mean()
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(),
self.args.clip)
self.optimizer.step()
self.scheduler.step()
bar.set_postfix_str(f" logP: {logP.mean():.4f}")
if self.args.em_alg:
self.model.step()
def _train(self, loader):
self.model.train()
bar, metric = progress_bar(loader), ChartMetric()
for i, (words, *feats, labels) in enumerate(bar, 1):
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
s_edge, s_sib, s_cop, s_grd, s_label = self.model(words, feats)
loss, s_edge = self.model.loss(s_edge, s_sib, s_cop, s_grd,
s_label, labels, mask)
loss = loss / self.args.update_steps
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
if i % self.args.update_steps == 0:
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
label_preds = self.model.decode(s_edge, s_label)
metric(label_preds.masked_fill(~mask, -1),
labels.masked_fill(~mask, -1))
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f} - {metric}"
)
logger.info(f"{bar.postfix}")
def _predict(self, loader):
self.model.eval()
preds, probs = {'trees': []}, []
for words, feats, trees in progress_bar(loader):
batch_size, seq_len = words.shape
lens = words.ne(self.args.pad_index).sum(1) - 1
mask = lens.new_tensor(range(seq_len - 1)) < lens.view(-1, 1, 1)
mask = mask & mask.new_ones(seq_len - 1, seq_len - 1).triu_(1)
s_span, s_label = self.model(words, feats)
if self.args.mbr:
s_span = self.model.crf(s_span, mask, mbr=True)
chart_preds = self.model.decode(s_span, s_label, mask)
preds['trees'].extend([
Tree.build(tree, [(i, j, self.CHART.vocab[label])
for i, j, label in chart])
for tree, chart in zip(trees, chart_preds)
])
if self.args.prob:
probs.extend([
prob[:i - 1, 1:i].cpu()
for i, prob in zip(lens, s_span.unbind())
])
if self.args.prob:
preds['probs'] = probs
return preds
def _predict(self, loader):
self.model.eval()
preds = {'trees': [], 'probs': [] if self.args.prob else None}
for batch in progress_bar(loader):
words, *feats, trees = batch
word_mask = words.ne(self.args.pad_index)[:, 1:]
mask = word_mask if len(words.shape) < 3 else word_mask.any(-1)
mask = (mask.unsqueeze(1) & mask.unsqueeze(2)).triu_(1)
lens = mask[:, 0].sum(-1)
s_span, s_label = self.model(words, feats)
s_span = ConstituencyCRF(
s_span, mask[:,
0].sum(-1)).marginals if self.args.mbr else s_span
chart_preds = self.model.decode(s_span, s_label, mask)
preds['trees'].extend([
Tree.build(tree, [(i, j, self.CHART.vocab[label])
for i, j, label in chart])
for tree, chart in zip(trees, chart_preds)
])
if self.args.prob:
preds['probs'].extend(
[prob[:i - 1, 1:i].cpu() for i, prob in zip(lens, s_span)])
return preds
def _train(self, loader):
self.model.train()
bar, metric = progress_bar(loader), AttachmentMetric()
for words, feats, arcs, sibs, rels in bar:
self.optimizer.zero_grad()
mask = words.ne(self.WORD.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
s_arc, s_sib, s_rel = self.model(words, feats)
loss, s_arc = self.model.loss(s_arc, s_sib, s_rel, arcs, sibs, rels, mask, self.args.mbr, self.args.partial)
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
self.optimizer.step()
self.scheduler.step()
arc_preds, rel_preds = self.model.decode(s_arc, s_sib, s_rel, mask)
if self.args.partial:
mask &= arcs.ge(0)
# ignore all punctuation if not specified
if not self.args.punct:
mask &= words.unsqueeze(-1).ne(self.puncts).all(-1)
metric(arc_preds, rel_preds, arcs, rels, mask)
bar.set_postfix_str(f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f} - {metric}")
def _predict(self, loader):
self.model.eval()
preds = {}
arcs, rels, probs = [], [], []
for words, feats in progress_bar(loader):
mask = words.ne(self.WORD.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
lens = mask.sum(1).tolist()
s_arc, s_sib, s_rel = self.model(words, feats)
if self.args.mbr:
s_arc = self.model.crf((s_arc, s_sib), mask, mbr=True)
arc_preds, rel_preds = self.model.decode(s_arc, s_sib, s_rel, mask, self.args.tree, self.args.mbr, self.args.proj)
arcs.extend(arc_preds[mask].split(lens))
rels.extend(rel_preds[mask].split(lens))
if self.args.prob:
arc_probs = s_arc if self.args.mbr else s_arc.softmax(-1)
probs.extend([prob[1:i+1, :i+1].cpu() for i, prob in zip(lens, arc_probs.unbind())])
arcs = [seq.tolist() for seq in arcs]
rels = [self.REL.vocab[seq.tolist()] for seq in rels]
preds = {'arcs': arcs, 'rels': rels}
if self.args.prob:
preds['probs'] = probs
return preds
def _train(self, loader):
self.model.train()
bar = progress_bar(loader)
for i, batch in enumerate(bar, 1):
words, *feats, trees, charts = batch
word_mask = words.ne(self.args.pad_index)[:, 1:]
mask = word_mask if len(words.shape) < 3 else word_mask.any(-1)
mask = (mask.unsqueeze(1) & mask.unsqueeze(2)).triu_(1)
s_span, s_label = self.model(words, feats)
loss, _ = self.model.loss(s_span, s_label, charts, mask,
self.args.mbr)
loss = loss / self.args.update_steps
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
if i % self.args.update_steps == 0:
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f}"
)
logger.info(f"{bar.postfix}")
def _train(self, loader):
self.model.train()
bar, metric = progress_bar(loader), ChartMetric()
for words, *feats, edges, labels in bar:
self.optimizer.zero_grad()
mask = words.ne(self.WORD.pad_index)
mask = mask.unsqueeze(1) & mask.unsqueeze(2)
mask[:, 0] = 0
s_edge, s_sib, s_cop, s_grd, s_label = self.model(words, feats)
loss, s_edge = self.model.loss(s_edge, s_sib, s_cop, s_grd,
s_label, edges, labels, mask)
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
self.optimizer.step()
self.scheduler.step()
edge_preds, label_preds = self.model.decode(s_edge, s_label)
metric(label_preds.masked_fill(~(edge_preds.gt(0) & mask), -1),
labels.masked_fill(~(edges.gt(0) & mask), -1))
bar.set_postfix_str(
f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f} - {metric}"
)
def _train(self, loader):
self.model.train()
bar, metric = progress_bar(loader), AttachmentMetric()
for i, (words, texts, *feats, arcs, rels) in enumerate(bar, 1):
word_mask = words.ne(self.args.pad_index)
mask = word_mask if len(words.shape) < 3 else word_mask.any(-1)
# ignore the first token of each sentence
mask[:, 0] = 0
s_arc, s_sib, s_rel = self.model(words, feats)
loss, s_arc = self.model.loss(s_arc, s_sib, s_rel, arcs, rels, mask)
loss = loss / self.args.update_steps
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
if i % self.args.update_steps == 0:
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
arc_preds, rel_preds = self.model.decode(s_arc, s_rel, mask)
if self.args.partial:
mask &= arcs.ge(0)
# ignore all punctuation if not specified
if not self.args.punct:
mask.masked_scatter_(mask, ~mask.new_tensor([ispunct(w) for s in texts for w in s]))
metric(arc_preds, rel_preds, arcs, rels, mask)
bar.set_postfix_str(f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f} - {metric}")
logger.info(f"{bar.postfix}")
def load(self, data, lang=None, max_len=None, **kwargs):
r"""
Args:
data (list[list] or str):
A list of instances or a filename.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
max_len (int):
Sentences exceeding the length will be discarded. Default: ``None``.
Returns:
A list of :class:`TreeSentence` instances.
"""
if isinstance(data, str) and os.path.exists(data):
with open(data, 'r') as f:
trees = [nltk.Tree.fromstring(s) for s in f]
self.root = trees[0].label()
else:
if lang is not None:
tokenizer = Tokenizer(lang)
data = [tokenizer(i) for i in ([data] if isinstance(data, str) else data)]
else:
data = [data] if isinstance(data[0], str) else data
trees = [self.totree(i, self.root) for i in data]
i, sentences = 0, []
for tree in progress_bar(trees):
sentences.append(TreeSentence(self, tree))
i += 1
if max_len is not None:
sentences = [i for i in sentences if len(i) < max_len]
return sentences
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 load(self, data, max_len=None, **kwargs):
r"""
Args:
data (list[list] or str):
A list of instances or a filename.
max_len (int):
Sentences exceeding the length will be discarded. Default: ``None``.
Returns:
A list of :class:`TreeSentence` instances.
"""
if isinstance(data, str):
with open(data, 'r') as f:
trees = [nltk.Tree.fromstring(string) for string in f]
self.root = trees[0].label()
else:
data = [data] if isinstance(data[0], str) else data
trees = [self.totree(i, self.root) for i in data]
i, sentences = 0, []
for tree in progress_bar(trees, leave=False):
if len(tree) == 1 and not isinstance(tree[0][0], nltk.Tree):
continue
sentences.append(TreeSentence(self, tree))
i += 1
if max_len is not None:
sentences = [i for i in sentences if len(i) < max_len]
return sentences
def _train(self, loader):
self.model.train()
bar, metric = progress_bar(loader), AttachmentMetric()
# words, feats, etc. come from loader! loader is train.loader, where train is Dataset
for words, feats, arcs, rels in bar:
self.optimizer.zero_grad()
if self.elmo:
feat_embs = self.elmo.embed_batch(feats)
else:
feat_embs = self.efml.sents2elmo(feats, output_layer=-2)
#TODO: dodaj mapping, ce in samo ce gre za vecmap
if self.args.map_method == 'vecmap':
# map feat_embs with vecmap, actually self.mapper defined in class init
feat_embs = self.mapper.map_batch(feat_embs)
mask = words.ne(self.WORD.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
feats0 = torch.zeros(words.shape+(1024,)) # words.clone()
feats1 = torch.zeros(words.shape+(1024,))
feats2 = torch.zeros(words.shape+(1024,))
# words get ignored, all input comes from feats - 3 elmo layers
# still inputting words due to reasons(tm)
#feats0 = feats0.unsqueeze(-1)
#feats0 = feats0.expand(words.shape+(1024,))
for sentence in range(len(feat_embs)):
for token in range(len(feat_embs[sentence][1])):
feats0[sentence][token] = torch.Tensor(feat_embs[sentence][0][token])
feats1[sentence][token] = torch.Tensor(feat_embs[sentence][1][token])
feats2[sentence][token] = torch.Tensor(feat_embs[sentence][2][token])
feats = torch.cat((feats0, feats1, feats2), -1)
if str(self.args.device) == '-1':
feats = feats.to('cpu')
else:
feats = feats.to('cuda:'+str(self.args.device)) #TODO: fix to allow cpu or gpu
s_arc, s_rel = self.model(words, feats) #INFO: here is the data input, y = model(x)
loss = self.model.loss(s_arc, s_rel, arcs, rels, mask, self.args.partial)
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
self.optimizer.step()
self.scheduler.step()
arc_preds, rel_preds = self.model.decode(s_arc, s_rel, mask)
if self.args.partial:
mask &= arcs.ge(0)
# ignore all punctuation if not specified
if not self.args.punct:
mask &= words.unsqueeze(-1).ne(self.puncts).all(-1)
metric(arc_preds, rel_preds, arcs, rels, mask)
bar.set_postfix_str(f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f} - {metric}")
def load(self, data, lang=None, proj=False, max_len=None, **kwargs):
r"""
Loads the data in CoNLL-X format.
Also supports for loading data from CoNLL-U file with comments and non-integer IDs.
Args:
data (list[list] or str):
A list of instances or a filename.
lang (str):
Language code (e.g., ``en``) or language name (e.g., ``English``) for the text to tokenize.
``None`` if tokenization is not required.
Default: ``None``.
proj (bool):
If ``True``, discards all non-projective sentences. Default: ``False``.
max_len (int):
Sentences exceeding the length will be discarded. Default: ``None``.
Returns:
A list of :class:`CoNLLSentence` instances.
"""
if isinstance(data, str) and os.path.exists(data):
with open(data, 'r') as f:
lines = [line.strip() for line in f]
else:
if lang is not None:
tokenizer = Tokenizer(lang)
data = [
tokenizer(i)
for i in ([data] if isinstance(data, str) else data)
]
else:
data = [data] if isinstance(data[0], str) else data
lines = '\n'.join([self.toconll(i) for i in data]).split('\n')
i, start, sentences = 0, 0, []
for line in progress_bar(lines):
if not line:
sentences.append(CoNLLSentence(self, lines[start:i]))
start = i + 1
i += 1
if proj:
sentences = [
i for i in sentences
if self.isprojective(list(map(int, i.arcs)))
]
if max_len is not None:
sentences = [i for i in sentences if len(i) < max_len]
return sentences
def _predict(self, loader):
self.model.eval()
preds = {}
tags = []
for words, in progress_bar(loader):
mask = words.ne(self.WORD.pad_index)
lens = mask.sum(1).tolist()
emit_probs, trans_probs = self.model(words, mask)
tag_preds = self.model.decode(emit_probs, trans_probs, mask)
tags.extend(tag_preds[mask].split(lens))
tags = [[f"#C{t}#" for t in seq.tolist()] for seq in tags]
preds = {'tags': tags}
return preds
def _predict(self, loader):
self.model.eval()
preds = {}
arcs, rels, probs = [], [], []
for words, feats in progress_bar(loader):
if self.elmo:
feat_embs = self.elmo.embed_batch(feats)
else:
feat_embs = self.efml.sents2elmo(feats, output_layer=-2)
if self.mapper:
# map feat_embs with self.mapper defined in class init
feat_embs = self.mapper.map_batch(feat_embs)
mask = words.ne(self.WORD.pad_index)
# ignore the first token of each sentence
mask[:, 0] = 0
lens = mask.sum(1).tolist()
feats0 = torch.zeros(words.shape+(1024,))
feats1 = torch.zeros(words.shape+(1024,))
feats2 = torch.zeros(words.shape+(1024,))
for sentence in range(len(feat_embs)):
for token in range(len(feat_embs[sentence][1])):
feats0[sentence][token] = torch.Tensor(feat_embs[sentence][0][token])
feats1[sentence][token] = torch.Tensor(feat_embs[sentence][1][token])
feats2[sentence][token] = torch.Tensor(feat_embs[sentence][2][token])
feats = torch.cat((feats0, feats1, feats2), -1)
if str(self.args.device) == '-1':
feats = feats.to('cpu')
else:
feats = feats.to('cuda:'+str(self.args.device))
s_arc, s_rel = self.model(words, feats)
arc_preds, rel_preds = self.model.decode(s_arc, s_rel, mask,
self.args.tree,
self.args.proj)
arcs.extend(arc_preds[mask].split(lens))
rels.extend(rel_preds[mask].split(lens))
if self.args.prob:
arc_probs = s_arc.softmax(-1)
probs.extend([prob[1:i+1, :i+1].cpu() for i, prob in zip(lens, arc_probs.unbind())])
arcs = [seq.tolist() for seq in arcs]
rels = [self.REL.vocab[seq.tolist()] for seq in rels]
preds = {'arcs': arcs, 'rels': rels}
if self.args.prob:
preds['probs'] = probs
return preds
def _predict(self, loader):
self.model.eval()
preds = {'arcs': [], 'rels': [], 'probs': [] if self.args.prob else None}
for words, texts, *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)
# ignore the first token of each sentence
mask[:, 0] = 0
lens = mask.sum(1).tolist()
s_arc, s_rel = self.model(words, feats)
arc_preds, rel_preds = self.model.decode(s_arc, s_rel, mask, self.args.tree, self.args.proj)
preds['arcs'].extend(arc_preds[mask].split(lens))
preds['rels'].extend(rel_preds[mask].split(lens))
if self.args.prob:
preds['probs'].extend([prob[1:i+1, :i+1].cpu() for i, prob in zip(lens, s_arc.softmax(-1).unbind())])
preds['arcs'] = [seq.tolist() for seq in preds['arcs']]
preds['rels'] = [self.REL.vocab[seq.tolist()] for seq in preds['rels']]
return preds
def _train(self, loader):
self.model.train()
bar = progress_bar(loader)
for words, feats, trees, charts in bar:
self.optimizer.zero_grad()
batch_size, seq_len = words.shape
lens = words.ne(self.args.pad_index).sum(1) - 1
mask = lens.new_tensor(range(seq_len - 1)) < lens.view(-1, 1, 1)
mask = mask & mask.new_ones(seq_len-1, seq_len-1).triu_(1)
s_span, s_label = self.model(words, feats)
loss, _ = self.model.loss(s_span, s_label, charts, mask, self.args.mbr)
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), self.args.clip)
self.optimizer.step()
self.scheduler.step()
bar.set_postfix_str(f"lr: {self.scheduler.get_last_lr()[0]:.4e} - loss: {loss:.4f}")
def load(self, data, proj=False, max_len=None, **kwargs):
r"""
Loads the data in CoNLL-X format.
Also supports for loading data from CoNLL-U file with comments and non-integer IDs.
Args:
data (list[list] or str):
A list of instances or a filename.
proj (bool):
If ``True``, discards all non-projective sentences. Default: ``False``.
max_len (int):
Sentences exceeding the length will be discarded. Default: ``None``.
Returns:
A list of :class:`CoNLLSentence` instances.
"""
if isinstance(data, str):
with open(data, 'r') as f:
lines = [line.strip() for line in f]
else:
data = [data] if isinstance(data[0], str) else data
lines = '\n'.join([self.toconll(i) for i in data]).split('\n')
i, start, sentences = 0, 0, []
for line in progress_bar(lines, leave=False):
if not line:
sentences.append(CoNLLSentence(self, lines[start:i]))
start = i + 1
i += 1
if proj:
sentences = [
i for i in sentences
if self.isprojective(list(map(int, i.arcs)))
]
if max_len is not None:
sentences = [i for i in sentences if len(i) < max_len]
return sentences
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
def __call__(self, sentences):
# numericalize the fields of each sentence
for sentence in progress_bar(sentences):
for f in self.flattened_fields:
sentence.transformed[f.name] = f.transform([getattr(sentence, f.name)])[0]
return self.flattened_fields
