/
seq2labels_model.py
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/
seq2labels_model.py
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"""Basic model. Predicts tags for every token"""
from typing import Dict, Optional, List, Any
import numpy
import torch
import torch.nn.functional as F
from allennlp.data import Vocabulary
from allennlp.models.model import Model
from allennlp.modules import TimeDistributed, TextFieldEmbedder
from allennlp.nn import InitializerApplicator, RegularizerApplicator
from allennlp.nn.util import get_text_field_mask, sequence_cross_entropy_with_logits
from allennlp.training.metrics import CategoricalAccuracy
from overrides import overrides
from torch.nn.modules.linear import Linear
@Model.register("seq2labels")
class Seq2Labels(Model):
"""
This ``Seq2Labels`` simply encodes a sequence of text with a stacked ``Seq2SeqEncoder``, then
predicts a tag (or couple tags) for each token in the sequence.
Parameters
----------
vocab : ``Vocabulary``, required
A Vocabulary, required in order to compute sizes for input/output projections.
text_field_embedder : ``TextFieldEmbedder``, required
Used to embed the ``tokens`` ``TextField`` we get as input to the model.
encoder : ``Seq2SeqEncoder``
The encoder (with its own internal stacking) that we will use in between embedding tokens
and predicting output tags.
calculate_span_f1 : ``bool``, optional (default=``None``)
Calculate span-level F1 metrics during training. If this is ``True``, then
``label_encoding`` is required. If ``None`` and
label_encoding is specified, this is set to ``True``.
If ``None`` and label_encoding is not specified, it defaults
to ``False``.
label_encoding : ``str``, optional (default=``None``)
Label encoding to use when calculating span f1.
Valid options are "BIO", "BIOUL", "IOB1", "BMES".
Required if ``calculate_span_f1`` is true.
labels_namespace : ``str``, optional (default=``labels``)
This is needed to compute the SpanBasedF1Measure metric, if desired.
Unless you did something unusual, the default value should be what you want.
verbose_metrics : ``bool``, optional (default = False)
If true, metrics will be returned per label class in addition
to the overall statistics.
initializer : ``InitializerApplicator``, optional (default=``InitializerApplicator()``)
Used to initialize the model parameters.
regularizer : ``RegularizerApplicator``, optional (default=``None``)
If provided, will be used to calculate the regularization penalty during training.
"""
def __init__(self, vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
predictor_dropout=0.0,
labels_namespace: str = "labels",
detect_namespace: str = "d_tags",
verbose_metrics: bool = False,
label_smoothing: float = 0.0,
confidence: float = 0.0,
del_confidence: float = 0.0,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super(Seq2Labels, self).__init__(vocab, regularizer)
self.label_namespaces = [labels_namespace,
detect_namespace]
self.text_field_embedder = text_field_embedder
self.num_labels_classes = self.vocab.get_vocab_size(labels_namespace)
self.num_detect_classes = self.vocab.get_vocab_size(detect_namespace)
self.label_smoothing = label_smoothing
self.confidence = confidence
self.del_conf = del_confidence
self.incorr_index = self.vocab.get_token_index("INCORRECT",
namespace=detect_namespace)
self._verbose_metrics = verbose_metrics
self.predictor_dropout = TimeDistributed(torch.nn.Dropout(predictor_dropout))
self.tag_labels_projection_layer = TimeDistributed(
Linear(text_field_embedder._token_embedders['bert'].get_output_dim(), self.num_labels_classes))
self.tag_detect_projection_layer = TimeDistributed(
Linear(text_field_embedder._token_embedders['bert'].get_output_dim(), self.num_detect_classes))
self.metrics = {"accuracy": CategoricalAccuracy()}
initializer(self)
@overrides
def forward(self, # type: ignore
tokens: Dict[str, torch.LongTensor],
labels: torch.LongTensor = None,
d_tags: torch.LongTensor = None,
metadata: List[Dict[str, Any]] = None) -> Dict[str, torch.Tensor]:
# pylint: disable=arguments-differ
"""
Parameters
----------
tokens : Dict[str, torch.LongTensor], required
The output of ``TextField.as_array()``, which should typically be passed directly to a
``TextFieldEmbedder``. This output is a dictionary mapping keys to ``TokenIndexer``
tensors. At its most basic, using a ``SingleIdTokenIndexer`` this is: ``{"tokens":
Tensor(batch_size, num_tokens)}``. This dictionary will have the same keys as were used
for the ``TokenIndexers`` when you created the ``TextField`` representing your
sequence. The dictionary is designed to be passed directly to a ``TextFieldEmbedder``,
which knows how to combine different word representations into a single vector per
token in your input.
labels : torch.LongTensor, optional (default = None)
A torch tensor representing the sequence of integer gold class labels of shape
``(batch_size, num_tokens)``.
d_tags : torch.LongTensor, optional (default = None)
A torch tensor representing the sequence of integer gold class labels of shape
``(batch_size, num_tokens)``.
metadata : ``List[Dict[str, Any]]``, optional, (default = None)
metadata containing the original words in the sentence to be tagged under a 'words' key.
Returns
-------
An output dictionary consisting of:
logits : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
unnormalised log probabilities of the tag classes.
class_probabilities : torch.FloatTensor
A tensor of shape ``(batch_size, num_tokens, tag_vocab_size)`` representing
a distribution of the tag classes per word.
loss : torch.FloatTensor, optional
A scalar loss to be optimised.
"""
encoded_text = self.text_field_embedder(tokens)
batch_size, sequence_length, _ = encoded_text.size()
mask = get_text_field_mask(tokens)
logits_labels = self.tag_labels_projection_layer(self.predictor_dropout(encoded_text))
logits_d = self.tag_detect_projection_layer(encoded_text)
class_probabilities_labels = F.softmax(logits_labels, dim=-1).view(
[batch_size, sequence_length, self.num_labels_classes])
class_probabilities_d = F.softmax(logits_d, dim=-1).view(
[batch_size, sequence_length, self.num_detect_classes])
error_probs = class_probabilities_d[:, :, self.incorr_index] * mask
incorr_prob = torch.max(error_probs, dim=-1)[0]
probability_change = [self.confidence, self.del_conf] + [0] * (self.num_labels_classes - 2)
class_probabilities_labels += torch.FloatTensor(probability_change).repeat(
(batch_size, sequence_length, 1)).to(class_probabilities_labels.device)
output_dict = {"logits_labels": logits_labels,
"logits_d_tags": logits_d,
"class_probabilities_labels": class_probabilities_labels,
"class_probabilities_d_tags": class_probabilities_d,
"max_error_probability": incorr_prob}
if labels is not None and d_tags is not None:
loss_labels = sequence_cross_entropy_with_logits(logits_labels, labels, mask,
label_smoothing=self.label_smoothing)
loss_d = sequence_cross_entropy_with_logits(logits_d, d_tags, mask)
for metric in self.metrics.values():
metric(logits_labels, labels, mask.float())
metric(logits_d, d_tags, mask.float())
output_dict["loss"] = loss_labels + loss_d
if metadata is not None:
output_dict["words"] = [x["words"] for x in metadata]
return output_dict
@overrides
def decode(self, output_dict: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
"""
Does a simple position-wise argmax over each token, converts indices to string labels, and
adds a ``"tags"`` key to the dictionary with the result.
"""
for label_namespace in self.label_namespaces:
all_predictions = output_dict[f'class_probabilities_{label_namespace}']
all_predictions = all_predictions.cpu().data.numpy()
if all_predictions.ndim == 3:
predictions_list = [all_predictions[i] for i in range(all_predictions.shape[0])]
else:
predictions_list = [all_predictions]
all_tags = []
for predictions in predictions_list:
argmax_indices = numpy.argmax(predictions, axis=-1)
tags = [self.vocab.get_token_from_index(x, namespace=label_namespace)
for x in argmax_indices]
all_tags.append(tags)
output_dict[f'{label_namespace}'] = all_tags
return output_dict
@overrides
def get_metrics(self, reset: bool = False) -> Dict[str, float]:
metrics_to_return = {metric_name: metric.get_metric(reset) for
metric_name, metric in self.metrics.items()}
return metrics_to_return