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from typing import Tuple
import numpy as np
import torch
import torch.nn
from torch.nn.functional import softmax
from torch.nn.utils.rnn import pack_padded_sequence
import flair
from flair.data import Dictionary, Label, List, Sentence
START_TAG: str = "<START>"
STOP_TAG: str = "<STOP>"
class ViterbiLoss(torch.nn.Module):
"""
Calculates the loss for each sequence up to its length t.
"""
def __init__(self, tag_dictionary: Dictionary):
"""
:param tag_dictionary: tag_dictionary of task
"""
super(ViterbiLoss, self).__init__()
self.tag_dictionary = tag_dictionary
self.tagset_size = len(tag_dictionary)
self.start_tag = tag_dictionary.get_idx_for_item(START_TAG)
self.stop_tag = tag_dictionary.get_idx_for_item(STOP_TAG)
def forward(self, features_tuple: tuple, targets: torch.Tensor) -> torch.Tensor:
"""
Forward propagation of Viterbi Loss
:param features_tuple: CRF scores from forward method in shape (batch size, seq len, tagset size, tagset size),
lengths of sentences in batch, transitions from CRF
:param targets: true tags for sentences which will be converted to matrix indices.
:return: average Viterbi Loss over batch size
"""
features, lengths, transitions = features_tuple
batch_size = features.size(0)
seq_len = features.size(1)
targets, targets_matrix_indices = self._format_targets(targets, lengths)
targets_matrix_indices = torch.tensor(targets_matrix_indices, dtype=torch.long).unsqueeze(2).to(flair.device)
# scores_at_targets[range(features.shape[0]), lengths.values -1]
# Squeeze crf scores matrices in 1-dim shape and gather scores at targets by matrix indices
scores_at_targets = torch.gather(features.view(batch_size, seq_len, -1), 2, targets_matrix_indices)
scores_at_targets = pack_padded_sequence(scores_at_targets, lengths, batch_first=True)[0]
transitions_to_stop = transitions[
np.repeat(self.stop_tag, features.shape[0]),
[target[length - 1] for target, length in zip(targets, lengths)],
]
gold_score = scores_at_targets.sum() + transitions_to_stop.sum()
scores_upto_t = torch.zeros(batch_size, self.tagset_size, device=flair.device)
for t in range(max(lengths)):
batch_size_t = sum(
[length > t for length in lengths]
) # since batch is ordered, we can save computation time by reducing our effective batch_size
if t == 0:
# Initially, get scores from <start> tag to all other tags
scores_upto_t[:batch_size_t] = (
scores_upto_t[:batch_size_t] + features[:batch_size_t, t, :, self.start_tag]
)
else:
# We add scores at current timestep to scores accumulated up to previous timestep, and log-sum-exp
# Remember, the cur_tag of the previous timestep is the prev_tag of this timestep
scores_upto_t[:batch_size_t] = self._log_sum_exp(
features[:batch_size_t, t, :, :] + scores_upto_t[:batch_size_t].unsqueeze(1), dim=2
)
all_paths_scores = self._log_sum_exp(scores_upto_t + transitions[self.stop_tag].unsqueeze(0), dim=1).sum()
viterbi_loss = all_paths_scores - gold_score
return viterbi_loss
@staticmethod
def _log_sum_exp(tensor, dim):
"""
Calculates the log-sum-exponent of a tensor's dimension in a numerically stable way.
:param tensor: tensor
:param dim: dimension to calculate log-sum-exp of
:return: log-sum-exp
"""
m, _ = torch.max(tensor, dim)
m_expanded = m.unsqueeze(dim).expand_as(tensor)
return m + torch.log(torch.sum(torch.exp(tensor - m_expanded), dim))
def _format_targets(self, targets: torch.Tensor, lengths: torch.IntTensor):
"""
Formats targets into matrix indices.
CRF scores contain per sentence, per token a (tagset_size x tagset_size) matrix, containing emission score for
token j + transition prob from previous token i. Means, if we think of our rows as "to tag" and our columns
as "from tag", the matrix in cell [10,5] would contain the emission score for tag 10 + transition score
from previous tag 5 and could directly be addressed through the 1-dim indices (10 + tagset_size * 5) = 70,
if our tagset consists of 12 tags.
:param targets: targets as in tag dictionary
:param lengths: lengths of sentences in batch
"""
targets_per_sentence = []
targets_list = targets.tolist()
for cut in lengths:
targets_per_sentence.append(targets_list[:cut])
targets_list = targets_list[cut:]
for t in targets_per_sentence:
t += [self.tag_dictionary.get_idx_for_item(STOP_TAG)] * (int(lengths.max().item()) - len(t))
matrix_indices = list(
map(
lambda s: [self.tag_dictionary.get_idx_for_item(START_TAG) + (s[0] * self.tagset_size)]
+ [s[i] + (s[i + 1] * self.tagset_size) for i in range(0, len(s) - 1)],
targets_per_sentence,
)
)
return targets_per_sentence, matrix_indices
class ViterbiDecoder:
"""
Decodes a given sequence using the Viterbi algorithm.
"""
def __init__(self, tag_dictionary: Dictionary):
"""
:param tag_dictionary: Dictionary of tags for sequence labeling task
"""
self.tag_dictionary = tag_dictionary
self.tagset_size = len(tag_dictionary)
self.start_tag = tag_dictionary.get_idx_for_item(START_TAG)
self.stop_tag = tag_dictionary.get_idx_for_item(STOP_TAG)
def decode(
self, features_tuple: tuple, probabilities_for_all_classes: bool, sentences: List[Sentence]
) -> Tuple[List, List]:
"""
Decoding function returning the most likely sequence of tags.
:param features_tuple: CRF scores from forward method in shape (batch size, seq len, tagset size, tagset size),
lengths of sentence in batch, transitions of CRF
:param probabilities_for_all_classes: whether to return probabilities for all tags
:return: decoded sequences
"""
features, lengths, transitions = features_tuple
all_tags = []
batch_size = features.size(0)
seq_len = features.size(1)
# Create a tensor to hold accumulated sequence scores at each current tag
scores_upto_t = torch.zeros(batch_size, seq_len + 1, self.tagset_size).to(flair.device)
# Create a tensor to hold back-pointers
# i.e., indices of the previous_tag that corresponds to maximum accumulated score at current tag
# Let pads be the <end> tag index, since that was the last tag in the decoded sequence
backpointers = (
torch.ones((batch_size, seq_len + 1, self.tagset_size), dtype=torch.long, device=flair.device)
* self.stop_tag
)
for t in range(seq_len):
batch_size_t = sum([length > t for length in lengths]) # effective batch size (sans pads) at this timestep
terminates = [i for i, length in enumerate(lengths) if length == t + 1]
if t == 0:
scores_upto_t[:batch_size_t, t] = features[:batch_size_t, t, :, self.start_tag]
backpointers[:batch_size_t, t, :] = (
torch.ones((batch_size_t, self.tagset_size), dtype=torch.long) * self.start_tag
)
else:
# We add scores at current timestep to scores accumulated up to previous timestep, and
# choose the previous timestep that corresponds to the max. accumulated score for each current timestep
scores_upto_t[:batch_size_t, t], backpointers[:batch_size_t, t, :] = torch.max(
features[:batch_size_t, t, :, :] + scores_upto_t[:batch_size_t, t - 1].unsqueeze(1), dim=2
)
# If sentence is over, add transition to STOP-tag
if terminates:
scores_upto_t[terminates, t + 1], backpointers[terminates, t + 1, :] = torch.max(
scores_upto_t[terminates, t].unsqueeze(1) + transitions[self.stop_tag].unsqueeze(0), dim=2
)
# Decode/trace best path backwards
decoded = torch.zeros((batch_size, backpointers.size(1)), dtype=torch.long, device=flair.device)
pointer = torch.ones((batch_size, 1), dtype=torch.long, device=flair.device) * self.stop_tag
for t in list(reversed(range(backpointers.size(1)))):
decoded[:, t] = torch.gather(backpointers[:, t, :], 1, pointer).squeeze(1)
pointer = decoded[:, t].unsqueeze(1)
# Sanity check
assert torch.equal(
decoded[:, 0], torch.ones((batch_size), dtype=torch.long, device=flair.device) * self.start_tag
)
# remove start-tag and backscore to stop-tag
scores_upto_t = scores_upto_t[:, :-1, :]
decoded = decoded[:, 1:]
# Max + Softmax to get confidence score for predicted label and append label to each token
scores = softmax(scores_upto_t, dim=2)
confidences = torch.max(scores, dim=2)
tags = []
for tag_seq, tag_seq_conf, length_seq in zip(decoded, confidences.values, lengths):
tags.append(
[
(self.tag_dictionary.get_item_for_index(tag), conf.item())
for tag, conf in list(zip(tag_seq, tag_seq_conf))[:length_seq]
]
)
if probabilities_for_all_classes:
all_tags = self._all_scores_for_token(scores.cpu(), lengths, sentences)
return tags, all_tags
def _all_scores_for_token(self, scores: torch.Tensor, lengths: torch.IntTensor, sentences: List[Sentence]):
"""
Returns all scores for each tag in tag dictionary.
:param scores: Scores for current sentence.
"""
scores = scores.numpy()
prob_tags_per_sentence = []
for scores_sentence, length, sentence in zip(scores, lengths, sentences):
scores_sentence = scores_sentence[:length]
prob_tags_per_sentence.append(
[
[
Label(token, self.tag_dictionary.get_item_for_index(score_id), score)
for score_id, score in enumerate(score_dist)
]
for score_dist, token in zip(scores_sentence, sentence)
]
)
return prob_tags_per_sentence |