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bleu.py
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bleu.py
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# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# referenced from
# Library Name: torchtext
# Authors: torchtext authors and @sluks
# Date: 2020-07-18
# Link: https://pytorch.org/text/_modules/torchtext/data/metrics.html#bleu_score
from typing import Any, Callable, Optional, Sequence
import torch
from torch import Tensor, tensor
from torchmetrics import Metric
from torchmetrics.functional.text.bleu import _bleu_score_compute, _bleu_score_update
class BLEUScore(Metric):
"""Calculate `BLEU score <https://en.wikipedia.org/wiki/BLEU>`_ of machine translated text with one or more
references.
Args:
n_gram:
Gram value ranged from 1 to 4 (Default 4)
smooth:
Whether or not to apply smoothing – see [2]
compute_on_step:
Forward only calls ``update()`` and returns None if this is set to False. default: True
dist_sync_on_step:
Synchronize metric state across processes at each ``forward()``
before returning the value at the step.
process_group:
Specify the process group on which synchronization is called. default: None (which selects the entire world)
dist_sync_fn:
Callback that performs the allgather operation on the metric state. When `None`, DDP
will be used to perform the allgather.
Example:
>>> translate_corpus = ['the cat is on the mat'.split()]
>>> reference_corpus = [['there is a cat on the mat'.split(), 'a cat is on the mat'.split()]]
>>> metric = BLEUScore()
>>> metric(reference_corpus, translate_corpus)
tensor(0.7598)
References:
[1] BLEU: a Method for Automatic Evaluation of Machine Translation by Papineni,
Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu http://www.aclweb.org/anthology/P02-1040.pdf
[2] Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence
and Skip-Bigram Statistics by Chin-Yew Lin and Franz Josef Och https://aclanthology.org/P04-1077.pdf
"""
trans_len: Tensor
ref_len: Tensor
numerator: Tensor
denominator: Tensor
def __init__(
self,
n_gram: int = 4,
smooth: bool = False,
compute_on_step: bool = True,
dist_sync_on_step: bool = False,
process_group: Optional[Any] = None,
dist_sync_fn: Optional[Callable] = None,
):
super().__init__(
compute_on_step=compute_on_step,
dist_sync_on_step=dist_sync_on_step,
process_group=process_group,
dist_sync_fn=dist_sync_fn,
)
self.n_gram = n_gram
self.smooth = smooth
self.add_state("trans_len", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state("ref_len", tensor(0, dtype=torch.float), dist_reduce_fx="sum")
self.add_state("numerator", torch.zeros(self.n_gram), dist_reduce_fx="sum")
self.add_state("denominator", torch.zeros(self.n_gram), dist_reduce_fx="sum")
def update( # type: ignore
self, reference_corpus: Sequence[Sequence[Sequence[str]]], translate_corpus: Sequence[Sequence[str]]
) -> None:
"""Compute Precision Scores.
Args:
reference_corpus: An iterable of iterables of reference corpus
translate_corpus: An iterable of machine translated corpus
"""
self.trans_len, self.ref_len = _bleu_score_update(
reference_corpus,
translate_corpus,
self.numerator,
self.denominator,
self.trans_len,
self.ref_len,
self.n_gram,
)
def compute(self) -> Tensor:
"""Calculate BLEU score.
Return:
Tensor with BLEU Score
"""
return _bleu_score_compute(
self.trans_len, self.ref_len, self.numerator, self.denominator, self.n_gram, self.smooth
)
@property
def is_differentiable(self) -> bool:
return False