[Feature] Add ROUGE to mmeval

.pre-commit-config-zh-cn.yaml

@@ -4,8 +4,8 @@ repos:
     rev: 5.0.4
     hooks:
       - id: flake8
-  - repo: https://gitee.com/openmmlab/mirrors-isort
-    rev: 5.10.1
+  - repo: https://gitee.com/zhouzaida/mirrors-isort
+    rev: 5.12.1
     hooks:
       - id: isort
   - repo: https://gitee.com/openmmlab/mirrors-yapf

.pre-commit-config.yaml

@@ -4,8 +4,8 @@ repos:
     rev: 5.0.4
     hooks:
       - id: flake8
-  - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+  - repo: https://github.com/zhouzaida/isort
+    rev: 5.12.1
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

docs/en/api/metrics.rst

@@ -47,3 +47,4 @@ Metrics
    MattingMSE
    ConnectivityError
    DOTAMeanAP
+   ROUGE

docs/zh_cn/api/metrics.rst

@@ -47,3 +47,4 @@ Metrics
    MattingMSE
    ConnectivityError
    DOTAMeanAP
+   ROUGE

mmeval/metrics/__init__.py

@@ -19,6 +19,7 @@
 from .pck_accuracy import JhmdbPCKAccuracy, MpiiPCKAccuracy, PCKAccuracy
 from .proposal_recall import ProposalRecall
 from .psnr import PSNR
+from .rouge import ROUGE
 from .sad import SAD
 from .single_label import SingleLabelMetric
 from .snr import SNR
@@ -31,5 +32,5 @@
     'PCKAccuracy', 'MpiiPCKAccuracy', 'JhmdbPCKAccuracy', 'ProposalRecall',
     'PSNR', 'MAE', 'MSE', 'SSIM', 'SNR', 'MultiLabelMetric',
     'AveragePrecision', 'AVAMeanAP', 'BLEU', 'DOTAMeanAP', 'SAD',
-    'GradientError', 'MattingMSE', 'ConnectivityError'
+    'GradientError', 'MattingMSE', 'ConnectivityError', 'ROUGE'
 ]

mmeval/metrics/bleu.py

@@ -3,40 +3,10 @@
 # <https://github.com/Lightning-AI/metrics/blob/master/src/torchmetrics/text/bleu.py>`_.
 import numpy as np
 from collections import Counter
-from typing import List, Optional, Sequence, Tuple
+from typing import Callable, List, Optional, Sequence, Tuple, Union
 
 from mmeval import BaseMetric
-
-
-def get_n_gram(token: Sequence[str], n_gram: int) -> Counter:
-    """A function get n_gram of sentences.
-
-    Args:
-        token (Sequence[str]): A series of tokens about sentences.
-        n_gram (int): The maximum number of words contained in a phrase
-            when calculating word fragments. Defaults to 4.
-
-    Returns:
-        Counter: The n_gram contained in sentences with Counter format.
-    """
-    counter: Counter = Counter()
-    for i in range(1, n_gram + 1):
-        for j in range(len(token) - i + 1):
-            key = tuple(token[j:(i + j)])
-            counter[key] += 1
-    return counter
-
-
-def tokenizer_fn(sentence: str) -> List[str]:
-    """This function is used to segment a sentence.
-
-    Args:
-        sentence (str): A sentence.
-
-    Returns:
-        List[str]: A list of tokens after word segmentation.
-    """
-    return sentence.split()
+from mmeval.metrics.utils import get_n_gram, get_tokenizer, infer_language
 
 
 def _get_brevity_penalty(pred_len: np.array,
@@ -67,9 +37,12 @@ class BLEU(BaseMetric):
         n_gram (int): The maximum number of words contained in a phrase
             when calculating word fragments. Defaults to 4.
         smooth (bool): Whether or not to apply to smooth. Defaults to False.
-        ngram_weights(Sequence[float], optional): Weights used
+        ngram_weights (Sequence[float], optional): Weights used
             for unigrams, bigrams, etc. to calculate BLEU score.
             If not provided, uniform weights are used. Defaults to None.
+        tokenizer_fn (Union[Callable, str, None]): A user's own tokenizer function.
+            Defaults to None.
+            New in version 0.3.0.
         **kwargs: Keyword parameters passed to :class:`BaseMetric`.
 
     Examples:
@@ -93,6 +66,7 @@ def __init__(self,
                  n_gram: int = 4,
                  smooth: bool = False,
                  ngram_weights: Optional[Sequence[float]] = None,
+                 tokenizer_fn: Union[Callable, str, None] = None,
                  **kwargs) -> None:
         super().__init__(**kwargs)
         self.n_gram = n_gram
@@ -105,21 +79,35 @@ def __init__(self,
             ngram_weights = [1.0 / n_gram] * n_gram
         self.ngram_weights = ngram_weights
 
+        # Select tokenizer according to the entered value.
+        self.tokenizer_fn = None
+        if callable(tokenizer_fn):
+            self.tokenizer_fn = tokenizer_fn
+        elif isinstance(tokenizer_fn, str):
+            self.tokenizer_fn = get_tokenizer(tokenizer_fn)
+            if self.tokenizer_fn is None:
+                raise ValueError('Right now, `tokenizer_fn` only supports '
+                                 "pre-defined 'en' or 'cn'.")
+        else:
+            assert tokenizer_fn is None, \
+                f'`tokenizer_fn` supports Callable, str or None, but not `{type(tokenizer_fn)}`'  # noqa: E501
+
     def add(self, predictions: Sequence[str], references: Sequence[Sequence[str]]) -> None:  # type: ignore # yapf: disable # noqa: E501
         """Add the intermediate results to ``self._results``.
 
         Args:
-             predictions (Sequence[str]): An iterable of machine
-                translated corpus.
-             references (Sequence[Sequence[str]]): An iterable of
-                iterables of reference corpus.
+             predictions (Sequence[str]): An iterable of predicted sentences.
+             references (Sequence[Sequence[str]): An iterable of
+                 referenced sentences.
         """
-
+        if self.tokenizer_fn is None:
+            language = infer_language(predictions[0])
+            self.tokenizer_fn = get_tokenizer(language)
         references_token: Sequence[Sequence[Sequence[str]]] = [
-            [tokenizer_fn(line) for line in r] for r in references
+            [self.tokenizer_fn(line) for line in r] for r in references
         ]
         predictions_token: Sequence[Sequence[str]] = [
-            tokenizer_fn(line) for line in predictions
+            self.tokenizer_fn(line) for line in predictions
         ]
         for prediction, references in zip(predictions_token, references_token):
             pred_len = len(prediction)