[Feature] Add WordAccuracy for OCR Task

docs/en/api/metrics.rst

@@ -53,3 +53,4 @@ Metrics
    KeypointEndPointError
    KeypointAUC
    KeypointNME
+   WordAccuracy

docs/zh_cn/api/metrics.rst

@@ -53,3 +53,4 @@ Metrics
    KeypointEndPointError
    KeypointAUC
    KeypointNME
+   WordAccuracy

mmeval/metrics/__init__.py

@@ -32,6 +32,7 @@
 from .snr import SignalNoiseRatio
 from .ssim import StructuralSimilarity
 from .voc_map import VOCMeanAP
+from .word_accuracy import WordAccuracy
 
 __all__ = [
     'Accuracy', 'MeanIoU', 'VOCMeanAP', 'OIDMeanAP', 'EndPointError',
@@ -42,7 +43,8 @@
     'AveragePrecision', 'AVAMeanAP', 'BLEU', 'DOTAMeanAP',
     'SumAbsoluteDifferences', 'GradientError', 'MattingMeanSquaredError',
     'ConnectivityError', 'ROUGE', 'Perplexity', 'KeypointEndPointError',
-    'KeypointAUC', 'KeypointNME', 'NaturalImageQualityEvaluator'
+    'KeypointAUC', 'KeypointNME', 'NaturalImageQualityEvaluator',
+    'WordAccuracy'
 ]
 
 _deprecated_msg = (

mmeval/metrics/word_accuracy.py

@@ -0,0 +1,107 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import re
+from typing import Dict, List, Sequence, Tuple, Union
+
+from mmeval.core import BaseMetric
+
+
+class WordAccuracy(BaseMetric):
+    r"""Calculate the word level accuracy.
+
+    Args:
+        mode (str or list[str]): Options are:
+
+            - 'exact': Accuracy at word level.
+            - 'ignore_case': Accuracy at word level, ignoring letter
+              case.
+            - 'ignore_case_symbol': Accuracy at word level, ignoring
+              letter case and symbol. (Default metric for academic evaluation)
+
+            If mode is a list, then metrics in mode will be calculated
+            separately. Defaults to 'ignore_case_symbol'.
+        invalid_symbol (str): A regular expression to filter out invalid or
+            not cared characters. Defaults to '[^A-Za-z0-9\u4e00-\u9fa5]'
+        **kwargs: Keyword parameters passed to :class:`BaseMetric`.
+
+    Examples:
+        >>> from mmeval import WordAccuracy
+        >>> metric = WordAccuracy()
+        >>> metric(['hello', 'hello', 'hello'], ['hello', 'HELLO', '$HELLO$'])
+        {'ignore_case_symbol_accuracy': 1.0}
+        >>> metric = WordAccuracy(mode=['exact', 'ignore_case',
+        >>>                             'ignore_case_symbol'])
+        >>> metric(['hello', 'hello', 'hello'], ['hello', 'HELLO', '$HELLO$'])
+        {'accuracy': 0.333333333,
+         'ignore_case_accuracy': 0.666666667,
+         'ignore_case_symbol_accuracy': 1.0}
+    """
+
+    def __init__(self,
+                 mode: Union[str, Sequence[str]] = 'ignore_case_symbol',
+                 invalid_symbol: str = '[^A-Za-z0-9\u4e00-\u9fa5]',
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.mode = mode
+        self.invalid_symbol = re.compile(invalid_symbol)
+        assert isinstance(mode, (str, list))
+        if isinstance(mode, str):
+            mode = [mode]
+        assert all(isinstance(item, str) for item in mode)
+        self.mode = set(mode)  # type: ignore
+        assert set(self.mode).issubset(
+            {'exact', 'ignore_case', 'ignore_case_symbol'})
+
+    def add(self, predictions: Sequence[str], groundtruths: Sequence[str]) -> None:  # type: ignore # yapf: disable # noqa: E501
+        """Process one batch of data and predictions.
+
+        Args:
+            predictions (list[str]): The prediction texts.
+            groundtruths (list[str]): The ground truth texts.
+        """
+        for pred, label in zip(predictions, groundtruths):
+            num, ignore_case_num, ignore_case_symbol_num = 0, 0, 0
+            if 'exact' in self.mode:
+                num = pred == label
+            if 'ignore_case' in self.mode or 'ignore_case_symbol' in self.mode:
+                pred_lower = pred.lower()
+                label_lower = label.lower()
+                ignore_case_num = pred_lower == label_lower
+            if 'ignore_case_symbol' in self.mode:
+                label_lower_ignore = self.invalid_symbol.sub('', label_lower)
+                pred_lower_ignore = self.invalid_symbol.sub('', pred_lower)
+                ignore_case_symbol_num =\
+                    label_lower_ignore == pred_lower_ignore
+            self._results.append(
+                (num, ignore_case_num, ignore_case_symbol_num))
+
+    def compute_metric(self, results: List[Tuple[int, int, int]]) -> Dict:
+        """Compute the metrics from processed results.
+
+        Args:
+            results (list[float]): The processed results of each batch.
+
+        Returns:
+            dict[str, float]: Nested dicts as results. Provided keys are:
+
+            - accuracy (float): Accuracy at word level.
+            - ignore_case_accuracy (float): Accuracy at word level, ignoring
+              letter case.
+            - ignore_case_symbol_accuracy (float): Accuracy at word level,
+              ignoring letter case and symbol.
+        """
+        metric_results = {}
+        gt_word_num = max(len(results), 1.0)
+        exact_sum, ignore_case_sum, ignore_case_symbol_sum = 0.0, 0.0, 0.0
+        for exact, ignore_case, ignore_case_symbol in results:
+            exact_sum += exact
+            ignore_case_sum += ignore_case
+            ignore_case_symbol_sum += ignore_case_symbol
+        if 'exact' in self.mode:
+            metric_results['accuracy'] = exact_sum / gt_word_num
+        if 'ignore_case' in self.mode:
+            metric_results[
+                'ignore_case_accuracy'] = ignore_case_sum / gt_word_num
+        if 'ignore_case_symbol' in self.mode:
+            metric_results['ignore_case_symbol_accuracy'] =\
+                ignore_case_symbol_sum / gt_word_num
+        return metric_results

tests/test_metrics/test_word_accuracy.py

@@ -0,0 +1,30 @@
+import pytest
+
+from mmeval import WordAccuracy
+
+
+def test_init():
+    with pytest.raises(AssertionError):
+        WordAccuracy(mode=1)
+    with pytest.raises(AssertionError):
+        WordAccuracy(mode=[1, 2])
+    with pytest.raises(AssertionError):
+        WordAccuracy(mode='micro')
+    metric = WordAccuracy(mode=['ignore_case', 'ignore_case', 'exact'])
+    assert metric.mode == {'ignore_case', 'ignore_case', 'exact'}
+
+
+def test_word_accuracy():
+    metric = WordAccuracy(mode=['exact', 'ignore_case', 'ignore_case_symbol'])
+    res = metric(['hello', 'hello', 'hello'], ['hello', 'HELLO', '$HELLO$'])
+    assert abs(res['accuracy'] - 1. / 3) < 1e-7
+    assert abs(res['ignore_case_accuracy'] - 2. / 3) < 1e-7
+    assert abs(res['ignore_case_symbol_accuracy'] - 1.0) < 1e-7
+    metric.reset()
+    for pred, label in zip(['hello', 'hello', 'hello'],
+                           ['hello', 'HELLO', '$HELLO$']):
+        metric.add([pred], [label])
+    res = metric.compute()
+    assert abs(res['accuracy'] - 1. / 3) < 1e-7
+    assert abs(res['ignore_case_accuracy'] - 2. / 3) < 1e-7
+    assert abs(res['ignore_case_symbol_accuracy'] - 1.0) < 1e-7