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# Copyright (c) OpenMMLab. All rights reserved.
import re
from difflib import SequenceMatcher
from typing import Dict, Optional, Sequence, Union
import mmengine
from mmengine.evaluator import BaseMetric
from rapidfuzz.distance import Levenshtein
from mmocr.registry import METRICS
@METRICS.register_module()
class WordMetric(BaseMetric):
"""Word metrics for text recognition task.
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'
valid_symbol (str): Valid characters. Defaults to
'[^A-Z^a-z^0-9^\u4e00-\u9fa5]'
collect_device (str): Device name used for collecting results from
different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Defaults to None.
"""
default_prefix: Optional[str] = 'recog'
def __init__(self,
mode: Union[str, Sequence[str]] = 'ignore_case_symbol',
valid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
collect_device: str = 'cpu',
prefix: Optional[str] = None) -> None:
super().__init__(collect_device, prefix)
self.valid_symbol = re.compile(valid_symbol)
if isinstance(mode, str):
mode = [mode]
assert mmengine.is_seq_of(mode, str)
assert set(mode).issubset(
{'exact', 'ignore_case', 'ignore_case_symbol'})
self.mode = set(mode)
def process(self, data_batch: Sequence[Dict],
data_samples: Sequence[Dict]) -> None:
"""Process one batch of data_samples. The processed results should be
stored in ``self.results``, which will be used to compute the metrics
when all batches have been processed.
Args:
data_batch (Sequence[Dict]): A batch of gts.
data_samples (Sequence[Dict]): A batch of outputs from the model.
"""
for data_sample in data_samples:
match_num = 0
match_ignore_case_num = 0
match_ignore_case_symbol_num = 0
pred_text = data_sample.get('pred_text').get('item')
gt_text = data_sample.get('gt_text').get('item')
if 'ignore_case' in self.mode or 'ignore_case_symbol' in self.mode:
pred_text_lower = pred_text.lower()
gt_text_lower = gt_text.lower()
if 'ignore_case_symbol' in self.mode:
gt_text_lower_ignore = self.valid_symbol.sub('', gt_text_lower)
pred_text_lower_ignore = self.valid_symbol.sub(
'', pred_text_lower)
match_ignore_case_symbol_num =\
gt_text_lower_ignore == pred_text_lower_ignore
if 'ignore_case' in self.mode:
match_ignore_case_num = pred_text_lower == gt_text_lower
if 'exact' in self.mode:
match_num = pred_text == gt_text
result = dict(
match_num=match_num,
match_ignore_case_num=match_ignore_case_num,
match_ignore_case_symbol_num=match_ignore_case_symbol_num)
self.results.append(result)
def compute_metrics(self, results: Sequence[Dict]) -> Dict:
"""Compute the metrics from processed results.
Args:
results (list[Dict]): The processed results of each batch.
Returns:
Dict: The computed metrics. The keys are the names of the metrics,
and the values are corresponding results.
"""
eps = 1e-8
eval_res = {}
gt_word_num = len(results)
if 'exact' in self.mode:
match_nums = [result['match_num'] for result in results]
match_nums = sum(match_nums)
eval_res['word_acc'] = 1.0 * match_nums / (eps + gt_word_num)
if 'ignore_case' in self.mode:
match_ignore_case_num = [
result['match_ignore_case_num'] for result in results
]
match_ignore_case_num = sum(match_ignore_case_num)
eval_res['word_acc_ignore_case'] = 1.0 *\
match_ignore_case_num / (eps + gt_word_num)
if 'ignore_case_symbol' in self.mode:
match_ignore_case_symbol_num = [
result['match_ignore_case_symbol_num'] for result in results
]
match_ignore_case_symbol_num = sum(match_ignore_case_symbol_num)
eval_res['word_acc_ignore_case_symbol'] = 1.0 *\
match_ignore_case_symbol_num / (eps + gt_word_num)
for key, value in eval_res.items():
eval_res[key] = float(f'{value:.4f}')
return eval_res
@METRICS.register_module()
class CharMetric(BaseMetric):
"""Character metrics for text recognition task.
Args:
valid_symbol (str): Valid characters.
Defaults to '[^A-Z^a-z^0-9^\u4e00-\u9fa5]'
collect_device (str): Device name used for collecting results from
different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Defaults to None.
"""
default_prefix: Optional[str] = 'recog'
def __init__(self,
valid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
collect_device: str = 'cpu',
prefix: Optional[str] = None) -> None:
super().__init__(collect_device, prefix)
self.valid_symbol = re.compile(valid_symbol)
def process(self, data_batch: Sequence[Dict],
data_samples: Sequence[Dict]) -> None:
"""Process one batch of data_samples. The processed results should be
stored in ``self.results``, which will be used to compute the metrics
when all batches have been processed.
Args:
data_batch (Sequence[Dict]): A batch of gts.
data_samples (Sequence[Dict]): A batch of outputs from the model.
"""
for data_sample in data_samples:
pred_text = data_sample.get('pred_text').get('item')
gt_text = data_sample.get('gt_text').get('item')
gt_text_lower = gt_text.lower()
pred_text_lower = pred_text.lower()
gt_text_lower_ignore = self.valid_symbol.sub('', gt_text_lower)
pred_text_lower_ignore = self.valid_symbol.sub('', pred_text_lower)
# number to calculate char level recall & precision
result = dict(
gt_char_num=len(gt_text_lower_ignore),
pred_char_num=len(pred_text_lower_ignore),
true_positive_char_num=self._cal_true_positive_char(
pred_text_lower_ignore, gt_text_lower_ignore))
self.results.append(result)
def compute_metrics(self, results: Sequence[Dict]) -> Dict:
"""Compute the metrics from processed results.
Args:
results (list[Dict]): The processed results of each batch.
Returns:
Dict: The computed metrics. The keys are the names of the
metrics, and the values are corresponding results.
"""
gt_char_num = [result['gt_char_num'] for result in results]
pred_char_num = [result['pred_char_num'] for result in results]
true_positive_char_num = [
result['true_positive_char_num'] for result in results
]
gt_char_num = sum(gt_char_num)
pred_char_num = sum(pred_char_num)
true_positive_char_num = sum(true_positive_char_num)
eps = 1e-8
char_recall = 1.0 * true_positive_char_num / (eps + gt_char_num)
char_precision = 1.0 * true_positive_char_num / (eps + pred_char_num)
eval_res = {}
eval_res['char_recall'] = char_recall
eval_res['char_precision'] = char_precision
for key, value in eval_res.items():
eval_res[key] = float(f'{value:.4f}')
return eval_res
def _cal_true_positive_char(self, pred: str, gt: str) -> int:
"""Calculate correct character number in prediction.
Args:
pred (str): Prediction text.
gt (str): Ground truth text.
Returns:
true_positive_char_num (int): The true positive number.
"""
all_opt = SequenceMatcher(None, pred, gt)
true_positive_char_num = 0
for opt, _, _, s2, e2 in all_opt.get_opcodes():
if opt == 'equal':
true_positive_char_num += (e2 - s2)
else:
pass
return true_positive_char_num
@METRICS.register_module()
class OneMinusNEDMetric(BaseMetric):
"""One minus NED metric for text recognition task.
Args:
valid_symbol (str): Valid characters. Defaults to
'[^A-Z^a-z^0-9^\u4e00-\u9fa5]'
collect_device (str): Device name used for collecting results from
different ranks during distributed training. Must be 'cpu' or
'gpu'. Defaults to 'cpu'.
prefix (str, optional): The prefix that will be added in the metric
names to disambiguate homonymous metrics of different evaluators.
If prefix is not provided in the argument, self.default_prefix
will be used instead. Defaults to None
"""
default_prefix: Optional[str] = 'recog'
def __init__(self,
valid_symbol: str = '[^A-Z^a-z^0-9^\u4e00-\u9fa5]',
collect_device: str = 'cpu',
prefix: Optional[str] = None) -> None:
super().__init__(collect_device, prefix)
self.valid_symbol = re.compile(valid_symbol)
def process(self, data_batch: Sequence[Dict],
data_samples: Sequence[Dict]) -> None:
"""Process one batch of data_samples. The processed results should be
stored in ``self.results``, which will be used to compute the metrics
when all batches have been processed.
Args:
data_batch (Sequence[Dict]): A batch of gts.
data_samples (Sequence[Dict]): A batch of outputs from the model.
"""
for data_sample in data_samples:
pred_text = data_sample.get('pred_text').get('item')
gt_text = data_sample.get('gt_text').get('item')
gt_text_lower = gt_text.lower()
pred_text_lower = pred_text.lower()
gt_text_lower_ignore = self.valid_symbol.sub('', gt_text_lower)
pred_text_lower_ignore = self.valid_symbol.sub('', pred_text_lower)
norm_ed = Levenshtein.normalized_distance(pred_text_lower_ignore,
gt_text_lower_ignore)
result = dict(norm_ed=norm_ed)
self.results.append(result)
def compute_metrics(self, results: Sequence[Dict]) -> Dict:
"""Compute the metrics from processed results.
Args:
results (list[Dict]): The processed results of each batch.
Returns:
Dict: The computed metrics. The keys are the names of the
metrics, and the values are corresponding results.
"""
gt_word_num = len(results)
norm_ed = [result['norm_ed'] for result in results]
norm_ed_sum = sum(norm_ed)
normalized_edit_distance = norm_ed_sum / max(1, gt_word_num)
eval_res = {}
eval_res['1-N.E.D'] = 1.0 - normalized_edit_distance
for key, value in eval_res.items():
eval_res[key] = float(f'{value:.4f}')
return eval_res
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