mirror of https://github.com/open-mmlab/mmocr.git
125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
|
|
import unittest
|
|
|
|
from mmengine.structures import LabelData
|
|
|
|
from mmocr.evaluation import CharMetric, OneMinusNEDMetric, WordMetric
|
|
from mmocr.structures import TextRecogDataSample
|
|
|
|
|
|
class TestWordMetric(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
|
|
self.pred = []
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'hello'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'hello'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'hello'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'HELLO'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'hello'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = '$HELLO$'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
|
|
def test_word_acc_metric(self):
|
|
metric = WordMetric(mode='exact')
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=3)
|
|
self.assertAlmostEqual(eval_res['recog/word_acc'], 1. / 3, 4)
|
|
|
|
def test_word_acc_ignore_case_metric(self):
|
|
metric = WordMetric(mode='ignore_case')
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=3)
|
|
self.assertAlmostEqual(eval_res['recog/word_acc_ignore_case'], 2. / 3,
|
|
4)
|
|
|
|
def test_word_acc_ignore_case_symbol_metric(self):
|
|
metric = WordMetric(mode='ignore_case_symbol')
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=3)
|
|
self.assertEqual(eval_res['recog/word_acc_ignore_case_symbol'], 1.0)
|
|
|
|
def test_all_metric(self):
|
|
metric = WordMetric(
|
|
mode=['exact', 'ignore_case', 'ignore_case_symbol'])
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=3)
|
|
self.assertAlmostEqual(eval_res['recog/word_acc'], 1. / 3, 4)
|
|
self.assertAlmostEqual(eval_res['recog/word_acc_ignore_case'], 2. / 3,
|
|
4)
|
|
self.assertEqual(eval_res['recog/word_acc_ignore_case_symbol'], 1.0)
|
|
|
|
|
|
class TestCharMetric(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
self.pred = []
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'helL'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'hello'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'HEL'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'HELLO'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
|
|
def test_char_recall_precision_metric(self):
|
|
metric = CharMetric()
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=2)
|
|
self.assertEqual(eval_res['recog/char_recall'], 0.7)
|
|
self.assertEqual(eval_res['recog/char_precision'], 1)
|
|
|
|
|
|
class TestOneMinusNED(unittest.TestCase):
|
|
|
|
def setUp(self):
|
|
self.pred = []
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'pred_helL'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'hello'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
data_sample = TextRecogDataSample()
|
|
pred_text = LabelData()
|
|
pred_text.item = 'HEL'
|
|
data_sample.pred_text = pred_text
|
|
gt_text = LabelData()
|
|
gt_text.item = 'HELLO'
|
|
data_sample.gt_text = gt_text
|
|
self.pred.append(data_sample)
|
|
|
|
def test_one_minus_ned_metric(self):
|
|
metric = OneMinusNEDMetric()
|
|
metric.process(None, self.pred)
|
|
eval_res = metric.evaluate(size=2)
|
|
self.assertEqual(eval_res['recog/1-N.E.D'], 0.4875)
|