def test_directory_parsing_classification_dataset(self):
dataset = DirectoryParsingClassificationDataset(self.tmp_dir,
color=self.color)
if self.depth == 1:
expected_legnth = self.n_img_per_class * self.n_class
elif self.depth == 2:
expected_legnth =\
self.n_img_per_class * self.n_sub_directory * self.n_class
self.assertEqual(len(dataset), expected_legnth)
assert_is_classification_dataset(dataset,
self.n_class,
color=self.color)
label_names = directory_parsing_label_names(self.tmp_dir)
self.assertEqual(label_names,
['class_{}'.format(i) for i in range(self.n_class)])
if self.depth == 1:
self.assertEqual(dataset.img_paths, [
'{}/class_{}/img{}.{}'.format(self.tmp_dir, i, j, self.suffix)
for i in range(self.n_class)
for j in range(self.n_img_per_class)
])
elif self.depth == 2:
self.assertEqual(dataset.img_paths, [
'{}/class_{}/nested_{}/img{}.{}'.format(
self.tmp_dir, i, j, k, self.suffix)
for i in range(self.n_class)
for j in range(self.n_sub_directory)
for k in range(self.n_img_per_class)
])
def test_numerical_sort(self):
dataset = DirectoryParsingClassificationDataset(self.tmp_dir,
numerical_sort=True)
assert_is_classification_dataset(dataset, self.n_class)
label_names = directory_parsing_label_names(self.tmp_dir,
numerical_sort=True)
self.assertEqual(label_names,
['{}'.format(i) for i in range(self.n_class)])
def test_assert_is_classification_dataset(self):
if hasattr(self, 'option'):
dataset = self.dataset(self.color, self.option)
else:
dataset = self.dataset(self.color)
if self.valid:
assert_is_classification_dataset(dataset, 20, color=self.color)
else:
with self.assertRaises(AssertionError):
assert_is_classification_dataset(dataset, 20, color=self.color)