def test__pandas_generator__nb_frames_yielded_as_specified(self):
nb_frames = 2
gen = PandasGenerator(self.source,
self.data_path,
nb_frames=nb_frames,
batch_size=1)
samples, _ = gen.__getitem__(0)
self.assertEqual(nb_frames, samples.shape[1])
def test__pandas_generator__batch_size_yielded_as_specified(self):
batch_size = self.nb_samples
gen = PandasGenerator(self.source,
self.data_path,
nb_frames=2,
batch_size=batch_size)
samples, labels = gen.__getitem__(0)
self.assertEqual(batch_size, samples.shape[0])
self.assertEqual(batch_size, labels.shape[0])
def test__pandas_generator__labels(self):
classes = self.source.loc[:, 1].unique()
class_label_map = GeneratorUtils.generate_class_to_label_mapper(
classes, 'categorical')
expected = list(map(lambda c: class_label_map[c], self.source.iloc[:, 1].values))
gen = PandasGenerator(self.source,
self.data_path,
nb_frames=5,
batch_size=self.nb_samples)
_, labels = gen.__getitem__(0)
npt.assert_equal(actual=labels, desired=expected)
def test__pandas_generator__labels_for_categorical_labels(self):
gen = PandasGenerator(self.source,
self.data_path)
classes = sorted(self.source.iloc[:, 1].unique())
expected = GeneratorUtils.generate_class_to_label_mapper(classes, 'categorical')
npt.assert_equal(expected, gen.class_label_map)
def test__pandas_generator__labels_for_binary_labels(self):
source = pd.DataFrame([[1, 'a'], [2, 'b']])
gen = PandasGenerator(source,
self.data_path,
labelling_strategy='binary')
classes = sorted(source.iloc[:, 1].unique())
expected = GeneratorUtils.generate_class_to_label_mapper(classes, 'binary')
npt.assert_equal(expected, gen.class_label_map)
def test__pandas_generator__prints_sample_stats(self):
mock_printer = Mock()
PandasGenerator(self.source,
self.data_path,
nb_frames=5,
batch_size=1,
printer=mock_printer)
mock_printer.assert_any_call(f'Sample size: {self.nb_samples}')
for _class, count in self.source.iloc[:, 1].value_counts().items():
mock_printer.assert_any_call(f'Class {_class}: {count}')
def test__pandas_generator__ignores_samples_that_have_less_frames_than_nb_frames(self):
gen = PandasGenerator(self.source,
self.data_path,
nb_frames=6,
batch_size=1)
batches = []
for samples, _ in gen:
batches.append(samples)
self.assertEqual(0, len(batches))
def test__pandas_generator__number_of_batches_yielded(self):
batch_size = 2
gen = PandasGenerator(self.source,
self.data_path,
nb_frames=5,
batch_size=batch_size)
batches = []
for samples, _ in gen:
batches.append(samples)
self.assertEqual(math.ceil(self.nb_samples / batch_size), len(batches))
def test__pandas_generator__yields_sample_images_correctly(self):
transformations = [A.HorizontalFlip(p=1)]
nb_frames = 5
batch_size = self.nb_samples
frame_size = (10, 10)
gen = PandasGenerator(self.source,
self.data_path,
batch_size=batch_size,
nb_frames=nb_frames,
transformations=transformations,
frame_size=frame_size)
expected = []
for i in range(1, batch_size + 1):
imgs = GeneratorUtils.get_sample_images(Path(f'fake_dataset/{i}'))
imgs = GeneratorUtils.pick_at_intervals(imgs, nb_frames, math.floor)
imgs = [GeneratorUtils.process_img(img_path, frame_size)
for img_path in imgs]
imgs = GeneratorUtils.augment(imgs, transformations)
expected.append(imgs)
expected = np.stack(expected)
sample, _ = gen.__getitem__(0)
npt.assert_equal(actual=sample, desired=expected)
def test_when_data_path_non_existent(self):
with self.assertRaises(FileNotFoundError):
PandasGenerator(self.source, Path('non existent'))
def test_when_source_not_dataframe(self):
with self.assertRaises(TypeError):
PandasGenerator('not a DataFrame', Path('images'))
def test__pandas_generator__returns_data_of_specified_type(self):
gen = PandasGenerator(self.source, self.data_path, nb_frames=3, dtype=np.uint32)
self.assertEqual('uint32', gen.__getitem__(0)[0].dtype)