def test_coco_instance_segmentation_dataset(self):
assert_is_instance_segmentation_dataset(
self.dataset,
len(coco_instance_segmentation_label_names),
n_example=10)
if self.return_area:
for _ in range(10):
i = np.random.randint(0, len(self.dataset))
_, mask, _, area = self.dataset[i][:4]
self.assertIsInstance(area, np.ndarray)
self.assertEqual(area.dtype, np.float32)
self.assertEqual(area.shape, (mask.shape[0], ))
if self.return_crowded:
for _ in range(10):
i = np.random.randint(0, len(self.dataset))
example = self.dataset[i]
crowded = example[-1]
mask = example[1]
self.assertIsInstance(crowded, np.ndarray)
self.assertEqual(crowded.dtype, np.bool)
self.assertEqual(crowded.shape, (mask.shape[0], ))
if not self.use_crowded:
np.testing.assert_equal(crowded, 0)
def test_assert_is_semantic_segmentation_dataset(self):
if self.valid:
assert_is_instance_segmentation_dataset(self.dataset, 20)
else:
with self.assertRaises(AssertionError):
assert_is_instance_segmentation_dataset(self.dataset, 20)
def test_sbd_instance_segmentation_dataset(self):
assert_is_instance_segmentation_dataset(
self.dataset,
len(sbd_instance_segmentation_label_names),
n_example=10)
def test_assert_is_semantic_segmentation_dataset(self):
if self.valid:
assert_is_instance_segmentation_dataset(self.dataset, 20)
else:
with self.assertRaises(AssertionError):
assert_is_instance_segmentation_dataset(self.dataset, 20)
