def test_assert_is_point_dataset(self):
if self.valid:
assert_is_point_dataset(self.dataset, self.n_point, 20,
self.no_mask)
else:
with self.assertRaises(AssertionError):
assert_is_point_dataset(self.dataset, self.n_point, 20,
self.no_mask)
def test_cub_point_dataset(self):
assert_is_point_dataset(self.dataset, n_point=15, n_example=10)
idx = np.random.choice(np.arange(10))
if self.return_bb:
if self.return_prob_map:
bb = self.dataset[idx][-2]
else:
bb = self.dataset[idx][-1]
assert_is_bbox(bb[np.newaxis])
if self.return_prob_map:
img = self.dataset[idx][0]
prob_map = self.dataset[idx][-1]
self.assertEqual(prob_map.dtype, np.float32)
self.assertEqual(prob_map.shape, img.shape[1:])
self.assertTrue(np.min(prob_map) >= 0)
self.assertTrue(np.max(prob_map) <= 1)
def test_coco_keypoint_dataset(self):
human_id = 0
assert_is_point_dataset(
self.dataset, len(coco_keypoint_names[human_id]),
n_example=30)
for _ in range(10):
i = np.random.randint(0, len(self.dataset))
img, point, _, label, bbox = self.dataset[i][:5]
assert_is_bbox(bbox, img.shape[1:])
self.assertEqual(len(bbox), len(point))
self.assertIsInstance(label, np.ndarray)
self.assertEqual(label.dtype, np.int32)
self.assertEqual(label.shape, (point.shape[0],))
if self.return_area:
for _ in range(10):
i = np.random.randint(0, len(self.dataset))
_, point, _, _, _, area = self.dataset[i][:6]
self.assertIsInstance(area, np.ndarray)
self.assertEqual(area.dtype, np.float32)
self.assertEqual(area.shape, (point.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]
point = example[1]
self.assertIsInstance(crowded, np.ndarray)
self.assertEqual(crowded.dtype, np.bool)
self.assertEqual(crowded.shape, (point.shape[0],))
if not self.use_crowded:
np.testing.assert_equal(crowded, 0)