def test_type_shape(self, input_data, expected_type, expected_count,
expected_shape):
results = []
for p in TEST_NDARRAYS + (None, ):
input_data = deepcopy(input_data)
if p is not None:
for k in ["fg_indices", "bg_indices"]:
input_data[k] = p(input_data[k])
set_determinism(0)
result = generate_pos_neg_label_crop_centers(**input_data)
self.assertIsInstance(result, expected_type)
self.assertEqual(len(result), expected_count)
self.assertEqual(len(result[0]), expected_shape)
# check for consistency between numpy, torch and torch.cuda
results.append(result)
if len(results) > 1:
# compare every crop center
for x, y in zip(results[0], results[-1]):
assert_allclose(x, y, type_test=False)
def test_type_shape(self, input_data, expected_type, expected_count,
expected_shape):
result = generate_pos_neg_label_crop_centers(**input_data)
self.assertIsInstance(result, expected_type)
self.assertEqual(len(result), expected_count)
self.assertEqual(len(result[0]), expected_shape)
