def test_get_balanced_sampling_probability(self):
instance_ids = self._get_instance_id_example()
probabilities = sampling_utils.get_balanced_sampling_probability(
instance_ids=instance_ids)
expected_probabilities = np.array([[
1.0 / 12.0, 1.0 / 12.0, 1.0 / 12.0, 1.0 / 8.0, 1.0 / 4.0,
1.0 / 8.0, 1.0 / 4.0
], [1.0 / 4.0, 1.0 / 4.0, 0.1, 0.1, 0.1, 0.1, 0.1]],
dtype=np.float32)
self.assertAllClose(probabilities.numpy(), expected_probabilities)
def test_get_balanced_sampling_probability_with_valid_mask(self):
instance_ids = self._get_instance_id_example()
valid_mask = self._get_valid_mask()
probabilities = sampling_utils.get_balanced_sampling_probability(
instance_ids=instance_ids,
valid_mask=valid_mask,
max_instance_id=4)
expected_probabilities = np.array(
[[1.0 / 9.0, 1.0 / 9.0, 1.0 / 9.0, 1.0 / 3.0, 1.0 / 3.0, 0.0, 0.0],
[0.5, 0.0, 1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0, 0.0, 0.0]],
dtype=np.float32)
self.assertAllClose(probabilities.numpy(), expected_probabilities)