def test_crf_multi_tag_sequence_score(dtype):
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=dtype)
# Test both the length-1 and regular cases.
sequence_lengths_list = [
np.array(3, dtype=np.int32),
np.array(1, dtype=np.int32),
]
inputs_list = [
np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=dtype),
np.array([[4, 5, -3]], dtype=dtype),
]
tag_bitmap_list = [
np.array(
[
[True, True, False],
[True, False, True],
[False, True, True],
[True, False, True],
],
dtype=np.bool,
),
np.array([[True, True, False]], dtype=np.bool),
]
for sequence_lengths, inputs, tag_bitmap in zip(
sequence_lengths_list, inputs_list, tag_bitmap_list
):
sequence_score = text.crf_multitag_sequence_score(
inputs=tf.expand_dims(inputs, 0),
tag_bitmap=tf.expand_dims(tag_bitmap, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params),
)
sequence_score = tf.squeeze(sequence_score, [0])
all_indices_list = [
single_index_bitmap.nonzero()[0]
for single_index_bitmap in tag_bitmap[:sequence_lengths]
]
expected_sequence_scores = [
calculate_sequence_score(
inputs, transition_params, indices, sequence_lengths
)
for indices in itertools.product(*all_indices_list)
]
expected_log_sum_exp_sequence_scores = np.logaddexp.reduce(
expected_sequence_scores
)
test_utils.assert_allclose_according_to_type(
sequence_score, expected_log_sum_exp_sequence_scores
)
def testCrfMultiTagSequenceScore(self):
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]],
dtype=np.float32)
# Test both the length-1 and regular cases.
sequence_lengths_list = [
np.array(3, dtype=np.int32),
np.array(1, dtype=np.int32)
]
inputs_list = [
np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32),
np.array([[4, 5, -3]], dtype=np.float32),
]
tag_bitmap_list = [
np.array([[True, True, False], [True, False, True],
[False, True, True], [True, False, True]],
dtype=np.bool),
np.array([[True, True, False]], dtype=np.bool)
]
for sequence_lengths, inputs, tag_bitmap in zip(
sequence_lengths_list, inputs_list, tag_bitmap_list):
sequence_score = text.crf_multitag_sequence_score(
inputs=tf.expand_dims(inputs, 0),
tag_bitmap=tf.expand_dims(tag_bitmap, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params))
sequence_score = tf.squeeze(sequence_score, [0])
tf_sum_sequence_score = self.evaluate(sequence_score)
all_indices_list = [
single_index_bitmap.nonzero()[0]
for single_index_bitmap in tag_bitmap[:sequence_lengths]
]
expected_sequence_scores = [
self.calculateSequenceScore(inputs, transition_params, indices,
sequence_lengths)
for indices in itertools.product(*all_indices_list)
]
expected_log_sum_exp_sequence_scores = np.logaddexp.reduce(
expected_sequence_scores)
self.assertAllClose(tf_sum_sequence_score,
expected_log_sum_exp_sequence_scores)