def test_viterbi_decode(dtype):
inputs = np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=dtype)
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=dtype)
sequence_lengths = np.array(3, dtype=np.int32)
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
all_sequence_scores = []
all_sequences = []
# Compare the dynamic program with brute force computation.
for tag_indices in itertools.product(range(num_tags), repeat=sequence_lengths):
tag_indices = list(tag_indices)
tag_indices.extend([0] * (num_words - sequence_lengths))
all_sequences.append(tag_indices)
sequence_score = text.crf_sequence_score(
inputs=tf.expand_dims(inputs, 0),
tag_indices=tf.expand_dims(tag_indices, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params),
)
sequence_score = tf.squeeze(sequence_score, [0])
all_sequence_scores.append(sequence_score)
expected_max_sequence_index = np.argmax(all_sequence_scores)
expected_max_sequence = all_sequences[expected_max_sequence_index]
expected_max_score = all_sequence_scores[expected_max_sequence_index]
actual_max_sequence, actual_max_score = text.viterbi_decode(
inputs[:sequence_lengths], transition_params
)
test_utils.assert_allclose_according_to_type(actual_max_score, expected_max_score)
assert actual_max_sequence == expected_max_sequence[:sequence_lengths]
def testViterbiDecode(self):
inputs = np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]],
dtype=np.float32)
transition_params = np.array([[-3, 5, -2], [3, 4, 1], [1, 2, 1]],
dtype=np.float32)
sequence_lengths = np.array(3, dtype=np.int32)
# TODO: https://github.com/PyCQA/pylint/issues/3139
# pylint: disable=E1136
num_words = inputs.shape[0]
num_tags = inputs.shape[1]
# pylint: enable=E1136
all_sequence_scores = []
all_sequences = []
# Compare the dynamic program with brute force computation.
for tag_indices in itertools.product(range(num_tags),
repeat=sequence_lengths):
tag_indices = list(tag_indices)
tag_indices.extend([0] * (num_words - sequence_lengths))
all_sequences.append(tag_indices)
sequence_score = text.crf_sequence_score(
inputs=tf.expand_dims(inputs, 0),
tag_indices=tf.expand_dims(tag_indices, 0),
sequence_lengths=tf.expand_dims(sequence_lengths, 0),
transition_params=tf.constant(transition_params),
)
sequence_score = tf.squeeze(sequence_score, [0])
all_sequence_scores.append(sequence_score)
tf_all_sequence_scores = self.evaluate(all_sequence_scores)
expected_max_sequence_index = np.argmax(tf_all_sequence_scores)
expected_max_sequence = all_sequences[expected_max_sequence_index]
expected_max_score = tf_all_sequence_scores[
expected_max_sequence_index]
actual_max_sequence, actual_max_score = text.viterbi_decode(
inputs[:sequence_lengths], transition_params)
self.assertAllClose(actual_max_score, expected_max_score)
self.assertEqual(actual_max_sequence,
expected_max_sequence[:sequence_lengths])