def test_sequence_with_none_permissions_multi_input(self):
use_log_space = True
use_start_and_end_states = False
scores = np.array([[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0, 10.0]])
# pyformat: disable
# pylint: disable=bad-whitespace
# pylint: disable=bad-continuation
transition_weights = np.array([[-1.0, 1.0, -2.0, 2.0],
[ 3.0, -3.0, 4.0, -4.0],
[ 5.0, 1.0, 10.0, 1.0],
[-7.0, 7.0, -8.0, 8.0]], dtype=np.float32)
# pyformat: enable
# pylint: enable=bad-whitespace
# pylint: enable=bad-continuation
sequence = self._decode_greedy_sequence(
scores,
transition_weights,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
# Test a multi-item batch.
multiple_input = np.array([scores, scores, scores], dtype=np.float32)
multiple_sequence_op = tftext.greedy_constrained_sequence(
multiple_input, [2, 2, 2],
transition_weights=transition_weights,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
multiple_sequence_result = self.evaluate(multiple_sequence_op)
self.assertRaggedEqual(multiple_sequence_result,
[sequence, sequence, sequence])
def test_ragged_inputs(self):
use_log_space = True
use_start_and_end_states = False
input_1 = np.array([[10.0, 13.0, 6.0, 4.0], [13.0, 12.0, 11.0, 10.0],
[13.0, 12.0, 11.0, 10.0]])
input_2 = np.array([[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0, 10.0]])
# TODO(momernick): Extend RT support to lists-of-ndarrays.
scores = tf.ragged.constant([input_1.tolist(), input_2.tolist()])
# pyformat: disable
# pylint: disable=bad-whitespace
# pylint: disable=bad-continuation
transition_weights = np.array([[-1.0, 1.0, -2.0, 2.0],
[3.0, -3.0, 4.0, -4.0],
[5.0, 1.0, 10.0, 1.0],
[-7.0, 7.0, -8.0, 8.0]],
dtype=np.float32)
allowed_transitions = np.array([[ True, True, True, True],
[ True, True, True, True],
[ True, False, True, False],
[ True, True, True, True]])
# pyformat: enable
# pylint: enable=bad-whitespace
# pylint: enable=bad-continuation
sequence_1 = self._decode_greedy_sequence(
input_1,
transition_weights,
allowed_transitions,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
sequence_2 = self._decode_greedy_sequence(
input_2,
transition_weights,
allowed_transitions,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
expected_sequence = tf.ragged.constant([sequence_1, sequence_2])
# Test a ragged batch.
ragged_op = tftext.greedy_constrained_sequence(
scores,
allowed_transitions=allowed_transitions,
transition_weights=transition_weights,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
ragged_result = self.evaluate(ragged_op)
self.assertRaggedEqual(ragged_result, expected_sequence)
def test_sequence_in_exp_space_with_start_end_states_multi_batch_item(self):
use_log_space = False
use_start_and_end_states = True
scores = np.array([[10.0, 12.0, 6.0, 4.0], [13.0, 12.0, 11.0, 10.0]])
# pyformat: disable
# pylint: disable=bad-whitespace
# pylint: disable=bad-continuation
transition_weights = np.array([[ .1, .2, .3, .4, .1],
[ .5, .6, .7, .8, .1],
[ .9, 1, .15, 1, .1],
[.25, .35, .45, .55, .5],
[ .1, .5, .1, .1, 1]], dtype=np.float32)
allowed_transitions = np.array([[ True, True, True, True, True],
[ True, True, True, True, True],
[ True, False, True, False, True],
[ True, True, True, True, True],
[ True, True, True, True, False]])
# pyformat: enable
# pylint: enable=bad-whitespace
# pylint: enable=bad-continuation
sequence = self._decode_greedy_sequence(
scores,
transition_weights,
allowed_transitions,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
# Test a multi-item batch.
multiple_input = np.array([scores, scores, scores], dtype=np.float32)
multiple_sequence_op = tftext.greedy_constrained_sequence(
multiple_input, [2, 2, 2],
allowed_transitions=allowed_transitions,
transition_weights=transition_weights,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
multiple_sequence_result = self.evaluate(multiple_sequence_op)
self.assertRaggedEqual(multiple_sequence_result,
[sequence, sequence, sequence])
def test_sequence_in_log_space_with_start_end_states_single_batch_item(self):
use_log_space = True
use_start_and_end_states = True
scores = np.array([[10.0, 12.0, 7.0, 4.0], [13.0, 12.0, 11.0, 10.0]])
# pyformat: disable
# pylint: disable=bad-whitespace
# pylint: disable=bad-continuation
transition_weights = np.array([[-1.0, 1.0, -2.0, 2.0, 0.0],
[ 3.0, -3.0, 4.0, -4.0, 0.0],
[ 5.0, 1.0, 10.0, 1.0, 1.0],
[-7.0, 7.0, -8.0, 8.0, 0.0],
[ 0.0, 1.0, 2.0, 3.0, 0.0]],
dtype=np.float32)
allowed_transitions = np.array([[ True, True, True, True, True],
[ True, True, True, True, True],
[ True, False, True, False, False],
[ True, True, True, True, True],
[ True, False, True, True, True]])
# pyformat: enable
# pylint: enable=bad-whitespace
# pylint: enable=bad-continuation
sequence = self._decode_greedy_sequence(
scores,
transition_weights,
allowed_transitions,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
# Test a single-item batch.
single_input = np.array([scores], dtype=np.float32)
single_sequence_op = tftext.greedy_constrained_sequence(
single_input, [2],
allowed_transitions=allowed_transitions,
transition_weights=transition_weights,
use_log_space=use_log_space,
use_start_and_end_states=use_start_and_end_states)
single_result = self.evaluate(single_sequence_op)
self.assertRaggedEqual(single_result, [sequence])