def decode(self, src_sentence):
"""This is a generalization to NMT ensembles of
``BeamSearch.search``.
Args:
src_sentence (list): List of source word ids without <S> or
</S> which make up the source sentence
Returns:
list. A list of ``Hypothesis`` instances ordered by their
score.
"""
for search in self.beam_searches:
if not search.compiled:
search.compile()
seq = self.src_sparse_feat_map.words2dense(
utils.oov_to_unk(src_sentence,
self.src_vocab_size)) + [self.src_eos]
if self.src_sparse_feat_map.dim > 1: # sparse src feats
input_ = np.transpose(np.tile(seq, (self.beam_size, 1, 1)),
(2, 0, 1))
else: # word ids on the source side
input_ = np.tile(seq, (self.beam_size, 1))
contexts_and_states = []
for sys_idx in xrange(self.n_networks):
contexts, states, _ = \
self.beam_searches[sys_idx].compute_initial_states_and_contexts(
{self.nmt_models[sys_idx].sampling_input: input_})
contexts_and_states.append(
(contexts, states, self.beam_searches[sys_idx]))
# This array will store all generated outputs, including those from
# previous step and those from already finished sequences.
all_outputs = states['outputs'][None, :]
all_masks = np.ones_like(all_outputs, dtype=config.floatX)
all_costs = np.zeros_like(all_outputs, dtype=config.floatX)
for i in range(3 * len(src_sentence)):
if all_masks[-1].sum() == 0:
break
logprobs_lst = []
for contexts, states, search in contexts_and_states:
logprobs_lst.append(search.compute_logprobs(contexts, states))
logprobs = np.sum(logprobs_lst, axis=0)
next_costs = (all_costs[-1, :, None] +
logprobs * all_masks[-1, :, None])
(finished, ) = np.where(all_masks[-1] == 0)
next_costs[finished, :utils.EOS_ID] = np.inf
next_costs[finished, utils.EOS_ID + 1:] = np.inf
# The `i == 0` is required because at the first step the beam
# size is effectively only 1.
(indexes, outputs), chosen_costs = BeamSearch._smallest(
next_costs, self.beam_size, only_first_row=i == 0)
all_outputs = all_outputs[:, indexes]
all_masks = all_masks[:, indexes]
all_costs = all_costs[:, indexes]
# Rearrange everything
for contexts, states, search in contexts_and_states:
for name in states:
states[name] = states[name][indexes]
states.update(
search.compute_next_states(contexts, states, outputs))
all_outputs = np.vstack([all_outputs, outputs[None, :]])
all_costs = np.vstack([all_costs, chosen_costs[None, :]])
mask = outputs != utils.EOS_ID
if i == 0:
mask[:] = 1
all_masks = np.vstack([all_masks, mask[None, :]])
all_outputs = all_outputs[1:]
all_masks = all_masks[:-1]
all_costs = all_costs[1:] - all_costs[:-1]
result = all_outputs, all_masks, all_costs
trans, costs = BeamSearch.result_to_lists(result)
hypos = []
max_len = 0
for idx in xrange(len(trans)):
max_len = max(max_len, len(trans[idx]))
hypo = Hypothesis(trans[idx], -costs[idx])
hypo.score_breakdown = len(trans[idx]) * [[(0.0, 1.0)]]
hypo.score_breakdown[0] = [(-costs[idx], 1.0)]
hypos.append(hypo)
self.apply_predictors_count = max_len * self.beam_size
return hypos
def test_beam_search_smallest():
a = numpy.array([[3, 6, 4], [1, 2, 7]])
ind, mins = BeamSearch._smallest(a, 2)
assert numpy.all(numpy.array(ind) == numpy.array([[1, 1], [0, 1]]))
assert numpy.all(mins == [1, 2])
def test_beam_search_smallest():
a = numpy.array([[3, 6, 4], [1, 2, 7]])
ind, mins = BeamSearch._smallest(a, 2)
assert numpy.all(numpy.array(ind) == numpy.array([[1, 1], [0, 1]]))
assert numpy.all(mins == [1, 2])
def decode(self, src_sentence):
"""This is a generalization to NMT ensembles of
``BeamSearch.search``.
Args:
src_sentence (list): List of source word ids without <S> or
</S> which make up the source sentence
Returns:
list. A list of ``Hypothesis`` instances ordered by their
score.
"""
for search in self.beam_searches:
if not search.compiled:
search.compile()
seq = self.src_sparse_feat_map.words2dense(utils.oov_to_unk(
src_sentence,
self.src_vocab_size)) + [self.src_eos]
if self.src_sparse_feat_map.dim > 1: # sparse src feats
input_ = np.transpose(
np.tile(seq, (self.beam_size, 1, 1)),
(2,0,1))
else: # word ids on the source side
input_ = np.tile(seq, (self.beam_size, 1))
contexts_and_states = []
for sys_idx in xrange(self.n_networks):
contexts, states, _ = \
self.beam_searches[sys_idx].compute_initial_states_and_contexts(
{self.nmt_models[sys_idx].sampling_input: input_})
contexts_and_states.append((contexts,
states,
self.beam_searches[sys_idx]))
# This array will store all generated outputs, including those from
# previous step and those from already finished sequences.
all_outputs = states['outputs'][None, :]
all_masks = np.ones_like(all_outputs, dtype=config.floatX)
all_costs = np.zeros_like(all_outputs, dtype=config.floatX)
for i in range(3*len(src_sentence)):
if all_masks[-1].sum() == 0:
break
logprobs_lst = []
for contexts, states, search in contexts_and_states:
logprobs_lst.append(search.compute_logprobs(contexts, states))
logprobs = np.sum(logprobs_lst, axis=0)
next_costs = (all_costs[-1, :, None] +
logprobs * all_masks[-1, :, None])
(finished,) = np.where(all_masks[-1] == 0)
next_costs[finished, :utils.EOS_ID] = np.inf
next_costs[finished, utils.EOS_ID + 1:] = np.inf
# The `i == 0` is required because at the first step the beam
# size is effectively only 1.
(indexes, outputs), chosen_costs = BeamSearch._smallest(
next_costs, self.beam_size, only_first_row=i == 0)
all_outputs = all_outputs[:, indexes]
all_masks = all_masks[:, indexes]
all_costs = all_costs[:, indexes]
# Rearrange everything
for contexts, states, search in contexts_and_states:
for name in states:
states[name] = states[name][indexes]
states.update(search.compute_next_states(contexts,
states,
outputs))
all_outputs = np.vstack([all_outputs, outputs[None, :]])
all_costs = np.vstack([all_costs, chosen_costs[None, :]])
mask = outputs != utils.EOS_ID
if i == 0:
mask[:] = 1
all_masks = np.vstack([all_masks, mask[None, :]])
all_outputs = all_outputs[1:]
all_masks = all_masks[:-1]
all_costs = all_costs[1:] - all_costs[:-1]
result = all_outputs, all_masks, all_costs
trans, costs = BeamSearch.result_to_lists(result)
hypos = []
max_len = 0
for idx in xrange(len(trans)):
max_len = max(max_len, len(trans[idx]))
hypo = Hypothesis(trans[idx], -costs[idx])
hypo.score_breakdown = len(trans[idx]) * [[(0.0,1.0)]]
hypo.score_breakdown[0] = [(-costs[idx],1.0)]
hypos.append(hypo)
self.apply_predictors_count = max_len * self.beam_size
return hypos
