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add min cutoff & top n cutoff

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pull/2/head
Yibing Liu 7 years ago
parent a661941ae7
commit a0c89ae7e0
5 changed files with 75 additions and 36 deletions
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14
deploy.py
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@ -18,7 +18,7 @@ import time
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--num_samples",
default=4,
default=10,
type=int,
help="Number of samples for inference. (default: %(default)s)")
parser.add_argument(
@ -95,12 +95,12 @@ parser.add_argument(
help="Path for language model. (default: %(default)s)")
parser.add_argument(
"--alpha",
default=0.26,
default=1.5,
type=float,
help="Parameter associated with language model. (default: %(default)f)")
parser.add_argument(
"--beta",
default=0.1,
default=0.3,
type=float,
help="Parameter associated with word count. (default: %(default)f)")
parser.add_argument(
@ -109,6 +109,12 @@ parser.add_argument(
type=float,
help="The cutoff probability of pruning"
"in beam search. (default: %(default)f)")
parser.add_argument(
"--cutoff_top_n",
default=40,
type=int,
help="The cutoff number of pruning"
"in beam search. (default: %(default)f)")
args = parser.parse_args()
@ -184,6 +190,7 @@ def infer():
vocabulary=data_generator.vocab_list,
blank_id=len(data_generator.vocab_list),
cutoff_prob=args.cutoff_prob,
cutoff_top_n=args.cutoff_top_n,
ext_scoring_func=ext_scorer, )
batch_beam_results += [beam_result]
else:
@ -194,6 +201,7 @@ def infer():
blank_id=len(data_generator.vocab_list),
num_processes=args.num_processes_beam_search,
cutoff_prob=args.cutoff_prob,
cutoff_top_n=args.cutoff_top_n,
ext_scoring_func=ext_scorer, )
for i, beam_result in enumerate(batch_beam_results):

71
deploy/ctc_decoders.cpp
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@ -62,6 +62,7 @@ std::vector<std::pair<double, std::string> >
std::vector<std::string> vocabulary,
int blank_id,
double cutoff_prob,
int cutoff_top_n,
Scorer *ext_scorer)
{
// dimension check
@ -116,19 +117,33 @@ std::vector<std::pair<double, std::string> >
prob_idx.push_back(std::pair<int, double>(i, prob[i]));
}
float min_cutoff = -NUM_FLT_INF;
bool full_beam = false;
if (ext_scorer != nullptr) {
int num_prefixes = std::min((int)prefixes.size(), beam_size);
std::sort(prefixes.begin(), prefixes.begin() + num_prefixes,
prefix_compare);
min_cutoff = prefixes[num_prefixes-1]->_score + log(prob[blank_id])
- std::max(0.0, ext_scorer->beta);
full_beam = (num_prefixes == beam_size);
}
// pruning of vacobulary
int cutoff_len = prob.size();
if (cutoff_prob < 1.0) {
if (cutoff_prob < 1.0 || cutoff_top_n < prob.size()) {
std::sort(prob_idx.begin(),
prob_idx.end(),
pair_comp_second_rev<int, double>);
double cum_prob = 0.0;
cutoff_len = 0;
for (int i=0; i<prob_idx.size(); i++) {
cum_prob += prob_idx[i].second;
cutoff_len += 1;
if (cum_prob >= cutoff_prob) break;
if (cutoff_prob < 1.0) {
double cum_prob = 0.0;
cutoff_len = 0;
for (int i=0; i<prob_idx.size(); i++) {
cum_prob += prob_idx[i].second;
cutoff_len += 1;
if (cum_prob >= cutoff_prob) break;
}
}
cutoff_len = std::min(cutoff_len, cutoff_top_n);
prob_idx = std::vector<std::pair<int, double> >( prob_idx.begin(),
prob_idx.begin() + cutoff_len);
}
@ -138,15 +153,17 @@ std::vector<std::pair<double, std::string> >
log_prob_idx.push_back(std::pair<int, float>
(prob_idx[i].first, log(prob_idx[i].second + NUM_FLT_MIN)));
}
// loop over chars
for (int index = 0; index < log_prob_idx.size(); index++) {
auto c = log_prob_idx[index].first;
float log_prob_c = log_prob_idx[index].second;
//float log_probs_prev;
for (int i = 0; i < prefixes.size() && i<beam_size; i++) {
auto prefix = prefixes[i];
if (full_beam && log_prob_c + prefix->_score < min_cutoff) {
break;
}
// blank
if (c == blank_id) {
prefix->_log_prob_b_cur = log_sum_exp(
@ -178,7 +195,7 @@ std::vector<std::pair<double, std::string> >
(c == space_id || ext_scorer->is_character_based()) ) {
PathTrie *prefix_to_score = nullptr;
// don't score the space
// skip scoring the space
if (ext_scorer->is_character_based()) {
prefix_to_score = prefix_new;
} else {
@ -202,10 +219,10 @@ std::vector<std::pair<double, std::string> >
} // end of loop over chars
prefixes.clear();
// update log probabilities
// update log probs
root.iterate_to_vec(prefixes);
// sort prefixes by score
// preserve top beam_size prefixes
if (prefixes.size() >= beam_size) {
std::nth_element(prefixes.begin(),
prefixes.begin() + beam_size,
@ -218,18 +235,20 @@ std::vector<std::pair<double, std::string> >
}
}
// compute aproximate ctc score as the return score
for (size_t i = 0; i < beam_size && i < prefixes.size(); i++) {
double approx_ctc = prefixes[i]->_score;
// remove word insert:
std::vector<int> output;
prefixes[i]->get_path_vec(output);
size_t prefix_length = output.size();
// remove language model weight:
if (ext_scorer != nullptr) {
// auto words = split_labels(output);
// approx_ctc = approx_ctc - path_length * ext_scorer->beta;
// approx_ctc -= (_lm->get_sent_log_prob(words)) * ext_scorer->alpha;
std::vector<int> output;
prefixes[i]->get_path_vec(output);
size_t prefix_length = output.size();
auto words = ext_scorer->split_labels(output);
// remove word insert
approx_ctc = approx_ctc - prefix_length * ext_scorer->beta;
// remove language model weight:
approx_ctc -= (ext_scorer->get_sent_log_prob(words))
* ext_scorer->alpha;
}
prefixes[i]->_approx_ctc = approx_ctc;
@ -253,11 +272,9 @@ std::vector<std::pair<double, std::string> >
for (int j = 0; j < output.size(); j++) {
output_str += vocabulary[output[j]];
}
std::pair<double, std::string> output_pair(space_prefixes[i]->_score,
output_str);
output_vecs.emplace_back(
output_pair
);
std::pair<double, std::string>
output_pair(-space_prefixes[i]->_approx_ctc, output_str);
output_vecs.emplace_back(output_pair);
}
return output_vecs;
@ -272,6 +289,7 @@ std::vector<std::vector<std::pair<double, std::string> > >
int blank_id,
int num_processes,
double cutoff_prob,
int cutoff_top_n,
Scorer *ext_scorer
) {
if (num_processes <= 0) {
@ -295,7 +313,8 @@ std::vector<std::vector<std::pair<double, std::string> > >
for (int i = 0; i < batch_size; i++) {
res.emplace_back(
pool.enqueue(ctc_beam_search_decoder, probs_split[i],
beam_size, vocabulary, blank_id, cutoff_prob, ext_scorer)
beam_size, vocabulary, blank_id, cutoff_prob,
cutoff_top_n, ext_scorer)
);
}
// get decoding results

2
deploy/ctc_decoders.h
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@ -39,6 +39,7 @@ std::vector<std::pair<double, std::string> >
std::vector<std::string> vocabulary,
int blank_id,
double cutoff_prob=1.0,
int cutoff_top_n=40,
Scorer *ext_scorer=NULL
);
@ -66,6 +67,7 @@ std::vector<std::vector<std::pair<double, std::string>>>
int blank_id,
int num_processes,
double cutoff_prob=1.0,
int cutoff_top_n=40,
Scorer *ext_scorer=NULL
);

2
deploy/scorer.h
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@ -50,6 +50,7 @@ public:
void fill_dictionary(bool add_space);
// set char map
void set_char_map(std::vector<std::string> char_list);
std::vector<std::string> split_labels(const std::vector<int> &labels);
// expose to decoder
double alpha;
double beta;
@ -60,7 +61,6 @@ protected:
void load_LM(const char* filename);
double get_log_prob(const std::vector<std::string>& words);
std::string vec2str(const std::vector<int> &input);
std::vector<std::string> split_labels(const std::vector<int> &labels);
private:
void* _language_model;

22
deploy/swig_decoders_wrapper.py
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@ -43,6 +43,7 @@ def ctc_beam_search_decoder(probs_seq,
vocabulary,
blank_id,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the CTC Beam Search Decoder.
@ -59,6 +60,10 @@ def ctc_beam_search_decoder(probs_seq,
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in vocabulary will be
used in beam search, default 40.
:type cutoff_top_n: int
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
@ -67,9 +72,9 @@ def ctc_beam_search_decoder(probs_seq,
results, in descending order of the probability.
:rtype: list
"""
return swig_decoders.ctc_beam_search_decoder(probs_seq.tolist(), beam_size,
vocabulary, blank_id,
cutoff_prob, ext_scoring_func)
return swig_decoders.ctc_beam_search_decoder(
probs_seq.tolist(), beam_size, vocabulary, blank_id, cutoff_prob,
cutoff_top_n, ext_scoring_func)
def ctc_beam_search_decoder_batch(probs_split,
@ -78,6 +83,7 @@ def ctc_beam_search_decoder_batch(probs_split,
blank_id,
num_processes,
cutoff_prob=1.0,
cutoff_top_n=40,
ext_scoring_func=None):
"""Wrapper for the batched CTC beam search decoder.
@ -92,11 +98,15 @@ def ctc_beam_search_decoder_batch(probs_split,
:type blank_id: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param cutoff_prob: Cutoff probability in pruning,
:param cutoff_prob: Cutoff probability in vocabulary pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in vocabulary will be
used in beam search, default 40.
:type cutoff_top_n: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:type cutoff_prob: float
:param ext_scoring_func: External scoring function for
partially decoded sentence, e.g. word count
or language model.
@ -109,4 +119,4 @@ def ctc_beam_search_decoder_batch(probs_split,
return swig_decoders.ctc_beam_search_decoder_batch(
probs_split, beam_size, vocabulary, blank_id, num_processes,
cutoff_prob, ext_scoring_func)
cutoff_prob, cutoff_top_n, ext_scoring_func)

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