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authorgraehl <graehl@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-05 21:47:50 +0000
committergraehl <graehl@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-07-05 21:47:50 +0000
commit206d5fc0efb735d404837d2a89a7e64a304f27ed (patch)
tree05c22b7c69e99715254fab91ea3b94937169ad1c /decoder
parent864995a44648f8de8042d26b30a92ed137acba28 (diff)
verbose feature info, cdec --keep_prelm_cube_order
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@142 ec762483-ff6d-05da-a07a-a48fb63a330f
Diffstat (limited to 'decoder')
-rw-r--r--decoder/cdec.cc14
1 files changed, 11 insertions, 3 deletions
diff --git a/decoder/cdec.cc b/decoder/cdec.cc
index c940fdac..e3a2435d 100644
--- a/decoder/cdec.cc
+++ b/decoder/cdec.cc
@@ -38,6 +38,8 @@ using namespace std::tr1;
using boost::shared_ptr;
namespace po = boost::program_options;
+bool verbose_feature_functions=true;
+
// some globals ...
boost::shared_ptr<RandomNumberGenerator<boost::mt19937> > rng;
static const double kMINUS_EPSILON = -1e-6; // don't be too strict
@@ -63,6 +65,7 @@ void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
("weights,w",po::value<string>(),"Feature weights file")
("prelm_weights",po::value<string>(),"Feature weights file for prelm_beam_prune. Requires --weights.")
("prelm_copy_weights","use --weights as value for --prelm_weights.")
+ ("keep_prelm_cube_order","when forest rescoring with final models, use the edge ordering from the prelm pruning features*weights. only meaningful if --prelm_weights given. UNTESTED but assume that cube pruning gives a sensible result, and that 'good' (as tuned for bleu w/ prelm features) edges come first.")
("no_freeze_feature_set,Z", "Do not freeze feature set after reading feature weights file")
("feature_function,F",po::value<vector<string> >()->composing(), "Additional feature function(s) (-L for list)")
@@ -348,8 +351,10 @@ int main(int argc, char** argv) {
// TODO check that multiple features aren't trying to set the same fid
pffs.push_back(pff);
late_ffs.push_back(p);
+ int nbyte=p->NumBytesContext();
+ if (verbose_feature_functions)
+ cerr<<"State is "<<nbyte<<" bytes for feature "<<ff<<endl;
if (has_prelm_models) {
- int nbyte=p->NumBytesContext();
if (nbyte==0)
prelm_ffs.push_back(p);
else
@@ -357,6 +362,9 @@ int main(int argc, char** argv) {
}
}
}
+ if (has_prelm_models)
+ cerr << "prelm rescoring with "<<prelm_ffs.size()<<" 0-state feature functions. +LM pass will use "<<late_ffs.size()<<" features (not counting rule features)."<<endl;
+
ModelSet late_models(feature_weights, late_ffs);
int palg = 1;
@@ -454,7 +462,6 @@ int main(int argc, char** argv) {
if (has_prelm_models) {
ModelSet prelm_models(prelm_feature_weights, prelm_ffs);
- cerr << "Rescoring with rule probabilities and "<<prelm_ffs.size()<<" 0-state feature functions. +LM pass will use "<<late_ffs.size()<<" features."<<endl;
Timer t("prelm rescoring");
forest.Reweight(prelm_feature_weights);
forest.SortInEdgesByEdgeWeights();
@@ -482,7 +489,8 @@ int main(int argc, char** argv) {
if (has_late_models) {
Timer t("Forest rescoring:");
forest.Reweight(feature_weights);
- forest.SortInEdgesByEdgeWeights();
+ if (!has_prelm_models || conf.count("keep_prelm_cube_order"))
+ forest.SortInEdgesByEdgeWeights();
Hypergraph lm_forest;
ApplyModelSet(forest,
smeta,