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Commit 160337da authored by Davis King's avatar Davis King
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Made the one_vs_one_trainer and one_vs_all_trainer objects multithreaded 

so they can run each binary trainer on a different core.
parent 525f2a52
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Showing with 254 additions and 76 deletions
dlib/svm.h
View file @ 160337da
......@@ -32,7 +32,6 @@
#include "svm/svr_trainer.h"
#include "svm/one_vs_one_decision_function.h"
#include "svm/one_vs_one_trainer.h"
#include "svm/multiclass_tools.h"
#include "svm/cross_validate_multiclass_trainer.h"
#include "svm/cross_validate_regression_trainer.h"
......@@ -42,7 +41,6 @@
#include "svm/cross_validate_assignment_trainer.h"
#include "svm/one_vs_all_decision_function.h"
#include "svm/one_vs_all_trainer.h"
#include "svm/structural_svm_problem.h"
#include "svm/sequence_labeler.h"
......
dlib/svm/cross_validate_multiclass_trainer.h
View file @ 160337da
......@@ -5,7 +5,6 @@
#include <vector>
#include "../matrix.h"
#include "one_vs_one_trainer.h"
#include "cross_validate_multiclass_trainer_abstract.h"
#include <sstream>
......
dlib/svm/one_vs_all_trainer.h
View file @ 160337da
......@@ -16,6 +16,7 @@
#include "../any.h"
#include <map>
#include <set>
#include "../threads.h"
namespace dlib
{
......@@ -39,7 +40,8 @@ namespace dlib
one_vs_all_trainer (
) :
verbose(false)
verbose(false),
num_threads(4)
{}
void set_trainer (
......@@ -70,6 +72,19 @@ namespace dlib
verbose = false;
}
void set_num_threads (
unsigned long num
)
{
num_threads = num;
}
unsigned long get_num_threads (
) const
{
return num_threads;
}
struct invalid_label : public dlib::error
{
invalid_label(const std::string& msg, const label_type& l_
......@@ -96,62 +111,117 @@ namespace dlib
const std::vector<label_type> distinct_labels = select_all_distinct_labels(all_labels);
std::vector<scalar_type> labels;
typename trained_function_type::binary_function_table dfs;
// make sure we have a trainer object for each of the label types.
for (unsigned long i = 0; i < distinct_labels.size(); ++i)
{
labels.clear();
const label_type l = distinct_labels[i];
const typename binary_function_table::const_iterator itr = trainers.find(l);
// setup one of the one vs all training sets
for (unsigned long k = 0; k < all_samples.size(); ++k)
if (itr == trainers.end() && default_trainer.is_empty())
{
if (all_labels[k] == l)
labels.push_back(+1);
else
labels.push_back(-1);
std::ostringstream sout;
sout << "In one_vs_all_trainer, no trainer registered for the " << l << " label.";
throw invalid_label(sout.str(), l);
}
}
if (verbose)
{
std::cout << "Training classifier for " << l << " vs. all" << std::endl;
}
// now do the training
parallel_for_helper helper(all_samples,all_labels,default_trainer,trainers,verbose,distinct_labels);
parallel_for(num_threads, 0, distinct_labels.size(), helper, 500);
// now train a binary classifier using the samples we selected
const typename binary_function_table::const_iterator itr = trainers.find(l);
if (helper.error_message.size() != 0)
{
throw dlib::error("binary trainer threw while training one vs. all classifier. Error was: " + helper.error_message);
}
return trained_function_type(helper.dfs);
}
if (itr != trainers.end())
{
dfs[l] = itr->second.train(all_samples, labels);
}
else if (default_trainer.is_empty() == false)
private:
typedef std::map<label_type, any_trainer> binary_function_table;
struct parallel_for_helper
{
parallel_for_helper(
const std::vector<sample_type>& all_samples_,
const std::vector<label_type>& all_labels_,
const any_trainer& default_trainer_,
const binary_function_table& trainers_,
const bool verbose_,
const std::vector<label_type>& distinct_labels_
) :
all_samples(all_samples_),
all_labels(all_labels_),
default_trainer(default_trainer_),
trainers(trainers_),
verbose(verbose_),
distinct_labels(distinct_labels_)
{}
void operator()(long i) const
{
try
{
dfs[l] = default_trainer.train(all_samples, labels);
std::vector<scalar_type> labels;
const label_type l = distinct_labels[i];
// setup one of the one vs all training sets
for (unsigned long k = 0; k < all_samples.size(); ++k)
{
if (all_labels[k] == l)
labels.push_back(+1);
else
labels.push_back(-1);
}
if (verbose)
{
auto_mutex lock(class_mutex);
std::cout << "Training classifier for " << l << " vs. all" << std::endl;
}
any_trainer trainer;
// now train a binary classifier using the samples we selected
{ auto_mutex lock(class_mutex);
const typename binary_function_table::const_iterator itr = trainers.find(l);
if (itr != trainers.end())
trainer = itr->second;
else
trainer = default_trainer;
}
any_decision_function<sample_type,scalar_type> binary_df = trainer.train(all_samples, labels);
auto_mutex lock(class_mutex);
dfs[l] = binary_df;
}
else
catch (std::exception& e)
{
std::ostringstream sout;
sout << "In one_vs_all_trainer, no trainer registered for the " << l << " label.";
throw invalid_label(sout.str(), l);
auto_mutex lock(class_mutex);
error_message = e.what();
}
}
return trained_function_type(dfs);
}
mutable typename trained_function_type::binary_function_table dfs;
mutex class_mutex;
mutable std::string error_message;
private:
const std::vector<sample_type>& all_samples;
const std::vector<label_type>& all_labels;
const any_trainer& default_trainer;
const binary_function_table& trainers;
const bool verbose;
const std::vector<label_type>& distinct_labels;
};
any_trainer default_trainer;
typedef std::map<label_type, any_trainer> binary_function_table;
binary_function_table trainers;
bool verbose;
unsigned long num_threads;
};
......
dlib/svm/one_vs_all_trainer_abstract.h
View file @ 160337da
......@@ -55,10 +55,11 @@ namespace dlib
);
/*!
ensures
- this object is properly initialized
- this object will not be verbose unless be_verbose() is called
- no binary trainers are associated with *this. I.e. you have to
call set_trainer() before calling train()
- This object is properly initialized.
- This object will not be verbose unless be_verbose() is called.
- No binary trainers are associated with *this. I.e. you have to
call set_trainer() before calling train().
- #get_num_threads() == 4
!*/
void set_trainer (
......@@ -96,6 +97,23 @@ namespace dlib
- this object will not print anything to standard out
!*/
void set_num_threads (
unsigned long num
);
/*!
ensures
- #get_num_threads() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- returns the number of threads used during training. You should
usually set this equal to the number of processing cores on your
machine.
!*/
struct invalid_label : public dlib::error
{
/*!
......
dlib/svm/one_vs_one_trainer.h
View file @ 160337da
......@@ -17,6 +17,7 @@
#include "../any.h"
#include <map>
#include <set>
#include "../threads.h"
namespace dlib
{
......@@ -40,7 +41,8 @@ namespace dlib
one_vs_one_trainer (
) :
verbose(false)
verbose(false),
num_threads(4)
{}
void set_trainer (
......@@ -72,6 +74,19 @@ namespace dlib
verbose = false;
}
void set_num_threads (
unsigned long num
)
{
num_threads = num;
}
unsigned long get_num_threads (
) const
{
return num_threads;
}
struct invalid_label : public dlib::error
{
invalid_label(const std::string& msg, const label_type& l1_, const label_type& l2_
......@@ -98,20 +113,70 @@ namespace dlib
const std::vector<label_type> distinct_labels = select_all_distinct_labels(all_labels);
std::vector<sample_type> samples;
std::vector<scalar_type> labels;
typename trained_function_type::binary_function_table dfs;
// fill pairs with all the pairs of labels.
std::vector<unordered_pair<label_type> > pairs;
for (unsigned long i = 0; i < distinct_labels.size(); ++i)
{
for (unsigned long j = i+1; j < distinct_labels.size(); ++j)
{
samples.clear();
labels.clear();
pairs.push_back(unordered_pair<label_type>(distinct_labels[i], distinct_labels[j]));
const unordered_pair<label_type> p(distinct_labels[i], distinct_labels[j]);
// make sure we have a trainer for this pair
const typename binary_function_table::const_iterator itr = trainers.find(pairs.back());
if (itr == trainers.end() && default_trainer.is_empty())
{
std::ostringstream sout;
sout << "In one_vs_one_trainer, no trainer registered for the ("
<< pairs.back().first << ", " << pairs.back().second << ") label pair.";
throw invalid_label(sout.str(), pairs.back().first, pairs.back().second);
}
}
}
// Now train on all the label pairs.
parallel_for_helper helper(all_samples,all_labels,default_trainer,trainers,verbose,pairs);
parallel_for(num_threads, 0, pairs.size(), helper, 500);
if (helper.error_message.size() != 0)
{
throw dlib::error("binary trainer threw while training one vs. one classifier. Error was: " + helper.error_message);
}
return trained_function_type(helper.dfs);
}
private:
typedef std::map<unordered_pair<label_type>, any_trainer> binary_function_table;
struct parallel_for_helper
{
parallel_for_helper(
const std::vector<sample_type>& all_samples_,
const std::vector<label_type>& all_labels_,
const any_trainer& default_trainer_,
const binary_function_table& trainers_,
const bool verbose_,
const std::vector<unordered_pair<label_type> >& pairs_
) :
all_samples(all_samples_),
all_labels(all_labels_),
default_trainer(default_trainer_),
trainers(trainers_),
verbose(verbose_),
pairs(pairs_)
{}
void operator()(long i) const
{
try
{
std::vector<sample_type> samples;
std::vector<scalar_type> labels;
const unordered_pair<label_type> p = pairs[i];
// pick out the samples corresponding to these two classes
for (unsigned long k = 0; k < all_samples.size(); ++k)
......@@ -128,43 +193,51 @@ namespace dlib
}
}
if (verbose)
{
auto_mutex lock(class_mutex);
std::cout << "Training classifier for " << p.first << " vs. " << p.second << std::endl;
}
any_trainer trainer;
// now train a binary classifier using the samples we selected
{ auto_mutex lock(class_mutex);
const typename binary_function_table::const_iterator itr = trainers.find(p);
if (itr != trainers.end())
{
dfs[p] = itr->second.train(samples, labels);
}
else if (default_trainer.is_empty() == false)
{
dfs[p] = default_trainer.train(samples, labels);
}
else
{
std::ostringstream sout;
sout << "In one_vs_one_trainer, no trainer registered for the (" << p.first << ", " << p.second << ") label pair.";
throw invalid_label(sout.str(), p.first, p.second);
trainer = itr->second;
else
trainer = default_trainer;
}
any_decision_function<sample_type,scalar_type> binary_df = trainer.train(samples, labels);
auto_mutex lock(class_mutex);
dfs[p] = binary_df;
}
catch (std::exception& e)
{
auto_mutex lock(class_mutex);
error_message = e.what();
}
}
return trained_function_type(dfs);
}
mutable typename trained_function_type::binary_function_table dfs;
mutex class_mutex;
mutable std::string error_message;
private:
const std::vector<sample_type>& all_samples;
const std::vector<label_type>& all_labels;
const any_trainer& default_trainer;
const binary_function_table& trainers;
const bool verbose;
const std::vector<unordered_pair<label_type> >& pairs;
};
any_trainer default_trainer;
typedef std::map<unordered_pair<label_type>, any_trainer> binary_function_table;
binary_function_table trainers;
bool verbose;
unsigned long num_threads;
};
......
dlib/svm/one_vs_one_trainer_abstract.h
View file @ 160337da
......@@ -55,10 +55,11 @@ namespace dlib
);
/*!
ensures
- this object is properly initialized
- this object will not be verbose unless be_verbose() is called
- no binary trainers are associated with *this. I.e. you have to
call set_trainer() before calling train()
- This object is properly initialized
- This object will not be verbose unless be_verbose() is called.
- No binary trainers are associated with *this. I.e. you have to
call set_trainer() before calling train().
- #get_num_threads() == 4
!*/
void set_trainer (
......@@ -99,6 +100,23 @@ namespace dlib
- this object will not print anything to standard out
!*/
void set_num_threads (
unsigned long num
);
/*!
ensures
- #get_num_threads() == num
!*/
unsigned long get_num_threads (
) const;
/*!
ensures
- returns the number of threads used during training. You should
usually set this equal to the number of processing cores on your
machine.
!*/
struct invalid_label : public dlib::error
{
/*!
......
dlib/svm_threaded.h
View file @ 160337da
......@@ -19,6 +19,8 @@
#include "svm/structural_graph_labeling_trainer.h"
#include "svm/cross_validate_graph_labeling_trainer.h"
#include "svm/svm_multiclass_linear_trainer.h"
#include "svm/one_vs_one_trainer.h"
#include "svm/one_vs_all_trainer.h"
#endif // DLIB_SVm_THREADED_HEADER
......
dlib/test/one_vs_all_trainer.cpp
View file @ 160337da
......@@ -2,7 +2,7 @@
// License: Boost Software License See LICENSE.txt for the full license.
#include "tester.h"
#include <dlib/svm.h>
#include <dlib/svm_threaded.h>
#include <vector>
#include <sstream>
......
dlib/test/one_vs_one_trainer.cpp
View file @ 160337da
......@@ -2,7 +2,7 @@
// License: Boost Software License See LICENSE.txt for the full license.
#include "tester.h"
#include <dlib/svm.h>
#include <dlib/svm_threaded.h>
#include <dlib/statistics.h>
#include <vector>
#include <sstream>
......
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