void memcpy (
gpu_data& dest,
size_t dest_offset,
const gpu_data& src,
size_t src_offset,
size_t num
)
{
DLIB_CASSERT(dest_offset + num <= dest.size());
DLIB_CASSERT(src_offset + num <= src.size());
if (num == 0)
return;
// if there is aliasing
if (&dest == &src && std::max(dest_offset, src_offset) < std::min(dest_offset,src_offset)+num)
{
// if they perfectly alias each other then there is nothing to do
if (dest_offset == src_offset)
return;
else
std::memmove(dest.host()+dest_offset, src.host()+src_offset, sizeof(float)*num);
}
else
{
// if we write to the entire thing then we can use device_write_only()
if (dest_offset == 0 && num == dest.size())
{
// copy the memory efficiently based on which copy is current in each object.
if (src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.device_write_only(), src.device()+src_offset, num*sizeof(float), cudaMemcpyDeviceToDevice));
else
CHECK_CUDA(cudaMemcpy(dest.device_write_only(), src.host()+src_offset, num*sizeof(float), cudaMemcpyHostToDevice));
}
else
{
// copy the memory efficiently based on which copy is current in each object.
if (dest.device_ready() && src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.device()+dest_offset, src.device()+src_offset, num*sizeof(float), cudaMemcpyDeviceToDevice));
else if (!dest.device_ready() && src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.host()+dest_offset, src.device()+src_offset, num*sizeof(float), cudaMemcpyDeviceToHost));
else if (dest.device_ready() && !src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.device()+dest_offset, src.host()+src_offset, num*sizeof(float), cudaMemcpyHostToDevice));
else
CHECK_CUDA(cudaMemcpy(dest.host()+dest_offset, src.host()+src_offset, num*sizeof(float), cudaMemcpyHostToHost));
}
}
}
void binary_relation_detector_trainer::
set_beta (
double new_beta
)
{
DLIB_CASSERT(new_beta >= 0, "Invalid beta");
beta = new_beta;
}
named_entity_extractor::
named_entity_extractor(
const std::vector<std::string>& tag_name_strings_,
const total_word_feature_extractor& fe_,
const dlib::sequence_segmenter<ner_feature_extractor>& segmenter_,
const dlib::multiclass_linear_decision_function<dlib::sparse_linear_kernel<ner_sample_type>,unsigned long>& df_
) : tag_name_strings(tag_name_strings_), fe(fe_), segmenter(segmenter_), df(df_)
{
// make sure the requirements are not violated.
DLIB_CASSERT(df.number_of_classes() >= tag_name_strings.size(),"invalid inputs");
DLIB_CASSERT(segmenter.get_feature_extractor().num_features() == fe.get_num_dimensions(),"invalid inputs");
std::set<unsigned long> df_tags(df.get_labels().begin(), df.get_labels().end());
for (unsigned long i = 0; i < tag_name_strings.size(); ++i)
{
DLIB_CASSERT(df_tags.count(i) == 1, "The classifier must be capable of predicting each possible tag as output.");
}
compute_fingerprint();
}
void scale_rows (
tensor& out,
const tensor& m,
const tensor& v
)
{
DLIB_CASSERT(have_same_dimensions(out,m));
DLIB_CASSERT(is_vector(v));
if (m.size() == 0 && v.size() == 0)
return;
DLIB_CASSERT(m.size() != 0);
DLIB_CASSERT(m.num_samples() == v.size());
#ifdef DLIB_USE_CUDA
cuda::scale_rows(out, m, v);
#else
out = scale_rows(mat(m), mat(v));
#endif
}
void binary_relation_detector_trainer::
add_negative_binary_relation (
const std::vector<std::string>& tokens,
unsigned long arg1_start,
unsigned long arg1_length,
unsigned long arg2_start,
unsigned long arg2_length
)
{
DLIB_CASSERT(arg1_length > 0 && arg2_length > 0, "Invalid Inputs");
DLIB_CASSERT(mitie_entities_overlap(arg1_start,arg1_length,arg2_start,arg2_length) == 0,
"Binary relation arguments can't overlap.");
DLIB_CASSERT(arg1_start+arg1_length <= tokens.size() && arg2_start+arg2_length <= tokens.size(),
"Invalid Inputs");
neg_sentences.push_back(tokens);
neg_arg1s.push_back(std::make_pair(arg1_start, arg1_start+arg1_length));
neg_arg2s.push_back(std::make_pair(arg2_start, arg2_start+arg2_length));
}
void memcpy (
gpu_data& dest,
const gpu_data& src
)
{
DLIB_CASSERT(dest.size() == src.size());
if (src.size() == 0 || &dest == &src)
return;
memcpy(dest,0, src, 0, src.size());
}
void scale_columns (
tensor& out,
const tensor& m,
const tensor& v
)
{
DLIB_CASSERT(have_same_dimensions(out,m));
DLIB_CASSERT(is_vector(v));
if (m.size() == 0 && v.size() == 0)
return;
DLIB_CASSERT(m.size() != 0);
DLIB_CASSERT(m.size()/m.num_samples() == v.size());
#ifdef DLIB_USE_CUDA
cuda::scale_columns(out, m, v);
#else
DLIB_CASSERT(false, "shouldn't be called right now");
out = scale_columns(mat(m), mat(v));
#endif
}
void log10 (
tensor& dest,
const tensor& src
)
{
DLIB_CASSERT(dest.size() == src.size());
#ifdef DLIB_USE_CUDA
cuda::log10(dest,src);
#else
dest = log10(mat(src));
#endif
}
void scale_rows2 (
float beta,
tensor& out,
const tensor& m1,
const tensor& m2,
const tensor& v1,
const tensor& v2
)
{
DLIB_CASSERT(have_same_dimensions(out,m1));
DLIB_CASSERT(have_same_dimensions(out,m2));
DLIB_CASSERT(have_same_dimensions(v1,v2));
DLIB_CASSERT(is_vector(mat(v1)));
DLIB_CASSERT(v1.size() == m1.num_samples());
#ifdef DLIB_USE_CUDA
cuda::scale_rows2(beta, out, m1, m2, v1, v2);
#else
if (beta == 0)
out = scale_rows(mat(m1) - scale_rows(mat(m2),mat(v1)), mat(v2));
else
out = beta*mat(out) + scale_rows(mat(m1) - scale_rows(mat(m2),mat(v1)), mat(v2));
#endif
}
void server::
start (
)
{
// make sure requires clause is not broken
DLIB_CASSERT(
this->is_running() == false,
"\tvoid server::start"
<< "\n\tis_running() == " << this->is_running()
<< "\n\tthis: " << this
);
start_accepting_connections();
}
void server::
set_max_connections (
int max
)
{
// make sure requires clause is not broken
DLIB_CASSERT(
max >= 0 ,
"\tvoid server::set_max_connections"
<< "\n\tmax == " << max
<< "\n\tthis: " << this
);
max_connections_mutex.lock();
max_connections = max;
max_connections_mutex.unlock();
}
void memcpy (
gpu_data& dest,
const gpu_data& src
)
{
DLIB_CASSERT(dest.size() == src.size(), "");
if (src.size() == 0)
return;
// copy the memory efficiently based on which copy is current in each object.
if (dest.device_ready() && src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.device(), src.device(), src.size()*sizeof(float), cudaMemcpyDeviceToDevice));
else if (!dest.device_ready() && src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.host_write_only(), src.device(), src.size()*sizeof(float), cudaMemcpyDeviceToHost));
else if (dest.device_ready() && !src.device_ready())
CHECK_CUDA(cudaMemcpy(dest.device(), src.host(), src.size()*sizeof(float), cudaMemcpyHostToDevice));
else
CHECK_CUDA(cudaMemcpy(dest.host_write_only(), src.host(), src.size()*sizeof(float), cudaMemcpyHostToHost));
}
void server::
set_listening_ip (
const std::string& ip
)
{
// make sure requires clause is not broken
DLIB_CASSERT(
( ( is_ip_address(ip) || ip == "" ) &&
this->is_running() == false ),
"\tvoid server::set_listening_ip"
<< "\n\tip == " << ip
<< "\n\tis_running() == " << this->is_running()
<< "\n\tthis: " << this
);
listening_ip_mutex.lock();
listening_ip = ip;
listening_ip_mutex.unlock();
}
void server::
set_listening_port (
int port
)
{
// make sure requires clause is not broken
DLIB_CASSERT(
( port >= 0 &&
this->is_running() == false ),
"\tvoid server::set_listening_port"
<< "\n\tport == " << port
<< "\n\tis_running() == " << this->is_running()
<< "\n\tthis: " << this
);
listening_port_mutex.lock();
listening_port = port;
listening_port_mutex.unlock();
}
void linker::
link (
connection& a,
connection& b
)
{
// make sure requires clause is not broken
DLIB_CASSERT(
this->is_running() == false ,
"\tvoid linker::link"
<< "\n\tis_running() == " << this->is_running()
<< "\n\tthis: " << this
);
running_mutex.lock();
running = true;
running_mutex.unlock();
cons_mutex.lock();
A = &a;
B = &b;
cons_mutex.unlock();
service_connection_running_mutex.lock();
service_connection_running = true;
service_connection_running_mutex.unlock();
service_connection_error_mutex.lock();
service_connection_error = false;
service_connection_error_mutex.unlock();
// if we fail to make the thread
if (!create_new_thread(service_connection,this))
{
a.shutdown();
b.shutdown();
service_connection_running_mutex.lock();
service_connection_running = false;
service_connection_running_mutex.unlock();
cons_mutex.lock();
A = 0;
B = 0;
cons_mutex.unlock();
running_mutex.lock();
running = false;
running_mutex.unlock();
throw dlib::thread_error (
ECREATE_THREAD,
"failed to make new thread in linker::link()"
);
}
// forward data from a to b
char buf[200];
int status;
bool error = false; // becomes true if one of the connections returns an error
while (true)
{
status = a.read(buf,sizeof(buf));
// if there was an error reading from the socket
if (status == OTHER_ERROR)
{
error = true;
break;
}
else if (status == SHUTDOWN)
{
b.shutdown();
}
if (status <= 0)
{
// if a has closed normally
if (status == 0)
b.shutdown_outgoing();
break;
}
status = b.write(buf,status);
// if there was an error writing to the socket then break
if (status == OTHER_ERROR)
{
error = true;
break;
}
if (status <= 0)
break;
}
// if there was an error then shutdown both connections
if (error)
{
a.shutdown();
b.shutdown();
}
// wait for the other thread to end
service_connection_running_mutex.lock();
while(service_connection_running)
{
service_connection_running_signaler.wait();
}
service_connection_running_mutex.unlock();
// make sure connections are shutdown
a.shutdown();
b.shutdown();
// both threads have ended so the connections are no longer needed
cons_mutex.lock();
A = 0;
B = 0;
cons_mutex.unlock();
// if service_connection terminated due to an error then set error to true
service_connection_error_mutex.lock();
if (service_connection_error)
error = true;
service_connection_error_mutex.unlock();
// if we are ending because of an error
if (error)
{
// signal that the link() function is ending
running_mutex.lock();
running = false;
running_signaler.broadcast();
running_mutex.unlock();
// throw the exception for this error
throw dlib::socket_error (
ECONNECTION,
"a connection returned an error in linker::link()"
);
}
// signal that the link() function is ending
running_mutex.lock();
running = false;
running_signaler.broadcast();
running_mutex.unlock();
}
binary_relation_detector binary_relation_detector_trainer::
train (
) const
{
DLIB_CASSERT(num_positive_examples() > 0, "Not enough training data given.");
DLIB_CASSERT(num_negative_examples() > 0, "Not enough training data given.");
std::vector<sparse_vector_type> samples;
std::vector<double> labels;
for (unsigned long i = 0; i < pos_sentences.size(); ++i)
{
samples.push_back(extract_binary_relation(pos_sentences[i], pos_arg1s[i], pos_arg2s[i], tfe).feats);
labels.push_back(+1);
}
for (unsigned long i = 0; i < neg_sentences.size(); ++i)
{
samples.push_back(extract_binary_relation(neg_sentences[i], neg_arg1s[i], neg_arg2s[i], tfe).feats);
labels.push_back(-1);
}
randomize_samples(samples, labels);
const int cv_folds = 6;
brdt_cv_objective obj(num_threads, cv_folds, beta, samples, labels);
matrix<double,2,1> params;
params = 5000.0/samples.size(), 5000.0/samples.size();
// We do the parameter search in log space.
params = log(params);
// can't do the parameter search if we don't have enough data. So if we don't
// have much data then just use the default parameters.
if (pos_sentences.size() > (unsigned)cv_folds)
{
matrix<double,2,1> lower_params, upper_params;
lower_params = 1.0/samples.size(), 1.0/samples.size();
upper_params = 100000.0/samples.size(), 100000.0/samples.size();
lower_params = log(lower_params);
upper_params = log(upper_params);
const double rho_begin = min(upper_params-lower_params)*0.15;
const double rho_end = log(1.2/samples.size()) - log(1.0/samples.size());
find_max_bobyqa(obj, params, params.size()*2+1, lower_params, upper_params, rho_begin, rho_end, 200);
}
// Note that we rescale the parameters to account for the fact that the cross
// validation was done on a dataset slightly smaller than the one we ultimately train
// on and the C parameters of this trainer are not normalized by the number of training
// samples.
params = exp(params) * (cv_folds-1.0)/cv_folds;
svm_c_linear_dcd_trainer<sparse_linear_kernel<sparse_vector_type> > trainer;
trainer.set_c_class1(params(0));
trainer.set_c_class2(params(1));
cout << "using parameters of: " << trans(params);
cout << "now doing training..." << endl;
binary_relation_detector bd;
bd.df = trainer.train(samples, labels);
bd.relation_type = relation_name;
bd.total_word_feature_extractor_fingerprint = tfe.get_fingerprint();
cout << "test on train: " << test_binary_decision_function(bd.df, samples, labels) << endl;
return bd;
}
