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Commit d207348a authored by Davis King's avatar Davis King
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merged

parents 523489e9 517858ad
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Showing with 71 additions and 45 deletions
dlib/dnn/trainer.h
View file @ d207348a
......@@ -328,12 +328,14 @@ namespace dlib
rs.clear();
}
void set_setep_size (
void set_step_size (
double ss
)
{
DLIB_CASSERT(ss > 0,"");
wait_for_thread_to_pause();
if (step_size != ss)
previous_loss_values.clear();
step_size = ss;
}
......@@ -391,24 +393,33 @@ namespace dlib
resizable_tensor t;
};
template <typename T>
void run_update(job_t& next_job, const T&)
void record_loss(double loss)
{
double loss = net.update(next_job.t, next_job.labels.begin(), make_sstack(solvers),step_size);
// Say that we will check if the gradient is bad 200 times during each
// iter_between_step_size_adjust interval of network updates. This kind of
// budgeting causes our gradient checking to use a fixed amount of
// computational resources, regardless of the size of
// iter_between_step_size_adjust.
gradient_check_budget += 200;
rs.add(loss);
previous_loss_values.push_back(loss);
if (previous_loss_values.size() > iter_between_step_size_adjust)
previous_loss_values.pop_front();
}
template <typename T>
void run_update(job_t& next_job, const T&)
{
double loss = net.update(next_job.t, next_job.labels.begin(), make_sstack(solvers),step_size);
record_loss(loss);
}
void run_update(job_t& next_job, const no_label_type&)
{
no_label_type pick_wich_run_update;
double loss = net.update(next_job.t, make_sstack(solvers), step_size);
rs.add(loss);
previous_loss_values.push_back(loss);
if (previous_loss_values.size() > iter_between_step_size_adjust)
previous_loss_values.pop_front();
record_loss(loss);
}
void thread() try
......@@ -425,9 +436,14 @@ namespace dlib
run_update(next_job, pick_wich_run_update);
// If we have been running for a while then check if the loss is still
// dropping. If it isn't then we will reduce the step size.
if (previous_loss_values.size() >= iter_between_step_size_adjust)
// dropping. If it isn't then we will reduce the step size. Note that we
// have a "budget" that prevents us from calling
// probability_gradient_greater_than() every iteration. We do this because
// it can be expensive to compute when previous_loss_values is large.
if (previous_loss_values.size() >= iter_between_step_size_adjust &&
gradient_check_budget > previous_loss_values.size())
{
gradient_check_budget = 0;
if (probability_gradient_greater_than(previous_loss_values, 0) > 0.49)
{
step_size = step_size_shrink*step_size;
......@@ -458,12 +474,13 @@ namespace dlib
verbose = false;
cuda_device_id = dlib::cuda::get_device();
step_size = 1;
min_step_size = 1e-4;
min_step_size = 1e-3;
iter_between_step_size_adjust = 2000;
step_size_shrink = 0.1;
epoch_iteration = 0;
epoch_pos = 0;
train_one_step_calls = 0;
gradient_check_budget = 0;
start();
}
......@@ -575,7 +592,7 @@ namespace dlib
std::vector<solver_type> solvers;
std::atomic<double> step_size;
double min_step_size;
std::atomic<long> iter_between_step_size_adjust;
std::atomic<unsigned long> iter_between_step_size_adjust;
std::atomic<double> step_size_shrink;
std::chrono::time_point<std::chrono::system_clock> last_sync_time;
std::string sync_filename;
......@@ -584,6 +601,7 @@ namespace dlib
unsigned long epoch_pos;
std::chrono::time_point<std::chrono::system_clock> last_time;
unsigned long long train_one_step_calls;
unsigned long gradient_check_budget;
// The job object is not logically part of the state of this object. It is here
// only to avoid reallocating it over and over.
......
dlib/dnn/trainer_abstract.h
View file @ d207348a
......@@ -60,7 +60,7 @@ namespace dlib
- #get_max_num_epochs() == 10000
- #get_mini_batch_size() == 128
- #get_step_size() == 1
- #get_min_step_size() == 1e-4
- #get_min_step_size() == 1e-3
- #get_iterations_between_step_size_adjust() == 2000
- #get_step_size_shrink() == 0.1
!*/
......@@ -149,7 +149,7 @@ namespace dlib
- #get_max_num_epochs() == num
!*/
void set_setep_size (
void set_step_size (
double ss
);
/*!
......
examples/dnn_mit67_ex.cpp
View file @ d207348a
......@@ -41,7 +41,9 @@ void randomly_crop_image (
)
{
// figure out what rectangle we want to crop from the image
auto scale = 1-rnd.get_random_double()*0.2;
//auto scale = 1-rnd.get_random_double()*0.2;
double mins = 0.466666666, maxs = 0.875;
auto scale = mins + rnd.get_random_double()*(maxs-mins);
auto size = scale*std::min(img.nr(), img.nc());
rectangle rect(size, size);
// randomly shift the box around
......@@ -49,8 +51,8 @@ void randomly_crop_image (
rnd.get_random_32bit_number()%(img.nr()-rect.height()));
rect = move_rect(rect, offset);
// now crop it out as a 250x250 image.
extract_image_chip(img, chip_details(rect, chip_dims(250,250)), crop);
// now crop it out as a 224x224 image.
extract_image_chip(img, chip_details(rect, chip_dims(224,224)), crop);
// Also randomly flip the image
if (rnd.get_random_double() > 0.5)
......@@ -71,7 +73,9 @@ void randomly_crop_images (
for (long i = 0; i < num_crops; ++i)
{
// figure out what rectangle we want to crop from the image
auto scale = 1-rnd.get_random_double()*0.2;
//auto scale = 1-rnd.get_random_double()*0.2;
double mins = 0.466666666, maxs = 0.875;
auto scale = mins + rnd.get_random_double()*(maxs-mins);
auto size = scale*std::min(img.nr(), img.nc());
rectangle rect(size, size);
// randomly shift the box around
......@@ -79,7 +83,7 @@ void randomly_crop_images (
rnd.get_random_32bit_number()%(img.nr()-rect.height()));
rect = move_rect(rect, offset);
dets.push_back(chip_details(rect, chip_dims(250,250)));
dets.push_back(chip_details(rect, chip_dims(224,224)));
}
extract_image_chips(img, dets, crops);
......@@ -104,7 +108,7 @@ struct image_info
unsigned long numeric_label;
};
std::vector<image_info> get_mit67_listing(
std::vector<image_info> get_imagenet_listing(
const std::string& images_folder
)
{
......@@ -147,9 +151,10 @@ int main(int argc, char** argv) try
return 1;
}
auto listing = get_mit67_listing(argv[1]);
auto listing = get_imagenet_listing(argv[1]);
cout << "images in dataset: " << listing.size() << endl;
if (listing.size() == 0 || listing.back().numeric_label != 66)
const auto number_of_classes = listing.back().numeric_label+1;
if (listing.size() == 0 || number_of_classes != 1000)
{
cout << "Didn't find the MIT 67 scene dataset. Are you sure you gave the correct folder?" << endl;
cout << "Give the Images folder as an argument to this program." << endl;
......@@ -161,21 +166,21 @@ int main(int argc, char** argv) try
const double weight_decay = sa = argv[2];
typedef loss_multiclass_log<fc<avg_pool<
res<res<
res<res<
res<res<
res<res<
res<res<res<
res<res<res<res<res<res<
res<res<res<res<
res<res<res<
max_pool<relu<bn<con<
input<matrix<rgb_pixel>
>>>>>>>>>>>>>>>> net_type;
>>>>>>>>>>>>>>>>>>>>>>>> net_type;
net_type net(fc_(67),
net_type net(fc_(number_of_classes),
avg_pool_(1000,1000,1000,1000),
res_(512),res_(512,2),
res_(256),res_(256,2),
res_(128),res_(128,2),
res_(64), res_(64),
res_(512),res_(512),res_(512,2),
res_(256),res_(256),res_(256),res_(256),res_(256),res_(256,2),
res_(128),res_(128),res_(128),res_(128,2),
res_(64), res_(64), res_(64),
max_pool_(3,3,2,2), relu_(), bn_(CONV_MODE), con_(64,7,7,2,2)
);
......@@ -185,12 +190,13 @@ int main(int argc, char** argv) try
dnn_trainer<net_type> trainer(net,sgd(initial_step_size, weight_decay));
trainer.be_verbose();
trainer.set_synchronization_file("mit67_sync3_"+cast_to_string(weight_decay), std::chrono::minutes(5));
trainer.set_synchronization_file("sync_imagenet_full_training_set_40000_minstep_"+cast_to_string(weight_decay), std::chrono::minutes(5));
trainer.set_iterations_between_step_size_adjust(40000);
std::vector<matrix<rgb_pixel>> samples;
std::vector<unsigned long> labels;
randomize_samples(listing);
const size_t training_part = listing.size()*0.7;
const size_t training_part = listing.size()*1.0;
dlib::rand rnd;
......@@ -198,14 +204,14 @@ int main(int argc, char** argv) try
const bool do_training = true;
if (do_training)
{
while(trainer.get_step_size() >= 1e-4)
while(trainer.get_step_size() >= 1e-3)
{
samples.clear();
labels.clear();
// make a 64 image mini-batch
// make a 128 image mini-batch
matrix<rgb_pixel> img, crop;
while(samples.size() < 64)
while(samples.size() < 128)
{
auto l = listing[rnd.get_random_32bit_number()%training_part];
load_image(img, l.filename);
......@@ -222,25 +228,25 @@ int main(int argc, char** argv) try
net.clean();
cout << "saving network" << endl;
serialize("mit67_network3_"+cast_to_string(weight_decay)+".dat") << net;
serialize("imagenet_full_training_set_40000_minstep_"+cast_to_string(weight_decay)+".dat") << net;
}
const bool test_network = true;
const bool test_network = false;
if (test_network)
{
typedef loss_multiclass_log<fc<avg_pool<
ares<ares<
ares<ares<
ares<ares<
ares<ares<
ares<ares<ares<
ares<ares<ares<ares<ares<ares<
ares<ares<ares<ares<
ares<ares<ares<
max_pool<relu<affine<con<
input<matrix<rgb_pixel>
>>>>>>>>>>>>>>>> anet_type;
>>>>>>>>>>>>>>>>>>>>>>>> anet_type;
anet_type net;
deserialize("mit67_network3_"+cast_to_string(weight_decay)+".dat") >> net;
deserialize("imagenet_network3_"+cast_to_string(weight_decay)+".dat") >> net;
dlib::array<matrix<rgb_pixel>> images;
std::vector<unsigned long> labels;
......@@ -249,6 +255,7 @@ int main(int argc, char** argv) try
int num_right = 0;
int num_wrong = 0;
console_progress_indicator pbar(training_part);
/*
for (size_t i = 0; i < training_part; ++i)
{
pbar.print_status(i);
......@@ -261,6 +268,7 @@ int main(int argc, char** argv) try
else
++num_wrong;
}
*/
cout << "\ntraining num_right: " << num_right << endl;
cout << "training num_wrong: " << num_wrong << endl;
......
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