dlib C++ Library - dnn_imagenet_train_ex.cpp

// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
/*
 This program was used to train the resnet34_1000_imagenet_classifier.dnn
 network used by the dnn_imagenet_ex.cpp example program. 
 You should be familiar with dlib's DNN module before reading this example
 program. So read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp first. 
*/
#include <dlib/dnn.h>
#include <iostream>
#include <dlib/data_io.h>
#include <dlib/image_transforms.h>
#include <dlib/dir_nav.h>
#include <iterator>
#include <thread>
using namespace std;
using namespace dlib;
 
// ----------------------------------------------------------------------------------------
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
using residual_down = add_prev2<avg_pool<2,2,2,2,skip1<tag2<block<N,BN,2,tag1<SUBNET>>>>>>;
template <int N, template <typename> class BN, int stride, typename SUBNET> 
using block = BN<con<N,3,3,1,1,relu<BN<con<N,3,3,stride,stride,SUBNET>>>>>;
template <int N, typename SUBNET> using res = relu<residual<block,N,bn_con,SUBNET>>;
template <int N, typename SUBNET> using ares = relu<residual<block,N,affine,SUBNET>>;
template <int N, typename SUBNET> using res_down = relu<residual_down<block,N,bn_con,SUBNET>>;
template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
// ----------------------------------------------------------------------------------------
template <typename SUBNET> using level1 = res<512,res<512,res_down<512,SUBNET>>>;
template <typename SUBNET> using level2 = res<256,res<256,res<256,res<256,res<256,res_down<256,SUBNET>>>>>>;
template <typename SUBNET> using level3 = res<128,res<128,res<128,res_down<128,SUBNET>>>>;
template <typename SUBNET> using level4 = res<64,res<64,res<64,SUBNET>>>;
template <typename SUBNET> using alevel1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
template <typename SUBNET> using alevel2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>;
template <typename SUBNET> using alevel3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>;
template <typename SUBNET> using alevel4 = ares<64,ares<64,ares<64,SUBNET>>>;
// training network type
using net_type = loss_multiclass_log<fc<1000,avg_pool_everything<
 level1<
 level2<
 level3<
 level4<
 max_pool<3,3,2,2,relu<bn_con<con<64,7,7,2,2,
 input_rgb_image_sized<227>
 >>>>>>>>>>>;
// testing network type (replaced batch normalization with fixed affine transforms)
using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
 alevel1<
 alevel2<
 alevel3<
 alevel4<
 max_pool<3,3,2,2,relu<affine<con<64,7,7,2,2,
 input_rgb_image_sized<227>
 >>>>>>>>>>>;
// ----------------------------------------------------------------------------------------
rectangle make_random_cropping_rect_resnet(
 const matrix<rgb_pixel>& img,
 dlib::rand& rnd
)
{
 // figure out what rectangle we want to crop from the image
 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
 point offset(rnd.get_random_32bit_number()%(img.nc()-rect.width()),
 rnd.get_random_32bit_number()%(img.nr()-rect.height()));
 return move_rect(rect, offset);
}
// ----------------------------------------------------------------------------------------
void randomly_crop_image (
 const matrix<rgb_pixel>& img,
 matrix<rgb_pixel>& crop,
 dlib::rand& rnd
)
{
 auto rect = make_random_cropping_rect_resnet(img, rnd);
 // now crop it out as a 227x227 image.
 extract_image_chip(img, chip_details(rect, chip_dims(227,227)), crop);
 // Also randomly flip the image
 if (rnd.get_random_double() > 0.5)
 crop = fliplr(crop);
 // And then randomly adjust the colors.
 apply_random_color_offset(crop, rnd);
}
void randomly_crop_images (
 const matrix<rgb_pixel>& img,
 dlib::array<matrix<rgb_pixel>>& crops,
 dlib::rand& rnd,
 long num_crops
)
{
 std::vector<chip_details> dets;
 for (long i = 0; i < num_crops; ++i)
 {
 auto rect = make_random_cropping_rect_resnet(img, rnd);
 dets.push_back(chip_details(rect, chip_dims(227,227)));
 }
 extract_image_chips(img, dets, crops);
 for (auto&& img : crops)
 {
 // Also randomly flip the image
 if (rnd.get_random_double() > 0.5)
 img = fliplr(img);
 // And then randomly adjust the colors.
 apply_random_color_offset(img, rnd);
 }
}
// ----------------------------------------------------------------------------------------
struct image_info
{
 string filename;
 string label;
 long numeric_label;
};
std::vector<image_info> get_imagenet_train_listing(
 const std::string& images_folder
)
{
 std::vector<image_info> results;
 image_info temp;
 temp.numeric_label = 0;
 // We will loop over all the label types in the dataset, each is contained in a subfolder.
 auto subdirs = directory(images_folder).get_dirs();
 // But first, sort the sub directories so the numeric labels will be assigned in sorted order.
 std::sort(subdirs.begin(), subdirs.end());
 for (auto subdir : subdirs)
 {
 // Now get all the images in this label type
 temp.label = subdir.name();
 for (auto image_file : subdir.get_files())
 {
 temp.filename = image_file;
 results.push_back(temp);
 }
 ++temp.numeric_label;
 }
 return results;
}
std::vector<image_info> get_imagenet_val_listing(
 const std::string& imagenet_root_dir,
 const std::string& validation_images_file 
)
{
 ifstream fin(validation_images_file);
 string label, filename;
 std::vector<image_info> results;
 image_info temp;
 temp.numeric_label = -1;
 while(fin >> label >> filename)
 {
 temp.filename = imagenet_root_dir+"/"+filename;
 if (!file_exists(temp.filename))
 {
 cerr << "file doesn't exist! " << temp.filename << endl;
 exit(1);
 }
 if (label != temp.label)
 ++temp.numeric_label;
 temp.label = label;
 results.push_back(temp);
 }
 return results;
}
// ----------------------------------------------------------------------------------------
int main(int argc, char** argv) try
{
 if (argc != 3)
 {
 cout << "To run this program you need a copy of the imagenet ILSVRC2015 dataset and" << endl;
 cout << "also the file http://dlib.net/files/imagenet2015_validation_images.txt.bz2" << endl;
 cout << endl;
 cout << "With those things, you call this program like this: " << endl;
 cout << "./dnn_imagenet_train_ex /path/to/ILSVRC2015 imagenet2015_validation_images.txt" << endl;
 return 1;
 }
 cout << "\nSCANNING IMAGENET DATASET\n" << endl;
 auto listing = get_imagenet_train_listing(string(argv[1])+"/Data/CLS-LOC/train/");
 cout << "images in dataset: " << listing.size() << endl;
 const auto number_of_classes = listing.back().numeric_label+1;
 if (listing.size() == 0 || number_of_classes != 1000)
 {
 cout << "Didn't find the imagenet dataset. " << endl;
 return 1;
 }
 
 set_dnn_prefer_smallest_algorithms();
 const double initial_learning_rate = 0.1;
 const double weight_decay = 0.0001;
 const double momentum = 0.9;
 net_type net;
 dnn_trainer<net_type> trainer(net,sgd(weight_decay, momentum));
 trainer.be_verbose();
 trainer.set_learning_rate(initial_learning_rate);
 trainer.set_synchronization_file("imagenet_trainer_state_file.dat", std::chrono::minutes(10));
 // This threshold is probably excessively large. You could likely get good results
 // with a smaller value but if you aren't in a hurry this value will surely work well.
 trainer.set_iterations_without_progress_threshold(20000);
 // Since the progress threshold is so large might as well set the batch normalization
 // stats window to something big too.
 set_all_bn_running_stats_window_sizes(net, 1000);
 std::vector<matrix<rgb_pixel>> samples;
 std::vector<unsigned long> labels;
 // Start a bunch of threads that read images from disk and pull out random crops. It's
 // important to be sure to feed the GPU fast enough to keep it busy. Using multiple
 // thread for this kind of data preparation helps us do that. Each thread puts the
 // crops into the data queue.
 dlib::pipe<std::pair<image_info,matrix<rgb_pixel>>> data(200);
 auto f = [&data, &listing](time_t seed)
 {
 dlib::rand rnd(time(0)+seed);
 matrix<rgb_pixel> img;
 std::pair<image_info, matrix<rgb_pixel>> temp;
 while(data.is_enabled())
 {
 temp.first = listing[rnd.get_random_32bit_number()%listing.size()];
 load_image(img, temp.first.filename);
 randomly_crop_image(img, temp.second, rnd);
 data.enqueue(temp);
 }
 };
 std::thread data_loader1([f](){ f(1); });
 std::thread data_loader2([f](){ f(2); });
 std::thread data_loader3([f](){ f(3); });
 std::thread data_loader4([f](){ f(4); });
 // The main training loop. Keep making mini-batches and giving them to the trainer.
 // We will run until the learning rate has dropped by a factor of 1e-3.
 while(trainer.get_learning_rate() >= initial_learning_rate*1e-3)
 {
 samples.clear();
 labels.clear();
 // make a 160 image mini-batch
 std::pair<image_info, matrix<rgb_pixel>> img;
 while(samples.size() < 160)
 {
 data.dequeue(img);
 samples.push_back(std::move(img.second));
 labels.push_back(img.first.numeric_label);
 }
 trainer.train_one_step(samples, labels);
 }
 // Training done, tell threads to stop and make sure to wait for them to finish before
 // moving on.
 data.disable();
 data_loader1.join();
 data_loader2.join();
 data_loader3.join();
 data_loader4.join();
 // also wait for threaded processing to stop in the trainer.
 trainer.get_net();
 net.clean();
 cout << "saving network" << endl;
 serialize("resnet34.dnn") << net;
 // Now test the network on the imagenet validation dataset. First, make a testing
 // network with softmax as the final layer. We don't have to do this if we just wanted
 // to test the "top1 accuracy" since the normal network outputs the class prediction.
 // But this snet object will make getting the top5 predictions easy as it directly
 // outputs the probability of each class as its final output.
 softmax<anet_type::subnet_type> snet; snet.subnet() = net.subnet();
 cout << "Testing network on imagenet validation dataset..." << endl;
 int num_right = 0;
 int num_wrong = 0;
 int num_right_top1 = 0;
 int num_wrong_top1 = 0;
 dlib::rand rnd(time(0));
 // loop over all the imagenet validation images
 for (auto l : get_imagenet_val_listing(argv[1], argv[2]))
 {
 dlib::array<matrix<rgb_pixel>> images;
 matrix<rgb_pixel> img;
 load_image(img, l.filename);
 // Grab 16 random crops from the image. We will run all of them through the
 // network and average the results.
 const int num_crops = 16;
 randomly_crop_images(img, images, rnd, num_crops);
 // p(i) == the probability the image contains object of class i.
 matrix<float,1,1000> p = sum_rows(mat(snet(images.begin(), images.end())))/num_crops;
 // check top 1 accuracy
 if (index_of_max(p) == l.numeric_label)
 ++num_right_top1;
 else
 ++num_wrong_top1;
 // check top 5 accuracy
 bool found_match = false;
 for (int k = 0; k < 5; ++k)
 {
 long predicted_label = index_of_max(p);
 p(predicted_label) = 0;
 if (predicted_label == l.numeric_label)
 {
 found_match = true;
 break;
 }
 }
 if (found_match)
 ++num_right;
 else
 ++num_wrong;
 }
 cout << "val top5 accuracy: " << num_right/(double)(num_right+num_wrong) << endl;
 cout << "val top1 accuracy: " << num_right_top1/(double)(num_right_top1+num_wrong_top1) << endl;
}
catch(std::exception& e)
{
 cout << e.what() << endl;
}