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Diffstat (limited to 'ml/dlib/examples/dnn_imagenet_train_ex.cpp')
-rw-r--r-- | ml/dlib/examples/dnn_imagenet_train_ex.cpp | 368 |
1 files changed, 0 insertions, 368 deletions
diff --git a/ml/dlib/examples/dnn_imagenet_train_ex.cpp b/ml/dlib/examples/dnn_imagenet_train_ex.cpp deleted file mode 100644 index e672018d3..000000000 --- a/ml/dlib/examples/dnn_imagenet_train_ex.cpp +++ /dev/null @@ -1,368 +0,0 @@ -// 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; -} - |