# dlib C++ Library - dnn_yolo_train_ex.cpp

    // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    /*
        This is an example illustrating the use of the deep learning tools from the dlib C++
        Library.  I'm assuming you have already read the dnn_introduction_ex.cpp, the
        dnn_introduction2_ex.cpp and the dnn_introduction3_ex.cpp examples.  In this example
        program we are going to show how one can train a YOLO detector.  In particular, we will train
        the YOLOv3 model like the one introduced in this paper:
        "YOLOv3: An Incremental Improvement" by Joseph Redmon and  Ali Farhadi.

        This example program will work with any imglab dataset, such as:
            - faces: http://dlib.net/files/data/dlib_face_detection_dataset-2016-09-30.tar.gz
            - vehicles: http://dlib.net/files/data/dlib_rear_end_vehicles_v1.tar
        Just uncompress the dataset and give the directory containing the training.xml and testing.xml
        files as an argument to this program.
    */

    #include <dlib/cmd_line_parser.h>
    #include <dlib/data_io.h>
    #include <dlib/dnn.h>
    #include <dlib/gui_widgets.h>
    #include <dlib/image_io.h>
    #include <tools/imglab/src/metadata_editor.h>

    using namespace std;
    using namespace dlib;

    // In the darknet namespace we define:
    // - the network architecture: DarkNet53 backbone and detection head for YOLO.
    // - a helper function to setup the detector: change the number of classes, etc.
    namespace darknet
    {
        // backbone tags
        template <typename SUBNET> using btag8 = add_tag_layer<8008, SUBNET>;
        template <typename SUBNET> using btag16 = add_tag_layer<8016, SUBNET>;
        template <typename SUBNET> using bskip8 = add_skip_layer<btag8, SUBNET>;
        template <typename SUBNET> using bskip16 = add_skip_layer<btag16, SUBNET>;
        // neck tags
        template <typename SUBNET> using ntag8 = add_tag_layer<6008, SUBNET>;
        template <typename SUBNET> using ntag16 = add_tag_layer<6016, SUBNET>;
        template <typename SUBNET> using ntag32 = add_tag_layer<6032, SUBNET>;
        template <typename SUBNET> using nskip8 = add_skip_layer<ntag8, SUBNET>;
        template <typename SUBNET> using nskip16 = add_skip_layer<ntag16, SUBNET>;
        template <typename SUBNET> using nskip32 = add_skip_layer<ntag32, SUBNET>;
        // head tags
        template <typename SUBNET> using htag8 = add_tag_layer<7008, SUBNET>;
        template <typename SUBNET> using htag16 = add_tag_layer<7016, SUBNET>;
        template <typename SUBNET> using htag32 = add_tag_layer<7032, SUBNET>;
        template <typename SUBNET> using hskip8 = add_skip_layer<htag8, SUBNET>;
        template <typename SUBNET> using hskip16 = add_skip_layer<htag16, SUBNET>;
        // yolo tags
        template <typename SUBNET> using ytag8 = add_tag_layer<4008, SUBNET>;
        template <typename SUBNET> using ytag16 = add_tag_layer<4016, SUBNET>;
        template <typename SUBNET> using ytag32 = add_tag_layer<4032, SUBNET>;

        template <template <typename> class ACT, template <typename> class BN>
        struct def
        {
            template <long nf, long ks, int s, typename SUBNET>
            using conblock = ACT<BN<add_layer<con_<nf, ks, ks, s, s, ks / 2, ks / 2>, SUBNET>>>;

            template <long nf, typename SUBNET>
            using residual = add_prev1<conblock<nf, 3, 1, conblock<nf / 2, 1, 1, tag1<SUBNET>>>>;

            template <long nf, long factor, typename SUBNET>
            using conblock5 = conblock<nf, 1, 1,
                              conblock<nf * factor, 3, 1,
                              conblock<nf, 1, 1,
                              conblock<nf * factor, 3, 1,
                              conblock<nf, 1, 1, SUBNET>>>>>;

            template <typename SUBNET> using res_64 = residual<64, SUBNET>;
            template <typename SUBNET> using res_128 = residual<128, SUBNET>;
            template <typename SUBNET> using res_256 = residual<256, SUBNET>;
            template <typename SUBNET> using res_512 = residual<512, SUBNET>;
            template <typename SUBNET> using res_1024 = residual<1024, SUBNET>;

            template <typename INPUT>
            using backbone53 = repeat<4, res_1024, conblock<1024, 3, 2,
                        btag16<repeat<8, res_512,  conblock<512, 3, 2,
                         btag8<repeat<8, res_256,  conblock<256, 3, 2,
                               repeat<2, res_128,  conblock<128, 3, 2,
                                         res_64<   conblock<64, 3, 2,
                                                   conblock<32, 3, 1,
                                                   INPUT>>>>>>>>>>>>>;

            // This is the layer that will be passed to the loss layer to get the detections from the network.
            // The main thing to pay attention to when defining the YOLO output layer is that it should be
            // a tag layer, followed by a sigmoid layer and a 1x1 convolution.  The tag layer should be unique
            // in the whole network definition, as the loss layer will use it to get the outputs.  The number of
            // filters in the convolutional layer should be (1 + 4 + num_classes) * num_anchors at that output.
            // The 1 corresponds to the objectness in the loss layer and the 4 to the bounding box coordinates.
            template <long num_classes, long nf, template <typename> class YTAG, template <typename> class NTAG, typename SUBNET>
            using yolo = YTAG<sig<con<3 * (num_classes + 5), 1, 1, 1, 1,
                         conblock<nf, 3, 1,
                    NTAG<conblock5<nf / 2, 2,
                         SUBNET>>>>>>;

            template <long num_classes>
            using yolov3 = yolo<num_classes, 256, ytag8, ntag8,
                           concat2<htag8, btag8,
                     htag8<upsample<2, conblock<128, 1, 1,
                           nskip16<
                           yolo<num_classes, 512, ytag16, ntag16,
                           concat2<htag16, btag16,
                    htag16<upsample<2, conblock<256, 1, 1,
                           nskip32<
                           yolo<num_classes, 1024, ytag32, ntag32,
                           backbone53<input_rgb_image>>>>>>>>>>>>>>;

        };

        using yolov3_train_type = loss_yolo<ytag8, ytag16, ytag32, def<leaky_relu, bn_con>::yolov3<80>>;
        using yolov3_infer_type = loss_yolo<ytag8, ytag16, ytag32, def<leaky_relu, affine>::yolov3<80>>;

        void setup_detector(yolov3_train_type& net, const yolo_options& options)
        {
            // remove bias from bn inputs
            disable_duplicative_biases(net);
            // setup leaky relus
            visit_computational_layers(net, [](leaky_relu_& l) { l = leaky_relu_(0.1); });
            // enlarge the batch normalization stats window
            set_all_bn_running_stats_window_sizes(net, 1000);
            // set the number of filters for detection layers (they are located after the tag and sig layers)
            const long nfo1 = options.anchors.at(tag_id<ytag8>::id).size() * (options.labels.size() + 5);
            const long nfo2 = options.anchors.at(tag_id<ytag16>::id).size() * (options.labels.size() + 5);
            const long nfo3 = options.anchors.at(tag_id<ytag32>::id).size() * (options.labels.size() + 5);
            layer<ytag8, 2>(net).layer_details().set_num_filters(nfo1);
            layer<ytag16, 2>(net).layer_details().set_num_filters(nfo2);
            layer<ytag32, 2>(net).layer_details().set_num_filters(nfo3);
        }
    }

    // In this example, YOLO expects square images, and we choose to transform them by letterboxing them.
    rectangle_transform preprocess_image(const matrix<rgb_pixel>& image, matrix<rgb_pixel>& output, const long image_size)
    {
        return rectangle_transform(inv(letterbox_image(image, output, image_size)));
    }

    // YOLO outputs the bounding boxes in the coordinate system of the input (letterboxed) image, so we need to convert them
    // back to the original image.
    void postprocess_detections(const rectangle_transform& tform, std::vector<yolo_rect>& detections)
    {
        for (auto& d : detections)
            d.rect = tform(d.rect);
    }

    int main(const int argc, const char** argv)
    try
    {
        command_line_parser parser;
        parser.add_option("size", "image size for training (default: 416)", 1);
        parser.add_option("learning-rate", "initial learning rate (default: 0.001)", 1);
        parser.add_option("batch-size", "mini batch size (default: 8)", 1);
        parser.add_option("burnin", "learning rate burnin steps (default: 1000)", 1);
        parser.add_option("patience", "number of steps without progress (default: 10000)", 1);
        parser.add_option("workers", "number of worker threads to load data (default: 4)", 1);
        parser.add_option("gpus", "number of GPUs to run the training on (default: 1)", 1);
        parser.add_option("test", "test the detector with a threshold (default: 0.01)", 1);
        parser.add_option("visualize", "visualize data augmentation instead of training");
        parser.add_option("map", "compute the mean average precision");
        parser.add_option("anchors", "Do nothing but compute <arg1> anchor boxes using K-Means and print their shapes.", 1);
        parser.set_group_name("Help Options");
        parser.add_option("h", "alias of --help");
        parser.add_option("help", "display this message and exit");
        parser.parse(argc, argv);
        if (parser.number_of_arguments() == 0 || parser.option("h") || parser.option("help"))
        {
            parser.print_options();
            cout << "Give the path to a folder containing the training.xml file." << endl;
            return 0;
        }
        const double learning_rate = get_option(parser, "learning-rate", 0.001);
        const size_t patience = get_option(parser, "patience", 10000);
        const size_t batch_size = get_option(parser, "batch-size", 8);
        const size_t burnin = get_option(parser, "burnin", 1000);
        const size_t image_size = get_option(parser, "size", 416);
        const size_t num_workers = get_option(parser, "workers", 4);
        const size_t num_gpus = get_option(parser, "gpus", 1);

        const string data_directory = parser[0];
        const string sync_file_name = "yolov3_sync";

        image_dataset_metadata::dataset dataset;
        image_dataset_metadata::load_image_dataset_metadata(dataset, data_directory + "/training.xml");
        cout << "# images: " << dataset.images.size() << endl;
        std::map<string, size_t> labels;
        size_t num_objects = 0;
        for (const auto& im : dataset.images)
        {
            for (const auto& b : im.boxes)
            {
                labels[b.label]++;
                ++num_objects;
            }
        }
        cout << "# labels: " << labels.size() << endl;

        yolo_options options;
        color_mapper string_to_color;
        for (const auto& label : labels)
        {
            cout <<  " - " << label.first << ": " << label.second;
            cout << " (" << (100.0*label.second)/num_objects << "%)\n";
            options.labels.push_back(label.first);
            string_to_color(label.first);
        }

        // If the default anchor boxes don't fit your data well you should recompute them.
        // Here's a simple way to do it using K-Means clustering.  Note that the approach
        // shown below is suboptimal, since it doesn't group the bounding boxes by size.
        // Grouping the bounding boxes by size and computing the K-Means on each group
        // would make more sense, since each stride of the network is meant to output
        // boxes at a particular size, but that is very specific to the network architecture
        // and the dataset itself.
        if (parser.option("anchors"))
        {
            const auto num_clusers = std::stoul(parser.option("anchors").argument());
            std::vector<dpoint> samples;
            // First we need to rescale the bounding boxes to match the image size at training time.
            for (const auto& image_info : dataset.images)
            {
                const auto scale = image_size / std::max<double>(image_info.width, image_info.height);
                for (const auto& box : image_info.boxes)
                {
                    dpoint sample(box.rect.width(), box.rect.height());
                    samples.push_back(sample*scale);
                }
            }
            // Now we can compute K-Means clustering
            randomize_samples(samples);
            cout << "Computing anchors for " << samples.size() << " samples" << endl;
            std::vector<dpoint> anchors;
            pick_initial_centers(num_clusers, anchors, samples);
            find_clusters_using_kmeans(samples, anchors);
            std::sort(anchors.begin(), anchors.end(), [](const dpoint& a, const dpoint& b){ return prod(a) < prod(b); });
            for (const dpoint& c : anchors)
                cout << round(c(0)) << 'x' << round(c(1)) << endl;
            // And check the average IoU of the newly computed anchor boxes and the training samples.
            double average_iou = 0;
            for (const dpoint& s : samples)
            {
                drectangle sample = centered_drect(dpoint(0, 0), s.x(), s.y());
                double best_iou = 0;
                for (const dpoint& a : anchors)
                {
                    drectangle anchor = centered_drect(dpoint(0, 0), a.x(), a.y());
                    best_iou = std::max(best_iou, box_intersection_over_union(sample, anchor));
                }
                average_iou += best_iou;
            }
            cout << "Average IoU: " << average_iou / samples.size() << endl;
            return EXIT_SUCCESS;
        }

        // When computing the objectness loss in YOLO, predictions that do not have an IoU
        // with any ground truth box of at least options.iou_ignore_threshold, will be
        // treated as not capable of detecting an object, an therefore incur loss.
        // Similarly, predictions above this threshold are considered correct predictions
        // by the loss.  Typical settings for this threshold are in the range 0.5 to 0.7.
        options.iou_ignore_threshold = 0.7;
        // By setting this to a value < 1, we are telling the model to update all the predictions
        // as long as the anchor box has an IoU > 0.2 with a ground truth.
        options.iou_anchor_threshold = 0.2;
        // These are the anchors computed on COCO dataset, presented in the YOLOv3 paper.
        options.add_anchors<darknet::ytag8>({{10, 13}, {16, 30}, {33, 23}});
        options.add_anchors<darknet::ytag16>({{30, 61}, {62, 45}, {59, 119}});
        options.add_anchors<darknet::ytag32>({{116, 90}, {156, 198}, {373, 326}});
        darknet::yolov3_train_type net(options);
        darknet::setup_detector(net, options);

        // The training process can be unstable at the beginning.  For this reason, we exponentially
        // increase the learning rate during the first burnin steps.
        const matrix<double> learning_rate_schedule = learning_rate * pow(linspace(1e-12, 1, burnin), 4);

        // In case we have several GPUs, we can tell the dnn_trainer to make use of them.
        std::vector<int> gpus(num_gpus);
        iota(gpus.begin(), gpus.end(), 0);
        // We initialize the trainer here, as it will be used in several contexts, depending on the
        // arguments passed the the program.
        dnn_trainer<darknet::yolov3_train_type> trainer(net, sgd(0.0005, 0.9), gpus);
        trainer.be_verbose();
        trainer.set_mini_batch_size(batch_size);
        trainer.set_learning_rate_schedule(learning_rate_schedule);
        trainer.set_synchronization_file(sync_file_name, chrono::minutes(15));
        cout << trainer;

        // If the training has started and a synchronization file has already been saved to disk,
        // we can re-run this program with the --test option and a confidence threshold to see
        // how the training is going.
        if (parser.option("test"))
        {
            if (!file_exists(sync_file_name))
            {
                cout << "Could not find file " << sync_file_name << endl;
                return EXIT_FAILURE;
            }
            const double threshold = get_option(parser, "test", 0.01);
            image_window win;
            matrix<rgb_pixel> image, resized;
            for (const auto& im : dataset.images)
            {
                win.clear_overlay();
                load_image(image, data_directory + "/" + im.filename);
                win.set_title(im.filename);
                win.set_image(image);
                const auto tform = preprocess_image(image, resized, image_size);
                auto detections = net.process(resized, threshold);
                postprocess_detections(tform, detections);
                cout << "# detections: " << detections.size() << endl;
                for (const auto& det : detections)
                {
                    win.add_overlay(det.rect, string_to_color(det.label), det.label);
                    cout << det.label << ": " << det.rect << " " << det.detection_confidence << endl;
                }
                cin.get();
            }
            return EXIT_SUCCESS;
        }

        // If the training has started and a synchronization file has already been saved to disk,
        // we can re-run this program with the --map option to compute the mean average precision
        // on the test set.
        if (parser.option("map"))
        {
            image_dataset_metadata::dataset dataset;
            image_dataset_metadata::load_image_dataset_metadata(dataset, data_directory + "/testing.xml");
            if (!file_exists(sync_file_name))
            {
                cout << "Could not find file " << sync_file_name << endl;
                return EXIT_FAILURE;
            }
            matrix<rgb_pixel> image, resized;
            std::map<std::string, std::vector<std::pair<double, bool>>> hits;
            std::map<std::string, unsigned long> missing;
            for (const auto& label : options.labels)
            {
                hits[label] = std::vector<std::pair<double, bool>>();
                missing[label] = 0;
            }
            cout << "computing mean average precision for " << dataset.images.size() << " images..." << endl;
            for (size_t i = 0; i < dataset.images.size(); ++i)
            {
                const auto& im = dataset.images[i];
                load_image(image, data_directory + "/" + im.filename);
                const auto tform = preprocess_image(image, resized, image_size);
                auto dets = net.process(resized, 0.005);
                postprocess_detections(tform, dets);
                std::vector<bool> used(dets.size(), false);
                // true positives: truths matched by detections
                for (size_t t = 0; t < im.boxes.size(); ++t)
                {
                    bool found_match = false;
                    for (size_t d = 0; d < dets.size(); ++d)
                    {
                        if (used[d])
                            continue;
                        if (im.boxes[t].label == dets[d].label &&
                            box_intersection_over_union(drectangle(im.boxes[t].rect), dets[d].rect) > 0.5)
                        {
                            used[d] = true;
                            found_match = true;
                            hits.at(dets[d].label).emplace_back(dets[d].detection_confidence, true);
                            break;
                        }
                    }
                    // false negatives: truths not matched
                    if (!found_match)
                        missing.at(im.boxes[t].label)++;
                }
                // false positives: detections not matched
                for (size_t d = 0; d < dets.size(); ++d)
                {
                    if (!used[d])
                        hits.at(dets[d].label).emplace_back(dets[d].detection_confidence, false);
                }
                cout << "progress: " << i << '/' << dataset.images.size() << "\t\t\t\r" << flush;
            }
            double map = 0;
            for (auto& item : hits)
            {
                std::sort(item.second.rbegin(), item.second.rend());
                const double ap = average_precision(item.second, missing[item.first]);
                cout << rpad(item.first + ": ", 16, " ") << ap * 100 << '%' << endl;
                map += ap;
            }
            cout << rpad(string("mAP: "), 16, " ") << map / hits.size() * 100 << '%' << endl;
            return EXIT_SUCCESS;
        }

        // Create some data loaders which will load the data, and perform some data augmentation.
        dlib::pipe<std::pair<matrix<rgb_pixel>, std::vector<yolo_rect>>> train_data(1000);
        const auto loader = [&dataset, &data_directory, &train_data, &image_size](time_t seed)
        {
            dlib::rand rnd(time(nullptr) + seed);
            matrix<rgb_pixel> image, rotated;
            std::pair<matrix<rgb_pixel>, std::vector<yolo_rect>> temp;
            random_cropper cropper;
            cropper.set_seed(time(nullptr) + seed);
            cropper.set_chip_dims(image_size, image_size);
            cropper.set_max_object_size(0.9);
            cropper.set_min_object_size(10, 10);
            cropper.set_max_rotation_degrees(10);
            cropper.set_translate_amount(0.5);
            cropper.set_randomly_flip(true);
            cropper.set_background_crops_fraction(0);
            cropper.set_min_object_coverage(0.8);
            while (train_data.is_enabled())
            {
                const auto idx = rnd.get_random_32bit_number() % dataset.images.size();
                load_image(image, data_directory + "/" + dataset.images[idx].filename);
                for (const auto& box : dataset.images[idx].boxes)
                    temp.second.emplace_back(box.rect, 1, box.label);

                // We alternate between augmenting the full image and random cropping
                if (rnd.get_random_double() > 0.5)
                {
                    rectangle_transform tform = rotate_image(
                        image,
                        rotated,
                        rnd.get_double_in_range(-5 * pi / 180, 5 * pi / 180),
                        interpolate_bilinear());
                    for (auto& box : temp.second)
                        box.rect = tform(box.rect);

                    tform = letterbox_image(rotated, temp.first, image_size);
                    for (auto& box : temp.second)
                        box.rect = tform(box.rect);

                    if (rnd.get_random_double() > 0.5)
                    {
                        tform = flip_image_left_right(temp.first);
                        for (auto& box : temp.second)
                            box.rect = tform(box.rect);
                    }
                }
                else
                {
                    std::vector<yolo_rect> boxes = temp.second;
                    cropper(image, boxes, temp.first, temp.second);
                }
                disturb_colors(temp.first, rnd);
                train_data.enqueue(temp);
            }
        };

        std::vector<thread> data_loaders;
        for (size_t i = 0; i < num_workers; ++i)
            data_loaders.emplace_back([loader, i]() { loader(i + 1); });

        // It is always a good idea to visualize the training samples.  By passing the --visualize
        // flag, we can see the training samples that will be fed to the dnn_trainer.
        if (parser.option("visualize"))
        {
            image_window win;
            while (true)
            {
                std::pair<matrix<rgb_pixel>, std::vector<yolo_rect>> temp;
                train_data.dequeue(temp);
                win.clear_overlay();
                win.set_image(temp.first);
                for (const auto& r : temp.second)
                {
                    auto color = string_to_color(r.label);
                    // make semi-transparent and cross-out the ignored boxes
                    if (r.ignore)
                    {
                        color.alpha = 128;
                        win.add_overlay(r.rect.tl_corner(), r.rect.br_corner(), color);
                        win.add_overlay(r.rect.tr_corner(), r.rect.bl_corner(), color);
                    }
                    win.add_overlay(r.rect, color, r.label);
                }
                cout << "Press enter to visualize the next training sample.";
                cin.get();
            }
        }

        std::vector<matrix<rgb_pixel>> images;
        std::vector<std::vector<yolo_rect>> bboxes;

        // The main training loop, that we will reuse for the warmup and the rest of the training.
        const auto train = [&images, &bboxes, &train_data, &trainer]()
        {
            images.clear();
            bboxes.clear();
            pair<matrix<rgb_pixel>, std::vector<yolo_rect>> temp;
            while (images.size() < trainer.get_mini_batch_size())
            {
                train_data.dequeue(temp);
                images.push_back(move(temp.first));
                bboxes.push_back(move(temp.second));
            }
            trainer.train_one_step(images, bboxes);
        };

        cout << "training started with " << burnin << " burn-in steps" << endl;
        while (trainer.get_train_one_step_calls() < burnin)
            train();

        cout << "burn-in finished" << endl;
        trainer.get_net();
        trainer.set_learning_rate(learning_rate);
        trainer.set_min_learning_rate(learning_rate * 1e-3);
        trainer.set_learning_rate_shrink_factor(0.1);
        trainer.set_iterations_without_progress_threshold(patience);
        cout << trainer << endl;

        while (trainer.get_learning_rate() >= trainer.get_min_learning_rate())
            train();

        cout << "training done" << endl;

        trainer.get_net();
        train_data.disable();
        for (auto& worker : data_loaders)
            worker.join();

        // Before saving the network, we can assign it to the "infer" version, so that it won't
        // perform batch normalization with batch sizes larger than one, as usual.  Moreover,
        // we can also fuse the batch normalization (affine) layers into the convolutional
        // layers, so that the network can run a bit faster.  Notice that, after fusing the
        // layers, the network can no longer be used for training, so you should save the
        // yolov3_train_type network if you plan to further train or finetune the network.
        darknet::yolov3_infer_type inet(net);
        fuse_layers(inet);

        serialize("yolov3.dnn") << inet;
        return EXIT_SUCCESS;
    }
    catch (const std::exception& e)
    {
        cout << e.what() << endl;
        return EXIT_FAILURE;
    }