Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 390 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/dnn_semantic_segmentation_train_ex.cpp b/src/ml/dlib/examples/dnn_semantic_segmentation_train_ex.cpp
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index 000000000..0de8c9f43
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+++ b/src/ml/dlib/examples/dnn_semantic_segmentation_train_ex.cpp
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+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    This example shows how to train a semantic segmentation net using the PASCAL VOC2012
+    dataset.  For an introduction to what segmentation is, see the accompanying header file
+    dnn_semantic_segmentation_ex.h.
+
+    Instructions how to run the example:
+    1. Download the PASCAL VOC2012 data, and untar it somewhere.
+       http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
+    2. Build the dnn_semantic_segmentation_train_ex example program.
+    3. Run:
+       ./dnn_semantic_segmentation_train_ex /path/to/VOC2012
+    4. Wait while the network is being trained.
+    5. Build the dnn_semantic_segmentation_ex example program.
+    6. Run:
+       ./dnn_semantic_segmentation_ex /path/to/VOC2012-or-other-images
+
+    It would be a good idea to become familiar with dlib's DNN tooling before reading this
+    example.  So you should read dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp
+    before reading this example program.
+*/
+
+#include "dnn_semantic_segmentation_ex.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;
+
+// A single training sample. A mini-batch comprises many of these.
+struct training_sample
+{
+    matrix<rgb_pixel> input_image;
+    matrix<uint16_t> label_image; // The ground-truth label of each pixel.
+};
+
+// ----------------------------------------------------------------------------------------
+
+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>& input_image,
+    const matrix<uint16_t>& label_image,
+    training_sample& crop,
+    dlib::rand& rnd
+)
+{
+    const auto rect = make_random_cropping_rect_resnet(input_image, rnd);
+
+    const chip_details chip_details(rect, chip_dims(227, 227));
+
+    // Crop the input image.
+    extract_image_chip(input_image, chip_details, crop.input_image, interpolate_bilinear());
+
+    // Crop the labels correspondingly. However, note that here bilinear
+    // interpolation would make absolutely no sense - you wouldn't say that
+    // a bicycle is half-way between an aeroplane and a bird, would you?
+    extract_image_chip(label_image, chip_details, crop.label_image, interpolate_nearest_neighbor());
+
+    // Also randomly flip the input image and the labels.
+    if (rnd.get_random_double() > 0.5)
+    {
+        crop.input_image = fliplr(crop.input_image);
+        crop.label_image = fliplr(crop.label_image);
+    }
+
+    // And then randomly adjust the colors.
+    apply_random_color_offset(crop.input_image, rnd);
+}
+
+// ----------------------------------------------------------------------------------------
+
+// The names of the input image and the associated RGB label image in the PASCAL VOC 2012
+// data set.
+struct image_info
+{
+    string image_filename;
+    string label_filename;
+};
+
+// Read the list of image files belonging to either the "train", "trainval", or "val" set
+// of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_listing(
+    const std::string& voc2012_folder,
+    const std::string& file = "train" // "train", "trainval", or "val"
+)
+{
+    std::ifstream in(voc2012_folder + "/ImageSets/Segmentation/" + file + ".txt");
+
+    std::vector<image_info> results;
+
+    while (in)
+    {
+        std::string basename;
+        in >> basename;
+
+        if (!basename.empty())
+        {
+            image_info image_info;
+            image_info.image_filename = voc2012_folder + "/JPEGImages/" + basename + ".jpg";
+            image_info.label_filename = voc2012_folder + "/SegmentationClass/" + basename + ".png";
+            results.push_back(image_info);
+        }
+    }
+
+    return results;
+}
+
+// Read the list of image files belong to the "train" set of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_train_listing(
+    const std::string& voc2012_folder
+)
+{
+    return get_pascal_voc2012_listing(voc2012_folder, "train");
+}
+
+// Read the list of image files belong to the "val" set of the PASCAL VOC2012 data.
+std::vector<image_info> get_pascal_voc2012_val_listing(
+    const std::string& voc2012_folder
+)
+{
+    return get_pascal_voc2012_listing(voc2012_folder, "val");
+}
+
+// ----------------------------------------------------------------------------------------
+
+// The PASCAL VOC2012 dataset contains 20 ground-truth classes + background.  Each class
+// is represented using an RGB color value.  We associate each class also to an index in the
+// range [0, 20], used internally by the network.  To convert the ground-truth data to
+// something that the network can efficiently digest, we need to be able to map the RGB
+// values to the corresponding indexes.
+
+// Given an RGB representation, find the corresponding PASCAL VOC2012 class
+// (e.g., 'dog').
+const Voc2012class& find_voc2012_class(const dlib::rgb_pixel& rgb_label)
+{
+    return find_voc2012_class(
+        [&rgb_label](const Voc2012class& voc2012class)
+        {
+            return rgb_label == voc2012class.rgb_label;
+        }
+    );
+}
+
+// Convert an RGB class label to an index in the range [0, 20].
+inline uint16_t rgb_label_to_index_label(const dlib::rgb_pixel& rgb_label)
+{
+    return find_voc2012_class(rgb_label).index;
+}
+
+// Convert an image containing RGB class labels to a corresponding
+// image containing indexes in the range [0, 20].
+void rgb_label_image_to_index_label_image(
+    const dlib::matrix<dlib::rgb_pixel>& rgb_label_image,
+    dlib::matrix<uint16_t>& index_label_image
+)
+{
+    const long nr = rgb_label_image.nr();
+    const long nc = rgb_label_image.nc();
+
+    index_label_image.set_size(nr, nc);
+
+    for (long r = 0; r < nr; ++r)
+    {
+        for (long c = 0; c < nc; ++c)
+        {
+            index_label_image(r, c) = rgb_label_to_index_label(rgb_label_image(r, c));
+        }
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+// Calculate the per-pixel accuracy on a dataset whose file names are supplied as a parameter.
+double calculate_accuracy(anet_type& anet, const std::vector<image_info>& dataset)
+{
+    int num_right = 0;
+    int num_wrong = 0;
+
+    matrix<rgb_pixel> input_image;
+    matrix<rgb_pixel> rgb_label_image;
+    matrix<uint16_t> index_label_image;
+    matrix<uint16_t> net_output;
+
+    for (const auto& image_info : dataset)
+    {
+        // Load the input image.
+        load_image(input_image, image_info.image_filename);
+
+        // Load the ground-truth (RGB) labels.
+        load_image(rgb_label_image, image_info.label_filename);
+
+        // Create predictions for each pixel. At this point, the type of each prediction
+        // is an index (a value between 0 and 20). Note that the net may return an image
+        // that is not exactly the same size as the input.
+        const matrix<uint16_t> temp = anet(input_image);
+
+        // Convert the indexes to RGB values.
+        rgb_label_image_to_index_label_image(rgb_label_image, index_label_image);
+
+        // Crop the net output to be exactly the same size as the input.
+        const chip_details chip_details(
+            centered_rect(temp.nc() / 2, temp.nr() / 2, input_image.nc(), input_image.nr()),
+            chip_dims(input_image.nr(), input_image.nc())
+        );
+        extract_image_chip(temp, chip_details, net_output, interpolate_nearest_neighbor());
+
+        const long nr = index_label_image.nr();
+        const long nc = index_label_image.nc();
+
+        // Compare the predicted values to the ground-truth values.
+        for (long r = 0; r < nr; ++r)
+        {
+            for (long c = 0; c < nc; ++c)
+            {
+                const uint16_t truth = index_label_image(r, c);
+                if (truth != dlib::loss_multiclass_log_per_pixel_::label_to_ignore)
+                {
+                    const uint16_t prediction = net_output(r, c);
+                    if (prediction == truth)
+                    {
+                        ++num_right;
+                    }
+                    else
+                    {
+                        ++num_wrong;
+                    }
+                }
+            }
+        }
+    }
+
+    // Return the accuracy estimate.
+    return num_right / static_cast<double>(num_right + num_wrong);
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc != 2)
+    {
+        cout << "To run this program you need a copy of the PASCAL VOC2012 dataset." << endl;
+        cout << endl;
+        cout << "You call this program like this: " << endl;
+        cout << "./dnn_semantic_segmentation_train_ex /path/to/VOC2012" << endl;
+        return 1;
+    }
+
+    cout << "\nSCANNING PASCAL VOC2012 DATASET\n" << endl;
+
+    const auto listing = get_pascal_voc2012_train_listing(argv[1]);
+    cout << "images in dataset: " << listing.size() << endl;
+    if (listing.size() == 0)
+    {
+        cout << "Didn't find the VOC2012 dataset. " << endl;
+        return 1;
+    }
+        
+
+    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("pascal_voc2012_trainer_state_file.dat", std::chrono::minutes(10));
+    // This threshold is probably excessively large.
+    trainer.set_iterations_without_progress_threshold(5000);
+    // 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);
+
+    // Output training parameters.
+    cout << endl << trainer << endl;
+
+    std::vector<matrix<rgb_pixel>> samples;
+    std::vector<matrix<uint16_t>> 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<training_sample> data(200);
+    auto f = [&data, &listing](time_t seed)
+    {
+        dlib::rand rnd(time(0)+seed);
+        matrix<rgb_pixel> input_image;
+        matrix<rgb_pixel> rgb_label_image;
+        matrix<uint16_t> index_label_image;
+        training_sample temp;
+        while(data.is_enabled())
+        {
+            // Pick a random input image.
+            const image_info& image_info = listing[rnd.get_random_32bit_number()%listing.size()];
+
+            // Load the input image.
+            load_image(input_image, image_info.image_filename);
+
+            // Load the ground-truth (RGB) labels.
+            load_image(rgb_label_image, image_info.label_filename);
+
+            // Convert the indexes to RGB values.
+            rgb_label_image_to_index_label_image(rgb_label_image, index_label_image);
+
+            // Randomly pick a part of the image.
+            randomly_crop_image(input_image, index_label_image, temp, rnd);
+
+            // Push the result to be used by the trainer.
+            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-4.
+    while(trainer.get_learning_rate() >= 1e-4)
+    {
+        samples.clear();
+        labels.clear();
+
+        // make a 30-image mini-batch
+        training_sample temp;
+        while(samples.size() < 30)
+        {
+            data.dequeue(temp);
+
+            samples.push_back(std::move(temp.input_image));
+            labels.push_back(std::move(temp.label_image));
+        }
+
+        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("semantic_segmentation_voc2012net.dnn") << net;
+
+
+    // Make a copy of the network to use it for inference.
+    anet_type anet = net;
+
+    cout << "Testing the network..." << endl;
+
+    // Find the accuracy of the newly trained network on both the training and the validation sets.
+    cout << "train accuracy  :  " << calculate_accuracy(anet, get_pascal_voc2012_train_listing(argv[1])) << endl;
+    cout << "val accuracy    :  " << calculate_accuracy(anet, get_pascal_voc2012_val_listing(argv[1])) << endl;
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+