Adding upstream version 1.44.3.upstream/1.44.3upstream

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

1 files changed, 230 insertions, 0 deletions

diff --git a/ml/dlib/examples/dnn_mmod_ex.cpp b/ml/dlib/examples/dnn_mmod_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 CNN based object detector using dlib's 
+    loss_mmod loss layer.  This loss layer implements the Max-Margin Object
+    Detection loss as described in the paper:
+        Max-Margin Object Detection by Davis E. King (http://arxiv.org/abs/1502.00046).
+    This is the same loss used by the popular SVM+HOG object detector in dlib
+    (see fhog_object_detector_ex.cpp) except here we replace the HOG features
+    with a CNN and train the entire detector end-to-end.  This allows us to make
+    much more powerful detectors.
+
+    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.
+    
+    Just like in the fhog_object_detector_ex.cpp example, we are going to train
+    a simple face detector based on the very small training dataset in the
+    examples/faces folder.  As we will see, even with this small dataset the
+    MMOD method is able to make a working face detector.  However, for real
+    applications you should train with more data for an even better result.
+*/
+
+
+#include <iostream>
+#include <dlib/dnn.h>
+#include <dlib/data_io.h>
+#include <dlib/gui_widgets.h>
+
+using namespace std;
+using namespace dlib;
+
+// The first thing we do is define our CNN.  The CNN is going to be evaluated
+// convolutionally over an entire image pyramid.  Think of it like a normal
+// sliding window classifier.  This means you need to define a CNN that can look
+// at some part of an image and decide if it is an object of interest.  In this
+// example I've defined a CNN with a receptive field of a little over 50x50
+// pixels.  This is reasonable for face detection since you can clearly tell if
+// a 50x50 image contains a face.  Other applications may benefit from CNNs with
+// different architectures.  
+// 
+// In this example our CNN begins with 3 downsampling layers.  These layers will
+// reduce the size of the image by 8x and output a feature map with
+// 32 dimensions.  Then we will pass that through 4 more convolutional layers to
+// get the final output of the network.  The last layer has only 1 channel and
+// the values in that last channel are large when the network thinks it has
+// found an object at a particular location.
+
+
+// Let's begin the network definition by creating some network blocks.
+
+// A 5x5 conv layer that does 2x downsampling
+template <long num_filters, typename SUBNET> using con5d = con<num_filters,5,5,2,2,SUBNET>;
+// A 3x3 conv layer that doesn't do any downsampling
+template <long num_filters, typename SUBNET> using con3  = con<num_filters,3,3,1,1,SUBNET>;
+
+// Now we can define the 8x downsampling block in terms of conv5d blocks.  We
+// also use relu and batch normalization in the standard way.
+template <typename SUBNET> using downsampler  = relu<bn_con<con5d<32, relu<bn_con<con5d<32, relu<bn_con<con5d<32,SUBNET>>>>>>>>>;
+
+// The rest of the network will be 3x3 conv layers with batch normalization and
+// relu.  So we define the 3x3 block we will use here.
+template <typename SUBNET> using rcon3  = relu<bn_con<con3<32,SUBNET>>>;
+
+// Finally, we define the entire network.   The special input_rgb_image_pyramid
+// layer causes the network to operate over a spatial pyramid, making the detector
+// scale invariant.  
+using net_type  = loss_mmod<con<1,6,6,1,1,rcon3<rcon3<rcon3<downsampler<input_rgb_image_pyramid<pyramid_down<6>>>>>>>>;
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    // In this example we are going to train a face detector based on the
+    // small faces dataset in the examples/faces directory.  So the first
+    // thing we do is load that dataset.  This means you need to supply the
+    // path to this faces folder as a command line argument so we will know
+    // where it is.
+    if (argc != 2)
+    {
+        cout << "Give the path to the examples/faces directory as the argument to this" << endl;
+        cout << "program.  For example, if you are in the examples folder then execute " << endl;
+        cout << "this program by running: " << endl;
+        cout << "   ./dnn_mmod_ex faces" << endl;
+        cout << endl;
+        return 0;
+    }
+    const std::string faces_directory = argv[1];
+    // The faces directory contains a training dataset and a separate
+    // testing dataset.  The training data consists of 4 images, each
+    // annotated with rectangles that bound each human face.  The idea is 
+    // to use this training data to learn to identify human faces in new
+    // images.  
+    // 
+    // Once you have trained an object detector it is always important to
+    // test it on data it wasn't trained on.  Therefore, we will also load
+    // a separate testing set of 5 images.  Once we have a face detector
+    // created from the training data we will see how well it works by
+    // running it on the testing images. 
+    // 
+    // So here we create the variables that will hold our dataset.
+    // images_train will hold the 4 training images and face_boxes_train
+    // holds the locations of the faces in the training images.  So for
+    // example, the image images_train[0] has the faces given by the
+    // rectangles in face_boxes_train[0].
+    std::vector<matrix<rgb_pixel>> images_train, images_test;
+    std::vector<std::vector<mmod_rect>> face_boxes_train, face_boxes_test;
+
+    // Now we load the data.  These XML files list the images in each dataset
+    // and also contain the positions of the face boxes.  Obviously you can use
+    // any kind of input format you like so long as you store the data into
+    // images_train and face_boxes_train.  But for convenience dlib comes with
+    // tools for creating and loading XML image datasets.  Here you see how to
+    // load the data.  To create the XML files you can use the imglab tool which
+    // can be found in the tools/imglab folder.  It is a simple graphical tool
+    // for labeling objects in images with boxes.  To see how to use it read the
+    // tools/imglab/README.txt file.
+    load_image_dataset(images_train, face_boxes_train, faces_directory+"/training.xml");
+    load_image_dataset(images_test, face_boxes_test, faces_directory+"/testing.xml");
+
+
+    cout << "num training images: " << images_train.size() << endl;
+    cout << "num testing images:  " << images_test.size() << endl;
+
+
+    // The MMOD algorithm has some options you can set to control its behavior.  However,
+    // you can also call the constructor with your training annotations and a "target
+    // object size" and it will automatically configure itself in a reasonable way for your
+    // problem.  Here we are saying that faces are still recognizably faces when they are
+    // 40x40 pixels in size.  You should generally pick the smallest size where this is
+    // true.  Based on this information the mmod_options constructor will automatically
+    // pick a good sliding window width and height.  It will also automatically set the
+    // non-max-suppression parameters to something reasonable.  For further details see the
+    // mmod_options documentation.
+    mmod_options options(face_boxes_train, 40,40);
+    // The detector will automatically decide to use multiple sliding windows if needed.
+    // For the face data, only one is needed however.
+    cout << "num detector windows: "<< options.detector_windows.size() << endl;
+    for (auto& w : options.detector_windows)
+        cout << "detector window width by height: " << w.width << " x " << w.height << endl;
+    cout << "overlap NMS IOU thresh:             " << options.overlaps_nms.get_iou_thresh() << endl;
+    cout << "overlap NMS percent covered thresh: " << options.overlaps_nms.get_percent_covered_thresh() << endl;
+
+    // Now we are ready to create our network and trainer.  
+    net_type net(options);
+    // The MMOD loss requires that the number of filters in the final network layer equal
+    // options.detector_windows.size().  So we set that here as well.
+    net.subnet().layer_details().set_num_filters(options.detector_windows.size());
+    dnn_trainer<net_type> trainer(net);
+    trainer.set_learning_rate(0.1);
+    trainer.be_verbose();
+    trainer.set_synchronization_file("mmod_sync", std::chrono::minutes(5));
+    trainer.set_iterations_without_progress_threshold(300);
+
+
+    // Now let's train the network.  We are going to use mini-batches of 150
+    // images.   The images are random crops from our training set (see
+    // random_cropper_ex.cpp for a discussion of the random_cropper). 
+    std::vector<matrix<rgb_pixel>> mini_batch_samples;
+    std::vector<std::vector<mmod_rect>> mini_batch_labels; 
+    random_cropper cropper;
+    cropper.set_chip_dims(200, 200);
+    // Usually you want to give the cropper whatever min sizes you passed to the
+    // mmod_options constructor, which is what we do here.
+    cropper.set_min_object_size(40,40);
+    dlib::rand rnd;
+    // Run the trainer until the learning rate gets small.  This will probably take several
+    // hours.
+    while(trainer.get_learning_rate() >= 1e-4)
+    {
+        cropper(150, images_train, face_boxes_train, mini_batch_samples, mini_batch_labels);
+        // We can also randomly jitter the colors and that often helps a detector
+        // generalize better to new images.
+        for (auto&& img : mini_batch_samples)
+            disturb_colors(img, rnd);
+
+        trainer.train_one_step(mini_batch_samples, mini_batch_labels);
+    }
+    // wait for training threads to stop
+    trainer.get_net();
+    cout << "done training" << endl;
+
+    // Save the network to disk
+    net.clean();
+    serialize("mmod_network.dat") << net;
+
+
+    // Now that we have a face detector we can test it.  The first statement tests it
+    // on the training data.  It will print the precision, recall, and then average precision.
+    // This statement should indicate that the network works perfectly on the
+    // training data.
+    cout << "training results: " << test_object_detection_function(net, images_train, face_boxes_train) << endl;
+    // However, to get an idea if it really worked without overfitting we need to run
+    // it on images it wasn't trained on.  The next line does this.   Happily,
+    // this statement indicates that the detector finds most of the faces in the
+    // testing data.
+    cout << "testing results:  " << test_object_detection_function(net, images_test, face_boxes_test) << endl;
+
+
+    // If you are running many experiments, it's also useful to log the settings used
+    // during the training experiment.  This statement will print the settings we used to
+    // the screen.
+    cout << trainer << cropper << endl;
+
+    // Now lets run the detector on the testing images and look at the outputs.  
+    image_window win;
+    for (auto&& img : images_test)
+    {
+        pyramid_up(img);
+        auto dets = net(img);
+        win.clear_overlay();
+        win.set_image(img);
+        for (auto&& d : dets)
+            win.add_overlay(d);
+        cin.get();
+    }
+    return 0;
+
+    // Now that you finished this example, you should read dnn_mmod_train_find_cars_ex.cpp,
+    // which is a more advanced example.  It discusses many issues surrounding properly
+    // setting the MMOD parameters and creating a good training dataset.
+
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+
+
+
+