Adding upstream version 1.44.3.upstream/1.44.3upstream

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

1 files changed, 128 insertions, 0 deletions

diff --git a/ml/dlib/examples/dnn_metric_learning_ex.cpp b/ml/dlib/examples/dnn_metric_learning_ex.cpp
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+// 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.  In it, we will show how to use the loss_metric layer to do
+    metric learning.  
+
+    The main reason you might want to use this kind of algorithm is because you
+    would like to use a k-nearest neighbor classifier or similar algorithm, but
+    you don't know a good way to calculate the distance between two things.  A
+    popular example would be face recognition.  There are a whole lot of papers
+    that train some kind of deep metric learning algorithm that embeds face
+    images in some vector space where images of the same person are close to each
+    other and images of different people are far apart.  Then in that vector
+    space it's very easy to do face recognition with some kind of k-nearest
+    neighbor classifier.  
+
+    To keep this example as simple as possible we won't do face recognition.
+    Instead, we will create a very simple network and use it to learn a mapping
+    from 8D vectors to 2D vectors such that vectors with the same class labels
+    are near each other.  If you want to see a more complex example that learns
+    the kind of network you would use for something like face recognition read
+    the dnn_metric_learning_on_images_ex.cpp example.
+
+    You should also have read the examples that introduce the dlib DNN API before 
+    continuing.  These are dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp.
+*/
+
+
+#include <dlib/dnn.h>
+#include <iostream>
+
+using namespace std;
+using namespace dlib;
+
+
+int main() try
+{
+    // The API for doing metric learning is very similar to the API for
+    // multi-class classification.  In fact, the inputs are the same, a bunch of
+    // labeled objects.  So here we create our dataset.  We make up some simple
+    // vectors and label them with the integers 1,2,3,4.  The specific values of
+    // the integer labels don't matter.
+    std::vector<matrix<double,0,1>> samples;
+    std::vector<unsigned long> labels;
+
+    // class 1 training vectors
+    samples.push_back({1,0,0,0,0,0,0,0}); labels.push_back(1);
+    samples.push_back({0,1,0,0,0,0,0,0}); labels.push_back(1);
+
+    // class 2 training vectors
+    samples.push_back({0,0,1,0,0,0,0,0}); labels.push_back(2);
+    samples.push_back({0,0,0,1,0,0,0,0}); labels.push_back(2);
+
+    // class 3 training vectors
+    samples.push_back({0,0,0,0,1,0,0,0}); labels.push_back(3);
+    samples.push_back({0,0,0,0,0,1,0,0}); labels.push_back(3);
+
+    // class 4 training vectors
+    samples.push_back({0,0,0,0,0,0,1,0}); labels.push_back(4);
+    samples.push_back({0,0,0,0,0,0,0,1}); labels.push_back(4);
+
+
+    // Make a network that simply learns a linear mapping from 8D vectors to 2D
+    // vectors.
+    using net_type = loss_metric<fc<2,input<matrix<double,0,1>>>>; 
+    net_type net;
+    dnn_trainer<net_type> trainer(net);
+    trainer.set_learning_rate(0.1);
+
+    // It should be emphasized out that it's really important that each mini-batch contain
+    // multiple instances of each class of object.  This is because the metric learning
+    // algorithm needs to consider pairs of objects that should be close as well as pairs
+    // of objects that should be far apart during each training step.  Here we just keep
+    // training on the same small batch so this constraint is trivially satisfied.
+    while(trainer.get_learning_rate() >= 1e-4)
+        trainer.train_one_step(samples, labels);
+
+    // Wait for training threads to stop
+    trainer.get_net();
+    cout << "done training" << endl;
+
+
+    // Run all the samples through the network to get their 2D vector embeddings.
+    std::vector<matrix<float,0,1>> embedded = net(samples);
+
+    // Print the embedding for each sample to the screen.  If you look at the
+    // outputs carefully you should notice that they are grouped together in 2D
+    // space according to their label.
+    for (size_t i = 0; i < embedded.size(); ++i)
+        cout << "label: " << labels[i] << "\t" << trans(embedded[i]);
+
+    // Now, check if the embedding puts things with the same labels near each other and
+    // things with different labels far apart.
+    int num_right = 0;
+    int num_wrong = 0;
+    for (size_t i = 0; i < embedded.size(); ++i)
+    {
+        for (size_t j = i+1; j < embedded.size(); ++j)
+        {
+            if (labels[i] == labels[j])
+            {
+                // The loss_metric layer will cause things with the same label to be less
+                // than net.loss_details().get_distance_threshold() distance from each
+                // other.  So we can use that distance value as our testing threshold for
+                // "being near to each other".
+                if (length(embedded[i]-embedded[j]) < net.loss_details().get_distance_threshold())
+                    ++num_right;
+                else
+                    ++num_wrong;
+            }
+            else
+            {
+                if (length(embedded[i]-embedded[j]) >= net.loss_details().get_distance_threshold())
+                    ++num_right;
+                else
+                    ++num_wrong;
+            }
+        }
+    }
+
+    cout << "num_right: "<< num_right << endl;
+    cout << "num_wrong: "<< num_wrong << endl;
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+