From c21c3b0befeb46a51b6bf3758ffa30813bea0ff0 Mon Sep 17 00:00:00 2001
From: Daniel Baumann <daniel.baumann@progress-linux.org>
Date: Sat, 9 Mar 2024 14:19:22 +0100
Subject: Adding upstream version 1.44.3.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
---
 ml/dlib/examples/dnn_imagenet_ex.cpp | 171 +++++++++++++++++++++++++++++++++++
 1 file changed, 171 insertions(+)
 create mode 100644 ml/dlib/examples/dnn_imagenet_ex.cpp

(limited to 'ml/dlib/examples/dnn_imagenet_ex.cpp')

diff --git a/ml/dlib/examples/dnn_imagenet_ex.cpp b/ml/dlib/examples/dnn_imagenet_ex.cpp
new file mode 100644
index 000000000..d1fa82823
--- /dev/null
+++ b/ml/dlib/examples/dnn_imagenet_ex.cpp
@@ -0,0 +1,171 @@
+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    This example shows how to classify an image into one of the 1000 imagenet
+    categories using the deep learning tools from the dlib C++ Library.  We will
+    use the pretrained ResNet34 model available on the dlib website.
+
+    The ResNet34 architecture is from the paper Deep Residual Learning for Image
+    Recognition by He, Zhang, Ren, and Sun.  The model file that comes with dlib
+    was trained using the dnn_imagenet_train_ex.cpp program on a Titan X for
+    about 2 weeks.  This pretrained model has a top5 error of 7.572% on the 2012
+    imagenet validation dataset.
+
+    For an introduction to dlib's DNN module read the dnn_introduction_ex.cpp and
+    dnn_introduction2_ex.cpp example programs.
+
+    
+    Finally, these tools will use CUDA and cuDNN to drastically accelerate
+    network training and testing.  CMake should automatically find them if they
+    are installed and configure things appropriately.  If not, the program will
+    still run but will be much slower to execute.
+*/
+
+
+
+#include <dlib/dnn.h>
+#include <iostream>
+#include <dlib/data_io.h>
+#include <dlib/gui_widgets.h>
+#include <dlib/image_transforms.h>
+
+using namespace std;
+using namespace dlib;
+ 
+// ----------------------------------------------------------------------------------------
+
+// This block of statements defines the resnet-34 network
+
+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 ares      = relu<residual<block,N,affine,SUBNET>>;
+template <int N, typename SUBNET> using ares_down = relu<residual_down<block,N,affine,SUBNET>>;
+
+template <typename SUBNET> using level1 = ares<512,ares<512,ares_down<512,SUBNET>>>;
+template <typename SUBNET> using level2 = ares<256,ares<256,ares<256,ares<256,ares<256,ares_down<256,SUBNET>>>>>>;
+template <typename SUBNET> using level3 = ares<128,ares<128,ares<128,ares_down<128,SUBNET>>>>;
+template <typename SUBNET> using level4 = ares<64,ares<64,ares<64,SUBNET>>>;
+
+using anet_type = loss_multiclass_log<fc<1000,avg_pool_everything<
+                            level1<
+                            level2<
+                            level3<
+                            level4<
+                            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_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);
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv) try
+{
+    if (argc == 1)
+    {
+        cout << "Give this program image files as command line arguments.\n" << endl;
+        cout << "You will also need a copy of the file resnet34_1000_imagenet_classifier.dnn " << endl;
+        cout << "available at http://dlib.net/files/resnet34_1000_imagenet_classifier.dnn.bz2" << endl;
+        cout << endl;
+        return 1;
+    }
+
+    std::vector<string> labels;
+    anet_type net;
+    deserialize("resnet34_1000_imagenet_classifier.dnn") >> net >> labels;
+
+    // Make a network with softmax as the final layer.  We don't have to do this
+    // if we just want to output the single best prediction, since the anet_type
+    // already does this.  But if we instead want to get the probability of each
+    // class as output we need to replace the last layer of the network with a
+    // softmax layer, which we do as follows:
+    softmax<anet_type::subnet_type> snet; 
+    snet.subnet() = net.subnet();
+
+    dlib::array<matrix<rgb_pixel>> images;
+    matrix<rgb_pixel> img, crop;
+
+    dlib::rand rnd;
+    image_window win;
+
+    // Read images from the command prompt and print the top 5 best labels for each.
+    for (int i = 1; i < argc; ++i)
+    {
+        load_image(img, argv[i]);
+        const int num_crops = 16;
+        // Grab 16 random crops from the image.  We will run all of them through the
+        // network and average the results.
+        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;
+
+        win.set_image(img);
+        // Print the 5 most probable labels
+        for (int k = 0; k < 5; ++k)
+        {
+            unsigned long predicted_label = index_of_max(p);
+            cout << p(predicted_label) << ": " << labels[predicted_label] << endl;
+            p(predicted_label) = 0;
+        }
+
+        cout << "Hit enter to process the next image";
+        cin.get();
+    }
+
+}
+catch(std::exception& e)
+{
+    cout << e.what() << endl;
+}
+
-- 
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