Merging upstream version 1.44.3.

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

1 files changed, 200 insertions, 0 deletions

diff --git a/ml/dlib/examples/dnn_semantic_segmentation_ex.h b/ml/dlib/examples/dnn_semantic_segmentation_ex.h
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+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+    Semantic segmentation using the PASCAL VOC2012 dataset.
+
+    In segmentation, the task is to assign each pixel of an input image
+    a label - for example, 'dog'.  Then, the idea is that neighboring
+    pixels having the same label can be connected together to form a
+    larger region, representing a complete (or partially occluded) dog.
+    So technically, segmentation can be viewed as classification of
+    individual pixels (using the relevant context in the input images),
+    however the goal usually is to identify meaningful regions that
+    represent complete entities of interest (such as dogs).
+
+    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
+
+    An alternative to steps 2-4 above is to download a pre-trained network
+    from here: http://dlib.net/files/semantic_segmentation_voc2012net.dnn
+
+    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.
+*/
+
+#ifndef DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
+#define DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_
+
+#include <dlib/dnn.h>
+
+// ----------------------------------------------------------------------------------------
+
+inline bool operator == (const dlib::rgb_pixel& a, const dlib::rgb_pixel& b)
+{
+    return a.red == b.red && a.green == b.green && a.blue == b.blue;
+}
+
+// ----------------------------------------------------------------------------------------
+
+// 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.
+
+struct Voc2012class {
+    Voc2012class(uint16_t index, const dlib::rgb_pixel& rgb_label, const std::string& classlabel)
+        : index(index), rgb_label(rgb_label), classlabel(classlabel)
+    {}
+
+    // The index of the class. In the PASCAL VOC 2012 dataset, indexes from 0 to 20 are valid.
+    const uint16_t index = 0;
+
+    // The corresponding RGB representation of the class.
+    const dlib::rgb_pixel rgb_label;
+
+    // The label of the class in plain text.
+    const std::string classlabel;
+};
+
+namespace {
+    constexpr int class_count = 21; // background + 20 classes
+
+    const std::vector<Voc2012class> classes = {
+        Voc2012class(0, dlib::rgb_pixel(0, 0, 0), ""), // background
+
+        // The cream-colored `void' label is used in border regions and to mask difficult objects
+        // (see http://host.robots.ox.ac.uk/pascal/VOC/voc2012/htmldoc/devkit_doc.html)
+        Voc2012class(dlib::loss_multiclass_log_per_pixel_::label_to_ignore,
+            dlib::rgb_pixel(224, 224, 192), "border"),
+
+        Voc2012class(1,  dlib::rgb_pixel(128,   0,   0), "aeroplane"),
+        Voc2012class(2,  dlib::rgb_pixel(  0, 128,   0), "bicycle"),
+        Voc2012class(3,  dlib::rgb_pixel(128, 128,   0), "bird"),
+        Voc2012class(4,  dlib::rgb_pixel(  0,   0, 128), "boat"),
+        Voc2012class(5,  dlib::rgb_pixel(128,   0, 128), "bottle"),
+        Voc2012class(6,  dlib::rgb_pixel(  0, 128, 128), "bus"),
+        Voc2012class(7,  dlib::rgb_pixel(128, 128, 128), "car"),
+        Voc2012class(8,  dlib::rgb_pixel( 64,   0,   0), "cat"),
+        Voc2012class(9,  dlib::rgb_pixel(192,   0,   0), "chair"),
+        Voc2012class(10, dlib::rgb_pixel( 64, 128,   0), "cow"),
+        Voc2012class(11, dlib::rgb_pixel(192, 128,   0), "diningtable"),
+        Voc2012class(12, dlib::rgb_pixel( 64,   0, 128), "dog"),
+        Voc2012class(13, dlib::rgb_pixel(192,   0, 128), "horse"),
+        Voc2012class(14, dlib::rgb_pixel( 64, 128, 128), "motorbike"),
+        Voc2012class(15, dlib::rgb_pixel(192, 128, 128), "person"),
+        Voc2012class(16, dlib::rgb_pixel(  0,  64,   0), "pottedplant"),
+        Voc2012class(17, dlib::rgb_pixel(128,  64,   0), "sheep"),
+        Voc2012class(18, dlib::rgb_pixel(  0, 192,   0), "sofa"),
+        Voc2012class(19, dlib::rgb_pixel(128, 192,   0), "train"),
+        Voc2012class(20, dlib::rgb_pixel(  0,  64, 128), "tvmonitor"),
+    };
+}
+
+template <typename Predicate>
+const Voc2012class& find_voc2012_class(Predicate predicate)
+{
+    const auto i = std::find_if(classes.begin(), classes.end(), predicate);
+
+    if (i != classes.end())
+    {
+        return *i;
+    }
+    else
+    {
+        throw std::runtime_error("Unable to find a matching VOC2012 class");
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+
+// Introduce the building blocks used to define the segmentation network.
+// The network first does residual downsampling (similar to the dnn_imagenet_(train_)ex 
+// example program), and then residual upsampling. The network could be improved e.g.
+// by introducing skip connections from the input image, and/or the first layers, to the
+// last layer(s).  (See Long et al., Fully Convolutional Networks for Semantic Segmentation,
+// https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)
+
+template <int N, template <typename> class BN, int stride, typename SUBNET> 
+using block = BN<dlib::con<N,3,3,1,1, dlib::relu<BN<dlib::con<N,3,3,stride,stride,SUBNET>>>>>;
+
+template <int N, template <typename> class BN, int stride, typename SUBNET> 
+using blockt = BN<dlib::cont<N,3,3,1,1,dlib::relu<BN<dlib::cont<N,3,3,stride,stride,SUBNET>>>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual = dlib::add_prev1<block<N,BN,1,dlib::tag1<SUBNET>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual_down = dlib::add_prev2<dlib::avg_pool<2,2,2,2,dlib::skip1<dlib::tag2<block<N,BN,2,dlib::tag1<SUBNET>>>>>>;
+
+template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
+using residual_up = dlib::add_prev2<dlib::cont<N,2,2,2,2,dlib::skip1<dlib::tag2<blockt<N,BN,2,dlib::tag1<SUBNET>>>>>>;
+
+template <int N, typename SUBNET> using res       = dlib::relu<residual<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares      = dlib::relu<residual<block,N,dlib::affine,SUBNET>>;
+template <int N, typename SUBNET> using res_down  = dlib::relu<residual_down<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares_down = dlib::relu<residual_down<block,N,dlib::affine,SUBNET>>;
+template <int N, typename SUBNET> using res_up    = dlib::relu<residual_up<block,N,dlib::bn_con,SUBNET>>;
+template <int N, typename SUBNET> using ares_up   = dlib::relu<residual_up<block,N,dlib::affine,SUBNET>>;
+
+// ----------------------------------------------------------------------------------------
+
+template <typename SUBNET> using res512 = res<512, SUBNET>;
+template <typename SUBNET> using res256 = res<256, SUBNET>;
+template <typename SUBNET> using res128 = res<128, SUBNET>;
+template <typename SUBNET> using res64  = res<64, SUBNET>;
+template <typename SUBNET> using ares512 = ares<512, SUBNET>;
+template <typename SUBNET> using ares256 = ares<256, SUBNET>;
+template <typename SUBNET> using ares128 = ares<128, SUBNET>;
+template <typename SUBNET> using ares64  = ares<64, SUBNET>;
+
+
+template <typename SUBNET> using level1 = dlib::repeat<2,res512,res_down<512,SUBNET>>;
+template <typename SUBNET> using level2 = dlib::repeat<2,res256,res_down<256,SUBNET>>;
+template <typename SUBNET> using level3 = dlib::repeat<2,res128,res_down<128,SUBNET>>;
+template <typename SUBNET> using level4 = dlib::repeat<2,res64,res<64,SUBNET>>;
+
+template <typename SUBNET> using alevel1 = dlib::repeat<2,ares512,ares_down<512,SUBNET>>;
+template <typename SUBNET> using alevel2 = dlib::repeat<2,ares256,ares_down<256,SUBNET>>;
+template <typename SUBNET> using alevel3 = dlib::repeat<2,ares128,ares_down<128,SUBNET>>;
+template <typename SUBNET> using alevel4 = dlib::repeat<2,ares64,ares<64,SUBNET>>;
+
+template <typename SUBNET> using level1t = dlib::repeat<2,res512,res_up<512,SUBNET>>;
+template <typename SUBNET> using level2t = dlib::repeat<2,res256,res_up<256,SUBNET>>;
+template <typename SUBNET> using level3t = dlib::repeat<2,res128,res_up<128,SUBNET>>;
+template <typename SUBNET> using level4t = dlib::repeat<2,res64,res_up<64,SUBNET>>;
+
+template <typename SUBNET> using alevel1t = dlib::repeat<2,ares512,ares_up<512,SUBNET>>;
+template <typename SUBNET> using alevel2t = dlib::repeat<2,ares256,ares_up<256,SUBNET>>;
+template <typename SUBNET> using alevel3t = dlib::repeat<2,ares128,ares_up<128,SUBNET>>;
+template <typename SUBNET> using alevel4t = dlib::repeat<2,ares64,ares_up<64,SUBNET>>;
+
+// ----------------------------------------------------------------------------------------
+
+// training network type
+using net_type = dlib::loss_multiclass_log_per_pixel<
+                            dlib::cont<class_count,7,7,2,2,
+                            level4t<level3t<level2t<level1t<
+                            level1<level2<level3<level4<
+                            dlib::max_pool<3,3,2,2,dlib::relu<dlib::bn_con<dlib::con<64,7,7,2,2,
+                            dlib::input<dlib::matrix<dlib::rgb_pixel>>
+                            >>>>>>>>>>>>>>;
+
+// testing network type (replaced batch normalization with fixed affine transforms)
+using anet_type = dlib::loss_multiclass_log_per_pixel<
+                            dlib::cont<class_count,7,7,2,2,
+                            alevel4t<alevel3t<alevel2t<alevel1t<
+                            alevel1<alevel2<alevel3<alevel4<
+                            dlib::max_pool<3,3,2,2,dlib::relu<dlib::affine<dlib::con<64,7,7,2,2,
+                            dlib::input<dlib::matrix<dlib::rgb_pixel>>
+                            >>>>>>>>>>>>>>;
+
+// ----------------------------------------------------------------------------------------
+
+#endif // DLIB_DNn_SEMANTIC_SEGMENTATION_EX_H_