merged

dlib/dnn/layers.h

@@ -2386,6 +2386,106 @@ namespace dlib
     using mult_prev9_  = mult_prev_<tag9>;
     using mult_prev10_ = mult_prev_<tag10>;
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        template<typename> class tag
+        >
+    class resize_prev_to_tagged_
+    {
+    public:
+        const static unsigned long id = tag_id<tag>::id;
+
+        resize_prev_to_tagged_()
+        {
+        }
+
+        template <typename SUBNET>
+        void setup (const SUBNET& /*sub*/)
+        {
+        }
+
+        template <typename SUBNET>
+        void forward(const SUBNET& sub, resizable_tensor& output)
+        {
+            auto& prev = sub.get_output();
+            auto& tagged = layer<tag>(sub).get_output();
+
+            DLIB_CASSERT(prev.num_samples() == tagged.num_samples());
+
+            output.set_size(prev.num_samples(),
+                            prev.k(),
+                            tagged.nr(),
+                            tagged.nc());
+
+            if (prev.nr() == tagged.nr() && prev.nc() == tagged.nc())
+            {
+                tt::copy_tensor(false, output, 0, prev, 0, prev.k());
+            }
+            else
+            {
+                tt::resize_bilinear(output, prev);
+            }
+        }
+
+        template <typename SUBNET>
+        void backward(const tensor& gradient_input, SUBNET& sub, tensor& /*params_grad*/)
+        {
+            auto& prev = sub.get_gradient_input();
+
+            DLIB_CASSERT(prev.k() == gradient_input.k());
+            DLIB_CASSERT(prev.num_samples() == gradient_input.num_samples());
+
+            if (prev.nr() == gradient_input.nr() && prev.nc() == gradient_input.nc())
+            {
+                tt::copy_tensor(true, prev, 0, gradient_input, 0, prev.k());
+            }
+            else
+            {
+                tt::resize_bilinear_gradient(prev, gradient_input);
+            }
+        }
+
+        const tensor& get_layer_params() const { return params; }
+        tensor& get_layer_params() { return params; }
+
+        inline dpoint map_input_to_output (const dpoint& p) const { return p; }
+        inline dpoint map_output_to_input (const dpoint& p) const { return p; }
+
+        friend void serialize(const resize_prev_to_tagged_& , std::ostream& out)
+        {
+            serialize("resize_prev_to_tagged_", out);
+        }
+
+        friend void deserialize(resize_prev_to_tagged_& , std::istream& in)
+        {
+            std::string version;
+            deserialize(version, in);
+            if (version != "resize_prev_to_tagged_")
+                throw serialization_error("Unexpected version '"+version+"' found while deserializing dlib::resize_prev_to_tagged_.");
+        }
+
+        friend std::ostream& operator<<(std::ostream& out, const resize_prev_to_tagged_& item)
+        {
+            out << "resize_prev_to_tagged"<<id;
+            return out;
+        }
+
+        friend void to_xml(const resize_prev_to_tagged_& item, std::ostream& out)
+        {
+            out << "<resize_prev_to_tagged tag='"<<id<<"'/>\n";
+        }
+
+    private:
+        resizable_tensor params;
+    };
+
+    template <
+        template<typename> class tag,
+        typename SUBNET
+        >
+    using resize_prev_to_tagged = add_layer<resize_prev_to_tagged_<tag>, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     template <

dlib/dnn/layers_abstract.h

@@ -2382,6 +2382,56 @@ namespace dlib
     using mult_prev9_  = mult_prev_<tag9>;
     using mult_prev10_ = mult_prev_<tag10>;
 
+// ----------------------------------------------------------------------------------------
+
+    template <
+        template<typename> class tag
+        >
+    class resize_prev_to_tagged_
+    {
+        /*!
+            WHAT THIS OBJECT REPRESENTS
+                This is an implementation of the EXAMPLE_COMPUTATIONAL_LAYER_ interface
+                defined above.  This layer resizes the output channels of the previous layer
+                to have the same number of rows and columns as the output of the tagged layer.
+
+                This layer uses bilinear interpolation. If the sizes match already, then it
+                simply copies the data.
+
+                Therefore, you supply a tag via resize_prev_to_tagged's template argument that
+                tells it what layer to use for the target size.
+
+                If tensor PREV is resized to size of tensor TAGGED, then a tensor OUT is
+                produced such that:
+                    - OUT.num_samples() == PREV.num_samples()
+                    - OUT.k()  == PREV.k()
+                    - OUT.nr() == TAGGED.nr()
+                    - OUT.nc() == TAGGED.nc()
+        !*/
+
+    public:
+        resize_prev_to_tagged_(
+        ); 
+
+        template <typename SUBNET> void setup(const SUBNET& sub);
+        template <typename SUBNET> void forward(const SUBNET& sub, resizable_tensor& output);
+        template <typename SUBNET> void backward(const tensor& gradient_input, SUBNET& sub, tensor& params_grad);
+        dpoint map_input_to_output(dpoint p) const;
+        dpoint map_output_to_input(dpoint p) const;
+        const tensor& get_layer_params() const; 
+        tensor& get_layer_params(); 
+        /*!
+            These functions are implemented as described in the EXAMPLE_COMPUTATIONAL_LAYER_ interface.
+        !*/
+    };
+
+
+    template <
+        template<typename> class tag,
+        typename SUBNET
+        >
+    using resize_prev_to_tagged = add_layer<resize_prev_to_tagged_<tag>, SUBNET>;
+
 // ----------------------------------------------------------------------------------------
 
     template <

dlib/external/libjpeg/jdhuff.cpp

@@ -293,7 +293,7 @@ jpeg_make_d_derived_tbl (j_decompress_ptr cinfo, int isDC, int tblno,
 
 GLOBAL(int)
 jpeg_fill_bit_buffer (bitread_working_state * state,
-		      bit_buf_type get_buffer, register int bits_left,
+		      bit_buf_type get_buffer, int bits_left,
 		      int nbits)
 /* Load up the bit buffer to a depth of at least nbits */
 {
@@ -399,7 +399,7 @@ jpeg_fill_bit_buffer (bitread_working_state * state,
 
 GLOBAL(int)
 jpeg_huff_decode (bitread_working_state * state,
-		  bit_buf_type get_buffer, register int bits_left,
+		  bit_buf_type get_buffer, int bits_left,
 		  d_derived_tbl * htbl, int min_bits)
 {
   int l = min_bits;

dlib/test/dnn.cpp

@@ -1910,7 +1910,7 @@ namespace
     template <typename SUBNET> 
     using pres  = prelu<add_prev1<bn_con<con<8,3,3,1,1,prelu<bn_con<con<8,3,3,1,1,tag1<SUBNET>>>>>>>>;
 
-    void test_visit_funcions()
+    void test_visit_functions()
     {
         using net_type2 = loss_multiclass_log<fc<10,
             avg_pool_everything<
@@ -3243,7 +3243,7 @@ namespace
             test_batch_normalize_conv();
             test_basic_tensor_ops();
             test_layers();
-            test_visit_funcions();
+            test_visit_functions();
             test_copy_tensor_cpu();
             test_copy_tensor_add_to_cpu();
             test_concat();

examples/dnn_semantic_segmentation_ex.cpp

@@ -16,7 +16,7 @@
        ./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
+    from here: http://dlib.net/files/semantic_segmentation_voc2012net_v2.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
@@ -111,16 +111,16 @@ int main(int argc, char** argv) try
         cout << "You call this program like this: " << endl;
         cout << "./dnn_semantic_segmentation_train_ex /path/to/images" << endl;
         cout << endl;
-        cout << "You will also need a trained 'semantic_segmentation_voc2012net.dnn' file." << endl;
+        cout << "You will also need a trained '" << semantic_segmentation_net_filename << "' file." << endl;
         cout << "You can either train it yourself (see example program" << endl;
         cout << "dnn_semantic_segmentation_train_ex), or download a" << endl;
-        cout << "copy from here: http://dlib.net/files/semantic_segmentation_voc2012net.dnn" << endl;
+        cout << "copy from here: http://dlib.net/files/" << semantic_segmentation_net_filename << endl;
         return 1;
     }
 
     // Read the file containing the trained network from the working directory.
     anet_type net;
-    deserialize("semantic_segmentation_voc2012net.dnn") >> net;
+    deserialize(semantic_segmentation_net_filename) >> net;
 
     // Show inference results in a window.
     image_window win;

examples/dnn_semantic_segmentation_ex.h

@@ -23,7 +23,7 @@
        ./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
+    from here: http://dlib.net/files/semantic_segmentation_voc2012net_v2.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
@@ -116,13 +116,13 @@ const Voc2012class& find_voc2012_class(Predicate predicate)
 
 // 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)
+// example program), and then residual upsampling. In addition, U-Net style skip
+// connections are used, so that not every simple detail needs to reprented on the low
+// levels. (See Ronneberger et al. (2015), U-Net: Convolutional Networks for Biomedical
+// Image Segmentation, https://arxiv.org/pdf/1505.04597.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>>>>>;
+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>>>>>;
@@ -145,55 +145,98 @@ template <int N, typename SUBNET> using ares_up   = dlib::relu<residual_up<block
 
 // ----------------------------------------------------------------------------------------
 
-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 res64 = res<64,SUBNET>;
+template <typename SUBNET> using res128 = res<128,SUBNET>;
+template <typename SUBNET> using res256 = res<256,SUBNET>;
+template <typename SUBNET> using res512 = res<512,SUBNET>;
+template <typename SUBNET> using ares64 = ares<64,SUBNET>;
+template <typename SUBNET> using ares128 = ares<128,SUBNET>;
+template <typename SUBNET> using ares256 = ares<256,SUBNET>;
+template <typename SUBNET> using ares512 = ares<512,SUBNET>;
+
+template <typename SUBNET> using level1 = dlib::repeat<2,res64,res<64,SUBNET>>;
+template <typename SUBNET> using level2 = dlib::repeat<2,res128,res_down<128,SUBNET>>;
+template <typename SUBNET> using level3 = dlib::repeat<2,res256,res_down<256,SUBNET>>;
+template <typename SUBNET> using level4 = dlib::repeat<2,res512,res_down<512,SUBNET>>;
+
+template <typename SUBNET> using alevel1 = dlib::repeat<2,ares64,ares<64,SUBNET>>;
+template <typename SUBNET> using alevel2 = dlib::repeat<2,ares128,ares_down<128,SUBNET>>;
+template <typename SUBNET> using alevel3 = dlib::repeat<2,ares256,ares_down<256,SUBNET>>;
+template <typename SUBNET> using alevel4 = dlib::repeat<2,ares512,ares_down<512,SUBNET>>;
+
+template <typename SUBNET> using level1t = dlib::repeat<2,res64,res_up<64,SUBNET>>;
+template <typename SUBNET> using level2t = dlib::repeat<2,res128,res_up<128,SUBNET>>;
+template <typename SUBNET> using level3t = dlib::repeat<2,res256,res_up<256,SUBNET>>;
+template <typename SUBNET> using level4t = dlib::repeat<2,res512,res_up<512,SUBNET>>;
+
+template <typename SUBNET> using alevel1t = dlib::repeat<2,ares64,ares_up<64,SUBNET>>;
+template <typename SUBNET> using alevel2t = dlib::repeat<2,ares128,ares_up<128,SUBNET>>;
+template <typename SUBNET> using alevel3t = dlib::repeat<2,ares256,ares_up<256,SUBNET>>;
+template <typename SUBNET> using alevel4t = dlib::repeat<2,ares512,ares_up<512,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 <
+    template<typename> class TAGGED,
+    template<typename> class PREV_RESIZED,
+    typename SUBNET
+>
+using resize_and_concat = dlib::add_layer<
+                          dlib::concat_<TAGGED,PREV_RESIZED>,
+                          PREV_RESIZED<dlib::resize_prev_to_tagged<TAGGED,SUBNET>>>;
+
+template <typename SUBNET> using utag1 = dlib::add_tag_layer<2100+1,SUBNET>;
+template <typename SUBNET> using utag2 = dlib::add_tag_layer<2100+2,SUBNET>;
+template <typename SUBNET> using utag3 = dlib::add_tag_layer<2100+3,SUBNET>;
+template <typename SUBNET> using utag4 = dlib::add_tag_layer<2100+4,SUBNET>;
+
+template <typename SUBNET> using utag1_ = dlib::add_tag_layer<2110+1,SUBNET>;
+template <typename SUBNET> using utag2_ = dlib::add_tag_layer<2110+2,SUBNET>;
+template <typename SUBNET> using utag3_ = dlib::add_tag_layer<2110+3,SUBNET>;
+template <typename SUBNET> using utag4_ = dlib::add_tag_layer<2110+4,SUBNET>;
+
+template <typename SUBNET> using concat_utag1 = resize_and_concat<utag1,utag1_,SUBNET>;
+template <typename SUBNET> using concat_utag2 = resize_and_concat<utag2,utag2_,SUBNET>;
+template <typename SUBNET> using concat_utag3 = resize_and_concat<utag3,utag3_,SUBNET>;
+template <typename SUBNET> using concat_utag4 = resize_and_concat<utag4,utag4_,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>>;
+static const char* semantic_segmentation_net_filename = "semantic_segmentation_voc2012net_v2.dnn";
 
 // ----------------------------------------------------------------------------------------
 
 // 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,
+using bnet_type = dlib::loss_multiclass_log_per_pixel<
+                              dlib::cont<class_count,1,1,1,1,
+                              dlib::relu<dlib::bn_con<dlib::cont<64,7,7,2,2,
+                              concat_utag1<level1t<
+                              concat_utag2<level2t<
+                              concat_utag3<level3t<
+                              concat_utag4<level4t<
+                              level4<utag4<
+                              level3<utag3<
+                              level2<utag2<
+                              level1<dlib::max_pool<3,3,2,2,utag1<
+                              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::cont<class_count,1,1,1,1,
+                              dlib::relu<dlib::affine<dlib::cont<64,7,7,2,2,
+                              concat_utag1<alevel1t<
+                              concat_utag2<alevel2t<
+                              concat_utag3<alevel3t<
+                              concat_utag4<alevel4t<
+                              alevel4<utag4<
+                              alevel3<utag3<
+                              alevel2<utag2<
+                              alevel1<dlib::max_pool<3,3,2,2,utag1<
+                              dlib::relu<dlib::affine<dlib::con<64,7,7,2,2,
                               dlib::input<dlib::matrix<dlib::rgb_pixel>>
-                            >>>>>>>>>>>>>>;
+                              >>>>>>>>>>>>>>>>>>>>>>>>>;
 
 // ----------------------------------------------------------------------------------------
 

examples/dnn_semantic_segmentation_train_ex.cpp

@@ -41,7 +41,7 @@ struct training_sample
 
 // ----------------------------------------------------------------------------------------
 
-rectangle make_random_cropping_rect_resnet(
+rectangle make_random_cropping_rect(
     const matrix<rgb_pixel>& img,
     dlib::rand& rnd
 )
@@ -66,7 +66,7 @@ void randomly_crop_image (
     dlib::rand& rnd
 )
 {
-    const auto rect = make_random_cropping_rect_resnet(input_image, rnd);
+    const auto rect = make_random_cropping_rect(input_image, rnd);
 
     const chip_details chip_details(rect, chip_dims(227, 227));
 
@@ -259,12 +259,12 @@ double calculate_accuracy(anet_type& anet, const std::vector<image_info>& datase
 
 int main(int argc, char** argv) try
 {
-    if (argc != 2)
+    if (argc < 2 || argc > 3)
     {
         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;
+        cout << "./dnn_semantic_segmentation_train_ex /path/to/VOC2012 [minibatch-size]" << endl;
         return 1;
     }
 
@@ -278,13 +278,16 @@ int main(int argc, char** argv) try
         return 1;
     }
 
+    // a mini-batch smaller than the default can be used with GPUs having less memory
+    const int minibatch_size = argc == 3 ? std::stoi(argv[2]) : 23;
+    cout << "mini-batch size: " << minibatch_size << endl;
 
     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));
+    bnet_type bnet;
+    dnn_trainer<bnet_type> trainer(bnet,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));
@@ -292,7 +295,7 @@ int main(int argc, char** argv) try
     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);
+    set_all_bn_running_stats_window_sizes(bnet, 1000);
 
     // Output training parameters.
     cout << endl << trainer << endl;
@@ -345,9 +348,9 @@ int main(int argc, char** argv) try
         samples.clear();
         labels.clear();
 
-        // make a 30-image mini-batch
+        // make a mini-batch
         training_sample temp;
-        while(samples.size() < 30)
+        while(samples.size() < minibatch_size)
         {
             data.dequeue(temp);
 
@@ -369,13 +372,13 @@ int main(int argc, char** argv) try
     // also wait for threaded processing to stop in the trainer.
     trainer.get_net();
 
-    net.clean();
+    bnet.clean();
     cout << "saving network" << endl;
-    serialize("semantic_segmentation_voc2012net.dnn") << net;
+    serialize(semantic_segmentation_net_filename) << bnet;
 
 
     // Make a copy of the network to use it for inference.
-    anet_type anet = net;
+    anet_type anet = bnet;
 
     cout << "Testing the network..." << endl;