Fix input/output mappings with repeat layers (#2337)

* Fix input/output mappings with repeat layers

* add test for input/output tensor mappers

* fix output to input order

dlib/dnn/utilities.h

@@ -141,6 +141,15 @@ namespace dlib
                 (*this)(net.subnet());
                 p = net.layer_details().map_input_to_output(p);
             }
+            template <size_t N, template <typename> class R, typename U>
+            void operator()(const repeat<N, R, U>& net)
+            {
+                (*this)(net.subnet());
+                for (size_t i = 0; i < N; ++i)
+                {
+                    (*this)(net.get_repeated_layer(N-1-i).subnet());
+                }
+            }
 
 
             template <unsigned long ID, typename U, typename E>
@@ -201,6 +210,15 @@ namespace dlib
                 p = net.layer_details().map_output_to_input(p);
                 (*this)(net.subnet());
             }
+            template <size_t N, template <typename> class R, typename U>
+            void operator()(const repeat<N, R, U>& net)
+            {
+                for (size_t i = 0; i < N; ++i)
+                {
+                    (*this)(net.get_repeated_layer(i).subnet());
+                }
+                (*this)(net.subnet());
+            }
 
 
             template <unsigned long ID, typename U, typename E>

dlib/test/dnn.cpp

@@ -3970,6 +3970,20 @@ namespace
         DLIB_TEST(layer<4>(net).layer_details().get_learning_rate_multiplier() == 0.01);
     }
 
+// ----------------------------------------------------------------------------------------
+
+    template <typename SUBNET>
+    using conblock = relu<bn_con<add_layer<con_<16, 3, 3, 2, 2, 1, 1>, SUBNET>>>;
+
+    void test_input_ouput_mappers()
+    {
+        using net_type = loss_binary_log_per_pixel<con<1, 1, 1, 1, 1,repeat<3, conblock, tag1<input_rgb_image>>>>;
+        net_type net;
+        point p(32, 32);
+        DLIB_TEST(input_tensor_to_output_tensor(net, p) == p / 8);
+        DLIB_TEST(output_tensor_to_input_tensor(net, p) == p * 8);
+    }
+
 // ----------------------------------------------------------------------------------------
 
     // This test really just checks if the mmod loss goes negative when a whole lot of overlapping
@@ -4157,6 +4171,7 @@ namespace
             test_layers_scale_and_scale_prev();
             test_disable_duplicative_biases();
             test_set_learning_rate_multipliers();
+            test_input_ouput_mappers();
         }
 
         void perform_test()