Added input_tensor_to_output_tensor() and output_tensor_to_input_tensor() along

with the mapping functions necessary at each layer to support these routines.

dlib/dnn/layers.h

@@ -69,6 +69,23 @@ namespace dlib
         void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
         void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }
 
+        inline point map_input_to_output (
+            point p
+        ) const
+        {
+            p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
+            p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
+            return p;
+        }
+
+        inline point map_output_to_input (
+            point p
+        ) const
+        {
+            p.x() = p.x()*stride_x() - padding_x() + nc()/2;
+            p.y() = p.y()*stride_y() - padding_y() + nr()/2;
+            return p;
+        }
 
         con_ (
             const con_& item
@@ -317,6 +334,24 @@ namespace dlib
         long padding_y() const { return padding_y_; }
         long padding_x() const { return padding_x_; }
 
+        inline point map_input_to_output (
+            point p
+        ) const
+        {
+            p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
+            p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
+            return p;
+        }
+
+        inline point map_output_to_input (
+            point p
+        ) const
+        {
+            p.x() = p.x()*stride_x() - padding_x() + nc()/2;
+            p.y() = p.y()*stride_y() - padding_y() + nr()/2;
+            return p;
+        }
+
         max_pool_ (
             const max_pool_& item
         )  :
@@ -496,6 +531,24 @@ namespace dlib
         long padding_y() const { return padding_y_; }
         long padding_x() const { return padding_x_; }
 
+        inline point map_input_to_output (
+            point p
+        ) const
+        {
+            p.x() = (p.x()+padding_x()-nc()/2)/stride_x();
+            p.y() = (p.y()+padding_y()-nr()/2)/stride_y();
+            return p;
+        }
+
+        inline point map_output_to_input (
+            point p
+        ) const
+        {
+            p.x() = p.x()*stride_x() - padding_x() + nc()/2;
+            p.y() = p.y()*stride_y() - padding_y() + nr()/2;
+            return p;
+        }
+
         avg_pool_ (
             const avg_pool_& item
         )  :
@@ -683,6 +736,9 @@ namespace dlib
         void set_bias_learning_rate_multiplier(double val) { bias_learning_rate_multiplier = val; }
         void set_bias_weight_decay_multiplier(double val)  { bias_weight_decay_multiplier  = val; }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
 
         template <typename SUBNET>
         void setup (const SUBNET& sub)
@@ -1143,6 +1199,9 @@ namespace dlib
                 tt::multiply(true, data_grad, mask, gradient_input);
         }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 
@@ -1219,6 +1278,9 @@ namespace dlib
             tt::affine_transform(output, input, val);
         } 
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         void backward_inplace(
             const tensor& gradient_input, 
             tensor& data_grad, 
@@ -1324,6 +1386,9 @@ namespace dlib
 
         layer_mode get_mode() const { return mode; }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         template <typename SUBNET>
         void setup (const SUBNET& sub)
         {
@@ -1584,6 +1649,9 @@ namespace dlib
             tt::relu_gradient(data_grad, computed_output, gradient_input);
         }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 
@@ -1661,6 +1729,9 @@ namespace dlib
                 gradient_input, params, params_grad);
         }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 
@@ -1733,6 +1804,9 @@ namespace dlib
             tt::sigmoid_gradient(data_grad, computed_output, gradient_input);
         }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 
@@ -1783,6 +1857,9 @@ namespace dlib
         {
         }
 
+        inline point map_input_to_output (const point& p) const { return p; }
+        inline point map_output_to_input (const point& p) const { return p; }
+
         void forward_inplace(const tensor& input, tensor& output)
         {
             tt::tanh(output, input);
@@ -1994,6 +2071,9 @@ namespace dlib
             impl::concat_helper_impl<TAG_TYPES...>::split(gradient_input, sub, 0);
         }
 
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
+
         const tensor& get_layer_params() const { return params; }
         tensor& get_layer_params() { return params; }
 

dlib/dnn/layers_abstract.h

@@ -348,6 +348,16 @@ namespace dlib
                 - returns the parameters that define the behavior of forward().
         !*/
 
+
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
+        /*!
+            These two functions are optional.  If provided, they should map between
+            (column,row) coordinates in input and output tensors of forward().  Providing
+            these functions allows you to use global utility functions like
+            input_tensor_to_output_tensor().
+        !*/
+
     };
 
     std::ostream& operator<<(std::ostream& out, const EXAMPLE_COMPUTATIONAL_LAYER_& item);
@@ -740,6 +750,8 @@ namespace dlib
         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);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -798,6 +810,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -850,6 +864,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1048,6 +1064,8 @@ namespace dlib
         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);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1139,6 +1157,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1273,6 +1293,8 @@ namespace dlib
         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& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1416,6 +1438,8 @@ namespace dlib
         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& computed_output, const tensor& gradient_input, SUBNET& sub, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1461,6 +1485,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1514,6 +1540,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1545,6 +1573,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1578,6 +1608,8 @@ namespace dlib
         template <typename SUBNET> void setup (const SUBNET& sub);
         void forward_inplace(const tensor& input, tensor& output);
         void backward_inplace(const tensor& computed_output, const tensor& gradient_input, tensor& data_grad, tensor& params_grad);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const; 
         tensor& get_layer_params(); 
         /*!
@@ -1729,6 +1761,8 @@ namespace dlib
         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);
+        point map_input_to_output(point p) const;
+        point map_output_to_input(point p) const;
         const tensor& get_layer_params() const;
         tensor& get_layer_params();
         /*!

dlib/dnn/utilities.h

@@ -5,6 +5,7 @@
 
 #include "core.h"
 #include "utilities_abstract.h"
+#include "../geometry.h"
 
 namespace dlib
 {
@@ -107,6 +108,63 @@ namespace dlib
         out << "</net>\n";
     }
 
+// ----------------------------------------------------------------------------------------
+
+    namespace impl
+    {
+
+        class visitor_net_map_input_to_output
+        {
+        public:
+
+            visitor_net_map_input_to_output(point& p_) : p(p_) {}
+
+            point& p;
+
+            template<typename layer_type>
+            void operator()(size_t idx, const layer_type& net) 
+            {
+                p = net.layer_details().map_input_to_output(p);
+            }
+        };
+
+        class visitor_net_map_output_to_input
+        {
+        public:
+            visitor_net_map_output_to_input(point& p_) : p(p_) {}
+
+            point& p;
+
+            template<typename layer_type>
+            void operator()(size_t idx, const layer_type& net) 
+            {
+                p = net.layer_details().map_output_to_input(p);
+            }
+        };
+    }
+
+    template <typename net_type>
+    inline point input_tensor_to_output_tensor(
+        const net_type& net,
+        point p 
+    )
+    {
+        impl::visitor_net_map_input_to_output temp(p);
+        visit_layers_backwards_range<0,net_type::num_layers-1>(net, temp);
+        return p;
+    }
+
+    template <typename net_type>
+    inline point output_tensor_to_input_tensor(
+        const net_type& net,
+        point p  
+    )
+    {
+        impl::visitor_net_map_output_to_input temp(p);
+        visit_layers_range<0,net_type::num_layers-1>(net, temp);
+        return p;
+    }
+
 // ----------------------------------------------------------------------------------------
 
 }

dlib/dnn/utilities_abstract.h

@@ -4,6 +4,7 @@
 #ifdef DLIB_DNn_UTILITIES_ABSTRACT_H_
 
 #include "core_abstract.h"
+#include "../geometry/vector_abstract.h"
 
 namespace dlib
 {
@@ -55,6 +56,42 @@ namespace dlib
               stream.
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    template <typename net_type>
+    point input_tensor_to_output_tensor(
+        const net_type& net,
+        point p 
+    );
+    /*!
+        requires
+            - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer.
+            - All layers in the net must provide map_input_to_output() functions.
+        ensures
+            - Given a point (i.e. a row,column coordinate) in the input tensor given to
+              net, this function returns the corresponding point in the output tensor
+              net.get_output().  This kind of mapping is useful when working with fully
+              convolutional networks as you will often want to know what parts of the
+              output feature maps correspond to what parts of the input.
+    !*/
+
+// ----------------------------------------------------------------------------------------
+
+    template <typename net_type>
+    point output_tensor_to_input_tensor(
+        const net_type& net,
+        point p  
+    );
+    /*!
+        requires
+            - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer.
+            - All layers in the net must provide map_output_to_input() functions.
+        ensures
+            - This function provides the reverse mapping of input_tensor_to_output_tensor().
+              That is, given a point in net.get_output(), what is the corresponding point
+              in the input tensor?
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
 }