Relaxed the preconditions so that layers are allowed to output tensors

that contain a different number of samples than their input tensors.

dlib/dnn/core.h

@@ -1059,7 +1059,7 @@ namespace dlib
         {
             subnetwork.forward(x);
             const dimpl::subnet_wrapper<subnet_type> wsub(subnetwork);
-            loss.to_label(wsub, obegin);
+            loss.to_label(x, wsub, obegin);
         }
 
         template <typename input_iterator, typename output_iterator>

dlib/dnn/core_abstract.h

@@ -309,7 +309,6 @@ namespace dlib
                     return forward(temp_tensor);
                 - The return value from this function is also available in #get_output().
                   i.e. this function returns #get_output().
-                - #get_output().num_samples() == std::distance(ibegin,iend)*sample_expansion_factor.
                 - have_same_dimensions(#get_gradient_input(), #get_output()) == true.
                 - All elements of #get_gradient_input() are set to 0. 
                   i.e. calling this function clears out #get_gradient_input() and ensures
@@ -341,7 +340,6 @@ namespace dlib
                     layer_details().forward(subnet(), get_output());
                 - The return value from this function is also available in #get_output().
                   i.e. this function returns #get_output().
-                - #get_output().num_samples() == x.num_samples().
                 - have_same_dimensions(#get_gradient_input(), #get_output()) == true
                 - All elements of #get_gradient_input() are set to 0. 
                   i.e. calling this function clears out #get_gradient_input() and ensures
@@ -382,7 +380,6 @@ namespace dlib
         /*!
             requires
                 - forward(x) was called to forward propagate x though the network.
-                - x.num_samples() == get_output().num_samples()
                 - get_gradient_input() has been set equal to the gradient of this network's
                   output with respect to some loss function.
                 - This instance of solvers has only ever been used with this network.  That

dlib/dnn/layers_abstract.h

@@ -163,7 +163,6 @@ namespace dlib
                   output into #output.  In particular, forward() can use any of the outputs
                   in sub (e.g. sub.get_output(), sub.subnet().get_output(), etc.) to
                   compute whatever it wants.
-                - #output.num_samples() == sub.get_output().num_samples()
         !*/
 
         template <typename SUBNET>

dlib/dnn/loss.h

@@ -24,6 +24,7 @@ namespace dlib
             typename label_iterator
             >
         void to_label (
+            const tensor& input_tensor,
             const SUB_TYPE& sub,
             label_iterator iter
         ) const
@@ -32,7 +33,7 @@ namespace dlib
             DLIB_CASSERT(output_tensor.nr() == 1 && 
                          output_tensor.nc() == 1 && 
                          output_tensor.k() == 1,"");
-            DLIB_CASSERT(output_tensor.num_samples()%sample_expansion_factor == 0,"");
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
 
             const float* out_data = output_tensor.host();
             for (long i = 0; i < output_tensor.num_samples(); ++i)

dlib/dnn/loss_abstract.h

@@ -56,6 +56,7 @@ namespace dlib
             typename label_iterator
             >
         void to_label (
+            const tensor& input_tensor,
             const SUB_TYPE& sub,
             label_iterator iter
         ) const;
@@ -63,19 +64,19 @@ namespace dlib
             requires
                 - SUBNET implements the SUBNET interface defined at the top of
                   layers_abstract.h.
-                - sub.get_output().num_samples()%sample_expansion_factor == 0.
-                - All outputs in each layer of sub have the same number of samples.  That
-                  is, for all valid i: 
-                    - sub.get_output().num_samples() == layer<i>(sub).get_output().num_samples()
+                - input_tensor was given as input to the network sub and the outputs are
+                  now visible in layer<i>(sub).get_output(), for all valid i.
+                - input_tensor.num_samples() > 0
+                - input_tensor.num_samples()%sample_expansion_factor == 0.
                 - iter == an iterator pointing to the beginning of a range of
-                  sub.get_output().num_samples()/sample_expansion_factor elements.
-                  Moreover, they must be label_type elements.
+                  input_tensor.num_samples()/sample_expansion_factor elements.  Moreover,
+                  they must be label_type elements.
             ensures
                 - Converts the output of the provided network to label_type objects and
                   stores the results into the range indicated by iter.  In particular, for
-                  all valid i and j, it will be the case that:
-                    *(iter+i/sample_expansion_factor) is the output corresponding to the
-                    ith sample in layer<j>(sub).get_output().
+                  all valid i, it will be the case that:
+                    *(iter+i/sample_expansion_factor) is the element corresponding to the
+                    output of sub for the ith sample in input_tensor.
         !*/
 
         template <
@@ -96,15 +97,14 @@ namespace dlib
                 - input_tensor.num_samples() > 0
                 - input_tensor.num_samples()%sample_expansion_factor == 0.
                 - for all valid i:
-                    - layer<i>(sub).get_output().num_samples() == input_tensor.num_samples().
                     - layer<i>(sub).get_gradient_input() has the same dimensions as
                       layer<i>(sub).get_output().
                 - truth == an iterator pointing to the beginning of a range of
-                  input_tensor.num_samples()/sample_expansion_factor elements.  In
-                  particular, they must be label_type elements.
-                - for all valid i and j:
+                  input_tensor.num_samples()/sample_expansion_factor elements.  Moreover,
+                  they must be label_type elements.
+                - for all valid i:
                     - *(truth+i/sample_expansion_factor) is the label of the ith sample in
-                      layer<j>(sub).get_output().
+                      input_tensor.
             ensures
                 - This function computes a loss function that describes how well the output
                   of sub matches the expected labels given by truth.  Let's write the loss
@@ -154,6 +154,7 @@ namespace dlib
             typename label_iterator
             >
         void to_label (
+            const tensor& input_tensor,
             const SUB_TYPE& sub,
             label_iterator iter
         ) const;