Cleaned up assert statements a bit.

dlib/dnn/core.h

@@ -861,7 +861,7 @@ namespace dlib
         template <typename solver_type>
         void update_parameters(sstack<solver_type> solvers, double learning_rate)
         {
-            DLIB_CASSERT(solvers.size()>=num_computational_layers,"");
+            DLIB_CASSERT(solvers.size()>=num_computational_layers);
             // Don't try to adjust the parameters if this layer doesn't have any or the
             // learning rate is disabled for this layer.
             if (params_grad.size() != 0 && get_learning_rate_multiplier(details) != 0)
@@ -1158,7 +1158,7 @@ namespace dlib
         const tensor& forward (const tensor& x)
         {
             DLIB_CASSERT(sample_expansion_factor() != 0, "You must call to_tensor() before this function can be used.");
-            DLIB_CASSERT(x.num_samples()%sample_expansion_factor() == 0,"");
+            DLIB_CASSERT(x.num_samples()%sample_expansion_factor() == 0);
             subnet_wrapper wsub(x, grad_final, _sample_expansion_factor);
             if (!this_layer_setup_called)
             {
@@ -1224,7 +1224,7 @@ namespace dlib
         template <typename solver_type>
         void update_parameters(sstack<solver_type> solvers, double learning_rate)
         {
-            DLIB_CASSERT(solvers.size()>=num_computational_layers,"");
+            DLIB_CASSERT(solvers.size()>=num_computational_layers);
             // Don't try to adjust the parameters if this layer doesn't have any or the
             // learning rate is disabled for this layer.
             if (params_grad.size() != 0 && get_learning_rate_multiplier(details) != 0) 
@@ -1615,7 +1615,7 @@ namespace dlib
             size_t i 
         ) const
         { 
-            DLIB_CASSERT(i < num_repetitions(), "");
+            DLIB_CASSERT(i < num_repetitions());
             return details[i]; 
         }
 
@@ -1623,7 +1623,7 @@ namespace dlib
             size_t i 
         ) 
         { 
-            DLIB_CASSERT(i < num_repetitions(), "");
+            DLIB_CASSERT(i < num_repetitions());
             return details[i]; 
         }
 

dlib/dnn/cublas_dlibapi.cpp

@@ -119,25 +119,25 @@ namespace dlib
             {
                 DLIB_ASSERT( dest_nr == lhs_nc &&
                               dest_nc == rhs_nr &&
-                              lhs_nr == rhs_nc,"")
+                              lhs_nr == rhs_nc)
             }
             else if (!trans_lhs && trans_rhs)
             {
                 DLIB_ASSERT( dest_nr == lhs_nr &&
                               dest_nc == rhs_nr &&
-                              lhs_nc == rhs_nc,"")
+                              lhs_nc == rhs_nc)
             }
             else if (trans_lhs && !trans_rhs)
             {
                 DLIB_ASSERT( dest_nr == lhs_nc &&
                               dest_nc == rhs_nc &&
-                              lhs_nr == rhs_nr,"")
+                              lhs_nr == rhs_nr)
             }
             else
             {
                 DLIB_ASSERT( dest_nr == lhs_nr &&
                               dest_nc == rhs_nc &&
-                              lhs_nc == rhs_nr,"")
+                              lhs_nc == rhs_nr)
             }
 
             const int k = trans_rhs ? rhs_nc : rhs_nr;

dlib/dnn/cuda_dlib.cu

@@ -173,11 +173,11 @@ namespace dlib
 
             DLIB_CASSERT(dest.k() == src1.k() && src1.k() == src2.k() &&
                 dest.nr() == src1.nr() && src1.nr() == src2.nr() &&
-                dest.nc() == src1.nc() && src1.nc() == src2.nc() ,"");
+                dest.nc() == src1.nc() && src1.nc() == src2.nc() );
             const long MD = std::max(std::max(dest.num_samples(),src1.num_samples()),src2.num_samples());
             DLIB_CASSERT((dest.num_samples()==1 || dest.num_samples()==MD) &&
                 (src1.num_samples()==1 || src1.num_samples()==MD) &&
-                (src2.num_samples()==1 || src2.num_samples()==MD) ,"");
+                (src2.num_samples()==1 || src2.num_samples()==MD) );
 
             if (dest.size() == 0)
                 return;
@@ -278,7 +278,7 @@ namespace dlib
         {
             if (have_same_dimensions(dest,src1))
             {
-                DLIB_CASSERT(src2.num_samples() == 1 && src2.nr() == 1 && src2.nc() == 1 && src2.k() == src1.k(),"");
+                DLIB_CASSERT(src2.num_samples() == 1 && src2.nr() == 1 && src2.nc() == 1 && src2.k() == src1.k());
                 if (dest.size() == 0)
                     return;
 
@@ -291,8 +291,8 @@ namespace dlib
             }
             else
             {
-                DLIB_CASSERT(have_same_dimensions(src1,src2),"");
-                DLIB_CASSERT(dest.num_samples() == 1 && dest.nr() == 1 && dest.nc() == 1 && dest.k() == src1.k(),"");
+                DLIB_CASSERT(have_same_dimensions(src1,src2));
+                DLIB_CASSERT(dest.num_samples() == 1 && dest.nr() == 1 && dest.nc() == 1 && dest.k() == src1.k());
                 if (dest.size() == 0)
                     return;
 
@@ -404,7 +404,7 @@ namespace dlib
             const float B
         )
         {
-            DLIB_CASSERT(dest.size()==src.size(),"");
+            DLIB_CASSERT(dest.size()==src.size());
             if (B != 0)
                 launch_kernel(_cuda_affine_transform1,max_jobs(dest.size()),dest.device(), src.device(), src.size(), A, B);
             else
@@ -417,7 +417,7 @@ namespace dlib
             const float A
         )
         {
-            DLIB_CASSERT(dest.size()==src.size(),"");
+            DLIB_CASSERT(dest.size()==src.size());
             launch_kernel(_cuda_affine_transform1_0,max_jobs(dest.size()),dest.device(), src.device(), src.size(), A);
         }
 
@@ -448,8 +448,8 @@ namespace dlib
             const float C
         )
         {
-            DLIB_CASSERT(dest.size()==src1.size(),"");
-            DLIB_CASSERT(dest.size()==src2.size(),"");
+            DLIB_CASSERT(dest.size()==src1.size());
+            DLIB_CASSERT(dest.size()==src2.size());
             if (C != 0)
                 launch_kernel(_cuda_affine_transform4,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(), dest.size(), A, B, C);
             else
@@ -464,8 +464,8 @@ namespace dlib
             const float B
         )
         {
-            DLIB_CASSERT(dest.size()==src1.size(),"");
-            DLIB_CASSERT(dest.size()==src2.size(),"");
+            DLIB_CASSERT(dest.size()==src1.size());
+            DLIB_CASSERT(dest.size()==src2.size());
             launch_kernel(_cuda_affine_transform4_0,max_jobs(dest.size()),dest.device(), src1.device(), src2.device(), dest.size(), A, B);
         }
 
@@ -485,7 +485,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(dest.size()==src.size(),"");
+            DLIB_CASSERT(dest.size()==src.size());
             launch_kernel(_cuda_add_scaled,max_jobs(dest.size()),dest.device(), src.device(), dest.size(), scale);
         }
 
@@ -512,9 +512,9 @@ namespace dlib
             const float D
         )
         {
-            DLIB_CASSERT(dest.size()==src1.size(),"");
-            DLIB_CASSERT(dest.size()==src2.size(),"");
-            DLIB_CASSERT(dest.size()==src3.size(),"");
+            DLIB_CASSERT(dest.size()==src1.size());
+            DLIB_CASSERT(dest.size()==src2.size());
+            DLIB_CASSERT(dest.size()==src3.size());
             launch_kernel(_cuda_affine_transform5,max_jobs(dest.size()),dest.device(), src1.device(),
                 src2.device(), src3.device(), dest.size(), A, B, C, D);
         }
@@ -544,10 +544,10 @@ namespace dlib
             const float C
         )
         {
-            DLIB_CASSERT(dest.size()==src1.size(),"");
-            DLIB_CASSERT(dest.size()==src2.size(),"");
-            DLIB_CASSERT(dest.size()==src3.size(),"");
-            DLIB_CASSERT(begin <= end && end <= dest.size(),"");
+            DLIB_CASSERT(dest.size()==src1.size());
+            DLIB_CASSERT(dest.size()==src2.size());
+            DLIB_CASSERT(dest.size()==src3.size());
+            DLIB_CASSERT(begin <= end && end <= dest.size());
             launch_kernel(_cuda_affine_transform_range,max_jobs(end-begin),
                 dest.device(), src1.device(),
                 src2.device(), src3.device(), begin, end, A, B, C);
@@ -577,10 +577,10 @@ namespace dlib
             const tensor& B
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest, src),"");
+            DLIB_CASSERT(have_same_dimensions(dest, src));
             DLIB_CASSERT(
                   ((A.num_samples()==1 && B.num_samples()==1) ||
-                  (A.num_samples()==src.num_samples() && B.num_samples()==src.num_samples())),"");
+                  (A.num_samples()==src.num_samples() && B.num_samples()==src.num_samples())));
             DLIB_CASSERT(
                   A.nr()==B.nr() && B.nr()==src.nr() &&
                   A.nc()==B.nc() && B.nc()==src.nc() &&
@@ -648,8 +648,8 @@ namespace dlib
             DLIB_CASSERT(s.size() == m.size() &&
                          s.size() == v.size() &&
                          s.size() == params.size() &&
-                         s.size() == params_grad.size(),"");
-            DLIB_CASSERT(begin <= end && end <= params.size(),"");
+                         s.size() == params_grad.size());
+            DLIB_CASSERT(begin <= end && end <= params.size());
             const float alpha = learning_rate*std::sqrt(1-std::pow(momentum2,t))/(1-std::pow(momentum1, t));
 
             launch_kernel(_cuda_compute_adam_update,max_jobs(end-begin),
@@ -675,9 +675,9 @@ namespace dlib
             const tensor& B
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest, src),"");
-            DLIB_CASSERT(have_same_dimensions(A, B),"");
-            DLIB_CASSERT(A.num_samples() == 1 && A.nr() == 1 && A.nc() == 1 && A.k() == src.k(),"");
+            DLIB_CASSERT(have_same_dimensions(dest, src));
+            DLIB_CASSERT(have_same_dimensions(A, B));
+            DLIB_CASSERT(A.num_samples() == 1 && A.nr() == 1 && A.nc() == 1 && A.k() == src.k());
 
             launch_kernel(_cuda_affine_transform_conv,max_jobs(dest.size()),
                     dest.device(), src.device(), src.size(), A.device(), B.device(), src.nr()*src.nc(), src.k());
@@ -705,7 +705,7 @@ namespace dlib
                   gradient_input.k() == grad.k() &&
                   gradient_input.nr() == grad.nr() &&
                   gradient_input.nc() == grad.nc() &&
-                  gradient_input.size() > 0,"");
+                  gradient_input.size() > 0);
 
             launch_kernel(_add_bias_gradient,max_jobs(grad.size()),grad.device(), gradient_input.device(), grad.size(), gradient_input.size());
         }
@@ -750,8 +750,8 @@ namespace dlib
             size_t idx
         )
         {
-            DLIB_CASSERT(a.size() == b.size(), "");
-            DLIB_CASSERT(idx < result.size(), "");
+            DLIB_CASSERT(a.size() == b.size());
+            DLIB_CASSERT(idx < result.size());
 
             launch_kernel(_cuda_dot, max_jobs(a.size()), a.device(), b.device(), a.size(), result.device()+idx);
         }

dlib/dnn/cudnn_dlibapi.cpp

@@ -326,7 +326,7 @@ namespace dlib
                   gradient_input.k() == grad.k() &&
                   gradient_input.size() > 0 &&
                   is_same_object(grad,gradient_input) == false
-                  ,"");
+                  );
 
             const float alpha = 1;
             const float beta = 0;
@@ -417,8 +417,8 @@ namespace dlib
         )
         {
             DLIB_CASSERT(0 <= averaging_factor && averaging_factor <= 1, "averaging_factor: " << averaging_factor);
-            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means),"");
-            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds),"");
+            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means));
+            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds));
             DLIB_CASSERT(
                 src.num_samples() > 1 &&
                 gamma.num_samples() == 1 && 
@@ -491,15 +491,15 @@ namespace dlib
         )
         {
             const long num = src.k()*src.nr()*src.nc();
-            DLIB_CASSERT(src.num_samples() > 1, "");
-            DLIB_CASSERT(num == (long)means.size(),"");
-            DLIB_CASSERT(num == (long)invstds.size(),"");
-            DLIB_CASSERT(num == (long)gamma.size(),"");
-            DLIB_CASSERT(num == (long)gamma_grad.size(),"");
-            DLIB_CASSERT(num == (long)beta_grad.size(),"");
-            DLIB_CASSERT(have_same_dimensions(gradient_input, src),"");
-            DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad),"");
-            DLIB_CASSERT(eps > 0,"");
+            DLIB_CASSERT(src.num_samples() > 1);
+            DLIB_CASSERT(num == (long)means.size());
+            DLIB_CASSERT(num == (long)invstds.size());
+            DLIB_CASSERT(num == (long)gamma.size());
+            DLIB_CASSERT(num == (long)gamma_grad.size());
+            DLIB_CASSERT(num == (long)beta_grad.size());
+            DLIB_CASSERT(have_same_dimensions(gradient_input, src));
+            DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad));
+            DLIB_CASSERT(eps > 0);
 
             const float in_scale = 1;
             const float out_scale = 1;
@@ -606,8 +606,8 @@ namespace dlib
         )
         {
             DLIB_CASSERT(0 <= averaging_factor && averaging_factor <= 1, "averaging_factor: " << averaging_factor);
-            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means),"");
-            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds),"");
+            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_means,means));
+            DLIB_CASSERT(averaging_factor==1 || have_same_dimensions(running_variances,invstds));
             DLIB_CASSERT(
                 src.num_samples() > 1 &&
                 gamma.num_samples() == 1 && 
@@ -680,14 +680,14 @@ namespace dlib
             tensor& beta_grad 
         )
         {
-            DLIB_CASSERT(src.k() == (long)means.size(),"");
-            DLIB_CASSERT(src.k() == (long)invstds.size(),"");
-            DLIB_CASSERT(src.k() == (long)gamma.size(),"");
-            DLIB_CASSERT(src.k() == (long)gamma_grad.size(),"");
-            DLIB_CASSERT(src.k() == (long)beta_grad.size(),"");
-            DLIB_CASSERT(have_same_dimensions(gradient_input, src),"");
-            DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad),"");
-            DLIB_CASSERT(eps > 0,"");
+            DLIB_CASSERT(src.k() == (long)means.size());
+            DLIB_CASSERT(src.k() == (long)invstds.size());
+            DLIB_CASSERT(src.k() == (long)gamma.size());
+            DLIB_CASSERT(src.k() == (long)gamma_grad.size());
+            DLIB_CASSERT(src.k() == (long)beta_grad.size());
+            DLIB_CASSERT(have_same_dimensions(gradient_input, src));
+            DLIB_CASSERT(have_same_dimensions(gradient_input, src_grad));
+            DLIB_CASSERT(eps > 0);
 
             const float in_scale = 1;
             const float out_scale = 1;
@@ -794,7 +794,7 @@ namespace dlib
             int padding_x_
         ) 
         {
-            DLIB_CASSERT(data.k() == filters.k(),"");
+            DLIB_CASSERT(data.k() == filters.k());
 
             // if the last call to setup gave the same exact settings then don't do
             // anything.
@@ -969,10 +969,10 @@ namespace dlib
             int padding_x
         )
         {
-            DLIB_CASSERT(is_same_object(output,data) == false,"");
-            DLIB_CASSERT(is_same_object(output,filters) == false,"");
-            DLIB_CASSERT(filters.k() == data.k(),"");
-            DLIB_CASSERT(stride_y > 0 && stride_x > 0,"");
+            DLIB_CASSERT(is_same_object(output,data) == false);
+            DLIB_CASSERT(is_same_object(output,filters) == false);
+            DLIB_CASSERT(filters.k() == data.k());
+            DLIB_CASSERT(stride_y > 0 && stride_x > 0);
             DLIB_CASSERT(filters.nc() <= data.nc() + 2*padding_x,
                 "Filter windows must be small enough to fit into the padded image."
                 << "\n\t filters.nc(): " << filters.nc() 
@@ -992,9 +992,9 @@ namespace dlib
             output.set_size(out_num_samples, out_k, out_nr, out_nc);
 
             DLIB_ASSERT(output.num_samples() == data.num_samples(),out_num_samples << "  " << data.num_samples());
-            DLIB_ASSERT(output.k() == filters.num_samples(),"");
-            DLIB_ASSERT(output.nr() == 1+(data.nr()+2*padding_y-filters.nr())/stride_y,"");
-            DLIB_ASSERT(output.nc() == 1+(data.nc()+2*padding_x-filters.nc())/stride_x,"");
+            DLIB_ASSERT(output.k() == filters.num_samples());
+            DLIB_ASSERT(output.nr() == 1+(data.nr()+2*padding_y-filters.nr())/stride_y);
+            DLIB_ASSERT(output.nc() == 1+(data.nc()+2*padding_x-filters.nc())/stride_x);
 
 
 
@@ -1221,8 +1221,8 @@ namespace dlib
 
             dest.set_size(outN,outC,outH,outW);
 
-            DLIB_CASSERT(dest.num_samples() == src.num_samples(),"");
-            DLIB_CASSERT(dest.k() == src.k(),"");
+            DLIB_CASSERT(dest.num_samples() == src.num_samples());
+            DLIB_CASSERT(dest.k() == src.k());
             DLIB_CASSERT(dest.nr() == 1 + (src.nr() + 2*padding_y - window_height)/stride_y, 
                 "\n stride_y:  " << stride_y  <<
                 "\n padding_y: " << padding_y  <<
@@ -1255,8 +1255,8 @@ namespace dlib
             tensor& grad 
         )
         {
-            DLIB_CASSERT(have_same_dimensions(gradient_input,dest),"");
-            DLIB_CASSERT(have_same_dimensions(src,grad),"");
+            DLIB_CASSERT(have_same_dimensions(gradient_input,dest));
+            DLIB_CASSERT(have_same_dimensions(src,grad));
 
             const float alpha = 1;
             const float beta = 1;
@@ -1282,7 +1282,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest,src),"");
+            DLIB_CASSERT(have_same_dimensions(dest,src));
             if (src.size() == 0)
                 return;
 
@@ -1309,7 +1309,7 @@ namespace dlib
         {
             DLIB_CASSERT(
                   have_same_dimensions(dest,gradient_input) == true &&
-                  have_same_dimensions(dest,grad) == true , "");
+                  have_same_dimensions(dest,grad) == true );
             if (dest.size() == 0)
                 return;
 
@@ -1336,7 +1336,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest,src),"");
+            DLIB_CASSERT(have_same_dimensions(dest,src));
             if (src.size() == 0)
                 return;
 
@@ -1360,7 +1360,7 @@ namespace dlib
         {
             DLIB_CASSERT(
                   have_same_dimensions(dest,gradient_input) == true &&
-                  have_same_dimensions(dest,grad) == true , "");
+                  have_same_dimensions(dest,grad) == true );
             if (dest.size() == 0)
                 return;
 
@@ -1387,7 +1387,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest,src),"");
+            DLIB_CASSERT(have_same_dimensions(dest,src));
             if (src.size() == 0)
                 return;
 
@@ -1411,7 +1411,7 @@ namespace dlib
         {
             DLIB_CASSERT(
                   have_same_dimensions(dest,gradient_input) == true &&
-                  have_same_dimensions(dest,grad) == true , "");
+                  have_same_dimensions(dest,grad) == true );
             if (dest.size() == 0)
                 return;
 
@@ -1438,7 +1438,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(have_same_dimensions(dest,src),"");
+            DLIB_CASSERT(have_same_dimensions(dest,src));
             if (src.size() == 0)
                 return;
 
@@ -1462,7 +1462,7 @@ namespace dlib
         {
             DLIB_CASSERT(
                   have_same_dimensions(dest,gradient_input) == true &&
-                  have_same_dimensions(dest,grad) == true, "");
+                  have_same_dimensions(dest,grad) == true);
             if (dest.size() == 0)
                 return;
 

dlib/dnn/gpu_data.cpp

@@ -23,7 +23,7 @@ namespace dlib
         const gpu_data& src
     )
     {
-        DLIB_CASSERT(dest.size() == src.size(), "");
+        DLIB_CASSERT(dest.size() == src.size());
         if (src.size() == 0 || &dest == &src)
             return;
 
@@ -38,8 +38,8 @@ namespace dlib
         size_t num
     )
     {
-        DLIB_CASSERT(dest_offset + num <= dest.size(), "");
-        DLIB_CASSERT(src_offset + num <= src.size(), "");
+        DLIB_CASSERT(dest_offset + num <= dest.size());
+        DLIB_CASSERT(src_offset + num <= src.size());
         if (num == 0)
             return;
 

dlib/dnn/gpu_data.h

@@ -221,7 +221,7 @@ namespace dlib
 
     inline void memcpy (gpu_data& dest, const gpu_data& src)
     {
-        DLIB_CASSERT(dest.size() == src.size(), "");
+        DLIB_CASSERT(dest.size() == src.size());
         if (src.size() == 0 || &dest == &src)
             return;
         std::memcpy(dest.host_write_only(), src.host(), sizeof(float)*src.size());
@@ -235,8 +235,8 @@ namespace dlib
         size_t num
     )
     {
-        DLIB_CASSERT(dest_offset + num <= dest.size(), "");
-        DLIB_CASSERT(src_offset + num <= src.size(), "");
+        DLIB_CASSERT(dest_offset + num <= dest.size());
+        DLIB_CASSERT(src_offset + num <= src.size());
         if (num == 0)
             return;
         if (&dest == &src && std::max(dest_offset, src_offset) < std::min(dest_offset,src_offset)+num)

dlib/dnn/input.h

@@ -64,7 +64,7 @@ namespace dlib
             resizable_tensor& data
         ) const
         {
-            DLIB_CASSERT(std::distance(ibegin,iend) > 0,"");
+            DLIB_CASSERT(std::distance(ibegin,iend) > 0);
             const auto nr = ibegin->nr();
             const auto nc = ibegin->nc();
             // make sure all the input matrices have the same dimensions
@@ -187,7 +187,7 @@ namespace dlib
             resizable_tensor& data
         ) const
         {
-            DLIB_CASSERT(std::distance(ibegin,iend) > 0,"");
+            DLIB_CASSERT(std::distance(ibegin,iend) > 0);
             // make sure all input images have the correct size
             for (auto i = ibegin; i != iend; ++i)
             {
@@ -305,7 +305,7 @@ namespace dlib
             resizable_tensor& data
         ) const
         {
-            DLIB_CASSERT(std::distance(ibegin,iend) > 0,"");
+            DLIB_CASSERT(std::distance(ibegin,iend) > 0);
             const auto nr = ibegin->nr();
             const auto nc = ibegin->nc();
             // make sure all the input matrices have the same dimensions
@@ -398,7 +398,7 @@ namespace dlib
             resizable_tensor& data
         ) const
         {
-            DLIB_CASSERT(std::distance(ibegin,iend) > 0,"");
+            DLIB_CASSERT(std::distance(ibegin,iend) > 0);
             const auto nr = ibegin->nr();
             const auto nc = ibegin->nc();
             // make sure all the input matrices have the same dimensions

dlib/dnn/layers.h

@@ -1092,7 +1092,7 @@ namespace dlib
             drop_rate(drop_rate_),
             rnd(std::rand())
         {
-            DLIB_CASSERT(0 <= drop_rate && drop_rate <= 1,"");
+            DLIB_CASSERT(0 <= drop_rate && drop_rate <= 1);
         }
 
         // We have to add a copy constructor and assignment operator because the rnd object

dlib/dnn/loss.h

@@ -29,13 +29,13 @@ namespace dlib
             label_iterator iter
         ) const
         {
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
 
             const tensor& output_tensor = sub.get_output();
             DLIB_CASSERT(output_tensor.nr() == 1 && 
                          output_tensor.nc() == 1 && 
-                         output_tensor.k() == 1,"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+                         output_tensor.k() == 1);
+            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)
@@ -57,14 +57,14 @@ namespace dlib
             const tensor& output_tensor = sub.get_output();
             tensor& grad = sub.get_gradient_input();
 
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
-            DLIB_CASSERT(input_tensor.num_samples() != 0,"");
-            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0,"");
-            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples(),"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() != 0);
+            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
+            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
             DLIB_CASSERT(output_tensor.nr() == 1 && 
                          output_tensor.nc() == 1 && 
-                         output_tensor.k() == 1,"");
+                         output_tensor.k() == 1);
 
             // The loss we output is the average loss over the mini-batch.
             const double scale = 1.0/output_tensor.num_samples();
@@ -136,13 +136,13 @@ namespace dlib
             label_iterator iter
         ) const
         {
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
 
             const tensor& output_tensor = sub.get_output();
             DLIB_CASSERT(output_tensor.nr() == 1 && 
                          output_tensor.nc() == 1 && 
-                         output_tensor.k() == 1,"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+                         output_tensor.k() == 1);
+            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)
@@ -165,17 +165,17 @@ namespace dlib
             const tensor& output_tensor = sub.get_output();
             tensor& grad = sub.get_gradient_input();
 
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
-            DLIB_CASSERT(input_tensor.num_samples() != 0,"");
-            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0,"");
-            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples(),"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() != 0);
+            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
+            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
             DLIB_CASSERT(output_tensor.nr() == 1 && 
                          output_tensor.nc() == 1 && 
-                         output_tensor.k() == 1,"");
+                         output_tensor.k() == 1);
             DLIB_CASSERT(grad.nr() == 1 && 
                          grad.nc() == 1 && 
-                         grad.k() == 1,"");
+                         grad.k() == 1);
 
             tt::sigmoid(grad, output_tensor);
 
@@ -253,10 +253,10 @@ namespace dlib
         ) const
         {
             const tensor& output_tensor = sub.get_output();
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
             DLIB_CASSERT(output_tensor.nr() == 1 && 
-                         output_tensor.nc() == 1 ,"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+                         output_tensor.nc() == 1 );
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
 
 
             // Note that output_tensor.k() should match the number of labels.
@@ -282,15 +282,15 @@ namespace dlib
             const tensor& output_tensor = sub.get_output();
             tensor& grad = sub.get_gradient_input();
 
-            DLIB_CASSERT(sub.sample_expansion_factor() == 1,"");
-            DLIB_CASSERT(input_tensor.num_samples() != 0,"");
-            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0,"");
-            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples(),"");
-            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples(),"");
+            DLIB_CASSERT(sub.sample_expansion_factor() == 1);
+            DLIB_CASSERT(input_tensor.num_samples() != 0);
+            DLIB_CASSERT(input_tensor.num_samples()%sub.sample_expansion_factor() == 0);
+            DLIB_CASSERT(input_tensor.num_samples() == grad.num_samples());
+            DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
             DLIB_CASSERT(output_tensor.nr() == 1 && 
-                         output_tensor.nc() == 1,"");
+                         output_tensor.nc() == 1);
             DLIB_CASSERT(grad.nr() == 1 && 
-                         grad.nc() == 1,"");
+                         grad.nc() == 1);
 
             tt::softmax(grad, output_tensor);
 

dlib/dnn/solvers.h

@@ -43,7 +43,7 @@ namespace dlib
         {
             const tensor& params = l.get_layer_params();
 
-            DLIB_CASSERT(params.size() != 0,"");
+            DLIB_CASSERT(params.size() != 0);
             if (v.size() == 0)
             {
                 v.copy_size(params_grad);
@@ -131,7 +131,7 @@ namespace dlib
         {
             const tensor& params = l.get_layer_params();
 
-            DLIB_CASSERT(params.size() != 0,"");
+            DLIB_CASSERT(params.size() != 0);
             if (v.size() == 0)
             {
                 v.copy_size(params_grad);
@@ -204,7 +204,7 @@ namespace dlib
         )
         {
             const tensor& params = l.get_layer_params();
-            DLIB_CASSERT(params.size() != 0,"");
+            DLIB_CASSERT(params.size() != 0);
             if (v.size() == 0)
             {
                 m.copy_size(params_grad);
@@ -305,7 +305,7 @@ namespace dlib
         )
         {
             const tensor& params = l.get_layer_params();
-            DLIB_CASSERT(params.size() != 0,"");
+            DLIB_CASSERT(params.size() != 0);
             if (v.size() == 0)
             {
                 m.copy_size(params_grad);

dlib/dnn/tensor.h

@@ -101,7 +101,7 @@ namespace dlib
         tensor& operator= (const matrix_exp<EXP>& item)
         {
             DLIB_CASSERT(num_samples() == item.nr() &&
-                         nr()*nc()*k() == item.nc(),"");
+                         nr()*nc()*k() == item.nc());
             static_assert((is_same_type<float, typename EXP::type>::value == true),
                 "To assign a matrix to a tensor the matrix must contain float values");
 
@@ -113,7 +113,7 @@ namespace dlib
         tensor& operator+= (const matrix_exp<EXP>& item)
         {
             DLIB_CASSERT(num_samples() == item.nr() &&
-                         nr()*nc()*k() == item.nc(),"");
+                         nr()*nc()*k() == item.nc());
             static_assert((is_same_type<float, typename EXP::type>::value == true),
                 "To assign a matrix to a tensor the matrix must contain float values");
             set_ptrm(host(), m_n, m_nr*m_nc*m_k) += item;
@@ -124,7 +124,7 @@ namespace dlib
         tensor& operator-= (const matrix_exp<EXP>& item)
         {
             DLIB_CASSERT(num_samples() == item.nr() &&
-                         nr()*nc()*k() == item.nc(),"");
+                         nr()*nc()*k() == item.nc());
             static_assert((is_same_type<float, typename EXP::type>::value == true),
                 "To assign a matrix to a tensor the matrix must contain float values");
             set_ptrm(host(), m_n, m_nr*m_nc*m_k) -= item;
@@ -137,8 +137,8 @@ namespace dlib
             const matrix_exp<EXP>& item
         )
         {
-            DLIB_CASSERT(idx < num_samples(), "");
-            DLIB_CASSERT(item.size() == nr()*nc()*k(), "");
+            DLIB_CASSERT(idx < num_samples());
+            DLIB_CASSERT(item.size() == nr()*nc()*k());
             static_assert((is_same_type<float, typename EXP::type>::value == true),
                 "To assign a matrix to a tensor the matrix must contain float values");
             set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) = item;
@@ -151,8 +151,8 @@ namespace dlib
             const matrix_exp<EXP>& item
         )
         {
-            DLIB_CASSERT(idx < num_samples(), "");
-            DLIB_CASSERT(item.size() == nr()*nc()*k(), "");
+            DLIB_CASSERT(idx < num_samples());
+            DLIB_CASSERT(item.size() == nr()*nc()*k());
             static_assert((is_same_type<float, typename EXP::type>::value == true),
                 "To assign a matrix to a tensor the matrix must contain float values");
             set_ptrm(host()+idx*item.size(), item.nr(), item.nc()) += item;
@@ -169,7 +169,7 @@ namespace dlib
             const tensor& src
         )
         {
-            DLIB_CASSERT(dest.size() == src.size(), "");
+            DLIB_CASSERT(dest.size() == src.size());
             memcpy(dest.data(), dest.get_alias_offset(),  
                    src.data(),  src.get_alias_offset(), 
                    src.size());
@@ -285,7 +285,7 @@ namespace dlib
             long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
         ) 
         {
-            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0,"");
+            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);
 
             set_size(n_,k_,nr_,nc_);
         }
@@ -351,7 +351,7 @@ namespace dlib
             long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
         )
         {
-            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0,"");
+            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);
 
             m_n = n_;
             m_k = k_;
@@ -469,7 +469,7 @@ namespace dlib
         const tensor& b
     )
     {
-        DLIB_CASSERT(a.size() == b.size(), "");
+        DLIB_CASSERT(a.size() == b.size());
         const float* da = a.host();
         const float* db = b.host();
         double sum = 0;
@@ -559,7 +559,7 @@ namespace dlib
             long n_, long k_ = 1, long nr_ = 1, long nc_ = 1
         ) 
         {
-            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0,"");
+            DLIB_ASSERT( n_ >= 0 && k_ >= 0 && nr_ >= 0 && nc_ >= 0);
 
             inst.m_n = n_;
             inst.m_k = k_;
@@ -588,7 +588,7 @@ namespace dlib
             size_t offset
         ) 
         {
-            DLIB_CASSERT(offset+size() <= t.size(),"");
+            DLIB_CASSERT(offset+size() <= t.size());
 
 #ifdef DLIB_USE_CUDA
             if (!inst.cudnn_descriptor)

dlib/dnn/tensor_tools.cpp

@@ -101,7 +101,7 @@ namespace dlib { namespace tt
         float stddev
     )
     {
-        DLIB_CASSERT(data.size()%2 == 0,"");
+        DLIB_CASSERT(data.size()%2 == 0);
 #ifdef DLIB_USE_CUDA
         rnd.fill_gaussian(data, mean, stddev);
 #else
@@ -135,11 +135,11 @@ namespace dlib { namespace tt
     {
         DLIB_CASSERT(dest.k() == src1.k() && src1.k() == src2.k() &&
             dest.nr() == src1.nr() && src1.nr() == src2.nr() &&
-            dest.nc() == src1.nc() && src1.nc() == src2.nc() ,"");
+            dest.nc() == src1.nc() && src1.nc() == src2.nc() );
         const long MD = std::max(std::max(dest.num_samples(),src1.num_samples()),src2.num_samples());
         DLIB_CASSERT((dest.num_samples()==1 || dest.num_samples()==MD) &&
                     (src1.num_samples()==1 || src1.num_samples()==MD) &&
-                    (src2.num_samples()==1 || src2.num_samples()==MD) ,"");
+                    (src2.num_samples()==1 || src2.num_samples()==MD) );
 #ifdef DLIB_USE_CUDA
         cuda::multiply(add_to, dest, src1, src2);
 #else

dlib/dnn/trainer.h

@@ -143,7 +143,7 @@ namespace dlib
             unsigned long batch_size 
         )
         {
-            DLIB_CASSERT(batch_size > 0,"");
+            DLIB_CASSERT(batch_size > 0);
             mini_batch_size = batch_size;
         }
 
@@ -154,7 +154,7 @@ namespace dlib
             unsigned long num
         )  
         {
-            DLIB_CASSERT(num > 0,"");
+            DLIB_CASSERT(num > 0);
             max_num_epochs = num;
         }
 
@@ -183,7 +183,7 @@ namespace dlib
             const std::vector<label_type>& labels 
         )
         {
-            DLIB_CASSERT(data.size() == labels.size() && data.size() > 0, "");
+            DLIB_CASSERT(data.size() == labels.size() && data.size() > 0);
 
             if (verbose)
             {
@@ -209,7 +209,7 @@ namespace dlib
             const std::vector<input_type>& data
         )
         {
-            DLIB_CASSERT(data.size() > 0, "");
+            DLIB_CASSERT(data.size() > 0);
             if (verbose)
             {
                 using namespace std::chrono;
@@ -234,7 +234,7 @@ namespace dlib
             const std::vector<label_type>& labels 
         ) 
         {
-            DLIB_CASSERT(data.size() == labels.size() && data.size() > 0, "");
+            DLIB_CASSERT(data.size() == labels.size() && data.size() > 0);
 
             bool updated_the_network = false;
             // The reason these two loops don't initialize their counter variables but
@@ -290,7 +290,7 @@ namespace dlib
             const std::vector<input_type>& data
         ) 
         {
-            DLIB_CASSERT(data.size() > 0, "");
+            DLIB_CASSERT(data.size() > 0);
 
             const bool has_unsupervised_loss = std::is_same<no_label_type, label_type>::value; 
             static_assert(has_unsupervised_loss, 
@@ -378,7 +378,7 @@ namespace dlib
             double lr
         )
         {
-            DLIB_CASSERT(lr > 0,"");
+            DLIB_CASSERT(lr > 0);
             wait_for_thread_to_pause();
             if (learning_rate != lr)
             {
@@ -399,7 +399,7 @@ namespace dlib
             double lr
         )
         {
-            DLIB_CASSERT(lr > 0,"");
+            DLIB_CASSERT(lr > 0);
             wait_for_thread_to_pause();
             lr_schedule.set_size(0);
             min_learning_rate = lr;
@@ -416,8 +416,8 @@ namespace dlib
             const matrix_exp<EXP>& schedule
         )
         {
-            DLIB_CASSERT(schedule.size() > 0,"");
-            DLIB_CASSERT(min(schedule) > 0,"");
+            DLIB_CASSERT(schedule.size() > 0);
+            DLIB_CASSERT(min(schedule) > 0);
             set_learning_rate(schedule(0,0));
             set_min_learning_rate(min(schedule));
             set_learning_rate_shrink_factor(1);
@@ -456,7 +456,7 @@ namespace dlib
             double shrink
         )
         {
-            DLIB_CASSERT(0 < shrink && shrink <= 1,"");
+            DLIB_CASSERT(0 < shrink && shrink <= 1);
             wait_for_thread_to_pause();
             lr_schedule.set_size(0);
             learning_rate_shrink = shrink;