Merge pull request #139 from e-fominov/dnn_vs2015_up3

DNN Visual Studio 2015 Update3 support

dlib/cmake

@@ -78,6 +78,12 @@ elseif (MSVC OR "${CMAKE_CXX_COMPILER_ID}" STREQUAL "MSVC") # else if using Visu
         message(STATUS "Enabling SSE2 instructions")
         add_definitions(-DDLIB_HAVE_SSE2)
     endif()
+
+   # By default Visual Studio does not support .obj files with more than 65k sections
+   # Code generated by file_to_code_ex and code using DNN module can have them
+   # this flag enables > 65k sections, but produces .obj files that will not be readable by
+   # VS 2005
+   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /bigobj")
 endif()
 
 

dlib/dnn.h

@@ -3,6 +3,12 @@
 #ifndef DLIB_DNn_
 #define DLIB_DNn_
 
+// DNN module uses template-based network declaration that leads to very long
+// type names. Visual Studio will produce Warning C4503 in such cases
+#ifdef _MSC_VER
+#   pragma warning( disable: 4503 )
+#endif
+
 #include "dnn/tensor.h"
 #include "dnn/input.h"
 #include "dnn/layers.h"

dlib/dnn/core.h

@@ -208,6 +208,8 @@ namespace dlib
         };
 
         template <typename T> struct alwaysbool { typedef bool type; };
+        // one more structure for VS 2015 UP3 support workaround
+        template <typename T> struct alwaysbool2 { typedef bool type; };
 
         resizable_tensor& rt();
 
@@ -254,7 +256,7 @@ namespace dlib
         constexpr auto has_inplace_backward(
             layer_type& layer,
             SUBNET& sub
-        ) -> typename alwaysbool<decltype(layer.backward(rt(),rt(),sub,rt()))>::type
+        ) -> typename alwaysbool2<decltype(layer.backward(rt(),rt(),sub,rt()))>::type
         {
             return false;
         }
@@ -263,7 +265,7 @@ namespace dlib
         constexpr auto has_inplace_backward(
             layer_type& layer,
             SUBNET& sub
-        ) -> typename alwaysbool<decltype(layer.backward(rt(),sub,rt()))>::type
+        ) -> typename alwaysbool2<decltype(layer.backward(rt(),sub,rt()))>::type
         {
             return false;
         }
@@ -272,7 +274,7 @@ namespace dlib
         constexpr auto has_inplace_backward(
             layer_type& layer,
             SUBNET& sub
-        ) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),rt(),sub.get_gradient_input(),rt()))>::type
+        ) -> typename alwaysbool2<decltype(layer.backward_inplace(rt(),rt(),sub.get_gradient_input(),rt()))>::type
         {
             return true;
         }
@@ -281,7 +283,7 @@ namespace dlib
         constexpr auto has_inplace_backward(
             layer_type& layer,
             SUBNET& sub
-        ) -> typename alwaysbool<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
+        ) -> typename alwaysbool2<decltype(layer.backward_inplace(rt(),sub.get_gradient_input(),rt()))>::type
         {
             return true;
         }
@@ -290,7 +292,7 @@ namespace dlib
         constexpr auto is_inplace_layer(
             layer_type& layer,
             const SUBNET& sub 
-        ) -> typename alwaysbool<decltype(layer.forward(sub,rt()))>::type
+        ) -> typename alwaysbool2<decltype(layer.forward(sub,rt()))>::type
         {
             return false;
         }
@@ -1363,7 +1365,7 @@ namespace dlib
         static_assert(sample_expansion_factor >= 1,
             "The input layer can't produce fewer output tensors than there are inputs.");
 
-        add_tag_layer() = default;
+        add_tag_layer() {};
         add_tag_layer(const add_tag_layer&) = default;
         add_tag_layer(add_tag_layer&&) = default;
         add_tag_layer& operator=(add_tag_layer&&) = default;
@@ -2552,7 +2554,7 @@ namespace dlib
         static_assert(sample_expansion_factor >= 1,
             "The input layer can't produce fewer output tensors than there are inputs.");
 
-        add_skip_layer() = default;
+        add_skip_layer() {};
         add_skip_layer(const add_skip_layer&) = default;
         add_skip_layer(add_skip_layer&&) = default;
         add_skip_layer& operator=(add_skip_layer&&) = default;

dlib/dnn/layers.h

@@ -2025,69 +2025,66 @@ namespace dlib
     using softmax = add_layer<softmax_, SUBNET>;
 
 // ----------------------------------------------------------------------------------------
-    namespace impl{
-        // helper classes for layer concat processing
-        template <template<typename> class... TAG_TYPES>
-        struct concat_helper_impl {
-        };
-        template <template<typename> class TAG_TYPE>
-        struct concat_helper_impl<TAG_TYPE>{
-            constexpr static size_t tag_count() {return 1;}
-            static void list_tags(std::ostream& out) 
-            { 
-                out << tag_id<TAG_TYPE>::id; 
+    namespace impl
+    {
+        template <template<typename> class TAG_TYPE, template<typename> class... TAG_TYPES>
+        struct concat_helper_impl{
+
+            constexpr static size_t tag_count() {return 1 + concat_helper_impl<TAG_TYPES...>::tag_count();}
+            static void list_tags(std::ostream& out)
+            {
+                out << tag_id<TAG_TYPE>::id << (tag_count() > 1 ? "," : "");
+                concat_helper_impl<TAG_TYPES...>::list_tags(out);
             }
 
             template<typename SUBNET>
             static void resize_out(resizable_tensor& out, const SUBNET& sub, long sum_k)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_output();
-                out.set_size(t.num_samples(), t.k() + sum_k, t.nr(), t.nc());
+                concat_helper_impl<TAG_TYPES...>::resize_out(out, sub, sum_k + t.k());
             }
             template<typename SUBNET>
             static void concat(tensor& out, const SUBNET& sub, size_t k_offset)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_output();
                 tt::copy_tensor(out, k_offset, t, 0, t.k());
+                k_offset += t.k();
+                concat_helper_impl<TAG_TYPES...>::concat(out, sub, k_offset);
             }
             template<typename SUBNET>
             static void split(const tensor& input, SUBNET& sub, size_t k_offset)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_gradient_input();
                 tt::copy_tensor(t, 0, input, k_offset, t.k());
+                k_offset += t.k();
+                concat_helper_impl<TAG_TYPES...>::split(input, sub, k_offset);
             }
         };
-        template <template<typename> class TAG_TYPE, template<typename> class... TAG_TYPES>
-        struct concat_helper_impl<TAG_TYPE, TAG_TYPES...>{
-
-            constexpr static size_t tag_count() {return 1 + concat_helper_impl<TAG_TYPES...>::tag_count();}
+        template <template<typename> class TAG_TYPE>
+        struct concat_helper_impl<TAG_TYPE>{
+            constexpr static size_t tag_count() {return 1;}
             static void list_tags(std::ostream& out) 
             { 
-                out << tag_id<TAG_TYPE>::id << ","; 
-                concat_helper_impl<TAG_TYPES...>::list_tags(out);
+                out << tag_id<TAG_TYPE>::id;
             }
 
             template<typename SUBNET>
             static void resize_out(resizable_tensor& out, const SUBNET& sub, long sum_k)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_output();
-                concat_helper_impl<TAG_TYPES...>::resize_out(out, sub, sum_k + t.k());
+                out.set_size(t.num_samples(), t.k() + sum_k, t.nr(), t.nc());
             }
             template<typename SUBNET>
             static void concat(tensor& out, const SUBNET& sub, size_t k_offset)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_output();
                 tt::copy_tensor(out, k_offset, t, 0, t.k());
-                k_offset += t.k();
-                concat_helper_impl<TAG_TYPES...>::concat(out, sub, k_offset);
             }
             template<typename SUBNET>
             static void split(const tensor& input, SUBNET& sub, size_t k_offset)
             {
                 auto& t = layer<TAG_TYPE>(sub).get_gradient_input();
                 tt::copy_tensor(t, 0, input, k_offset, t.k());
-                k_offset += t.k();
-                concat_helper_impl<TAG_TYPES...>::split(input, sub, k_offset);
             }
         };
     }

dlib/use_cpp_11.cmake

@@ -61,7 +61,6 @@ else()
        ";${CMAKE_CXX_COMPILE_FEATURES};" MATCHES ";cxx_delegating_constructors;" AND
        ";${CMAKE_CXX_COMPILE_FEATURES};" MATCHES ";cxx_thread_local;" AND
        ";${CMAKE_CXX_COMPILE_FEATURES};" MATCHES ";cxx_constexpr;" AND
-       ";${CMAKE_CXX_COMPILE_FEATURES};" MATCHES ";cxx_decltype_incomplete_return_types;" AND
        ";${CMAKE_CXX_COMPILE_FEATURES};" MATCHES ";cxx_auto_type;")
 
       set(COMPILER_CAN_DO_CPP_11 1)

examples/dnn_mnist_advanced_ex.cpp

@@ -10,7 +10,6 @@
         - Accessing and configuring layers in a network
 */
 
-
 #include <dlib/dnn.h>
 #include <iostream>
 #include <dlib/data_io.h>