Removing 'float' from the template to make it C++17 compatible

davisking · Sep 23, 2024 · cf53441 · cf53441
1 parent 078c9cb
commit cf53441
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Showing 4 changed files with 35 additions and 23 deletions.
diff --git a/dlib/dnn/layers.h b/dlib/dnn/layers.h
@@ -4637,11 +4637,16 @@ namespace dlib
 
 // ----------------------------------------------------------------------------------------
 
-    template <long diag_, float diag_value_>
+    template <typename T, T val>
+    struct float_constant {
+        static constexpr T value = val;
+    };
+
+    template <long diag_, typename diag_value_>
     class tril_
     {
     public:
-        tril_() : diag(diag_), diag_value(diag_value_) {}
+        tril_() : diag(diag_), diag_value(diag_value_::value) {}
 
         template <typename SUBNET>
         void setup(const SUBNET& sub) {
@@ -4728,13 +4733,13 @@ namespace dlib
     };
 
     template <typename SUBNET>
-    using tril = add_layer<tril_<0, 0.0f>, SUBNET>;
+    using tril = add_layer<tril_<0, float_constant<float, 0.0f>>, SUBNET>;
 
     template <typename SUBNET>
-    using tril_mask = add_layer<tril_<0, -std::numeric_limits<float>::infinity()>, SUBNET>;
+    using tril_mask = add_layer<tril_<0, float_constant<float, -std::numeric_limits<float>::infinity()>>, SUBNET>;
 
-    template <long diag, float diag_value, typename SUBNET>
-    using tril_diag = add_layer<tril_<diag, diag_value>, SUBNET>;
+    template <long diag, typename diag_value_type, typename SUBNET>
+    using tril_diag = add_layer<tril_<diag, diag_value_type>, SUBNET>;
 
 // ----------------------------------------------------------------------------------------
 

diff --git a/dlib/dnn/layers_abstract.h b/dlib/dnn/layers_abstract.h
@@ -3651,21 +3651,26 @@ namespace dlib
 
 // ----------------------------------------------------------------------------------------
 
-    template <long diag_, float diag_value_>
+    template <typename T, T val>
+    struct float_constant {
+        static constexpr T value = val;
+    };
+
+    template <long diag_, typename diag_value_>
     class tril_
     {
         /*!
             REQUIREMENTS ON diag_ and diag_value_
                 - diag_ must be a non-negative integer.
-                - diag_value_ must be a floating point number.
+                - diag_value_::value must be a floating point number.
 
             WHAT THIS OBJECT REPRESENTS
                 This object implements a layer in a deep neural network that applies a lower triangular mask to 
                 its input tensor. The mask is defined such that all elements above the specified diagonal are set
-                to a given value (diag_value_). The diagonal is specified by the diag_ parameter.
+                to a given value (diag_value_::value). The diagonal is specified by the diag_ parameter.
 
             EXAMPLE USAGE
-                tril_<0, -std::numeric_limits<float>::infinity()> layer;
+                tril_<0, float_constant<float, -std::numeric_limits<float>::infinity()>> layer;
                 // This creates a layer that masks all elements above the main diagonal with -inf.
 
             SERIALIZATION SUPPORT
@@ -3756,13 +3761,14 @@ namespace dlib
     };
 
     template <typename SUBNET>
-    using tril = add_layer<tril_<0, 0.0f>, SUBNET>;
+    using tril = add_layer<tril_<0, float_constant<float, 0.0f>>, SUBNET>;
 
     template <typename SUBNET>
-    using tril_mask = add_layer<tril_<0, -std::numeric_limits<float>::infinity()>, SUBNET>;
+    using tril_mask = add_layer<tril_<0, float_constant<float, -std::numeric_limits<float>::infinity()>>, SUBNET>;
+
+    template <long diag, typename diag_value_type, typename SUBNET>
+    using tril_diag = add_layer<tril_<diag, diag_value_type>, SUBNET>;
 
-    template <long diag, float diag_value, typename SUBNET>
-    using tril_diag = add_layer<tril_<diag, diag_value>, SUBNET>;
 
 // ----------------------------------------------------------------------------------------
 

diff --git a/dlib/dnn/visitors.h b/dlib/dnn/visitors.h
@@ -1021,15 +1021,15 @@ namespace dlib
                 update(i);
             }
 
-            template <long diag, float diag_value, typename U, typename E>
-            void operator()(size_t i, const add_layer<tril_<diag, diag_value>, U, E>&)
+            template <long diag, typename diag_value_type, typename U, typename E>
+            void operator()(size_t i, const add_layer<tril_<diag, diag_value_type>, U, E>&)
             {
                 start_node(i, "tril");
                 out << " | {diag|{" << diag << "}}";
-                out << " | {diag_value|{" << diag_value << "}}";
+                out << " | {diag_value|{" << diag_value_type::value << "}}";
                 end_node();
                 update(i);
-            }            
+            }       
 
             template <typename T, typename U, typename E>
             void operator()(size_t i, const add_layer<T, U, E>&)

diff --git a/dlib/test/dnn.cpp b/dlib/test/dnn.cpp
@@ -1994,7 +1994,8 @@ namespace
         }
         {
             print_spinner();
-            tril_<-5, -std::numeric_limits<float>::infinity()> l;
+            using specific_float = float_constant<float, -std::numeric_limits<float>::infinity()>;
+            tril_<-5, specific_float> l;
             auto res = test_layer(l);
             DLIB_TEST_MSG(res, res);
         }
@@ -4370,7 +4371,7 @@ void test_tril()
 {
     print_spinner();
 
-    constexpr float NEG_INF = -std::numeric_limits<float>::infinity();
+    using NEG_INF = float_constant<float, -std::numeric_limits<float>::infinity()>;
     using net_type = tag1<tril_diag<0, NEG_INF, tag2<input<matrix<float>>>>>;
     net_type net;
 
@@ -4397,9 +4398,9 @@ void test_tril()
     expected_output.copy_size(input_tensor);
     tt::copy_tensor(false, expected_output, 0, input_tensor, 0, input_tensor.k());
     for (int ii = 0; ii < n_samples; ++ii) {
-        expected_output.host()[tensor_index(expected_output, ii, 0, 0, 1)] = NEG_INF;
-        expected_output.host()[tensor_index(expected_output, ii, 0, 0, 2)] = NEG_INF;
-        expected_output.host()[tensor_index(expected_output, ii, 0, 1, 2)] = NEG_INF;
+        expected_output.host()[tensor_index(expected_output, ii, 0, 0, 1)] = -std::numeric_limits<float>::infinity();
+        expected_output.host()[tensor_index(expected_output, ii, 0, 0, 2)] = -std::numeric_limits<float>::infinity();
+        expected_output.host()[tensor_index(expected_output, ii, 0, 1, 2)] = -std::numeric_limits<float>::infinity();
     }
 
     // Compare output tensor with expected output