Made it so you can set the number of output filters for con_ layers at runtime.

dlib/dnn/layers.h

@@ -21,6 +21,12 @@ namespace dlib
 
 // ----------------------------------------------------------------------------------------
 
+    struct num_con_outputs
+    {
+        num_con_outputs(unsigned long n) : num_outputs(n) {}
+        unsigned long num_outputs;
+    };
+
     template <
         long _num_filters,
         long _nr,
@@ -43,16 +49,22 @@ namespace dlib
         static_assert(0 <= _padding_x && _padding_x < _nc, "The padding must be smaller than the filter size.");
 
         con_(
+            num_con_outputs o
         ) : 
             learning_rate_multiplier(1),
             weight_decay_multiplier(1),
             bias_learning_rate_multiplier(1),
             bias_weight_decay_multiplier(0),
             padding_y_(_padding_y),
-            padding_x_(_padding_x)
-        {}
+            padding_x_(_padding_x),
+            num_filters_(o.num_outputs)
+        {
+            DLIB_CASSERT(num_filters_ > 0);
+        }
 
-        long num_filters() const { return _num_filters; }
+        con_() : con_(num_con_outputs(_num_filters)) {}
+
+        long num_filters() const { return num_filters_; }
         long nr() const { return _nr; }
         long nc() const { return _nc; }
         long stride_y() const { return _stride_y; }
@@ -60,6 +72,14 @@ namespace dlib
         long padding_y() const { return padding_y_; }
         long padding_x() const { return padding_x_; }
 
+        void set_num_filters(long num) 
+        {
+            DLIB_CASSERT(num > 0);
+            DLIB_CASSERT(get_layer_params().size() == 0, 
+                "You can't change the number of filters in con_ if the parameter tensor has already been allocated.");
+            num_filters_ = num;
+        }
+
         double get_learning_rate_multiplier () const  { return learning_rate_multiplier; }
         double get_weight_decay_multiplier () const   { return weight_decay_multiplier; }
         void set_learning_rate_multiplier(double val) { learning_rate_multiplier = val; }
@@ -130,15 +150,15 @@ namespace dlib
         void setup (const SUBNET& sub)
         {
             long num_inputs = _nr*_nc*sub.get_output().k();
-            long num_outputs = _num_filters;
+            long num_outputs = num_filters_;
             // allocate params for the filters and also for the filter bias values.
-            params.set_size(num_inputs*_num_filters + _num_filters);
+            params.set_size(num_inputs*num_filters_ + num_filters_);
 
             dlib::rand rnd(std::rand());
             randomize_parameters(params, num_inputs+num_outputs, rnd);
 
-            filters = alias_tensor(_num_filters, sub.get_output().k(), _nr, _nc);
-            biases = alias_tensor(1,_num_filters);
+            filters = alias_tensor(num_filters_, sub.get_output().k(), _nr, _nc);
+            biases = alias_tensor(1,num_filters_);
 
             // set the initial bias values to zero
             biases(params,filters.size()) = 0;
@@ -182,7 +202,7 @@ namespace dlib
         {
             serialize("con_4", out);
             serialize(item.params, out);
-            serialize(_num_filters, out);
+            serialize(item.num_filters_, out);
             serialize(_nr, out);
             serialize(_nc, out);
             serialize(_stride_y, out);
@@ -201,7 +221,6 @@ namespace dlib
         {
             std::string version;
             deserialize(version, in);
-            long num_filters;
             long nr;
             long nc;
             int stride_y;
@@ -209,7 +228,7 @@ namespace dlib
             if (version == "con_4")
             {
                 deserialize(item.params, in);
-                deserialize(num_filters, in);
+                deserialize(item.num_filters_, in);
                 deserialize(nr, in);
                 deserialize(nc, in);
                 deserialize(stride_y, in);
@@ -224,14 +243,6 @@ namespace dlib
                 deserialize(item.bias_weight_decay_multiplier, in);
                 if (item.padding_y_ != _padding_y) throw serialization_error("Wrong padding_y found while deserializing dlib::con_");
                 if (item.padding_x_ != _padding_x) throw serialization_error("Wrong padding_x found while deserializing dlib::con_");
-                if (num_filters != _num_filters) 
-                {
-                    std::ostringstream sout;
-                    sout << "Wrong num_filters found while deserializing dlib::con_" << std::endl;
-                    sout << "expected " << _num_filters << " but found " << num_filters << std::endl;
-                    throw serialization_error(sout.str());
-                }
-
                 if (nr != _nr) throw serialization_error("Wrong nr found while deserializing dlib::con_");
                 if (nc != _nc) throw serialization_error("Wrong nc found while deserializing dlib::con_");
                 if (stride_y != _stride_y) throw serialization_error("Wrong stride_y found while deserializing dlib::con_");
@@ -247,7 +258,7 @@ namespace dlib
         friend std::ostream& operator<<(std::ostream& out, const con_& item)
         {
             out << "con\t ("
-                << "num_filters="<<_num_filters
+                << "num_filters="<<item.num_filters_
                 << ", nr="<<_nr
                 << ", nc="<<_nc
                 << ", stride_y="<<_stride_y
@@ -265,7 +276,7 @@ namespace dlib
         friend void to_xml(const con_& item, std::ostream& out)
         {
             out << "<con"
-                << " num_filters='"<<_num_filters<<"'"
+                << " num_filters='"<<item.num_filters_<<"'"
                 << " nr='"<<_nr<<"'"
                 << " nc='"<<_nc<<"'"
                 << " stride_y='"<<_stride_y<<"'"
@@ -290,6 +301,7 @@ namespace dlib
         double weight_decay_multiplier;
         double bias_learning_rate_multiplier;
         double bias_weight_decay_multiplier;
+        long num_filters_;
 
         // These are here only because older versions of con (which you might encounter
         // serialized to disk) used different padding settings.