merged

dlib/dnn/layers.h

@@ -164,7 +164,6 @@ namespace dlib
 
             // set the initial bias values to zero
             biases(params,filters.size()) = 0;
-
         }
 
         template <typename SUBNET>
@@ -346,17 +345,22 @@ namespace dlib
         static_assert(0 <= _padding_x && _padding_x < _nc, "The padding must be smaller than the filter size.");
 
         cont_(
+            num_con_outputs o
         ) : 
             learning_rate_multiplier(1),
             weight_decay_multiplier(1),
             bias_learning_rate_multiplier(1),
             bias_weight_decay_multiplier(0),
+            num_filters_(o.num_outputs),
             padding_y_(_padding_y),
             padding_x_(_padding_x)
         {
+            DLIB_CASSERT(num_filters_ > 0);
         }
 
-        long num_filters() const { return _num_filters; }
+        cont_() : cont_(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; }
@@ -364,6 +368,14 @@ 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 cont_ 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; }
@@ -402,6 +414,7 @@ namespace dlib
             weight_decay_multiplier(item.weight_decay_multiplier),
             bias_learning_rate_multiplier(item.bias_learning_rate_multiplier),
             bias_weight_decay_multiplier(item.bias_weight_decay_multiplier),
+            num_filters_(item.num_filters_),
             padding_y_(item.padding_y_),
             padding_x_(item.padding_x_)
         {
@@ -427,6 +440,7 @@ namespace dlib
             weight_decay_multiplier = item.weight_decay_multiplier;
             bias_learning_rate_multiplier = item.bias_learning_rate_multiplier;
             bias_weight_decay_multiplier = item.bias_weight_decay_multiplier;
+            num_filters_ = item.num_filters_;
             return *this;
         }
 
@@ -434,18 +448,18 @@ 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(sub.get_output().k(),_num_filters, _nr, _nc);
-            biases = alias_tensor(1,_num_filters);
+            filters = alias_tensor(sub.get_output().k(), num_filters_, _nr, _nc);
+            biases = alias_tensor(1,num_filters_);
 
             // set the initial bias values to zero
-            biases(params,_num_filters) = 0;
+            biases(params,filters.size()) = 0;
         }
 
         template <typename SUBNET>
@@ -484,7 +498,7 @@ namespace dlib
         {
             serialize("cont_1", out);
             serialize(item.params, out);
-            serialize(_num_filters, out);
+            serialize(item.num_filters_, out);
             serialize(_nr, out);
             serialize(_nc, out);
             serialize(_stride_y, out);
@@ -503,7 +517,6 @@ namespace dlib
         {
             std::string version;
             deserialize(version, in);
-            long num_filters;
             long nr;
             long nc;
             int stride_y;
@@ -511,7 +524,7 @@ namespace dlib
             if (version == "cont_1")
             {
                 deserialize(item.params, in);
-                deserialize(num_filters, in);
+                deserialize(item.num_filters_, in);
                 deserialize(nr, in);
                 deserialize(nc, in);
                 deserialize(stride_y, in);
@@ -526,14 +539,6 @@ 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_");
@@ -592,6 +597,7 @@ namespace dlib
         double weight_decay_multiplier;
         double bias_learning_rate_multiplier;
         double bias_weight_decay_multiplier;
+        long num_filters_;
 
         int padding_y_;
         int padding_x_;

dlib/dnn/layers_abstract.h

@@ -941,6 +941,24 @@ namespace dlib
                 - #get_bias_weight_decay_multiplier()  == 0
         !*/
 
+        cont_(
+            num_con_outputs o
+        );
+        /*!
+            ensures
+                - #num_filters() == o.num_outputs 
+                - #nr() == _nr
+                - #nc() == _nc
+                - #stride_y() == _stride_y
+                - #stride_x() == _stride_x
+                - #padding_y() == _padding_y
+                - #padding_x() == _padding_x
+                - #get_learning_rate_multiplier()      == 1
+                - #get_weight_decay_multiplier()       == 1
+                - #get_bias_learning_rate_multiplier() == 1
+                - #get_bias_weight_decay_multiplier()  == 0
+        !*/
+
         long num_filters(
         ) const; 
         /*!
@@ -950,6 +968,19 @@ namespace dlib
                   of filters.
         !*/
 
+        void set_num_filters(
+            long num
+        );
+        /*!
+            requires
+                - num > 0
+                - get_layer_params().size() == 0
+                  (i.e. You can't change the number of filters in cont_ if the parameter
+                  tensor has already been allocated.)
+            ensures
+                - #num_filters() == num
+        !*/
+
         long nr(
         ) const; 
         /*!

tools/python/src/face_recognition.cpp

@@ -166,7 +166,7 @@ boost::python::list chinese_whispers_clustering(boost::python::list descriptors,
                 edges.push_back(sample_pair(i,j));
         }
     }
-    const auto num_clusters = chinese_whispers(edges, labels);
+    chinese_whispers(edges, labels);
     for (size_t i = 0; i < labels.size(); ++i)
     {
         clusters.append(labels[i]);
@@ -242,7 +242,6 @@ boost::python::list get_face_chips (
 
     boost::python::list chips_list;
 
-    int num_faces = faces.size();
     std::vector<chip_details> dets;
     for (auto& f : faces)
         dets.push_back(get_face_chip_details(f, size, padding));
@@ -253,9 +252,9 @@ boost::python::list get_face_chips (
     {
         boost::python::list img;
         
-        for(int row=0; row<size; row++) {
+        for(size_t row=0; row<size; row++) {
             boost::python::list row_list;
-            for(int col=0; col<size; col++) {
+            for(size_t col=0; col<size; col++) {
                 rgb_pixel pixel = chip(row, col);
                 boost::python::list item;