From 846a5704795e5e3ad1a7beac2dee3790c9211a46 Mon Sep 17 00:00:00 2001
From: Davis King <davis@dlib.net>
Date: Sat, 23 Jan 2016 12:06:51 -0500
Subject: [PATCH] Added an overload of operator() that lets you easily run a
 network on an entire std::vector of objects.

---
 dlib/dnn/core.h          | 16 ++++++++++++++++
 dlib/dnn/core_abstract.h | 20 ++++++++++++++++++++
 2 files changed, 36 insertions(+)

diff --git a/dlib/dnn/core.h b/dlib/dnn/core.h
index 909be0b72..95bde45f1 100644
--- a/dlib/dnn/core.h
+++ b/dlib/dnn/core.h
@@ -15,6 +15,7 @@
 #include <utility>
 #include <tuple>
 #include <cmath>
+#include <vector>
 #include "tensor_tools.h"
 
 
@@ -1922,6 +1923,21 @@ namespace dlib
             return temp_label;
         }
 
+        std::vector<label_type> operator() (
+            const std::vector<input_type>& data,
+            size_t batch_size = 128
+        )
+        {
+            std::vector<label_type> results(data.size());
+            auto o = results.begin();
+            for (auto i = data.begin(); i < data.end(); i+=batch_size, o+=batch_size)
+            {
+                auto end = std::min(i+batch_size, data.end());
+                (*this)(i, end, o);
+            }
+            return results;
+        }
+
         template <typename label_iterator>
         double compute_loss (
             const tensor& x,
diff --git a/dlib/dnn/core_abstract.h b/dlib/dnn/core_abstract.h
index 50d7444ed..3f893ac49 100644
--- a/dlib/dnn/core_abstract.h
+++ b/dlib/dnn/core_abstract.h
@@ -7,6 +7,7 @@
 #include <memory>
 #include <type_traits>
 #include <tuple>
+#include <vector>
 #include "../rand.h"
 
 
@@ -687,6 +688,25 @@ namespace dlib
                   label_type.
         !*/
 
+        std::vector<label_type> operator() (
+            const std::vector<input_type>& data,
+            size_t batch_size = 128
+        );
+        /*!
+            requires
+                - batch_size > 0
+            ensures
+                - runs all the objects in data through the network and returns their
+                  predicted labels.  This means this function returns a vector V such that:
+                    - V.size() == data.size()
+                    - for all valid i: V[i] == the predicted label of data[i].
+                - Elements of data are run through the network in batches of batch_size
+                  items.  Using a batch_size > 1 can be faster because it better exploits
+                  the available hardware parallelism.
+                - loss_details().to_label() is used to convert the network output into a
+                  label_type.
+        !*/
+
     // -------------
 
         template <typename label_iterator>