From 81c23650e1b880279d29e9a6cef18d29e2cec69c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 16 Dec 2015 19:46:39 +0000
Subject: [PATCH] missing file

---
 src/network.c |   36 ++++++++++++++++++++++++++++++++++++
 1 files changed, 36 insertions(+), 0 deletions(-)

diff --git a/src/network.c b/src/network.c
index 9bcb264..8dee8cc 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,6 +8,7 @@
 
 #include "crop_layer.h"
 #include "connected_layer.h"
+#include "local_layer.h"
 #include "convolutional_layer.h"
 #include "deconvolutional_layer.h"
 #include "detection_layer.h"
@@ -18,6 +19,7 @@
 #include "softmax_layer.h"
 #include "dropout_layer.h"
 #include "route_layer.h"
+#include "shortcut_layer.h"
 
 int get_current_batch(network net)
 {
@@ -25,6 +27,17 @@
     return batch_num;
 }
 
+void reset_momentum(network net)
+{
+    if (net.momentum == 0) return;
+    net.learning_rate = 0;
+    net.momentum = 0;
+    net.decay = 0;
+    #ifdef GPU
+        if(gpu_index >= 0) update_network_gpu(net);
+    #endif
+}
+
 float get_current_rate(network net)
 {
     int batch_num = get_current_batch(net);
@@ -40,6 +53,7 @@
             for(i = 0; i < net.num_steps; ++i){
                 if(net.steps[i] > batch_num) return rate;
                 rate *= net.scales[i];
+                if(net.steps[i] > batch_num - 1) reset_momentum(net);
             }
             return rate;
         case EXP:
@@ -59,6 +73,8 @@
     switch(a){
         case CONVOLUTIONAL:
             return "convolutional";
+        case LOCAL:
+            return "local";
         case DECONVOLUTIONAL:
             return "deconvolutional";
         case CONNECTED:
@@ -79,6 +95,8 @@
             return "cost";
         case ROUTE:
             return "route";
+        case SHORTCUT:
+            return "shortcut";
         case NORMALIZATION:
             return "normalization";
         default:
@@ -104,6 +122,7 @@
 {
     int i;
     for(i = 0; i < net.n; ++i){
+        state.index = i;
         layer l = net.layers[i];
         if(l.delta){
             scal_cpu(l.outputs * l.batch, 0, l.delta, 1);
@@ -112,6 +131,8 @@
             forward_convolutional_layer(l, state);
         } else if(l.type == DECONVOLUTIONAL){
             forward_deconvolutional_layer(l, state);
+        } else if(l.type == LOCAL){
+            forward_local_layer(l, state);
         } else if(l.type == NORMALIZATION){
             forward_normalization_layer(l, state);
         } else if(l.type == DETECTION){
@@ -132,6 +153,8 @@
             forward_dropout_layer(l, state);
         } else if(l.type == ROUTE){
             forward_route_layer(l, net);
+        } else if(l.type == SHORTCUT){
+            forward_shortcut_layer(l, state);
         }
         state.input = l.output;
     }
@@ -150,6 +173,8 @@
             update_deconvolutional_layer(l, rate, net.momentum, net.decay);
         } else if(l.type == CONNECTED){
             update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
+        } else if(l.type == LOCAL){
+            update_local_layer(l, update_batch, rate, net.momentum, net.decay);
         }
     }
 }
@@ -192,6 +217,7 @@
     float *original_input = state.input;
     float *original_delta = state.delta;
     for(i = net.n-1; i >= 0; --i){
+        state.index = i;
         if(i == 0){
             state.input = original_input;
             state.delta = original_delta;
@@ -219,10 +245,14 @@
             if(i != 0) backward_softmax_layer(l, state);
         } else if(l.type == CONNECTED){
             backward_connected_layer(l, state);
+        } else if(l.type == LOCAL){
+            backward_local_layer(l, state);
         } else if(l.type == COST){
             backward_cost_layer(l, state);
         } else if(l.type == ROUTE){
             backward_route_layer(l, net);
+        } else if(l.type == SHORTCUT){
+            backward_shortcut_layer(l, state);
         }
     }
 }
@@ -234,6 +264,8 @@
     if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
 #endif
     network_state state;
+    state.index = 0;
+    state.net = net;
     state.input = x;
     state.delta = 0;
     state.truth = y;
@@ -286,6 +318,8 @@
 {
     int i,j;
     network_state state;
+    state.index = 0;
+    state.net = net;
     state.train = 1;
     state.delta = 0;
     float sum = 0;
@@ -422,6 +456,8 @@
 #endif
 
     network_state state;
+    state.net = net;
+    state.index = 0;
     state.input = input;
     state.truth = 0;
     state.train = 0;

--
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