From 9361292c429c0ba3400c31c7fa5d5e3d3cb6ab47 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 19 Jul 2016 21:50:01 +0000
Subject: [PATCH] updates

---
 src/network.c |   32 +++++++++++++++++++++++++++-----
 1 files changed, 27 insertions(+), 5 deletions(-)

diff --git a/src/network.c b/src/network.c
index ca485d6..6ed82ce 100644
--- a/src/network.c
+++ b/src/network.c
@@ -16,6 +16,7 @@
 #include "activation_layer.h"
 #include "deconvolutional_layer.h"
 #include "detection_layer.h"
+#include "region_layer.h"
 #include "normalization_layer.h"
 #include "batchnorm_layer.h"
 #include "maxpool_layer.h"
@@ -64,7 +65,10 @@
         case EXP:
             return net.learning_rate * pow(net.gamma, batch_num);
         case POLY:
+            if (batch_num < net.burn_in) return net.learning_rate * pow((float)batch_num / net.burn_in, net.power);
             return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
+        case RANDOM:
+            return net.learning_rate * pow(rand_uniform(0,1), net.power);
         case SIG:
             return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
         default:
@@ -100,6 +104,8 @@
             return "softmax";
         case DETECTION:
             return "detection";
+        case REGION:
+            return "region";
         case DROPOUT:
             return "dropout";
         case CROP:
@@ -135,6 +141,7 @@
 
 void forward_network(network net, network_state state)
 {
+    state.workspace = net.workspace;
     int i;
     for(i = 0; i < net.n; ++i){
         state.index = i;
@@ -156,6 +163,8 @@
             forward_batchnorm_layer(l, state);
         } else if(l.type == DETECTION){
             forward_detection_layer(l, state);
+        } else if(l.type == REGION){
+            forward_region_layer(l, state);
         } else if(l.type == CONNECTED){
             forward_connected_layer(l, state);
         } else if(l.type == RNN){
@@ -226,11 +235,7 @@
     float sum = 0;
     int count = 0;
     for(i = 0; i < net.n; ++i){
-        if(net.layers[i].type == COST){
-            sum += net.layers[i].cost[0];
-            ++count;
-        }
-        if(net.layers[i].type == DETECTION){
+        if(net.layers[i].cost){
             sum += net.layers[i].cost[0];
             ++count;
         }
@@ -250,6 +255,7 @@
     int i;
     float *original_input = state.input;
     float *original_delta = state.delta;
+    state.workspace = net.workspace;
     for(i = net.n-1; i >= 0; --i){
         state.index = i;
         if(i == 0){
@@ -279,6 +285,8 @@
             backward_dropout_layer(l, state);
         } else if(l.type == DETECTION){
             backward_detection_layer(l, state);
+        } else if(l.type == REGION){
+            backward_region_layer(l, state);
         } else if(l.type == SOFTMAX){
             if(i != 0) backward_softmax_layer(l, state);
         } else if(l.type == CONNECTED){
@@ -388,6 +396,11 @@
     int i;
     for(i = 0; i < net->n; ++i){
         net->layers[i].batch = b;
+        #ifdef CUDNN
+        if(net->layers[i].type == CONVOLUTIONAL){
+            cudnn_convolutional_setup(net->layers + i);
+        }
+        #endif
     }
 }
 
@@ -398,6 +411,7 @@
     net->w = w;
     net->h = h;
     int inputs = 0;
+    size_t workspace_size = 0;
     //fprintf(stderr, "Resizing to %d x %d...", w, h);
     //fflush(stderr);
     for (i = 0; i < net->n; ++i){
@@ -417,12 +431,20 @@
         }else{
             error("Cannot resize this type of layer");
         }
+        if(l.workspace_size > workspace_size) workspace_size = l.workspace_size;
         inputs = l.outputs;
         net->layers[i] = l;
         w = l.out_w;
         h = l.out_h;
         if(l.type == AVGPOOL) break;
     }
+#ifdef GPU
+        cuda_free(net->workspace);
+        net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+#else
+        free(net->workspace);
+        net->workspace = calloc(1, workspace_size);
+#endif
     //fprintf(stderr, " Done!\n");
     return 0;
 }

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