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 | 22 ++++++++++++++++------
1 files changed, 16 insertions(+), 6 deletions(-)
diff --git a/src/network.c b/src/network.c
index 88b7085..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"
@@ -64,6 +65,7 @@
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);
@@ -102,6 +104,8 @@
return "softmax";
case DETECTION:
return "detection";
+ case REGION:
+ return "region";
case DROPOUT:
return "dropout";
case CROP:
@@ -159,6 +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){
@@ -229,11 +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;
}
@@ -253,6 +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){
@@ -282,6 +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){
@@ -391,6 +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
}
}
@@ -433,7 +443,7 @@
net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
#else
free(net->workspace);
- net->workspace = calloc(1, (workspace_size-1)/sizeof(float)+1);
+ net->workspace = calloc(1, workspace_size);
#endif
//fprintf(stderr, " Done!\n");
return 0;
--
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