From 0e610b056dbcd85affa23f64f9f8da4d197f110a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 08 Sep 2016 05:46:10 +0000
Subject: [PATCH] and again
---
src/network.c | 55 ++++++++++++++++++++++++++++++++++++++++++++++---------
1 files changed, 46 insertions(+), 9 deletions(-)
diff --git a/src/network.c b/src/network.c
index ca485d6..c9a198f 100644
--- a/src/network.c
+++ b/src/network.c
@@ -16,9 +16,11 @@
#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"
+#include "reorg_layer.h"
#include "avgpool_layer.h"
#include "cost_layer.h"
#include "softmax_layer.h"
@@ -64,7 +66,10 @@
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:
@@ -94,12 +99,16 @@
return "crnn";
case MAXPOOL:
return "maxpool";
+ case REORG:
+ return "reorg";
case AVGPOOL:
return "avgpool";
case SOFTMAX:
return "softmax";
case DETECTION:
return "detection";
+ case REGION:
+ return "region";
case DROPOUT:
return "dropout";
case CROP:
@@ -135,6 +144,7 @@
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 +166,8 @@
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){
@@ -172,6 +184,8 @@
forward_softmax_layer(l, state);
} else if(l.type == MAXPOOL){
forward_maxpool_layer(l, state);
+ } else if(l.type == REORG){
+ forward_reorg_layer(l, state);
} else if(l.type == AVGPOOL){
forward_avgpool_layer(l, state);
} else if(l.type == DROPOUT){
@@ -213,7 +227,7 @@
float *get_network_output(network net)
{
#ifdef GPU
- return get_network_output_gpu(net);
+ if (gpu_index >= 0) return get_network_output_gpu(net);
#endif
int i;
for(i = net.n-1; i > 0; --i) if(net.layers[i].type != COST) break;
@@ -226,11 +240,7 @@
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 +260,7 @@
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){
@@ -273,12 +284,16 @@
backward_batchnorm_layer(l, state);
} else if(l.type == MAXPOOL){
if(i != 0) backward_maxpool_layer(l, state);
+ } else if(l.type == REORG){
+ backward_reorg_layer(l, state);
} else if(l.type == AVGPOOL){
backward_avgpool_layer(l, state);
} else if(l.type == DROPOUT){
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){
@@ -358,6 +373,7 @@
return (float)sum/(n*batch);
}
+
float train_network_batch(network net, data d, int n)
{
int i,j;
@@ -388,6 +404,11 @@
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
}
}
@@ -398,7 +419,8 @@
net->w = w;
net->h = h;
int inputs = 0;
- //fprintf(stderr, "Resizing to %d x %d...", w, h);
+ size_t workspace_size = 0;
+ //fprintf(stderr, "Resizing to %d x %d...\n", w, h);
//fflush(stderr);
for (i = 0; i < net->n; ++i){
layer l = net->layers[i];
@@ -408,6 +430,8 @@
resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
+ }else if(l.type == REORG){
+ resize_reorg_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
}else if(l.type == NORMALIZATION){
@@ -417,12 +441,25 @@
}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
+ if(gpu_index >= 0){
+ 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);
+ }
+#else
+ free(net->workspace);
+ net->workspace = calloc(1, workspace_size);
+#endif
//fprintf(stderr, " Done!\n");
return 0;
}
@@ -637,10 +674,10 @@
free_layer(net.layers[i]);
}
free(net.layers);
- #ifdef GPU
+#ifdef GPU
if(*net.input_gpu) cuda_free(*net.input_gpu);
if(*net.truth_gpu) cuda_free(*net.truth_gpu);
if(net.input_gpu) free(net.input_gpu);
if(net.truth_gpu) free(net.truth_gpu);
- #endif
+#endif
}
--
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