From f047cfff99e00e28c02eb59b6d32386c122f9af6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 08 Mar 2015 18:31:12 +0000
Subject: [PATCH] renamed sigmoid to logistic
---
src/network.c | 105 ++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 85 insertions(+), 20 deletions(-)
diff --git a/src/network.c b/src/network.c
index eb39054..b60f059 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,6 +8,8 @@
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
+#include "detection_layer.h"
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
@@ -20,12 +22,16 @@
switch(a){
case CONVOLUTIONAL:
return "convolutional";
+ case DECONVOLUTIONAL:
+ return "deconvolutional";
case CONNECTED:
return "connected";
case MAXPOOL:
return "maxpool";
case SOFTMAX:
return "softmax";
+ case DETECTION:
+ return "detection";
case NORMALIZATION:
return "normalization";
case DROPOUT:
@@ -42,8 +48,6 @@
return "none";
}
-
-
network make_network(int n, int batch)
{
network net;
@@ -61,7 +65,6 @@
return net;
}
-
void forward_network(network net, float *input, float *truth, int train)
{
int i;
@@ -71,6 +74,16 @@
forward_convolutional_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ forward_deconvolutional_layer(layer, input);
+ input = layer.output;
+ }
+ else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *)net.layers[i];
+ forward_detection_layer(layer, input, truth);
+ input = layer.output;
+ }
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input);
@@ -78,7 +91,7 @@
}
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
- forward_crop_layer(layer, input);
+ forward_crop_layer(layer, train, input);
input = layer.output;
}
else if(net.types[i] == COST){
@@ -125,18 +138,12 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer(layer);
}
- else if(net.types[i] == MAXPOOL){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- }
- else if(net.types[i] == SOFTMAX){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
- }
- else if(net.types[i] == NORMALIZATION){
- //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ update_deconvolutional_layer(layer);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- //secret_update_connected_layer((connected_layer *)net.layers[i]);
update_connected_layer(layer);
}
}
@@ -147,9 +154,15 @@
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ return layer.output;
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *)net.layers[i];
+ return layer.output;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output;
@@ -182,12 +195,18 @@
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.delta;
+ } else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ return layer.delta;
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.delta;
} else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta;
+ } else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *)net.layers[i];
+ return layer.delta;
} else if(net.types[i] == DROPOUT){
if(i == 0) return 0;
return get_network_delta_layer(net, i-1);
@@ -238,7 +257,7 @@
return max_index(out, k);
}
-void backward_network(network net, float *input)
+void backward_network(network net, float *input, float *truth)
{
int i;
float *prev_input;
@@ -251,9 +270,13 @@
prev_input = get_network_output_layer(net, i-1);
prev_delta = get_network_delta_layer(net, i-1);
}
+
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
backward_convolutional_layer(layer, prev_input, prev_delta);
+ } else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ backward_deconvolutional_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -263,6 +286,10 @@
dropout_layer layer = *(dropout_layer *)net.layers[i];
backward_dropout_layer(layer, prev_delta);
}
+ else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *)net.layers[i];
+ backward_detection_layer(layer, prev_input, prev_delta);
+ }
else if(net.types[i] == NORMALIZATION){
normalization_layer layer = *(normalization_layer *)net.layers[i];
if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
@@ -288,7 +315,7 @@
if(gpu_index >= 0) return train_network_datum_gpu(net, x, y);
#endif
forward_network(net, x, y, 1);
- backward_network(net, x);
+ backward_network(net, x, y);
float error = get_network_cost(net);
update_network(net);
return error;
@@ -342,7 +369,7 @@
float *x = d.X.vals[index];
float *y = d.y.vals[index];
forward_network(net, x, y, 1);
- backward_network(net, x);
+ backward_network(net, x, y);
sum += get_network_cost(net);
}
update_network(net);
@@ -372,7 +399,6 @@
}
}
-
void set_batch_network(network *net, int b)
{
net->batch = b;
@@ -381,6 +407,9 @@
if(net->types[i] == CONVOLUTIONAL){
convolutional_layer *layer = (convolutional_layer *)net->layers[i];
layer->batch = b;
+ }else if(net->types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer *layer = (deconvolutional_layer *)net->layers[i];
+ layer->batch = b;
}
else if(net->types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net->layers[i];
@@ -392,6 +421,9 @@
} else if(net->types[i] == DROPOUT){
dropout_layer *layer = (dropout_layer *) net->layers[i];
layer->batch = b;
+ } else if(net->types[i] == DETECTION){
+ detection_layer *layer = (detection_layer *) net->layers[i];
+ layer->batch = b;
}
else if(net->types[i] == FREEWEIGHT){
freeweight_layer *layer = (freeweight_layer *) net->layers[i];
@@ -419,6 +451,10 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.h*layer.w*layer.c;
}
+ if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ return layer.h*layer.w*layer.c;
+ }
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.h*layer.w*layer.c;
@@ -429,6 +465,9 @@
} else if(net.types[i] == DROPOUT){
dropout_layer layer = *(dropout_layer *) net.layers[i];
return layer.inputs;
+ } else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *) net.layers[i];
+ return layer.inputs;
} else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *) net.layers[i];
return layer.c*layer.h*layer.w;
@@ -452,6 +491,15 @@
image output = get_convolutional_image(layer);
return output.h*output.w*output.c;
}
+ else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ image output = get_deconvolutional_image(layer);
+ return output.h*output.w*output.c;
+ }
+ else if(net.types[i] == DETECTION){
+ detection_layer layer = *(detection_layer *)net.layers[i];
+ return get_detection_layer_output_size(layer);
+ }
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
image output = get_maxpool_image(layer);
@@ -487,21 +535,31 @@
for (i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer *layer = (convolutional_layer *)net.layers[i];
- resize_convolutional_layer(layer, h, w, c);
+ resize_convolutional_layer(layer, h, w);
image output = get_convolutional_image(*layer);
h = output.h;
w = output.w;
c = output.c;
+ } else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer *layer = (deconvolutional_layer *)net.layers[i];
+ resize_deconvolutional_layer(layer, h, w);
+ image output = get_deconvolutional_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
}else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
- resize_maxpool_layer(layer, h, w, c);
+ resize_maxpool_layer(layer, h, w);
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
+ }else if(net.types[i] == DROPOUT){
+ dropout_layer *layer = (dropout_layer *)net.layers[i];
+ resize_dropout_layer(layer, h*w*c);
}else if(net.types[i] == NORMALIZATION){
normalization_layer *layer = (normalization_layer *)net.layers[i];
- resize_normalization_layer(layer, h, w, c);
+ resize_normalization_layer(layer, h, w);
image output = get_normalization_image(*layer);
h = output.h;
w = output.w;
@@ -531,6 +589,10 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return get_convolutional_image(layer);
}
+ else if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
+ return get_deconvolutional_image(layer);
+ }
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
@@ -539,6 +601,9 @@
normalization_layer layer = *(normalization_layer *)net.layers[i];
return get_normalization_image(layer);
}
+ else if(net.types[i] == DROPOUT){
+ return get_network_image_layer(net, i-1);
+ }
else if(net.types[i] == CROP){
crop_layer layer = *(crop_layer *)net.layers[i];
return get_crop_image(layer);
--
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