From 0f645836f193e75c4c3b718369e6fab15b5d19c5 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 11 Feb 2015 03:41:03 +0000
Subject: [PATCH] Detection is back, baby\!
---
src/network.c | 115 +++++++++++++++++++++++++++++++++++++++++++++++----------
1 files changed, 95 insertions(+), 20 deletions(-)
diff --git a/src/network.c b/src/network.c
index 42253dc..bf0d63f 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,6 +8,7 @@
#include "crop_layer.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "cost_layer.h"
#include "normalization_layer.h"
@@ -15,6 +16,35 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
+char *get_layer_string(LAYER_TYPE a)
+{
+ switch(a){
+ case CONVOLUTIONAL:
+ return "convolutional";
+ case DECONVOLUTIONAL:
+ return "deconvolutional";
+ case CONNECTED:
+ return "connected";
+ case MAXPOOL:
+ return "maxpool";
+ case SOFTMAX:
+ return "softmax";
+ case NORMALIZATION:
+ return "normalization";
+ case DROPOUT:
+ return "dropout";
+ case FREEWEIGHT:
+ return "freeweight";
+ case CROP:
+ return "crop";
+ case COST:
+ return "cost";
+ default:
+ break;
+ }
+ return "none";
+}
+
network make_network(int n, int batch)
{
network net;
@@ -24,14 +54,14 @@
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
net.output = 0;
+ net.seen = 0;
#ifdef GPU
- net.input_cl = calloc(1, sizeof(cl_mem));
- net.truth_cl = calloc(1, sizeof(cl_mem));
+ net.input_gpu = calloc(1, sizeof(float *));
+ net.truth_gpu = calloc(1, sizeof(float *));
#endif
return net;
}
-
void forward_network(network net, float *input, float *truth, int train)
{
int i;
@@ -41,6 +71,11 @@
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] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input);
@@ -48,7 +83,7 @@
}
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){
@@ -78,9 +113,12 @@
}
else if(net.types[i] == FREEWEIGHT){
if(!train) continue;
- freeweight_layer layer = *(freeweight_layer *)net.layers[i];
- forward_freeweight_layer(layer, input);
+ //freeweight_layer layer = *(freeweight_layer *)net.layers[i];
+ //forward_freeweight_layer(layer, input);
}
+ //char buff[256];
+ //sprintf(buff, "layer %d", i);
+ //cuda_compare(get_network_output_gpu_layer(net, i), input, get_network_output_size_layer(net, i)*net.batch, buff);
}
}
@@ -92,19 +130,13 @@
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);
+ update_connected_layer(layer);
}
}
}
@@ -114,6 +146,9 @@
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;
@@ -149,6 +184,9 @@
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;
@@ -218,9 +256,13 @@
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];
@@ -348,6 +390,9 @@
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];
@@ -372,6 +417,10 @@
cost_layer *layer = (cost_layer *)net->layers[i];
layer->batch = b;
}
+ else if(net->types[i] == CROP){
+ crop_layer *layer = (crop_layer *)net->layers[i];
+ layer->batch = b;
+ }
}
}
@@ -382,6 +431,10 @@
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;
@@ -415,6 +468,11 @@
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] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
image output = get_maxpool_image(layer);
@@ -450,21 +508,31 @@
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;
@@ -494,6 +562,10 @@
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);
@@ -502,6 +574,9 @@
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);
@@ -549,7 +624,7 @@
float *network_predict(network net, float *input)
{
#ifdef GPU
- if(gpu_index >= 0) return network_predict_gpu(net, input);
+ if(gpu_index >= 0) return network_predict_gpu(net, input);
#endif
forward_network(net, input, 0, 0);
--
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