From 738cd4c2d7abcf62b85b759a5765b08380ee90e8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 17 Apr 2014 00:05:29 +0000
Subject: [PATCH] Visualizations?
---
src/network.c | 136 ++++++++++++++++++++++++++++++++-------------
1 files changed, 96 insertions(+), 40 deletions(-)
diff --git a/src/network.c b/src/network.c
index edae3c7..7d4b1fa 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,6 +8,7 @@
#include "convolutional_layer.h"
//#include "old_conv.h"
#include "maxpool_layer.h"
+#include "normalization_layer.h"
#include "softmax_layer.h"
network make_network(int n, int batch)
@@ -40,6 +41,17 @@
fprintf(fp, "data=");
for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
+ /*
+ int j,k;
+ for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
+ for(i = 0; i < l->n; ++i){
+ for(j = l->c-1; j >= 0; --j){
+ for(k = 0; k < l->size*l->size; ++k){
+ fprintf(fp, "%g,", l->filters[i*(l->c*l->size*l->size)+j*l->size*l->size+k]);
+ }
+ }
+ }
+ */
fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
@@ -48,9 +60,9 @@
fprintf(fp, "[connected]\n");
if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "output=%d\n"
- "activation=%s\n",
- l->outputs,
- get_activation_string(l->activation));
+ "activation=%s\n",
+ l->outputs,
+ get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
@@ -61,13 +73,27 @@
{
fprintf(fp, "[maxpool]\n");
if(first) fprintf(fp, "batch=%d\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n",
- l->batch,l->h, l->w, l->c);
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
fprintf(fp, "stride=%d\n\n", l->stride);
}
+void print_normalization_cfg(FILE *fp, normalization_layer *l, int first)
+{
+ fprintf(fp, "[localresponsenormalization]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "size=%d\n"
+ "alpha=%g\n"
+ "beta=%g\n"
+ "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
+}
+
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
fprintf(fp, "[softmax]\n");
@@ -88,6 +114,8 @@
print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
+ else if(net.types[i] == NORMALIZATION)
+ print_normalization_cfg(fp, (normalization_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
@@ -118,6 +146,11 @@
forward_maxpool_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ forward_normalization_layer(layer, input);
+ input = layer.output;
+ }
}
}
@@ -135,6 +168,9 @@
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] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer, step, momentum, decay);
@@ -156,6 +192,9 @@
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return layer.output;
}
return 0;
}
@@ -233,6 +272,10 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
if(i != 0) backward_maxpool_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);
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
@@ -272,7 +315,7 @@
error += err;
++pos;
}
-
+
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
@@ -341,34 +384,34 @@
}
/*
-int resize_network(network net, int h, int w, int c)
-{
- int i;
- for (i = 0; i < net.n; ++i){
- if(net.types[i] == CONVOLUTIONAL){
- convolutional_layer *layer = (convolutional_layer *)net.layers[i];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_convolutional_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];
- layer->h = h;
- layer->w = w;
- layer->c = c;
- image output = get_maxpool_image(*layer);
- h = output.h;
- w = output.w;
- c = output.c;
- }
- }
- return 0;
-}
-*/
+ int resize_network(network net, int h, int w, int c)
+ {
+ int i;
+ for (i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer *layer = (convolutional_layer *)net.layers[i];
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ image output = get_convolutional_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];
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ image output = get_maxpool_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ }
+ return 0;
+ }
+ */
int resize_network(network net, int h, int w, int c)
{
@@ -381,16 +424,21 @@
h = output.h;
w = output.w;
c = output.c;
- }
- else if(net.types[i] == MAXPOOL){
+ }else if(net.types[i] == MAXPOOL){
maxpool_layer *layer = (maxpool_layer *)net.layers[i];
resize_maxpool_layer(layer, h, w, c);
image output = get_maxpool_image(*layer);
h = output.h;
w = output.w;
c = output.c;
- }
- else{
+ }else if(net.types[i] == NORMALIZATION){
+ normalization_layer *layer = (normalization_layer *)net.layers[i];
+ resize_normalization_layer(layer, h, w, c);
+ image output = get_normalization_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }else{
error("Cannot resize this type of layer");
}
}
@@ -413,6 +461,10 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
}
+ else if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ return get_normalization_image(layer);
+ }
return make_empty_image(0,0,0);
}
@@ -437,6 +489,10 @@
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
prev = visualize_convolutional_layer(layer, buff, prev);
}
+ if(net.types[i] == NORMALIZATION){
+ normalization_layer layer = *(normalization_layer *)net.layers[i];
+ visualize_normalization_layer(layer, buff);
+ }
}
}
--
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