From fb9e0fe33681280112e4e33939c5844dba994dca Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 04 Mar 2015 22:56:38 +0000
Subject: [PATCH] Big changes to detection
---
src/parser.c | 37 +++++++++++++++++++++++++++++++++++++
1 files changed, 37 insertions(+), 0 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 850cc38..53e1f56 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -13,6 +13,7 @@
#include "normalization_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
+#include "detection_layer.h"
#include "freeweight_layer.h"
#include "list.h"
#include "option_list.h"
@@ -32,6 +33,7 @@
int is_softmax(section *s);
int is_crop(section *s);
int is_cost(section *s);
+int is_detection(section *s);
int is_normalization(section *s);
list *read_cfg(char *filename);
@@ -204,6 +206,24 @@
return layer;
}
+detection_layer *parse_detection(list *options, network *net, int count)
+{
+ int input;
+ if(count == 0){
+ input = option_find_int(options, "input",1);
+ net->batch = option_find_int(options, "batch",1);
+ net->seen = option_find_int(options, "seen",0);
+ }else{
+ input = get_network_output_size_layer(*net, count-1);
+ }
+ int coords = option_find_int(options, "coords", 1);
+ int classes = option_find_int(options, "classes", 1);
+ int rescore = option_find_int(options, "rescore", 1);
+ detection_layer *layer = make_detection_layer(net->batch, input, classes, coords, rescore);
+ option_unused(options);
+ return layer;
+}
+
cost_layer *parse_cost(list *options, network *net, int count)
{
int input;
@@ -368,6 +388,10 @@
cost_layer *layer = parse_cost(options, &net, count);
net.types[count] = COST;
net.layers[count] = layer;
+ }else if(is_detection(s)){
+ detection_layer *layer = parse_detection(options, &net, count);
+ net.types[count] = DETECTION;
+ net.layers[count] = layer;
}else if(is_softmax(s)){
softmax_layer *layer = parse_softmax(options, &net, count);
net.types[count] = SOFTMAX;
@@ -410,6 +434,10 @@
{
return (strcmp(s->type, "[cost]")==0);
}
+int is_detection(section *s)
+{
+ return (strcmp(s->type, "[detection]")==0);
+}
int is_deconvolutional(section *s)
{
return (strcmp(s->type, "[deconv]")==0
@@ -684,6 +712,13 @@
fprintf(fp, "\n");
}
+void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
+{
+ fprintf(fp, "[detection]\n");
+ fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\n", l->classes, l->coords, l->rescore);
+ fprintf(fp, "\n");
+}
+
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
@@ -815,6 +850,8 @@
print_normalization_cfg(fp, (normalization_layer *)net.layers[i], net, i);
else if(net.types[i] == SOFTMAX)
print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i);
+ else if(net.types[i] == DETECTION)
+ print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i);
else if(net.types[i] == COST)
print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
}
--
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