From 064d4462d3890fd5dda5c7845119e1a3854d477b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 26 Nov 2015 21:45:12 +0000
Subject: [PATCH] stuff
---
src/parser.c | 126 ++++++++++++++++++++++++-----------------
1 files changed, 73 insertions(+), 53 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 53e8461..277c6e2 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -14,8 +14,8 @@
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
-#include "region_layer.h"
#include "avgpool_layer.h"
+#include "local_layer.h"
#include "route_layer.h"
#include "list.h"
#include "option_list.h"
@@ -28,6 +28,7 @@
int is_network(section *s);
int is_convolutional(section *s);
+int is_local(section *s);
int is_deconvolutional(section *s);
int is_connected(section *s);
int is_maxpool(section *s);
@@ -38,7 +39,6 @@
int is_crop(section *s);
int is_cost(section *s);
int is_detection(section *s);
-int is_region(section *s);
int is_route(section *s);
list *read_cfg(char *filename);
@@ -109,6 +109,27 @@
return layer;
}
+local_layer parse_local(list *options, size_params params)
+{
+ int n = option_find_int(options, "filters",1);
+ int size = option_find_int(options, "size",1);
+ int stride = option_find_int(options, "stride",1);
+ int pad = option_find_int(options, "pad",0);
+ char *activation_s = option_find_str(options, "activation", "logistic");
+ ACTIVATION activation = get_activation(activation_s);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before local layer must output image.");
+
+ local_layer layer = make_local_layer(batch,h,w,c,n,size,stride,pad,activation);
+
+ return layer;
+}
+
convolutional_layer parse_convolutional(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
@@ -124,8 +145,9 @@
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before convolutional layer must output image.");
+ int batch_normalize = option_find_int_quiet(options, "batch_normalize", 0);
- convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
+ convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation, batch_normalize);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
@@ -167,25 +189,19 @@
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 0);
- int joint = option_find_int(options, "joint", 0);
- int objectness = option_find_int(options, "objectness", 0);
- int background = option_find_int(options, "background", 0);
- detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness);
- return layer;
-}
-
-region_layer parse_region(list *options, size_params params)
-{
- int coords = option_find_int(options, "coords", 1);
- int classes = option_find_int(options, "classes", 1);
- int rescore = option_find_int(options, "rescore", 0);
int num = option_find_int(options, "num", 1);
int side = option_find_int(options, "side", 7);
- region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
- int softmax = option_find_int(options, "softmax", 0);
- int sqrt = option_find_int(options, "sqrt", 0);
- layer.softmax = softmax;
- layer.sqrt = sqrt;
+ detection_layer layer = make_detection_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
+
+ layer.softmax = option_find_int(options, "softmax", 0);
+ layer.sqrt = option_find_int(options, "sqrt", 0);
+
+ layer.coord_scale = option_find_float(options, "coord_scale", 1);
+ layer.forced = option_find_int(options, "forced", 0);
+ layer.object_scale = option_find_float(options, "object_scale", 1);
+ layer.noobject_scale = option_find_float(options, "noobject_scale", 1);
+ layer.class_scale = option_find_float(options, "class_scale", 1);
+ layer.jitter = option_find_float(options, "jitter", .2);
return layer;
}
@@ -217,6 +233,7 @@
int noadjust = option_find_int_quiet(options, "noadjust",0);
crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
+ l.shift = option_find_float(options, "shift", 0);
l.noadjust = noadjust;
return l;
}
@@ -408,6 +425,8 @@
layer l = {0};
if(is_convolutional(s)){
l = parse_convolutional(options, params);
+ }else if(is_local(s)){
+ l = parse_local(options, params);
}else if(is_deconvolutional(s)){
l = parse_deconvolutional(options, params);
}else if(is_connected(s)){
@@ -418,8 +437,6 @@
l = parse_cost(options, params);
}else if(is_detection(s)){
l = parse_detection(options, params);
- }else if(is_region(s)){
- l = parse_region(options, params);
}else if(is_softmax(s)){
l = parse_softmax(options, params);
}else if(is_normalization(s)){
@@ -442,6 +459,7 @@
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
l.dontload = option_find_int_quiet(options, "dontload", 0);
+ l.dontloadscales = option_find_int_quiet(options, "dontloadscales", 0);
option_unused(options);
net.layers[count] = l;
free_section(s);
@@ -472,9 +490,9 @@
{
return (strcmp(s->type, "[detection]")==0);
}
-int is_region(section *s)
+int is_local(section *s)
{
- return (strcmp(s->type, "[region]")==0);
+ return (strcmp(s->type, "[local]")==0);
}
int is_deconvolutional(section *s)
{
@@ -527,24 +545,6 @@
return (strcmp(s->type, "[route]")==0);
}
-int read_option(char *s, list *options)
-{
- size_t i;
- size_t len = strlen(s);
- char *val = 0;
- for(i = 0; i < len; ++i){
- if(s[i] == '='){
- s[i] = '\0';
- val = s+i+1;
- break;
- }
- }
- if(i == len-1) return 0;
- char *key = s;
- option_insert(options, key, val);
- return 1;
-}
-
list *read_cfg(char *filename)
{
FILE *file = fopen(filename, "r");
@@ -641,19 +641,13 @@
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
- fwrite(l.filters, sizeof(float), num, fp);
- }
- if(l.type == DECONVOLUTIONAL){
-#ifdef GPU
- if(gpu_index >= 0){
- pull_deconvolutional_layer(l);
+ if (l.batch_normalize){
+ fwrite(l.scales, sizeof(float), l.n, fp);
+ fwrite(l.rolling_mean, sizeof(float), l.n, fp);
+ fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
-#endif
- int num = l.n*l.c*l.size*l.size;
- fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.filters, sizeof(float), num, fp);
- }
- if(l.type == CONNECTED){
+ } if(l.type == CONNECTED){
#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(l);
@@ -661,6 +655,16 @@
#endif
fwrite(l.biases, sizeof(float), l.outputs, fp);
fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
+ } if(l.type == LOCAL){
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_local_layer(l);
+ }
+#endif
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ fwrite(l.biases, sizeof(float), l.outputs, fp);
+ fwrite(l.filters, sizeof(float), size, fp);
}
}
fclose(fp);
@@ -690,6 +694,11 @@
if(l.type == CONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
+ if (l.batch_normalize && (!l.dontloadscales)){
+ fread(l.scales, sizeof(float), l.n, fp);
+ fread(l.rolling_mean, sizeof(float), l.n, fp);
+ fread(l.rolling_variance, sizeof(float), l.n, fp);
+ }
fread(l.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
@@ -716,6 +725,17 @@
}
#endif
}
+ if(l.type == LOCAL){
+ int locations = l.out_w*l.out_h;
+ int size = l.size*l.size*l.c*l.n*locations;
+ fread(l.biases, sizeof(float), l.outputs, fp);
+ fread(l.filters, sizeof(float), size, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_local_layer(l);
+ }
+#endif
+ }
}
fprintf(stderr, "Done!\n");
fclose(fp);
--
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