From 176d65b76583803cf10194c4c70bdc51897f2ae3 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 11 Aug 2014 19:52:07 +0000
Subject: [PATCH] Nist NIN testing multi-crop
---
src/parser.c | 116 +++++++++++++++++++++++++++++++++++++++++++--------------
1 files changed, 87 insertions(+), 29 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 1656346..5c991a5 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -4,6 +4,7 @@
#include "parser.h"
#include "activations.h"
+#include "crop_layer.h"
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
@@ -24,6 +25,7 @@
int is_maxpool(section *s);
int is_dropout(section *s);
int is_softmax(section *s);
+int is_crop(section *s);
int is_normalization(section *s);
list *read_cfg(char *filename);
@@ -43,6 +45,22 @@
free(s);
}
+void parse_data(char *data, float *a, int n)
+{
+ int i;
+ if(!data) return;
+ char *curr = data;
+ char *next = data;
+ int done = 0;
+ for(i = 0; i < n && !done; ++i){
+ while(*++next !='\0' && *next != ',');
+ if(*next == '\0') done = 1;
+ *next = '\0';
+ sscanf(curr, "%g", &a[i]);
+ curr = next+1;
+ }
+}
+
convolutional_layer *parse_convolutional(list *options, network *net, int count)
{
int i;
@@ -95,30 +113,8 @@
}
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
- if(biases){
- char *curr = biases;
- char *next = biases;
- int done = 0;
- for(i = 0; i < n && !done; ++i){
- while(*++next !='\0' && *next != ',');
- if(*next == '\0') done = 1;
- *next = '\0';
- sscanf(curr, "%g", &layer->biases[i]);
- curr = next+1;
- }
- }
- if(weights){
- char *curr = weights;
- char *next = weights;
- int done = 0;
- for(i = 0; i < c*n*size*size && !done; ++i){
- while(*++next !='\0' && *next != ',');
- if(*next == '\0') done = 1;
- *next = '\0';
- sscanf(curr, "%g", &layer->filters[i]);
- curr = next+1;
- }
- }
+ parse_data(biases, layer->biases, n);
+ parse_data(weights, layer->filters, c*n*size*size);
option_unused(options);
return layer;
}
@@ -164,6 +160,10 @@
curr = next+1;
}
}
+ char *weights = option_find_str(options, "weights", 0);
+ char *biases = option_find_str(options, "biases", 0);
+ parse_data(biases, layer->biases, output);
+ parse_data(weights, layer->weights, input*output);
option_unused(options);
return layer;
}
@@ -182,6 +182,36 @@
return layer;
}
+crop_layer *parse_crop(list *options, network *net, int count)
+{
+ float learning_rate, momentum, decay;
+ int h,w,c;
+ int crop_height = option_find_int(options, "crop_height",1);
+ int crop_width = option_find_int(options, "crop_width",1);
+ int flip = option_find_int(options, "flip",0);
+ if(count == 0){
+ h = option_find_int(options, "height",1);
+ w = option_find_int(options, "width",1);
+ c = option_find_int(options, "channels",1);
+ net->batch = option_find_int(options, "batch",1);
+ learning_rate = option_find_float(options, "learning_rate", .001);
+ momentum = option_find_float(options, "momentum", .9);
+ decay = option_find_float(options, "decay", .0001);
+ net->learning_rate = learning_rate;
+ net->momentum = momentum;
+ net->decay = decay;
+ }else{
+ image m = get_network_image_layer(*net, count-1);
+ h = m.h;
+ w = m.w;
+ c = m.c;
+ if(h == 0) error("Layer before crop layer must output image.");
+ }
+ crop_layer *layer = make_crop_layer(net->batch,h,w,c,crop_height,crop_width,flip);
+ option_unused(options);
+ return layer;
+}
+
maxpool_layer *parse_maxpool(list *options, network *net, int count)
{
int h,w,c;
@@ -261,6 +291,10 @@
connected_layer *layer = parse_connected(options, &net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
+ }else if(is_crop(s)){
+ crop_layer *layer = parse_crop(options, &net, count);
+ net.types[count] = CROP;
+ net.layers[count] = layer;
}else if(is_softmax(s)){
softmax_layer *layer = parse_softmax(options, &net, count);
net.types[count] = SOFTMAX;
@@ -290,6 +324,10 @@
return net;
}
+int is_crop(section *s)
+{
+ return (strcmp(s->type, "[crop]")==0);
+}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
@@ -389,11 +427,11 @@
l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay);
} else {
if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
+ fprintf(fp, "learning_rate=%g\n", l->learning_rate);
if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
+ fprintf(fp, "momentum=%g\n", l->momentum);
if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
+ fprintf(fp, "decay=%g\n", l->decay);
}
fprintf(fp, "filters=%d\n"
"size=%d\n"
@@ -432,12 +470,30 @@
"activation=%s\n",
l->outputs,
get_activation_string(l->activation));
- fprintf(fp, "data=");
+ fprintf(fp, "biases=");
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]);
+ fprintf(fp, "\n");
+ fprintf(fp, "weights=");
+ for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
fprintf(fp, "\n\n");
}
+void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
+{
+ fprintf(fp, "[crop]\n");
+ if(count == 0) {
+ fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n"
+ "learning_rate=%g\n"
+ "momentum=%g\n"
+ "decay=%g\n",
+ l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay);
+ }
+ fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
+}
+
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
{
fprintf(fp, "[maxpool]\n");
@@ -481,6 +537,8 @@
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
else if(net.types[i] == CONNECTED)
print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
+ else if(net.types[i] == CROP)
+ print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
else if(net.types[i] == MAXPOOL)
print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
else if(net.types[i] == NORMALIZATION)
--
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