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Nist NIN testing multi-crop

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This commit is contained in:
Joseph Redmon
2014-08-11 12:52:07 -07:00
parent 7add111509
commit 176d65b765
11 changed files with 288 additions and 51 deletions
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116
src/parser.c
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@ -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_connected(section *s);
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 @@ void free_section(section *s)
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 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
}
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 @@ connected_layer *parse_connected(list *options, network *net, int count)
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 @@ softmax_layer *parse_softmax(list *options, network *net, int count)
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 @@ network parse_network_cfg(char *filename)
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 @@ network parse_network_cfg(char *filename)
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 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int
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 @@ void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
"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 @@ void save_network(network net, char *filename)
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)