From 81751b47dd5d2e63f571f048bdd0a6a2a45617b0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 30 Mar 2015 19:04:03 +0000
Subject: [PATCH] ..... and back to coords
---
src/parser.c | 628 +++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 416 insertions(+), 212 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 1656346..e4ee17e 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -4,12 +4,16 @@
#include "parser.h"
#include "activations.h"
+#include "crop_layer.h"
+#include "cost_layer.h"
#include "convolutional_layer.h"
+#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
+#include "detection_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@@ -19,11 +23,16 @@
list *options;
}section;
+int is_network(section *s);
int is_convolutional(section *s);
+int is_deconvolutional(section *s);
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_cost(section *s);
+int is_detection(section *s);
int is_normalization(section *s);
list *read_cfg(char *filename);
@@ -43,246 +52,294 @@
free(s);
}
-convolutional_layer *parse_convolutional(list *options, network *net, int count)
+void parse_data(char *data, float *a, int n)
{
int i;
- int h,w,c;
- float learning_rate, momentum, decay;
+ 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;
+ }
+}
+
+typedef struct size_params{
+ int batch;
+ int inputs;
+ int h;
+ int w;
+ int c;
+} size_params;
+
+deconvolutional_layer *parse_deconvolutional(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);
+ 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 deconvolutional layer must output image.");
+
+ deconvolutional_layer *layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
+
+ char *weights = option_find_str(options, "weights", 0);
+ char *biases = option_find_str(options, "biases", 0);
+ parse_data(weights, layer->filters, c*n*size*size);
+ parse_data(biases, layer->biases, n);
+ #ifdef GPU
+ if(weights || biases) push_deconvolutional_layer(*layer);
+ #endif
+ option_unused(options);
+ return layer;
+}
+
+convolutional_layer *parse_convolutional(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", "sigmoid");
+ char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- learning_rate = option_find_float(options, "learning_rate", .001);
- momentum = option_find_float(options, "momentum", .9);
- decay = option_find_float(options, "decay", .0001);
- 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);
- net->learning_rate = learning_rate;
- net->momentum = momentum;
- net->decay = decay;
- }else{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- image m = get_network_image_layer(*net, count-1);
- h = m.h;
- w = m.w;
- c = m.c;
- if(h == 0) error("Layer before convolutional layer must output image.");
- }
- convolutional_layer *layer = make_convolutional_layer(net->batch,h,w,c,n,size,stride,pad,activation,learning_rate,momentum,decay);
- char *data = option_find_str(options, "data", 0);
- if(data){
- char *curr = data;
- char *next = data;
- for(i = 0; i < n; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->biases[i]);
- curr = next+1;
- }
- for(i = 0; i < c*n*size*size; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->filters[i]);
- curr = next+1;
- }
- }
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before convolutional layer must output image.");
+
+ convolutional_layer *layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
+
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(weights, layer->filters, c*n*size*size);
+ parse_data(biases, layer->biases, n);
+ #ifdef GPU
+ if(weights || biases) push_convolutional_layer(*layer);
+ #endif
option_unused(options);
return layer;
}
-connected_layer *parse_connected(list *options, network *net, int count)
+connected_layer *parse_connected(list *options, size_params params)
{
- int i;
- int input;
- float learning_rate, momentum, decay;
int output = option_find_int(options, "output",1);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
+ char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- input = option_find_int(options, "input",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{
- learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
- momentum = option_find_float_quiet(options, "momentum", net->momentum);
- decay = option_find_float_quiet(options, "decay", net->decay);
- input = get_network_output_size_layer(*net, count-1);
- }
- connected_layer *layer = make_connected_layer(net->batch, input, output, activation,learning_rate,momentum,decay);
- char *data = option_find_str(options, "data", 0);
- if(data){
- char *curr = data;
- char *next = data;
- for(i = 0; i < output; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->biases[i]);
- curr = next+1;
- }
- for(i = 0; i < input*output; ++i){
- while(*++next !='\0' && *next != ',');
- *next = '\0';
- sscanf(curr, "%g", &layer->weights[i]);
- curr = next+1;
- }
- }
+
+ connected_layer *layer = make_connected_layer(params.batch, params.inputs, output, activation);
+
+ 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, params.inputs*output);
+ #ifdef GPU
+ if(weights || biases) push_connected_layer(*layer);
+ #endif
option_unused(options);
return layer;
}
-softmax_layer *parse_softmax(list *options, network *net, int count)
+softmax_layer *parse_softmax(list *options, size_params params)
{
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- net->batch = option_find_int(options, "batch",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- softmax_layer *layer = make_softmax_layer(net->batch, input);
+ int groups = option_find_int(options, "groups",1);
+ softmax_layer *layer = make_softmax_layer(params.batch, params.inputs, groups);
option_unused(options);
return layer;
}
-maxpool_layer *parse_maxpool(list *options, network *net, int count)
+detection_layer *parse_detection(list *options, size_params params)
{
- int h,w,c;
+ int coords = option_find_int(options, "coords", 1);
+ int classes = option_find_int(options, "classes", 1);
+ int rescore = option_find_int(options, "rescore", 1);
+ int nuisance = option_find_int(options, "nuisance", 0);
+ int background = option_find_int(options, "background", 1);
+ detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background, nuisance);
+ option_unused(options);
+ return layer;
+}
+
+cost_layer *parse_cost(list *options, size_params params)
+{
+ char *type_s = option_find_str(options, "type", "sse");
+ COST_TYPE type = get_cost_type(type_s);
+ cost_layer *layer = make_cost_layer(params.batch, params.inputs, type);
+ option_unused(options);
+ return layer;
+}
+
+crop_layer *parse_crop(list *options, size_params params)
+{
+ 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);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before crop layer must output image.");
+
+ crop_layer *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip);
+ option_unused(options);
+ return layer;
+}
+
+maxpool_layer *parse_maxpool(list *options, size_params params)
+{
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
- 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);
- }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 convolutional layer must output image.");
- }
- maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before maxpool layer must output image.");
+
+ maxpool_layer *layer = make_maxpool_layer(batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
-dropout_layer *parse_dropout(list *options, network *net, int count)
+dropout_layer *parse_dropout(list *options, size_params params)
{
- int input;
float probability = option_find_float(options, "probability", .5);
- if(count == 0){
- net->batch = option_find_int(options, "batch",1);
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(*net, count-1);
- }
- dropout_layer *layer = make_dropout_layer(net->batch,input,probability);
+ dropout_layer *layer = make_dropout_layer(params.batch, params.inputs, probability);
option_unused(options);
return layer;
}
-normalization_layer *parse_normalization(list *options, network *net, int count)
+normalization_layer *parse_normalization(list *options, size_params params)
{
- int h,w,c;
int size = option_find_int(options, "size",1);
float alpha = option_find_float(options, "alpha", 0.);
float beta = option_find_float(options, "beta", 1.);
float kappa = option_find_float(options, "kappa", 1.);
- 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);
- }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 convolutional layer must output image.");
- }
- normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
+
+ int batch,h,w,c;
+ h = params.h;
+ w = params.w;
+ c = params.c;
+ batch=params.batch;
+ if(!(h && w && c)) error("Layer before normalization layer must output image.");
+
+ normalization_layer *layer = make_normalization_layer(batch,h,w,c,size, alpha, beta, kappa);
option_unused(options);
return layer;
}
+void parse_net_options(list *options, network *net)
+{
+ net->batch = option_find_int(options, "batch",1);
+ net->learning_rate = option_find_float(options, "learning_rate", .001);
+ net->momentum = option_find_float(options, "momentum", .9);
+ net->decay = option_find_float(options, "decay", .0001);
+ net->seen = option_find_int(options, "seen",0);
+ int subdivs = option_find_int(options, "subdivisions",1);
+ net->batch /= subdivs;
+ net->subdivisions = subdivs;
+
+ net->h = option_find_int_quiet(options, "height",0);
+ net->w = option_find_int_quiet(options, "width",0);
+ net->c = option_find_int_quiet(options, "channels",0);
+ net->inputs = option_find_int_quiet(options, "inputs", net->h * net->w * net->c);
+ if(!net->inputs && !(net->h && net->w && net->c)) error("No input parameters supplied");
+ option_unused(options);
+}
+
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
- network net = make_network(sections->size, 0);
-
node *n = sections->front;
+ if(!n) error("Config file has no sections");
+ network net = make_network(sections->size - 1);
+ size_params params;
+
+ section *s = (section *)n->val;
+ list *options = s->options;
+ if(!is_network(s)) error("First section must be [net] or [network]");
+ parse_net_options(options, &net);
+
+ params.h = net.h;
+ params.w = net.w;
+ params.c = net.c;
+ params.inputs = net.inputs;
+ params.batch = net.batch;
+
+ n = n->next;
int count = 0;
while(n){
- section *s = (section *)n->val;
- list *options = s->options;
+ fprintf(stderr, "%d: ", count);
+ s = (section *)n->val;
+ options = s->options;
if(is_convolutional(s)){
- convolutional_layer *layer = parse_convolutional(options, &net, count);
+ convolutional_layer *layer = parse_convolutional(options, params);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
+ }else if(is_deconvolutional(s)){
+ deconvolutional_layer *layer = parse_deconvolutional(options, params);
+ net.types[count] = DECONVOLUTIONAL;
+ net.layers[count] = layer;
}else if(is_connected(s)){
- connected_layer *layer = parse_connected(options, &net, count);
+ connected_layer *layer = parse_connected(options, params);
net.types[count] = CONNECTED;
net.layers[count] = layer;
+ }else if(is_crop(s)){
+ crop_layer *layer = parse_crop(options, params);
+ net.types[count] = CROP;
+ net.layers[count] = layer;
+ }else if(is_cost(s)){
+ cost_layer *layer = parse_cost(options, params);
+ net.types[count] = COST;
+ net.layers[count] = layer;
+ }else if(is_detection(s)){
+ detection_layer *layer = parse_detection(options, params);
+ net.types[count] = DETECTION;
+ net.layers[count] = layer;
}else if(is_softmax(s)){
- softmax_layer *layer = parse_softmax(options, &net, count);
+ softmax_layer *layer = parse_softmax(options, params);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
}else if(is_maxpool(s)){
- maxpool_layer *layer = parse_maxpool(options, &net, count);
+ maxpool_layer *layer = parse_maxpool(options, params);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
}else if(is_normalization(s)){
- normalization_layer *layer = parse_normalization(options, &net, count);
+ normalization_layer *layer = parse_normalization(options, params);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
}else if(is_dropout(s)){
- dropout_layer *layer = parse_dropout(options, &net, count);
+ dropout_layer *layer = parse_dropout(options, params);
net.types[count] = DROPOUT;
net.layers[count] = layer;
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
free_section(s);
- ++count;
n = n->next;
+ if(n){
+ image im = get_network_image_layer(net, count);
+ params.h = im.h;
+ params.w = im.w;
+ params.c = im.c;
+ params.inputs = get_network_output_size_layer(net, count);
+ }
+ ++count;
}
free_list(sections);
net.outputs = get_network_output_size(net);
@@ -290,11 +347,33 @@
return net;
}
+int is_crop(section *s)
+{
+ return (strcmp(s->type, "[crop]")==0);
+}
+int is_cost(section *s)
+{
+ 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
+ || strcmp(s->type, "[deconvolutional]")==0);
+}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
|| strcmp(s->type, "[convolutional]")==0);
}
+int is_network(section *s)
+{
+ return (strcmp(s->type, "[net]")==0
+ || strcmp(s->type, "[network]")==0);
+}
int is_connected(section *s)
{
return (strcmp(s->type, "[conn]")==0
@@ -323,8 +402,8 @@
int read_option(char *s, list *options)
{
- int i;
- int len = strlen(s);
+ size_t i;
+ size_t len = strlen(s);
char *val = 0;
for(i = 0; i < len; ++i){
if(s[i] == '='){
@@ -364,7 +443,7 @@
break;
default:
if(!read_option(line, current->options)){
- printf("Config file error line %d, could parse: %s\n", nu, line);
+ fprintf(stderr, "Config file error line %d, could parse: %s\n", nu, line);
free(line);
}
break;
@@ -376,25 +455,11 @@
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
{
+#ifdef GPU
+ if(gpu_index >= 0) pull_convolutional_layer(*l);
+#endif
int i;
fprintf(fp, "[convolutional]\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, l->learning_rate, l->momentum, l->decay);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
- }
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
@@ -409,54 +474,68 @@
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
fprintf(fp, "\n\n");
}
+
+void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
+{
+#ifdef GPU
+ if(gpu_index >= 0) pull_deconvolutional_layer(*l);
+#endif
+ int i;
+ fprintf(fp, "[deconvolutional]\n");
+ fprintf(fp, "filters=%d\n"
+ "size=%d\n"
+ "stride=%d\n"
+ "activation=%s\n",
+ l->n, l->size, l->stride,
+ get_activation_string(l->activation));
+ fprintf(fp, "biases=");
+ for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
+ fprintf(fp, "\n");
+ fprintf(fp, "weights=");
+ for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
+ fprintf(fp, "\n\n");
+}
+
+void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
+{
+ fprintf(fp, "[dropout]\n");
+ fprintf(fp, "probability=%g\n\n", l->probability);
+}
+
void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
{
+#ifdef GPU
+ if(gpu_index >= 0) pull_connected_layer(*l);
+#endif
int i;
fprintf(fp, "[connected]\n");
- if(count == 0){
- fprintf(fp, "batch=%d\n"
- "input=%d\n"
- "learning_rate=%g\n"
- "momentum=%g\n"
- "decay=%g\n",
- l->batch, l->inputs, l->learning_rate, l->momentum, l->decay);
- } else {
- if(l->learning_rate != net.learning_rate)
- fprintf(fp, "learning_rate=%g\n", l->learning_rate);
- if(l->momentum != net.momentum)
- fprintf(fp, "momentum=%g\n", l->momentum);
- if(l->decay != net.decay)
- fprintf(fp, "decay=%g\n", l->decay);
- }
fprintf(fp, "output=%d\n"
"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");
+ 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");
- if(count == 0) 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\nstride=%d\n\n", l->size, l->stride);
}
void print_normalization_cfg(FILE *fp, normalization_layer *l, network net, int count)
{
fprintf(fp, "[localresponsenormalization]\n");
- if(count == 0) 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"
@@ -466,10 +545,125 @@
void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
{
fprintf(fp, "[softmax]\n");
- if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
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\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance);
+ 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));
+ fprintf(fp, "\n");
+}
+
+void save_weights(network net, char *filename)
+{
+ fprintf(stderr, "Saving weights to %s\n", filename);
+ FILE *fp = fopen(filename, "w");
+ if(!fp) file_error(filename);
+
+ fwrite(&net.learning_rate, sizeof(float), 1, fp);
+ fwrite(&net.momentum, sizeof(float), 1, fp);
+ fwrite(&net.decay, sizeof(float), 1, fp);
+ fwrite(&net.seen, sizeof(int), 1, fp);
+
+ int i;
+ for(i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *) net.layers[i];
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_convolutional_layer(layer);
+ }
+#endif
+ int num = layer.n*layer.c*layer.size*layer.size;
+ fwrite(layer.biases, sizeof(float), layer.n, fp);
+ fwrite(layer.filters, sizeof(float), num, fp);
+ }
+ if(net.types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *) net.layers[i];
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_deconvolutional_layer(layer);
+ }
+#endif
+ int num = layer.n*layer.c*layer.size*layer.size;
+ fwrite(layer.biases, sizeof(float), layer.n, fp);
+ fwrite(layer.filters, sizeof(float), num, fp);
+ }
+ if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *) net.layers[i];
+#ifdef GPU
+ if(gpu_index >= 0){
+ pull_connected_layer(layer);
+ }
+#endif
+ fwrite(layer.biases, sizeof(float), layer.outputs, fp);
+ fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
+ }
+ }
+ fclose(fp);
+}
+
+void load_weights_upto(network *net, char *filename, int cutoff)
+{
+ fprintf(stderr, "Loading weights from %s\n", filename);
+ FILE *fp = fopen(filename, "r");
+ if(!fp) file_error(filename);
+
+ fread(&net->learning_rate, sizeof(float), 1, fp);
+ fread(&net->momentum, sizeof(float), 1, fp);
+ fread(&net->decay, sizeof(float), 1, fp);
+ fread(&net->seen, sizeof(int), 1, fp);
+
+ int i;
+ for(i = 0; i < net->n && i < cutoff; ++i){
+ if(net->types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *) net->layers[i];
+ int num = layer.n*layer.c*layer.size*layer.size;
+ fread(layer.biases, sizeof(float), layer.n, fp);
+ fread(layer.filters, sizeof(float), num, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_convolutional_layer(layer);
+ }
+#endif
+ }
+ if(net->types[i] == DECONVOLUTIONAL){
+ deconvolutional_layer layer = *(deconvolutional_layer *) net->layers[i];
+ int num = layer.n*layer.c*layer.size*layer.size;
+ fread(layer.biases, sizeof(float), layer.n, fp);
+ fread(layer.filters, sizeof(float), num, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_deconvolutional_layer(layer);
+ }
+#endif
+ }
+ if(net->types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *) net->layers[i];
+ fread(layer.biases, sizeof(float), layer.outputs, fp);
+ fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
+#ifdef GPU
+ if(gpu_index >= 0){
+ push_connected_layer(layer);
+ }
+#endif
+ }
+ }
+ fclose(fp);
+}
+
+void load_weights(network *net, char *filename)
+{
+ load_weights_upto(net, filename, net->n);
+}
+
void save_network(network net, char *filename)
{
FILE *fp = fopen(filename, "w");
@@ -479,14 +673,24 @@
{
if(net.types[i] == CONVOLUTIONAL)
print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
+ else if(net.types[i] == DECONVOLUTIONAL)
+ print_deconvolutional_cfg(fp, (deconvolutional_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] == DROPOUT)
+ print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
else if(net.types[i] == NORMALIZATION)
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);
}
fclose(fp);
}
--
Gitblit v1.10.0