From 2ea63c0e99a5358eaf38785ea83b9c5923fcc9cd Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 13 Mar 2014 04:57:34 +0000
Subject: [PATCH] Better VOC handling and resizing
---
src/parser.c | 202 ++++++++++++++++++++++++++++++++++++++------------
1 files changed, 153 insertions(+), 49 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 7541620..cf64b55 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -7,6 +7,7 @@
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
+#include "softmax_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@@ -19,13 +20,142 @@
int is_convolutional(section *s);
int is_connected(section *s);
int is_maxpool(section *s);
+int is_softmax(section *s);
list *read_cfg(char *filename);
+void free_section(section *s)
+{
+ free(s->type);
+ node *n = s->options->front;
+ while(n){
+ kvp *pair = (kvp *)n->val;
+ free(pair->key);
+ free(pair);
+ node *next = n->next;
+ free(n);
+ n = next;
+ }
+ free(s->options);
+ free(s);
+}
+
+convolutional_layer *parse_convolutional(list *options, network net, int count)
+{
+ int i;
+ int h,w,c;
+ 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", "sigmoid");
+ ACTIVATION activation = get_activation(activation_s);
+ 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.");
+ }
+ convolutional_layer *layer = make_convolutional_layer(net.batch,h,w,c,n,size,stride, activation);
+ 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;
+ }
+ }
+ option_unused(options);
+ return layer;
+}
+
+connected_layer *parse_connected(list *options, network net, int count)
+{
+ int i;
+ int input;
+ int output = option_find_int(options, "output",1);
+ char *activation_s = option_find_str(options, "activation", "sigmoid");
+ ACTIVATION activation = get_activation(activation_s);
+ 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);
+ }
+ connected_layer *layer = make_connected_layer(net.batch, input, output, activation);
+ 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;
+ }
+ }
+ option_unused(options);
+ return layer;
+}
+
+softmax_layer *parse_softmax(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);
+ }else{
+ input = get_network_output_size_layer(net, count-1);
+ }
+ softmax_layer *layer = make_softmax_layer(net.batch, input);
+ option_unused(options);
+ return layer;
+}
+
+maxpool_layer *parse_maxpool(list *options, network net, int count)
+{
+ int h,w,c;
+ int stride = option_find_int(options, "stride",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.");
+ }
+ maxpool_layer *layer = make_maxpool_layer(net.batch,h,w,c,stride);
+ option_unused(options);
+ return layer;
+}
network parse_network_cfg(char *filename)
{
list *sections = read_cfg(filename);
- network net = make_network(sections->size);
+ network net = make_network(sections->size, 0);
node *n = sections->front;
int count = 0;
@@ -33,67 +163,35 @@
section *s = (section *)n->val;
list *options = s->options;
if(is_convolutional(s)){
- int h,w,c;
- 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", "sigmoid");
- ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",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.");
- }
- convolutional_layer *layer = make_convolutional_layer(h,w,c,n,size,stride, activation);
+ convolutional_layer *layer = parse_convolutional(options, net, count);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
- option_unused(options);
- }
- else if(is_connected(s)){
- int input;
- int output = option_find_int(options, "output",1);
- char *activation_s = option_find_str(options, "activation", "sigmoid");
- ACTIVATION activation = get_activation(activation_s);
- if(count == 0){
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(net, count-1);
- }
- connected_layer *layer = make_connected_layer(input, output, activation);
+ net.batch = layer->batch;
+ }else if(is_connected(s)){
+ connected_layer *layer = parse_connected(options, net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
- option_unused(options);
+ net.batch = layer->batch;
+ }else if(is_softmax(s)){
+ softmax_layer *layer = parse_softmax(options, net, count);
+ net.types[count] = SOFTMAX;
+ net.layers[count] = layer;
+ net.batch = layer->batch;
}else if(is_maxpool(s)){
- int h,w,c;
- int stride = option_find_int(options, "stride",1);
- //char *activation_s = option_find_str(options, "activation", "sigmoid");
- if(count == 0){
- h = option_find_int(options, "height",1);
- w = option_find_int(options, "width",1);
- c = option_find_int(options, "channels",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(h,w,c,stride);
+ maxpool_layer *layer = parse_maxpool(options, net, count);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
- option_unused(options);
+ net.batch = layer->batch;
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
+ free_section(s);
++count;
n = n->next;
}
+ free_list(sections);
+ net.outputs = get_network_output_size(net);
+ net.output = get_network_output(net);
return net;
}
@@ -113,6 +211,12 @@
|| strcmp(s->type, "[maxpool]")==0);
}
+int is_softmax(section *s)
+{
+ return (strcmp(s->type, "[soft]")==0
+ || strcmp(s->type, "[softmax]")==0);
+}
+
int read_option(char *s, list *options)
{
int i;
--
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