From 76ee68f96d864a27312c9aa09856ddda559a5cd9 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 28 Aug 2014 02:11:46 +0000
Subject: [PATCH] Trying some stuff w/ dropout
---
src/parser.c | 478 ++++++++++++++++++++++++++++++++++++++++++++++++++++-------
1 files changed, 421 insertions(+), 57 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index dc1db2b..5c991a5 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -4,10 +4,13 @@
#include "parser.h"
#include "activations.h"
+#include "crop_layer.h"
#include "convolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
+#include "normalization_layer.h"
#include "softmax_layer.h"
+#include "dropout_layer.h"
#include "list.h"
#include "option_list.h"
#include "utils.h"
@@ -20,14 +23,260 @@
int is_convolutional(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_normalization(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);
+}
+
+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;
+ int h,w,c;
+ float learning_rate, momentum, decay;
+ 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");
+ 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;
+ }
+ }
+ char *weights = option_find_str(options, "weights", 0);
+ char *biases = option_find_str(options, "biases", 0);
+ parse_data(biases, layer->biases, n);
+ parse_data(weights, layer->filters, c*n*size*size);
+ option_unused(options);
+ return layer;
+}
+
+connected_layer *parse_connected(list *options, network *net, int count)
+{
+ 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");
+ 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;
+ }
+ }
+ 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;
+}
+
+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;
+}
+
+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;
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+dropout_layer *parse_dropout(list *options, network *net, int count)
+{
+ 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);
+ option_unused(options);
+ return layer;
+}
+
+normalization_layer *parse_normalization(list *options, network *net, int count)
+{
+ 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);
+ 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;
@@ -35,81 +284,50 @@
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);
+ }else if(is_connected(s)){
+ connected_layer *layer = parse_connected(options, &net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
- option_unused(options);
+ }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)){
- int input;
- if(count == 0){
- input = option_find_int(options, "input",1);
- }else{
- input = get_network_output_size_layer(net, count-1);
- }
- softmax_layer *layer = make_softmax_layer(input);
+ softmax_layer *layer = parse_softmax(options, &net, count);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
- option_unused(options);
}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);
+ }else if(is_normalization(s)){
+ normalization_layer *layer = parse_normalization(options, &net, count);
+ net.types[count] = NORMALIZATION;
+ net.layers[count] = layer;
+ }else if(is_dropout(s)){
+ dropout_layer *layer = parse_dropout(options, &net, count);
+ 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;
}
+ free_list(sections);
+ net.outputs = get_network_output_size(net);
+ net.output = get_network_output(net);
return net;
}
+int is_crop(section *s)
+{
+ return (strcmp(s->type, "[crop]")==0);
+}
int is_convolutional(section *s)
{
return (strcmp(s->type, "[conv]")==0
@@ -125,12 +343,21 @@
return (strcmp(s->type, "[max]")==0
|| strcmp(s->type, "[maxpool]")==0);
}
+int is_dropout(section *s)
+{
+ return (strcmp(s->type, "[dropout]")==0);
+}
int is_softmax(section *s)
{
return (strcmp(s->type, "[soft]")==0
|| strcmp(s->type, "[softmax]")==0);
}
+int is_normalization(section *s)
+{
+ return (strcmp(s->type, "[lrnorm]")==0
+ || strcmp(s->type, "[localresponsenormalization]")==0);
+}
int read_option(char *s, list *options)
{
@@ -185,3 +412,140 @@
return sections;
}
+void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
+{
+ 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"
+ "pad=%d\n"
+ "activation=%s\n",
+ l->n, l->size, l->stride, l->pad,
+ 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_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
+{
+ 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, "biases=");
+ for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[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");
+ 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"
+ "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
+}
+
+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 save_network(network net, char *filename)
+{
+ FILE *fp = fopen(filename, "w");
+ if(!fp) file_error(filename);
+ int i;
+ for(i = 0; i < net.n; ++i)
+ {
+ if(net.types[i] == CONVOLUTIONAL)
+ 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)
+ 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);
+ }
+ fclose(fp);
+}
+
--
Gitblit v1.10.0