From d9f1b0b16edeb59281355a855e18a8be343fc33c Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 08 Aug 2014 19:04:15 +0000
Subject: [PATCH] probably how maxpool layers should be
---
src/parser.c | 246 +++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 215 insertions(+), 31 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 5d6aa1c..1656346 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -9,6 +9,7 @@
#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"
@@ -21,6 +22,7 @@
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_normalization(section *s);
list *read_cfg(char *filename);
@@ -41,28 +43,39 @@
free(s);
}
-convolutional_layer *parse_convolutional(list *options, network net, int count)
+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->batch = option_find_int(options, "batch",1);
+ net->learning_rate = learning_rate;
+ net->momentum = momentum;
+ net->decay = decay;
}else{
- image m = get_network_image_layer(net, count-1);
+ 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, activation);
+ 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;
@@ -80,25 +93,60 @@
curr = next+1;
}
}
+ 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;
+ }
+ }
option_unused(options);
return layer;
}
-connected_layer *parse_connected(list *options, network net, int count)
+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);
- float dropout = option_find_float(options, "dropout", 0.);
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);
+ 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{
- input = get_network_output_size_layer(net, count-1);
+ 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, dropout, activation);
+ 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;
@@ -120,42 +168,58 @@
return layer;
}
-softmax_layer *parse_softmax(list *options, network net, int count)
+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);
+ net->batch = option_find_int(options, "batch",1);
}else{
- input = get_network_output_size_layer(net, count-1);
+ input = get_network_output_size_layer(*net, count-1);
}
- softmax_layer *layer = make_softmax_layer(net.batch, input);
+ softmax_layer *layer = make_softmax_layer(net->batch, input);
option_unused(options);
return layer;
}
-maxpool_layer *parse_maxpool(list *options, network net, int count)
+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);
+ net->batch = option_find_int(options, "batch",1);
}else{
- image m = get_network_image_layer(net, count-1);
+ 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);
+ maxpool_layer *layer = make_maxpool_layer(net->batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
-normalization_layer *parse_normalization(list *options, network net, int count)
+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);
@@ -166,15 +230,15 @@
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->batch = option_find_int(options, "batch",1);
}else{
- image m = get_network_image_layer(net, count-1);
+ 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);
+ normalization_layer *layer = make_normalization_layer(net->batch,h,w,c,size, alpha, beta, kappa);
option_unused(options);
return layer;
}
@@ -190,30 +254,29 @@
section *s = (section *)n->val;
list *options = s->options;
if(is_convolutional(s)){
- convolutional_layer *layer = parse_convolutional(options, net, count);
+ convolutional_layer *layer = parse_convolutional(options, &net, count);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
- net.batch = layer->batch;
}else if(is_connected(s)){
- connected_layer *layer = parse_connected(options, net, count);
+ connected_layer *layer = parse_connected(options, &net, count);
net.types[count] = CONNECTED;
net.layers[count] = layer;
- net.batch = layer->batch;
}else if(is_softmax(s)){
- softmax_layer *layer = parse_softmax(options, net, count);
+ 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)){
- maxpool_layer *layer = parse_maxpool(options, net, count);
+ maxpool_layer *layer = parse_maxpool(options, &net, count);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
- net.batch = layer->batch;
}else if(is_normalization(s)){
- normalization_layer *layer = parse_normalization(options, net, count);
+ normalization_layer *layer = parse_normalization(options, &net, count);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
- net.batch = layer->batch;
+ }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);
}
@@ -242,6 +305,10 @@
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)
{
@@ -307,3 +374,120 @@
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, "data=");
+ 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\n");
+}
+
+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] == 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);
+}
+
--
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