From 0f1a31648c5292fa49b35eac90a2ee676d6c13e6 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 31 Jan 2015 06:05:23 +0000
Subject: [PATCH] idk, probably something changed
---
src/parser.c | 97 +++++++++++++++++++++++-------------------------
1 files changed, 47 insertions(+), 50 deletions(-)
diff --git a/src/parser.c b/src/parser.c
index 9bd2eb7..a00feec 100644
--- a/src/parser.c
+++ b/src/parser.c
@@ -67,7 +67,6 @@
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);
@@ -87,6 +86,7 @@
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
+ net->seen = option_find_int(options, "seen",0);
}else{
learning_rate = option_find_float_quiet(options, "learning_rate", net->learning_rate);
momentum = option_find_float_quiet(options, "momentum", net->momentum);
@@ -98,34 +98,19 @@
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);
+ parse_data(biases, layer->biases, n);
+ #ifdef GPU
+ push_convolutional_layer(*layer);
+ #endif
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);
@@ -147,27 +132,13 @@
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);
+ #ifdef GPU
+ push_connected_layer(*layer);
+ #endif
option_unused(options);
return layer;
}
@@ -178,6 +149,7 @@
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
+ net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
@@ -192,10 +164,13 @@
if(count == 0){
input = option_find_int(options, "input",1);
net->batch = option_find_int(options, "batch",1);
+ net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
- cost_layer *layer = make_cost_layer(net->batch, input);
+ char *type_s = option_find_str(options, "type", "sse");
+ COST_TYPE type = get_cost_type(type_s);
+ cost_layer *layer = make_cost_layer(net->batch, input, type);
option_unused(options);
return layer;
}
@@ -218,6 +193,7 @@
net->learning_rate = learning_rate;
net->momentum = momentum;
net->decay = decay;
+ net->seen = option_find_int(options, "seen",0);
}else{
image m = get_network_image_layer(*net, count-1);
h = m.h;
@@ -240,6 +216,7 @@
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
+ net->seen = option_find_int(options, "seen",0);
}else{
image m = get_network_image_layer(*net, count-1);
h = m.h;
@@ -252,6 +229,7 @@
return layer;
}
+/*
freeweight_layer *parse_freeweight(list *options, network *net, int count)
{
int input;
@@ -265,6 +243,7 @@
option_unused(options);
return layer;
}
+*/
dropout_layer *parse_dropout(list *options, network *net, int count)
{
@@ -273,6 +252,13 @@
if(count == 0){
net->batch = option_find_int(options, "batch",1);
input = option_find_int(options, "input",1);
+ float learning_rate = option_find_float(options, "learning_rate", .001);
+ float momentum = option_find_float(options, "momentum", .9);
+ float decay = option_find_float(options, "decay", .0001);
+ net->learning_rate = learning_rate;
+ net->momentum = momentum;
+ net->decay = decay;
+ net->seen = option_find_int(options, "seen",0);
}else{
input = get_network_output_size_layer(*net, count-1);
}
@@ -293,6 +279,7 @@
w = option_find_int(options, "width",1);
c = option_find_int(options, "channels",1);
net->batch = option_find_int(options, "batch",1);
+ net->seen = option_find_int(options, "seen",0);
}else{
image m = get_network_image_layer(*net, count-1);
h = m.h;
@@ -348,9 +335,10 @@
net.types[count] = DROPOUT;
net.layers[count] = layer;
}else if(is_freeweight(s)){
- freeweight_layer *layer = parse_freeweight(options, &net, count);
- net.types[count] = FREEWEIGHT;
- net.layers[count] = layer;
+ //freeweight_layer *layer = parse_freeweight(options, &net, count);
+ //net.types[count] = FREEWEIGHT;
+ //net.layers[count] = layer;
+ fprintf(stderr, "Type not recognized: %s\n", s->type);
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
@@ -409,8 +397,8 @@
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] == '='){
@@ -462,6 +450,9 @@
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) {
@@ -471,8 +462,9 @@
"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);
+ "decay=%g\n"
+ "seen=%d\n",
+ l->batch,l->h, l->w, l->c, l->learning_rate, l->momentum, l->decay, net.seen);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
@@ -516,6 +508,9 @@
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){
@@ -523,8 +518,9 @@
"input=%d\n"
"learning_rate=%g\n"
"momentum=%g\n"
- "decay=%g\n",
- l->batch, l->inputs, l->learning_rate, l->momentum, l->decay);
+ "decay=%g\n"
+ "seen=%d\n",
+ l->batch, l->inputs, l->learning_rate, l->momentum, l->decay, net.seen);
} else {
if(l->learning_rate != net.learning_rate)
fprintf(fp, "learning_rate=%g\n", l->learning_rate);
@@ -555,8 +551,9 @@
"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);
+ "decay=%g\n"
+ "seen=%d\n",
+ l->batch,l->h, l->w, l->c, net.learning_rate, net.momentum, net.decay, net.seen);
}
fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
}
@@ -595,7 +592,7 @@
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
- fprintf(fp, "[cost]\n");
+ fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
if(count == 0) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
fprintf(fp, "\n");
}
--
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