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CUDA so fast

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This commit is contained in:
Joseph Redmon
2015-01-22 16:38:24 -08:00
parent 4ac78c8926
commit 809f924db2
57 changed files with 1116 additions and 2181 deletions
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34
src/parser.c
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@ -16,7 +16,6 @@
#include "list.h"
#include "option_list.h"
#include "utils.h"
#include "opencl.h"
typedef struct{
char *type;
@ -87,6 +86,7 @@ convolutional_layer *parse_convolutional(list *options, network *net, int count)
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);
@ -149,6 +149,7 @@ softmax_layer *parse_softmax(list *options, network *net, int count)
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);
}
@ -163,6 +164,7 @@ cost_layer *parse_cost(list *options, network *net, int count)
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);
}
@ -191,6 +193,7 @@ crop_layer *parse_crop(list *options, network *net, int count)
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;
@ -213,6 +216,7 @@ maxpool_layer *parse_maxpool(list *options, network *net, int count)
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;
@ -225,6 +229,7 @@ maxpool_layer *parse_maxpool(list *options, network *net, int count)
return layer;
}
/*
freeweight_layer *parse_freeweight(list *options, network *net, int count)
{
int input;
@ -238,6 +243,7 @@ freeweight_layer *parse_freeweight(list *options, network *net, int count)
option_unused(options);
return layer;
}
*/
dropout_layer *parse_dropout(list *options, network *net, int count)
{
@ -252,6 +258,7 @@ dropout_layer *parse_dropout(list *options, network *net, int count)
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);
}
@ -272,6 +279,7 @@ normalization_layer *parse_normalization(list *options, network *net, int count)
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;
@ -327,9 +335,10 @@ network parse_network_cfg(char *filename)
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);
}
@ -442,7 +451,7 @@ list *read_cfg(char *filename)
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
{
#ifdef GPU
if(gpu_index >= 0) pull_convolutional_layer(*l);
if(gpu_index >= 0) pull_convolutional_layer(*l);
#endif
int i;
fprintf(fp, "[convolutional]\n");
@ -453,8 +462,9 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int
"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);
@ -508,8 +518,9 @@ void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
"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);
@ -540,8 +551,9 @@ void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
"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);
}