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route handles input images well....ish

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This commit is contained in:
Joseph Redmon
2015-05-11 13:46:49 -07:00
parent dc0d7bb8a8
commit 516f019ba6
31 changed files with 1250 additions and 1819 deletions
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367
src/parser.c
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@ -10,7 +10,6 @@
#include "deconvolutional_layer.h"
#include "connected_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
#include "detection_layer.h"
@ -34,7 +33,6 @@ int is_softmax(section *s);
int is_crop(section *s);
int is_cost(section *s);
int is_detection(section *s);
int is_normalization(section *s);
int is_route(section *s);
list *read_cfg(char *filename);
@ -78,7 +76,7 @@ typedef struct size_params{
int c;
} size_params;
deconvolutional_layer *parse_deconvolutional(list *options, size_params params)
deconvolutional_layer parse_deconvolutional(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
@ -93,20 +91,20 @@ deconvolutional_layer *parse_deconvolutional(list *options, size_params params)
batch=params.batch;
if(!(h && w && c)) error("Layer before deconvolutional layer must output image.");
deconvolutional_layer *layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
deconvolutional_layer layer = make_deconvolutional_layer(batch,h,w,c,n,size,stride,activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer->filters, c*n*size*size);
parse_data(biases, layer->biases, n);
parse_data(weights, layer.filters, c*n*size*size);
parse_data(biases, layer.biases, n);
#ifdef GPU
if(weights || biases) push_deconvolutional_layer(*layer);
if(weights || biases) push_deconvolutional_layer(layer);
#endif
option_unused(options);
return layer;
}
convolutional_layer *parse_convolutional(list *options, size_params params)
convolutional_layer parse_convolutional(list *options, size_params params)
{
int n = option_find_int(options, "filters",1);
int size = option_find_int(options, "size",1);
@ -122,68 +120,68 @@ convolutional_layer *parse_convolutional(list *options, size_params params)
batch=params.batch;
if(!(h && w && c)) error("Layer before convolutional layer must output image.");
convolutional_layer *layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
convolutional_layer layer = make_convolutional_layer(batch,h,w,c,n,size,stride,pad,activation);
char *weights = option_find_str(options, "weights", 0);
char *biases = option_find_str(options, "biases", 0);
parse_data(weights, layer->filters, c*n*size*size);
parse_data(biases, layer->biases, n);
parse_data(weights, layer.filters, c*n*size*size);
parse_data(biases, layer.biases, n);
#ifdef GPU
if(weights || biases) push_convolutional_layer(*layer);
if(weights || biases) push_convolutional_layer(layer);
#endif
option_unused(options);
return layer;
}
connected_layer *parse_connected(list *options, size_params params)
connected_layer parse_connected(list *options, size_params params)
{
int output = option_find_int(options, "output",1);
char *activation_s = option_find_str(options, "activation", "logistic");
ACTIVATION activation = get_activation(activation_s);
connected_layer *layer = make_connected_layer(params.batch, params.inputs, output, activation);
connected_layer layer = make_connected_layer(params.batch, params.inputs, output, activation);
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, params.inputs*output);
parse_data(biases, layer.biases, output);
parse_data(weights, layer.weights, params.inputs*output);
#ifdef GPU
if(weights || biases) push_connected_layer(*layer);
if(weights || biases) push_connected_layer(layer);
#endif
option_unused(options);
return layer;
}
softmax_layer *parse_softmax(list *options, size_params params)
softmax_layer parse_softmax(list *options, size_params params)
{
int groups = option_find_int(options, "groups",1);
softmax_layer *layer = make_softmax_layer(params.batch, params.inputs, groups);
softmax_layer layer = make_softmax_layer(params.batch, params.inputs, groups);
option_unused(options);
return layer;
}
detection_layer *parse_detection(list *options, size_params params)
detection_layer parse_detection(list *options, size_params params)
{
int coords = option_find_int(options, "coords", 1);
int classes = option_find_int(options, "classes", 1);
int rescore = option_find_int(options, "rescore", 1);
int nuisance = option_find_int(options, "nuisance", 0);
int background = option_find_int(options, "background", 1);
detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background, nuisance);
detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background, nuisance);
option_unused(options);
return layer;
}
cost_layer *parse_cost(list *options, size_params params)
cost_layer parse_cost(list *options, size_params params)
{
char *type_s = option_find_str(options, "type", "sse");
COST_TYPE type = get_cost_type(type_s);
cost_layer *layer = make_cost_layer(params.batch, params.inputs, type);
cost_layer layer = make_cost_layer(params.batch, params.inputs, type);
option_unused(options);
return layer;
}
crop_layer *parse_crop(list *options, size_params params)
crop_layer parse_crop(list *options, size_params params)
{
int crop_height = option_find_int(options, "crop_height",1);
int crop_width = option_find_int(options, "crop_width",1);
@ -199,12 +197,12 @@ crop_layer *parse_crop(list *options, size_params params)
batch=params.batch;
if(!(h && w && c)) error("Layer before crop layer must output image.");
crop_layer *layer = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
crop_layer l = make_crop_layer(batch,h,w,c,crop_height,crop_width,flip, angle, saturation, exposure);
option_unused(options);
return layer;
return l;
}
maxpool_layer *parse_maxpool(list *options, size_params params)
maxpool_layer parse_maxpool(list *options, size_params params)
{
int stride = option_find_int(options, "stride",1);
int size = option_find_int(options, "size",stride);
@ -216,39 +214,20 @@ maxpool_layer *parse_maxpool(list *options, size_params params)
batch=params.batch;
if(!(h && w && c)) error("Layer before maxpool layer must output image.");
maxpool_layer *layer = make_maxpool_layer(batch,h,w,c,size,stride);
maxpool_layer layer = make_maxpool_layer(batch,h,w,c,size,stride);
option_unused(options);
return layer;
}
dropout_layer *parse_dropout(list *options, size_params params)
dropout_layer parse_dropout(list *options, size_params params)
{
float probability = option_find_float(options, "probability", .5);
dropout_layer *layer = make_dropout_layer(params.batch, params.inputs, probability);
dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability);
option_unused(options);
return layer;
}
normalization_layer *parse_normalization(list *options, size_params params)
{
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.);
int batch,h,w,c;
h = params.h;
w = params.w;
c = params.c;
batch=params.batch;
if(!(h && w && c)) error("Layer before normalization layer must output image.");
normalization_layer *layer = make_normalization_layer(batch,h,w,c,size, alpha, beta, kappa);
option_unused(options);
return layer;
}
route_layer *parse_route(list *options, size_params params, network net)
route_layer parse_route(list *options, size_params params, network net)
{
char *l = option_find(options, "layers");
int len = strlen(l);
@ -265,11 +244,26 @@ route_layer *parse_route(list *options, size_params params, network net)
int index = atoi(l);
l = strchr(l, ',')+1;
layers[i] = index;
sizes[i] = get_network_output_size_layer(net, index);
sizes[i] = net.layers[index].outputs;
}
int batch = params.batch;
route_layer *layer = make_route_layer(batch, n, layers, sizes);
route_layer layer = make_route_layer(batch, n, layers, sizes);
convolutional_layer first = net.layers[layers[0]];
layer.out_w = first.out_w;
layer.out_h = first.out_h;
layer.out_c = first.out_c;
for(i = 1; i < n; ++i){
int index = layers[i];
convolutional_layer next = net.layers[index];
if(next.out_w == first.out_w && next.out_h == first.out_h){
layer.out_c += next.out_c;
}else{
layer.out_h = layer.out_w = layer.out_c = 0;
}
}
option_unused(options);
return layer;
}
@ -318,61 +312,44 @@ network parse_network_cfg(char *filename)
fprintf(stderr, "%d: ", count);
s = (section *)n->val;
options = s->options;
layer l = {0};
if(is_convolutional(s)){
convolutional_layer *layer = parse_convolutional(options, params);
net.types[count] = CONVOLUTIONAL;
net.layers[count] = layer;
l = parse_convolutional(options, params);
}else if(is_deconvolutional(s)){
deconvolutional_layer *layer = parse_deconvolutional(options, params);
net.types[count] = DECONVOLUTIONAL;
net.layers[count] = layer;
l = parse_deconvolutional(options, params);
}else if(is_connected(s)){
connected_layer *layer = parse_connected(options, params);
net.types[count] = CONNECTED;
net.layers[count] = layer;
l = parse_connected(options, params);
}else if(is_crop(s)){
crop_layer *layer = parse_crop(options, params);
net.types[count] = CROP;
net.layers[count] = layer;
l = parse_crop(options, params);
}else if(is_cost(s)){
cost_layer *layer = parse_cost(options, params);
net.types[count] = COST;
net.layers[count] = layer;
l = parse_cost(options, params);
}else if(is_detection(s)){
detection_layer *layer = parse_detection(options, params);
net.types[count] = DETECTION;
net.layers[count] = layer;
l = parse_detection(options, params);
}else if(is_softmax(s)){
softmax_layer *layer = parse_softmax(options, params);
net.types[count] = SOFTMAX;
net.layers[count] = layer;
l = parse_softmax(options, params);
}else if(is_maxpool(s)){
maxpool_layer *layer = parse_maxpool(options, params);
net.types[count] = MAXPOOL;
net.layers[count] = layer;
}else if(is_normalization(s)){
normalization_layer *layer = parse_normalization(options, params);
net.types[count] = NORMALIZATION;
net.layers[count] = layer;
l = parse_maxpool(options, params);
}else if(is_route(s)){
route_layer *layer = parse_route(options, params, net);
net.types[count] = ROUTE;
net.layers[count] = layer;
l = parse_route(options, params, net);
}else if(is_dropout(s)){
dropout_layer *layer = parse_dropout(options, params);
net.types[count] = DROPOUT;
net.layers[count] = layer;
l = parse_dropout(options, params);
l.output = net.layers[count-1].output;
l.delta = net.layers[count-1].delta;
#ifdef GPU
l.output_gpu = net.layers[count-1].output_gpu;
l.delta_gpu = net.layers[count-1].delta_gpu;
#endif
}else{
fprintf(stderr, "Type not recognized: %s\n", s->type);
}
net.layers[count] = l;
free_section(s);
n = n->next;
if(n){
image im = get_network_image_layer(net, count);
params.h = im.h;
params.w = im.w;
params.c = im.c;
params.inputs = get_network_output_size_layer(net, count);
params.h = l.out_h;
params.w = l.out_w;
params.c = l.out_c;
params.inputs = l.outputs;
}
++count;
}
@ -429,11 +406,6 @@ 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 is_route(section *s)
{
return (strcmp(s->type, "[route]")==0);
@ -492,114 +464,6 @@ list *read_cfg(char *filename)
return sections;
}
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");
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_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
{
#ifdef GPU
if(gpu_index >= 0) pull_deconvolutional_layer(*l);
#endif
int i;
fprintf(fp, "[deconvolutional]\n");
fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
l->n, l->size, l->stride,
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_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
{
fprintf(fp, "[dropout]\n");
fprintf(fp, "probability=%g\n\n", l->probability);
}
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");
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");
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");
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");
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");
fprintf(fp, "\n");
}
void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
{
fprintf(fp, "[detection]\n");
fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance);
fprintf(fp, "\n");
}
void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
fprintf(fp, "\n");
}
void save_weights(network net, char *filename)
{
fprintf(stderr, "Saving weights to %s\n", filename);
@ -613,37 +477,35 @@ void save_weights(network net, char *filename)
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(layer);
pull_convolutional_layer(l);
}
#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.filters, sizeof(float), num, fp);
}
if(net.types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net.layers[i];
if(l.type == DECONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_deconvolutional_layer(layer);
pull_deconvolutional_layer(l);
}
#endif
int num = layer.n*layer.c*layer.size*layer.size;
fwrite(layer.biases, sizeof(float), layer.n, fp);
fwrite(layer.filters, sizeof(float), num, fp);
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.filters, sizeof(float), num, fp);
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
if(l.type == CONNECTED){
#ifdef GPU
if(gpu_index >= 0){
pull_connected_layer(layer);
pull_connected_layer(l);
}
#endif
fwrite(layer.biases, sizeof(float), layer.outputs, fp);
fwrite(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
fwrite(l.biases, sizeof(float), l.outputs, fp);
fwrite(l.weights, sizeof(float), l.outputs*l.inputs, fp);
}
}
fclose(fp);
@ -663,35 +525,33 @@ void load_weights_upto(network *net, char *filename, int cutoff)
int i;
for(i = 0; i < net->n && i < cutoff; ++i){
if(net->types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net->layers[i];
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
fread(l.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
push_convolutional_layer(layer);
push_convolutional_layer(l);
}
#endif
}
if(net->types[i] == DECONVOLUTIONAL){
deconvolutional_layer layer = *(deconvolutional_layer *) net->layers[i];
int num = layer.n*layer.c*layer.size*layer.size;
fread(layer.biases, sizeof(float), layer.n, fp);
fread(layer.filters, sizeof(float), num, fp);
if(l.type == DECONVOLUTIONAL){
int num = l.n*l.c*l.size*l.size;
fread(l.biases, sizeof(float), l.n, fp);
fread(l.filters, sizeof(float), num, fp);
#ifdef GPU
if(gpu_index >= 0){
push_deconvolutional_layer(layer);
push_deconvolutional_layer(l);
}
#endif
}
if(net->types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net->layers[i];
fread(layer.biases, sizeof(float), layer.outputs, fp);
fread(layer.weights, sizeof(float), layer.outputs*layer.inputs, fp);
if(l.type == CONNECTED){
fread(l.biases, sizeof(float), l.outputs, fp);
fread(l.weights, sizeof(float), l.outputs*l.inputs, fp);
#ifdef GPU
if(gpu_index >= 0){
push_connected_layer(layer);
push_connected_layer(l);
}
#endif
}
@ -704,34 +564,3 @@ void load_weights(network *net, char *filename)
load_weights_upto(net, filename, net->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] == DECONVOLUTIONAL)
print_deconvolutional_cfg(fp, (deconvolutional_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] == DROPOUT)
print_dropout_cfg(fp, (dropout_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);
else if(net.types[i] == DETECTION)
print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i);
else if(net.types[i] == COST)
print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
}
fclose(fp);
}