| | |
| | | #include "softmax_layer.h" |
| | | #include "dropout_layer.h" |
| | | #include "detection_layer.h" |
| | | #include "region_layer.h" |
| | | #include "avgpool_layer.h" |
| | | #include "route_layer.h" |
| | | #include "list.h" |
| | |
| | | int is_crop(section *s); |
| | | int is_cost(section *s); |
| | | int is_detection(section *s); |
| | | int is_region(section *s); |
| | | int is_route(section *s); |
| | | list *read_cfg(char *filename); |
| | | |
| | |
| | | int rescore = option_find_int(options, "rescore", 0); |
| | | int joint = option_find_int(options, "joint", 0); |
| | | int objectness = option_find_int(options, "objectness", 0); |
| | | int background = option_find_int(options, "background", 0); |
| | | int background = 0; |
| | | detection_layer layer = make_detection_layer(params.batch, params.inputs, classes, coords, joint, rescore, background, objectness); |
| | | return layer; |
| | | } |
| | | |
| | | region_layer parse_region(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", 0); |
| | | int num = option_find_int(options, "num", 1); |
| | | region_layer layer = make_region_layer(params.batch, params.inputs, num, classes, coords, rescore); |
| | | return layer; |
| | | } |
| | | |
| | | cost_layer parse_cost(list *options, size_params params) |
| | | { |
| | | char *type_s = option_find_str(options, "type", "sse"); |
| | |
| | | { |
| | | float probability = option_find_float(options, "probability", .5); |
| | | dropout_layer layer = make_dropout_layer(params.batch, params.inputs, probability); |
| | | layer.out_w = params.w; |
| | | layer.out_h = params.h; |
| | | layer.out_c = params.c; |
| | | return layer; |
| | | } |
| | | |
| | |
| | | net->learning_rate = option_find_float(options, "learning_rate", .001); |
| | | net->momentum = option_find_float(options, "momentum", .9); |
| | | net->decay = option_find_float(options, "decay", .0001); |
| | | net->seen = option_find_int(options, "seen",0); |
| | | int subdivs = option_find_int(options, "subdivisions",1); |
| | | net->batch /= subdivs; |
| | | net->subdivisions = subdivs; |
| | |
| | | l = parse_cost(options, params); |
| | | }else if(is_detection(s)){ |
| | | l = parse_detection(options, params); |
| | | }else if(is_region(s)){ |
| | | l = parse_region(options, params); |
| | | }else if(is_softmax(s)){ |
| | | l = parse_softmax(options, params); |
| | | }else if(is_normalization(s)){ |
| | |
| | | { |
| | | return (strcmp(s->type, "[detection]")==0); |
| | | } |
| | | int is_region(section *s) |
| | | { |
| | | return (strcmp(s->type, "[region]")==0); |
| | | } |
| | | int is_deconvolutional(section *s) |
| | | { |
| | | return (strcmp(s->type, "[deconv]")==0 |
| | |
| | | return sections; |
| | | } |
| | | |
| | | void save_weights(network net, char *filename) |
| | | void save_weights_upto(network net, char *filename, int cutoff) |
| | | { |
| | | fprintf(stderr, "Saving weights to %s\n", filename); |
| | | FILE *fp = fopen(filename, "w"); |
| | |
| | | fwrite(&net.seen, sizeof(int), 1, fp); |
| | | |
| | | int i; |
| | | for(i = 0; i < net.n; ++i){ |
| | | for(i = 0; i < net.n && i < cutoff; ++i){ |
| | | layer l = net.layers[i]; |
| | | if(l.type == CONVOLUTIONAL){ |
| | | #ifdef GPU |
| | |
| | | } |
| | | fclose(fp); |
| | | } |
| | | void save_weights(network net, char *filename) |
| | | { |
| | | save_weights_upto(net, filename, net.n); |
| | | } |
| | | |
| | | void load_weights_upto(network *net, char *filename, int cutoff) |
| | | { |