| | |
| | | #include "normalization_layer.h" |
| | | #include "softmax_layer.h" |
| | | #include "dropout_layer.h" |
| | | #include "detection_layer.h" |
| | | #include "freeweight_layer.h" |
| | | #include "list.h" |
| | | #include "option_list.h" |
| | |
| | | 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); |
| | | list *read_cfg(char *filename); |
| | | |
| | |
| | | return layer; |
| | | } |
| | | |
| | | detection_layer *parse_detection(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->seen = option_find_int(options, "seen",0); |
| | | }else{ |
| | | input = get_network_output_size_layer(*net, count-1); |
| | | } |
| | | int coords = option_find_int(options, "coords", 1); |
| | | int classes = option_find_int(options, "classes", 1); |
| | | int rescore = option_find_int(options, "rescore", 1); |
| | | detection_layer *layer = make_detection_layer(net->batch, input, classes, coords, rescore); |
| | | option_unused(options); |
| | | return layer; |
| | | } |
| | | |
| | | cost_layer *parse_cost(list *options, network *net, int count) |
| | | { |
| | | int input; |
| | |
| | | cost_layer *layer = parse_cost(options, &net, count); |
| | | net.types[count] = COST; |
| | | net.layers[count] = layer; |
| | | }else if(is_detection(s)){ |
| | | detection_layer *layer = parse_detection(options, &net, count); |
| | | net.types[count] = DETECTION; |
| | | net.layers[count] = layer; |
| | | }else if(is_softmax(s)){ |
| | | softmax_layer *layer = parse_softmax(options, &net, count); |
| | | net.types[count] = SOFTMAX; |
| | |
| | | { |
| | | return (strcmp(s->type, "[cost]")==0); |
| | | } |
| | | int is_detection(section *s) |
| | | { |
| | | return (strcmp(s->type, "[detection]")==0); |
| | | } |
| | | int is_deconvolutional(section *s) |
| | | { |
| | | return (strcmp(s->type, "[deconv]")==0 |
| | |
| | | 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\n", l->classes, l->coords, l->rescore); |
| | | 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)); |
| | |
| | | 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); |
| | | } |