| | |
| | | #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); |
| | | |
| | |
| | | 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; |
| | | } |
| | | |
| | |
| | | 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 |