| | |
| | | #include "crop_layer.h" |
| | | #include "cost_layer.h" |
| | | #include "convolutional_layer.h" |
| | | #include "normalization_layer.h" |
| | | #include "deconvolutional_layer.h" |
| | | #include "connected_layer.h" |
| | | #include "maxpool_layer.h" |
| | |
| | | int is_maxpool(section *s); |
| | | int is_dropout(section *s); |
| | | int is_softmax(section *s); |
| | | int is_normalization(section *s); |
| | | int is_crop(section *s); |
| | | int is_cost(section *s); |
| | | int is_detection(section *s); |
| | |
| | | return layer; |
| | | } |
| | | |
| | | layer parse_normalization(list *options, size_params params) |
| | | { |
| | | float alpha = option_find_float(options, "alpha", .0001); |
| | | float beta = option_find_float(options, "beta" , .75); |
| | | float kappa = option_find_float(options, "kappa", 1); |
| | | int size = option_find_int(options, "size", 5); |
| | | layer l = make_normalization_layer(params.batch, params.w, params.h, params.c, size, alpha, beta, kappa); |
| | | option_unused(options); |
| | | return l; |
| | | } |
| | | |
| | | route_layer parse_route(list *options, size_params params, network net) |
| | | { |
| | | char *l = option_find(options, "layers"); |
| | |
| | | l = parse_detection(options, params); |
| | | }else if(is_softmax(s)){ |
| | | l = parse_softmax(options, params); |
| | | }else if(is_normalization(s)){ |
| | | l = parse_normalization(options, params); |
| | | }else if(is_maxpool(s)){ |
| | | l = parse_maxpool(options, params); |
| | | }else if(is_route(s)){ |
| | |
| | | return (strcmp(s->type, "[dropout]")==0); |
| | | } |
| | | |
| | | int is_normalization(section *s) |
| | | { |
| | | return (strcmp(s->type, "[lrn]")==0 |
| | | || strcmp(s->type, "[normalization]")==0); |
| | | } |
| | | |
| | | int is_softmax(section *s) |
| | | { |
| | | return (strcmp(s->type, "[soft]")==0 |