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152 lines
3.2 KiB
152 lines
3.2 KiB
// Oh boy, why am I about to do this.... |
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#pragma once |
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#ifndef NETWORK_H |
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#define NETWORK_H |
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#include "define_inc.h" |
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#include "darknet.h" |
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#include "image.h" |
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#include "layer.h" |
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#include "data.h" |
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#include "tree.h" |
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typedef enum { |
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CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM |
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} learning_rate_policy; |
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typedef struct network { |
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int n; |
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int batch; |
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size_t *seen; |
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int *t; |
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float epoch; |
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int subdivisions; |
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layer *layers; |
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float *output; |
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learning_rate_policy policy; |
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float learning_rate; |
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float momentum; |
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float decay; |
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float gamma; |
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float scale; |
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float power; |
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int time_steps; |
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int step; |
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int max_batches; |
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float *scales; |
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int *steps; |
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int num_steps; |
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int burn_in; |
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int adam; |
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float B1; |
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float B2; |
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float eps; |
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int inputs; |
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int outputs; |
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int truths; |
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int notruth; |
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int h, w, c; |
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int max_crop; |
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int min_crop; |
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float max_ratio; |
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float min_ratio; |
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int center; |
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float angle; |
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float aspect; |
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float exposure; |
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float saturation; |
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float hue; |
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int random; |
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int gpu_index; |
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tree *hierarchy; |
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float *input; |
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float *truth; |
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float *delta; |
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float *workspace; |
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int train; |
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int index; |
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float *cost; |
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float clip; |
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#ifdef GPU |
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float *input_gpu; |
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float *truth_gpu; |
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float *delta_gpu; |
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float *output_gpu; |
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#endif |
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} network; |
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#ifdef GPU |
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void pull_network_output(network *net); |
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#endif |
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void compare_networks(network *n1, network *n2, data d); |
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char *get_layer_string(LAYER_TYPE a); |
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network *make_network(int n); |
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void forward_network(network *net); |
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void backward_network(network *net); |
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void update_network(network *net); |
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network *load_network(char *cfg, char *weights, int clear); |
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load_args get_base_args(network *net); |
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float network_accuracy_multi(network *net, data d, int n); |
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int get_predicted_class_network(network *net); |
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void print_network(network *net); |
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int resize_network(network *net, int w, int h); |
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void calc_network_cost(network *net); |
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float train_network_sgd(network *net, data d, int n); |
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float *network_accuracies(network *net, data d, int n); |
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float train_network_datum(network *net); |
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network *parse_network_cfg(char *filename); |
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void save_weights(network *net, char *filename); |
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void load_weights(network *net, char *filename); |
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void save_weights_upto(network *net, char *filename, int cutoff); |
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void load_weights_upto(network *net, char *filename, int start, int cutoff); |
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void free_network(network *net); |
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void set_batch_network(network *net, int b); |
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void set_temp_network(network *net, float t); |
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int resize_network(network *net, int w, int h); |
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float get_current_rate(network *net); |
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size_t get_current_batch(network *net); |
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image get_network_image_layer(network *net, int i); |
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layer get_network_output_layer(network *net); |
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void top_predictions(network *net, int n, int *index); |
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float network_accuracy(network *net, data d); |
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void visualize_network(network *net); |
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matrix network_predict_data(network *net, data test); |
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image get_network_image(network *net); |
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float *network_predict(network *net, float *input); |
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int network_width(network *net); |
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int network_height(network *net); |
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float *network_predict_image(network *net, image im); |
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detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num); |
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void reset_network_state(network *net, int b); |
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float train_network(network *net, data d); |
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#endif |
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