darknet/include/darknet.h

#ifndef DARKNET_API
#define DARKNET_API

#if defined(_MSC_VER) && _MSC_VER < 1900
#define inline __inline
#endif

#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <pthread.h>
#include <stdint.h>

#ifdef LIB_EXPORTS
#if defined(_MSC_VER)
#define LIB_API __declspec(dllexport)
#else
#define LIB_API __attribute__((visibility("default")))
#endif
#else
#if defined(_MSC_VER)
#define LIB_API
#else
#define LIB_API
#endif
#endif

#ifdef GPU
#define BLOCK 512

#include "cuda_runtime.h"
#include "curand.h"
#include "cublas_v2.h"

#ifdef CUDNN
#include "cudnn.h"
#endif
#endif

#ifdef __cplusplus
extern "C" {
#endif

struct network;
typedef struct network network;

struct network_state;
typedef struct network_state;

struct layer;
typedef struct layer layer;

struct image;
typedef struct image image;

struct detection;
typedef struct detection detection;

struct load_args;
typedef struct load_args load_args;

struct data;
typedef struct data data;

struct metadata;
typedef struct metadata metadata;

struct tree;
typedef struct tree tree;


#define SECRET_NUM -1234
extern int gpu_index;

// option_list.h
typedef struct metadata {
    int classes;
    char **names;
} metadata;


// tree.h
typedef struct tree {
    int *leaf;
    int n;
    int *parent;
    int *child;
    int *group;
    char **name;

    int groups;
    int *group_size;
    int *group_offset;
} tree;


// activations.h
typedef enum {
    LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU
}ACTIVATION;

// image.h
typedef enum{
    PNG, BMP, TGA, JPG
} IMTYPE;

// activations.h
typedef enum{
    MULT, ADD, SUB, DIV
} BINARY_ACTIVATION;

// layer.h
typedef enum {
    CONVOLUTIONAL,
    DECONVOLUTIONAL,
    CONNECTED,
    MAXPOOL,
    SOFTMAX,
    DETECTION,
    DROPOUT,
    CROP,
    ROUTE,
    COST,
    NORMALIZATION,
    AVGPOOL,
    LOCAL,
    SHORTCUT,
    ACTIVE,
    RNN,
    GRU,
    LSTM,
    CRNN,
    BATCHNORM,
    NETWORK,
    XNOR,
    REGION,
    YOLO,
    ISEG,
    REORG,
    REORG_OLD,
    UPSAMPLE,
    LOGXENT,
    L2NORM,
    BLANK
} LAYER_TYPE;

// layer.h
typedef enum{
    SSE, MASKED, L1, SEG, SMOOTH,WGAN
} COST_TYPE;

// layer.h
typedef struct update_args {
    int batch;
    float learning_rate;
    float momentum;
    float decay;
    int adam;
    float B1;
    float B2;
    float eps;
    int t;
} update_args;

// layer.h
struct layer {
    LAYER_TYPE type;
    ACTIVATION activation;
    COST_TYPE cost_type;
    void(*forward)   (struct layer, struct network_state);
    void(*backward)  (struct layer, struct network_state);
    void(*update)    (struct layer, int, float, float, float);
    void(*forward_gpu)   (struct layer, struct network_state);
    void(*backward_gpu)  (struct layer, struct network_state);
    void(*update_gpu)    (struct layer, int, float, float, float);
    int batch_normalize;
    int shortcut;
    int batch;
    int forced;
    int flipped;
    int inputs;
    int outputs;
    int nweights;
    int nbiases;
    int extra;
    int truths;
    int h, w, c;
    int out_h, out_w, out_c;
    int n;
    int max_boxes;
    int groups;
    int size;
    int side;
    int stride;
    int reverse;
    int flatten;
    int spatial;
    int pad;
    int sqrt;
    int flip;
    int index;
    int binary;
    int xnor;
    int use_bin_output;
    int steps;
    int hidden;
    int truth;
    float smooth;
    float dot;
    float angle;
    float jitter;
    float saturation;
    float exposure;
    float shift;
    float ratio;
    float learning_rate_scale;
    float clip;
    int focal_loss;
    int noloss;
    int softmax;
    int classes;
    int coords;
    int background;
    int rescore;
    int objectness;
    int does_cost;
    int joint;
    int noadjust;
    int reorg;
    int log;
    int tanh;
    int *mask;
    int total;
    float bflops;

    int adam;
    float B1;
    float B2;
    float eps;

    int t;

    float alpha;
    float beta;
    float kappa;

    float coord_scale;
    float object_scale;
    float noobject_scale;
    float mask_scale;
    float class_scale;
    int bias_match;
    int random;
    float ignore_thresh;
    float truth_thresh;
    float thresh;
    float focus;
    int classfix;
    int absolute;

    int onlyforward;
    int stopbackward;
    int dontload;
    int dontsave;
    int dontloadscales;
    int numload;

    float temperature;
    float probability;
    float scale;

    char  * cweights;
    int   * indexes;
    int   * input_layers;
    int   * input_sizes;
    int   * map;
    int   * counts;
    float ** sums;
    float * rand;
    float * cost;
    float * state;
    float * prev_state;
    float * forgot_state;
    float * forgot_delta;
    float * state_delta;
    float * combine_cpu;
    float * combine_delta_cpu;

    float *concat;
    float *concat_delta;

    float *binary_weights;

    float *biases;
    float *bias_updates;

    float *scales;
    float *scale_updates;

    float *weights;
    float *weight_updates;

    char *align_bit_weights_gpu;
    float *mean_arr_gpu;
    float *align_workspace_gpu;
    float *transposed_align_workspace_gpu;
    int align_workspace_size;

    char *align_bit_weights;
    float *mean_arr;
    int align_bit_weights_size;
    int lda_align;
    int new_lda;
    int bit_align;

    float *col_image;
    float * delta;
    float * output;
    float * loss;
    float * squared;
    float * norms;

    float * spatial_mean;
    float * mean;
    float * variance;

    float * mean_delta;
    float * variance_delta;

    float * rolling_mean;
    float * rolling_variance;

    float * x;
    float * x_norm;

    float * m;
    float * v;

    float * bias_m;
    float * bias_v;
    float * scale_m;
    float * scale_v;


    float *z_cpu;
    float *r_cpu;
    float *h_cpu;
    float * prev_state_cpu;

    float *temp_cpu;
    float *temp2_cpu;
    float *temp3_cpu;

    float *dh_cpu;
    float *hh_cpu;
    float *prev_cell_cpu;
    float *cell_cpu;
    float *f_cpu;
    float *i_cpu;
    float *g_cpu;
    float *o_cpu;
    float *c_cpu;
    float *dc_cpu;

    float * binary_input;

    struct layer *input_layer;
    struct layer *self_layer;
    struct layer *output_layer;

    struct layer *reset_layer;
    struct layer *update_layer;
    struct layer *state_layer;

    struct layer *input_gate_layer;
    struct layer *state_gate_layer;
    struct layer *input_save_layer;
    struct layer *state_save_layer;
    struct layer *input_state_layer;
    struct layer *state_state_layer;

    struct layer *input_z_layer;
    struct layer *state_z_layer;

    struct layer *input_r_layer;
    struct layer *state_r_layer;

    struct layer *input_h_layer;
    struct layer *state_h_layer;

    struct layer *wz;
    struct layer *uz;
    struct layer *wr;
    struct layer *ur;
    struct layer *wh;
    struct layer *uh;
    struct layer *uo;
    struct layer *wo;
    struct layer *uf;
    struct layer *wf;
    struct layer *ui;
    struct layer *wi;
    struct layer *ug;
    struct layer *wg;

    tree *softmax_tree;

    size_t workspace_size;

#ifdef GPU
    int *indexes_gpu;

    float *z_gpu;
    float *r_gpu;
    float *h_gpu;

    float *temp_gpu;
    float *temp2_gpu;
    float *temp3_gpu;

    float *dh_gpu;
    float *hh_gpu;
    float *prev_cell_gpu;
    float *cell_gpu;
    float *f_gpu;
    float *i_gpu;
    float *g_gpu;
    float *o_gpu;
    float *c_gpu;
    float *dc_gpu;

    // adam
    float *m_gpu;
    float *v_gpu;
    float *bias_m_gpu;
    float *scale_m_gpu;
    float *bias_v_gpu;
    float *scale_v_gpu;

    float * combine_gpu;
    float * combine_delta_gpu;

    float * prev_state_gpu;
    float * forgot_state_gpu;
    float * forgot_delta_gpu;
    float * state_gpu;
    float * state_delta_gpu;
    float * gate_gpu;
    float * gate_delta_gpu;
    float * save_gpu;
    float * save_delta_gpu;
    float * concat_gpu;
    float * concat_delta_gpu;

    float *binary_input_gpu;
    float *binary_weights_gpu;

    float * mean_gpu;
    float * variance_gpu;

    float * rolling_mean_gpu;
    float * rolling_variance_gpu;

    float * variance_delta_gpu;
    float * mean_delta_gpu;

    float * col_image_gpu;

    float * x_gpu;
    float * x_norm_gpu;
    float * weights_gpu;
    float * weight_updates_gpu;
    float * weight_change_gpu;

    float * weights_gpu16;
    float * weight_updates_gpu16;

    float * biases_gpu;
    float * bias_updates_gpu;
    float * bias_change_gpu;

    float * scales_gpu;
    float * scale_updates_gpu;
    float * scale_change_gpu;

    float * output_gpu;
    float * loss_gpu;
    float * delta_gpu;
    float * rand_gpu;
    float * squared_gpu;
    float * norms_gpu;
#ifdef CUDNN
    cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
    cudnnTensorDescriptor_t srcTensorDesc16, dstTensorDesc16;
    cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
    cudnnTensorDescriptor_t dsrcTensorDesc16, ddstTensorDesc16;
    cudnnTensorDescriptor_t normTensorDesc, normDstTensorDesc, normDstTensorDescF16;
    cudnnFilterDescriptor_t weightDesc, weightDesc16;
    cudnnFilterDescriptor_t dweightDesc, dweightDesc16;
    cudnnConvolutionDescriptor_t convDesc;
    cudnnConvolutionFwdAlgo_t fw_algo, fw_algo16;
    cudnnConvolutionBwdDataAlgo_t bd_algo, bd_algo16;
    cudnnConvolutionBwdFilterAlgo_t bf_algo, bf_algo16;
    cudnnPoolingDescriptor_t poolingDesc;
#endif  // CUDNN
#endif  // GPU
};


// network.h
typedef enum {
    CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
} learning_rate_policy;

// network.h
typedef struct network {
    int n;
    int batch;
    uint64_t *seen;
    int *t;
    float epoch;
    int subdivisions;
    layer *layers;
    float *output;
    learning_rate_policy policy;

    float learning_rate;
    float momentum;
    float decay;
    float gamma;
    float scale;
    float power;
    int time_steps;
    int step;
    int max_batches;
    float *scales;
    int   *steps;
    int num_steps;
    int burn_in;
    int cudnn_half;

    int adam;
    float B1;
    float B2;
    float eps;

    int inputs;
    int outputs;
    int truths;
    int notruth;
    int h, w, c;
    int max_crop;
    int min_crop;
    float max_ratio;
    float min_ratio;
    int center;
    int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1)
    float angle;
    float aspect;
    float exposure;
    float saturation;
    float hue;
    int random;
    int small_object;

    int gpu_index;
    tree *hierarchy;

    float *input;
    float *truth;
    float *delta;
    float *workspace;
    int train;
    int index;
    float *cost;
    float clip;

#ifdef GPU
    //float *input_gpu;
    //float *truth_gpu;
    float *delta_gpu;
    float *output_gpu;

    float *input_state_gpu;

    float **input_gpu;
    float **truth_gpu;
    float **input16_gpu;
    float **output16_gpu;
    size_t *max_input16_size;
    size_t *max_output16_size;
    int wait_stream;
#endif
} network;

// network.h
typedef struct network_state {
    float *truth;
    float *input;
    float *delta;
    float *workspace;
    int train;
    int index;
    network net;
} network_state;

//typedef struct {
//    int w;
//    int h;
//    float scale;
//    float rad;
//    float dx;
//    float dy;
//    float aspect;
//} augment_args;

// image.h
typedef struct image {
    int w;
    int h;
    int c;
    float *data;
} image;

//typedef struct {
//    int w;
//    int h;
//    int c;
//    float *data;
//} image;

// box.h
typedef struct box {
    float x, y, w, h;
} box;

// box.h
typedef struct detection{
    box bbox;
    int classes;
    float *prob;
    float *mask;
    float objectness;
    int sort_class;
} detection;

// matrix.h
typedef struct matrix {
    int rows, cols;
    float **vals;
} matrix;

// data.h
typedef struct data {
    int w, h;
    matrix X;
    matrix y;
    int shallow;
    int *num_boxes;
    box **boxes;
} data;

// data.h
typedef enum {
    CLASSIFICATION_DATA, DETECTION_DATA, CAPTCHA_DATA, REGION_DATA, IMAGE_DATA, COMPARE_DATA, WRITING_DATA, SWAG_DATA, TAG_DATA, OLD_CLASSIFICATION_DATA, STUDY_DATA, DET_DATA, SUPER_DATA, LETTERBOX_DATA, REGRESSION_DATA, SEGMENTATION_DATA, INSTANCE_DATA, ISEG_DATA
} data_type;

// data.h
typedef struct load_args {
    int threads;
    char **paths;
    char *path;
    int n;
    int m;
    char **labels;
    int h;
    int w;
    int c; // color depth
    int out_w;
    int out_h;
    int nh;
    int nw;
    int num_boxes;
    int min, max, size;
    int classes;
    int background;
    int scale;
    int center;
    int coords;
    int small_object;
    float jitter;
    int flip;
    float angle;
    float aspect;
    float saturation;
    float exposure;
    float hue;
    data *d;
    image *im;
    image *resized;
    data_type type;
    tree *hierarchy;
} load_args;

// data.h
typedef struct box_label {
    int id;
    float x, y, w, h;
    float left, right, top, bottom;
} box_label;

// list.h
//typedef struct node {
//    void *val;
//    struct node *next;
//    struct node *prev;
//} node;

// list.h
//typedef struct list {
//    int size;
//    node *front;
//    node *back;
//} list;

// -----------------------------------------------------


// parser.c
LIB_API network *load_network(char *cfg, char *weights, int clear);
LIB_API network *load_network_custom(char *cfg, char *weights, int clear, int batch);
LIB_API network *load_network(char *cfg, char *weights, int clear);

// network.c
LIB_API load_args get_base_args(network *net);

// box.h
LIB_API void do_nms_sort(detection *dets, int total, int classes, float thresh);
LIB_API void do_nms_obj(detection *dets, int total, int classes, float thresh);

// network.h
LIB_API float *network_predict(network net, float *input);
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
LIB_API void free_detections(detection *dets, int n);
LIB_API void fuse_conv_batchnorm(network net);
LIB_API void calculate_binary_weights(network net);

LIB_API layer* get_network_layer(network* net, int i);
LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
LIB_API detection *make_network_boxes(network *net, float thresh, int *num);
LIB_API void reset_rnn(network *net);
LIB_API float *network_predict_image(network *net, image im);
LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, network *existing_net);
LIB_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show, int calc_map);
LIB_API int network_width(network *net);
LIB_API int network_height(network *net);
LIB_API void optimize_picture(network *net, image orig, int max_layer, float scale, float rate, float thresh, int norm);

// image.h
LIB_API image resize_image(image im, int w, int h);
LIB_API image letterbox_image(image im, int w, int h);
LIB_API void rgbgr_image(image im);
LIB_API image make_image(int w, int h, int c);
LIB_API image load_image_color(char *filename, int w, int h);
LIB_API void free_image(image m);

// layer.h
LIB_API void free_layer(layer);

// data.c
LIB_API void free_data(data d);
LIB_API pthread_t load_data(load_args args);
LIB_API pthread_t load_data_in_thread(load_args args);

// cuda.h
LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n);
LIB_API void cuda_set_device(int n);

// utils.h
LIB_API void free_ptrs(void **ptrs, int n);
LIB_API void top_k(float *a, int n, int k, int *index);

// tree.h
LIB_API tree *read_tree(char *filename);

// option_list.h
LIB_API metadata get_metadata(char *file);


#ifdef __cplusplus
}
#endif  // __cplusplus
#endif  // DARKNET_API