From 168af40fe9a3cc81c6ee16b3e81f154780c36bdb Mon Sep 17 00:00:00 2001
From: Scheaven <xuepengqiang>
Date: 星期四, 03 六月 2021 15:03:27 +0800
Subject: [PATCH] up new v4
---
lib/detecter_tools/darknet/darknet.h | 2154 +++++++++++++++++++++++++++++++----------------------------
1 files changed, 1,120 insertions(+), 1,034 deletions(-)
diff --git a/lib/detecter_tools/darknet/darknet.h b/lib/detecter_tools/darknet/darknet.h
index 00d6412..f5170f3 100644
--- a/lib/detecter_tools/darknet/darknet.h
+++ b/lib/detecter_tools/darknet/darknet.h
@@ -1,1034 +1,1120 @@
-#ifndef DARKNET_API
-#define DARKNET_API
-
-#if defined(_MSC_VER) && _MSC_VER < 1900
-#define inline __inline
-#endif
-
-#if defined(DEBUG) && !defined(_CRTDBG_MAP_ALLOC)
-#define _CRTDBG_MAP_ALLOC
-#endif
-
-#include <stdlib.h>
-#include <stdio.h>
-#include <string.h>
-#include <stdint.h>
-#include <assert.h>
-#include <pthread.h>
-
-#ifndef LIB_API
-#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
-#endif
-
-#define SECRET_NUM -1234
-
-typedef enum { UNUSED_DEF_VAL } UNUSED_ENUM_TYPE;
-
-#ifdef GPU
-
-#include <cuda_runtime.h>
-#include <curand.h>
-#include <cublas_v2.h>
-
-#ifdef CUDNN
-#include <cudnn.h>
-#endif // CUDNN
-#endif // GPU
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-
-struct network;
-typedef struct network network;
-
-struct network_state;
-typedef struct network_state 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;
-
-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, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL
-}ACTIVATION;
-
-// parser.h
-typedef enum {
- IOU, GIOU, MSE, DIOU, CIOU
-} IOU_LOSS;
-
-// parser.h
-typedef enum {
- DEFAULT_NMS, GREEDY_NMS, DIOU_NMS, CORNERS_NMS
-} NMS_KIND;
-
-// parser.h
-typedef enum {
- YOLO_CENTER = 1 << 0, YOLO_LEFT_TOP = 1 << 1, YOLO_RIGHT_BOTTOM = 1 << 2
-} YOLO_POINT;
-
-// parser.h
-typedef enum {
- NO_WEIGHTS, PER_FEATURE, PER_CHANNEL
-} WEIGHTS_TYPE_T;
-
-// parser.h
-typedef enum {
- NO_NORMALIZATION, RELU_NORMALIZATION, SOFTMAX_NORMALIZATION
-} WEIGHTS_NORMALIZATION_T;
-
-// 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,
- LOCAL_AVGPOOL,
- SOFTMAX,
- DETECTION,
- DROPOUT,
- CROP,
- ROUTE,
- COST,
- NORMALIZATION,
- AVGPOOL,
- LOCAL,
- SHORTCUT,
- SCALE_CHANNELS,
- SAM,
- ACTIVE,
- RNN,
- GRU,
- LSTM,
- CONV_LSTM,
- CRNN,
- BATCHNORM,
- NETWORK,
- XNOR,
- REGION,
- YOLO,
- GAUSSIAN_YOLO,
- ISEG,
- REORG,
- REORG_OLD,
- UPSAMPLE,
- LOGXENT,
- L2NORM,
- EMPTY,
- 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, float);
- layer *share_layer;
- int train;
- int avgpool;
- int batch_normalize;
- int shortcut;
- int batch;
- int dynamic_minibatch;
- int forced;
- int flipped;
- int inputs;
- int outputs;
- float mean_alpha;
- 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 group_id;
- int size;
- int side;
- int stride;
- int stride_x;
- int stride_y;
- int dilation;
- int antialiasing;
- int maxpool_depth;
- int out_channels;
- int reverse;
- int flatten;
- int spatial;
- int pad;
- int sqrt;
- int flip;
- int index;
- int scale_wh;
- int binary;
- int xnor;
- int peephole;
- int use_bin_output;
- int keep_delta_gpu;
- int optimized_memory;
- int steps;
- int state_constrain;
- int hidden;
- int truth;
- float smooth;
- float dot;
- int deform;
- int grad_centr;
- int sway;
- int rotate;
- int stretch;
- int stretch_sway;
- float angle;
- float jitter;
- float resize;
- float saturation;
- float exposure;
- float shift;
- float ratio;
- float learning_rate_scale;
- float clip;
- int focal_loss;
- float *classes_multipliers;
- float label_smooth_eps;
- 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;
- float random;
- float ignore_thresh;
- float truth_thresh;
- float iou_thresh;
- float thresh;
- float focus;
- int classfix;
- int absolute;
- int assisted_excitation;
-
- int onlyforward;
- int stopbackward;
- int train_only_bn;
- int dont_update;
- int burnin_update;
- int dontload;
- int dontsave;
- int dontloadscales;
- int numload;
-
- float temperature;
- float probability;
- float dropblock_size_rel;
- int dropblock_size_abs;
- int dropblock;
- float scale;
-
- int receptive_w;
- int receptive_h;
- int receptive_w_scale;
- int receptive_h_scale;
-
- char * cweights;
- int * indexes;
- int * input_layers;
- int * input_sizes;
- float **layers_output;
- float **layers_delta;
- WEIGHTS_TYPE_T weights_type;
- WEIGHTS_NORMALIZATION_T weights_normalization;
- 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;
-
- float scale_x_y;
- int objectness_smooth;
- float max_delta;
- float uc_normalizer;
- float iou_normalizer;
- float cls_normalizer;
- IOU_LOSS iou_loss;
- IOU_LOSS iou_thresh_kind;
- NMS_KIND nms_kind;
- float beta_nms;
- YOLO_POINT yolo_point;
-
- 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 * activation_input;
- int delta_pinned;
- int output_pinned;
- 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 *stored_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 *stored_c_cpu;
- float *dc_cpu;
-
- float *binary_input;
- uint32_t *bin_re_packed_input;
- char *t_bit_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 *vo;
- struct layer *uf;
- struct layer *wf;
- struct layer *vf;
- struct layer *ui;
- struct layer *wi;
- struct layer *vi;
- 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 *stored_h_gpu;
-
- float *temp_gpu;
- float *temp2_gpu;
- float *temp3_gpu;
-
- float *dh_gpu;
- float *hh_gpu;
- float *prev_cell_gpu;
- float *prev_state_gpu;
- float *last_prev_state_gpu;
- float *last_prev_cell_gpu;
- float *cell_gpu;
- float *f_gpu;
- float *i_gpu;
- float *g_gpu;
- float *o_gpu;
- float *c_gpu;
- float *stored_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 * 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 *bin_conv_shortcut_in_gpu;
- float *bin_conv_shortcut_out_gpu;
-
- float * mean_gpu;
- float * variance_gpu;
- float * m_cbn_avg_gpu;
- float * v_cbn_avg_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_deform_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 * input_antialiasing_gpu;
- float * output_gpu;
- float * output_avg_gpu;
- float * activation_input_gpu;
- float * loss_gpu;
- float * delta_gpu;
- float * rand_gpu;
- float * drop_blocks_scale;
- float * drop_blocks_scale_gpu;
- float * squared_gpu;
- float * norms_gpu;
-
- float *gt_gpu;
- float *a_avg_gpu;
-
- int *input_sizes_gpu;
- float **layers_output_gpu;
- float **layers_delta_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;
-#else // CUDNN
- void* srcTensorDesc, *dstTensorDesc;
- void* srcTensorDesc16, *dstTensorDesc16;
- void* dsrcTensorDesc, *ddstTensorDesc;
- void* dsrcTensorDesc16, *ddstTensorDesc16;
- void* normTensorDesc, *normDstTensorDesc, *normDstTensorDescF16;
- void* weightDesc, *weightDesc16;
- void* dweightDesc, *dweightDesc16;
- void* convDesc;
- UNUSED_ENUM_TYPE fw_algo, fw_algo16;
- UNUSED_ENUM_TYPE bd_algo, bd_algo16;
- UNUSED_ENUM_TYPE bf_algo, bf_algo16;
- void* poolingDesc;
-#endif // CUDNN
-//#endif // GPU
-};
-
-
-// network.h
-typedef enum {
- CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM, SGDR
-} learning_rate_policy;
-
-// network.h
-typedef struct network {
- int n;
- int batch;
- uint64_t *seen;
- int *cur_iteration;
- float loss_scale;
- int *t;
- float epoch;
- int subdivisions;
- layer *layers;
- float *output;
- learning_rate_policy policy;
- int benchmark_layers;
-
- float learning_rate;
- float learning_rate_min;
- float learning_rate_max;
- int batches_per_cycle;
- int batches_cycle_mult;
- float momentum;
- float decay;
- float gamma;
- float scale;
- float power;
- int time_steps;
- int step;
- int max_batches;
- int num_boxes;
- int train_images_num;
- float *seq_scales;
- 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)
- int gaussian_noise;
- int blur;
- int mixup;
- float label_smooth_eps;
- int resize_step;
- int attention;
- int adversarial;
- float adversarial_lr;
- int letter_box;
- float angle;
- float aspect;
- float exposure;
- float saturation;
- float hue;
- int random;
- int track;
- int augment_speed;
- int sequential_subdivisions;
- int init_sequential_subdivisions;
- int current_subdivision;
- int try_fix_nan;
-
- 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_pinned_cpu;
- int input_pinned_cpu_flag;
-
- 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;
-
- float *global_delta_gpu;
- float *state_delta_gpu;
- size_t max_delta_gpu_size;
-//#endif // GPU
- int optimized_memory;
- int dynamic_minibatch;
- size_t workspace_size_limit;
-} 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 boxabs {
- float left, right, top, bot;
-} boxabs;
-
-// box.h
-typedef struct dxrep {
- float dt, db, dl, dr;
-} dxrep;
-
-// box.h
-typedef struct ious {
- float iou, giou, diou, ciou;
- dxrep dx_iou;
- dxrep dx_giou;
-} ious;
-
-
-// box.h
-typedef struct detection{
- box bbox;
- int classes;
- float *prob;
- float *mask;
- float objectness;
- int sort_class;
- float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty
- int points; // bit-0 - center, bit-1 - top-left-corner, bit-2 - bottom-right-corner
-} detection;
-
-// network.c -batch inference
-typedef struct det_num_pair {
- int num;
- detection *dets;
-} det_num_pair, *pdet_num_pair;
-
-// 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 mini_batch;
- int track;
- int augment_speed;
- int letter_box;
- int show_imgs;
- int dontuse_opencv;
- float jitter;
- float resize;
- int flip;
- int gaussian_noise;
- int blur;
- int mixup;
- float label_smooth_eps;
- 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);
-LIB_API void free_network(network net);
-
-// 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);
-LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1);
-
-// network.h
-LIB_API float *network_predict(network net, float *input);
-LIB_API float *network_predict_ptr(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 det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter);
-LIB_API void free_detections(detection *dets, int n);
-LIB_API void free_batch_detections(det_num_pair *det_num_pairs, int n);
-LIB_API void fuse_conv_batchnorm(network net);
-LIB_API void calculate_binary_weights(network net);
-LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename);
-
-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 *network_predict_image_letterbox(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, const int map_points, int letter_box, 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, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path);
-LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
- float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers);
-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 void make_image_red(image im);
-LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c);
-LIB_API image resize_image(image im, int w, int h);
-LIB_API void quantize_image(image im);
-LIB_API void copy_image_from_bytes(image im, char *pdata);
-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);
-LIB_API image crop_image(image im, int dx, int dy, int w, int h);
-LIB_API image resize_min(image im, int min);
-
-// layer.h
-LIB_API void free_layer_custom(layer l, int keep_cudnn_desc);
-LIB_API void free_layer(layer l);
-
-// data.c
-LIB_API void free_data(data d);
-LIB_API pthread_t load_data(load_args args);
-LIB_API void free_load_threads(void *ptr);
-LIB_API pthread_t load_data_in_thread(load_args args);
-LIB_API void *load_thread(void *ptr);
-
-// dark_cuda.h
-LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n);
-LIB_API void cuda_pull_array_async(float *x_gpu, float *x, size_t n);
-LIB_API void cuda_set_device(int n);
-LIB_API void *cuda_get_context();
-
-// 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);
-
-
-// http_stream.h
-LIB_API void delete_json_sender();
-LIB_API void send_json_custom(char const* send_buf, int port, int timeout);
-LIB_API double get_time_point();
-void start_timer();
-void stop_timer();
-double get_time();
-void stop_timer_and_show();
-void stop_timer_and_show_name(char *name);
-void show_total_time();
-
-// gemm.h
-LIB_API void init_cpu();
-
-#ifdef __cplusplus
-}
-#endif // __cplusplus
-#endif // DARKNET_API
+#ifndef DARKNET_API
+#define DARKNET_API
+
+#if defined(_MSC_VER) && _MSC_VER < 1900
+#define inline __inline
+#endif
+
+#if defined(DEBUG) && !defined(_CRTDBG_MAP_ALLOC)
+#define _CRTDBG_MAP_ALLOC
+#endif
+
+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include <stdint.h>
+#include <assert.h>
+#include <pthread.h>
+
+#ifndef LIB_API
+#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
+#endif
+
+#define SECRET_NUM -1234
+
+typedef enum { UNUSED_DEF_VAL } UNUSED_ENUM_TYPE;
+
+#ifdef GPU
+
+#include <cuda_runtime.h>
+#include <curand.h>
+#include <cublas_v2.h>
+
+#ifdef CUDNN
+#include <cudnn.h>
+#endif // CUDNN
+#endif // GPU
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct network;
+typedef struct network network;
+
+struct network_state;
+typedef struct network_state 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;
+
+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, RELU6, RELIE, LINEAR, RAMP, TANH, PLSE, REVLEAKY, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN, SELU, GELU, SWISH, MISH, HARD_MISH, NORM_CHAN, NORM_CHAN_SOFTMAX, NORM_CHAN_SOFTMAX_MAXVAL
+}ACTIVATION;
+
+// parser.h
+typedef enum {
+ IOU, GIOU, MSE, DIOU, CIOU
+} IOU_LOSS;
+
+// parser.h
+typedef enum {
+ DEFAULT_NMS, GREEDY_NMS, DIOU_NMS, CORNERS_NMS
+} NMS_KIND;
+
+// parser.h
+typedef enum {
+ YOLO_CENTER = 1 << 0, YOLO_LEFT_TOP = 1 << 1, YOLO_RIGHT_BOTTOM = 1 << 2
+} YOLO_POINT;
+
+// parser.h
+typedef enum {
+ NO_WEIGHTS, PER_FEATURE, PER_CHANNEL
+} WEIGHTS_TYPE_T;
+
+// parser.h
+typedef enum {
+ NO_NORMALIZATION, RELU_NORMALIZATION, SOFTMAX_NORMALIZATION
+} WEIGHTS_NORMALIZATION_T;
+
+// image.h
+typedef enum{
+ PNG, BMP, TGA, JPG
+} IMTYPE;
+
+// activations.h
+typedef enum{
+ MULT, ADD, SUB, DIV
+} BINARY_ACTIVATION;
+
+// blas.h
+typedef struct contrastive_params {
+ float sim;
+ float exp_sim;
+ float P;
+ int i, j;
+ int time_step_i, time_step_j;
+} contrastive_params;
+
+
+// layer.h
+typedef enum {
+ CONVOLUTIONAL,
+ DECONVOLUTIONAL,
+ CONNECTED,
+ MAXPOOL,
+ LOCAL_AVGPOOL,
+ SOFTMAX,
+ DETECTION,
+ DROPOUT,
+ CROP,
+ ROUTE,
+ COST,
+ NORMALIZATION,
+ AVGPOOL,
+ LOCAL,
+ SHORTCUT,
+ SCALE_CHANNELS,
+ SAM,
+ ACTIVE,
+ RNN,
+ GRU,
+ LSTM,
+ CONV_LSTM,
+ HISTORY,
+ CRNN,
+ BATCHNORM,
+ NETWORK,
+ XNOR,
+ REGION,
+ YOLO,
+ GAUSSIAN_YOLO,
+ ISEG,
+ REORG,
+ REORG_OLD,
+ UPSAMPLE,
+ LOGXENT,
+ L2NORM,
+ EMPTY,
+ BLANK,
+ CONTRASTIVE,
+ IMPLICIT
+} 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;
+ ACTIVATION lstm_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, float);
+ layer *share_layer;
+ int train;
+ int avgpool;
+ int batch_normalize;
+ int shortcut;
+ int batch;
+ int dynamic_minibatch;
+ int forced;
+ int flipped;
+ int inputs;
+ int outputs;
+ float mean_alpha;
+ 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 truth_size;
+ int groups;
+ int group_id;
+ int size;
+ int side;
+ int stride;
+ int stride_x;
+ int stride_y;
+ int dilation;
+ int antialiasing;
+ int maxpool_depth;
+ int maxpool_zero_nonmax;
+ int out_channels;
+ float reverse;
+ int coordconv;
+ int flatten;
+ int spatial;
+ int pad;
+ int sqrt;
+ int flip;
+ int index;
+ int scale_wh;
+ int binary;
+ int xnor;
+ int peephole;
+ int use_bin_output;
+ int keep_delta_gpu;
+ int optimized_memory;
+ int steps;
+ int history_size;
+ int bottleneck;
+ float time_normalizer;
+ int state_constrain;
+ int hidden;
+ int truth;
+ float smooth;
+ float dot;
+ int deform;
+ int grad_centr;
+ int sway;
+ int rotate;
+ int stretch;
+ int stretch_sway;
+ float angle;
+ float jitter;
+ float resize;
+ float saturation;
+ float exposure;
+ float shift;
+ float ratio;
+ float learning_rate_scale;
+ float clip;
+ int focal_loss;
+ float *classes_multipliers;
+ float label_smooth_eps;
+ int noloss;
+ int softmax;
+ int classes;
+ int detection;
+ int embedding_layer_id;
+ float *embedding_output;
+ int embedding_size;
+ float sim_thresh;
+ int track_history_size;
+ int dets_for_track;
+ int dets_for_show;
+ float track_ciou_norm;
+ 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;
+ float random;
+ float ignore_thresh;
+ float truth_thresh;
+ float iou_thresh;
+ float thresh;
+ float focus;
+ int classfix;
+ int absolute;
+ int assisted_excitation;
+
+ int onlyforward;
+ int stopbackward;
+ int train_only_bn;
+ int dont_update;
+ int burnin_update;
+ int dontload;
+ int dontsave;
+ int dontloadscales;
+ int numload;
+
+ float temperature;
+ float probability;
+ float dropblock_size_rel;
+ int dropblock_size_abs;
+ int dropblock;
+ float scale;
+
+ int receptive_w;
+ int receptive_h;
+ int receptive_w_scale;
+ int receptive_h_scale;
+
+ char * cweights;
+ int * indexes;
+ int * input_layers;
+ int * input_sizes;
+ float **layers_output;
+ float **layers_delta;
+ WEIGHTS_TYPE_T weights_type;
+ WEIGHTS_NORMALIZATION_T weights_normalization;
+ int * map;
+ int * counts;
+ float ** sums;
+ float * rand;
+ float * cost;
+ int *labels;
+ int *class_ids;
+ int contrastive_neg_max;
+ float *cos_sim;
+ float *exp_cos_sim;
+ float *p_constrastive;
+ contrastive_params *contrast_p_gpu;
+ 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_ema;
+ float *biases_ema;
+ float *scales_ema;
+
+ float *weights;
+ float *weight_updates;
+
+ float scale_x_y;
+ int objectness_smooth;
+ int new_coords;
+ int show_details;
+ float max_delta;
+ float uc_normalizer;
+ float iou_normalizer;
+ float obj_normalizer;
+ float cls_normalizer;
+ float delta_normalizer;
+ IOU_LOSS iou_loss;
+ IOU_LOSS iou_thresh_kind;
+ NMS_KIND nms_kind;
+ float beta_nms;
+ YOLO_POINT yolo_point;
+
+ 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 * activation_input;
+ int delta_pinned;
+ int output_pinned;
+ 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 *stored_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 *stored_c_cpu;
+ float *dc_cpu;
+
+ float *binary_input;
+ uint32_t *bin_re_packed_input;
+ char *t_bit_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 *vo;
+ struct layer *uf;
+ struct layer *wf;
+ struct layer *vf;
+ struct layer *ui;
+ struct layer *wi;
+ struct layer *vi;
+ struct layer *ug;
+ struct layer *wg;
+
+ tree *softmax_tree;
+
+ size_t workspace_size;
+
+//#ifdef GPU
+ int *indexes_gpu;
+
+ int stream;
+ int wait_stream_id;
+
+ float *z_gpu;
+ float *r_gpu;
+ float *h_gpu;
+ float *stored_h_gpu;
+ float *bottelneck_hi_gpu;
+ float *bottelneck_delta_gpu;
+
+ float *temp_gpu;
+ float *temp2_gpu;
+ float *temp3_gpu;
+
+ float *dh_gpu;
+ float *hh_gpu;
+ float *prev_cell_gpu;
+ float *prev_state_gpu;
+ float *last_prev_state_gpu;
+ float *last_prev_cell_gpu;
+ float *cell_gpu;
+ float *f_gpu;
+ float *i_gpu;
+ float *g_gpu;
+ float *o_gpu;
+ float *c_gpu;
+ float *stored_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 * 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 *bin_conv_shortcut_in_gpu;
+ float *bin_conv_shortcut_out_gpu;
+
+ float * mean_gpu;
+ float * variance_gpu;
+ float * m_cbn_avg_gpu;
+ float * v_cbn_avg_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_deform_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 * input_antialiasing_gpu;
+ float * output_gpu;
+ float * output_avg_gpu;
+ float * activation_input_gpu;
+ float * loss_gpu;
+ float * delta_gpu;
+ float * cos_sim_gpu;
+ float * rand_gpu;
+ float * drop_blocks_scale;
+ float * drop_blocks_scale_gpu;
+ float * squared_gpu;
+ float * norms_gpu;
+
+ float *gt_gpu;
+ float *a_avg_gpu;
+
+ int *input_sizes_gpu;
+ float **layers_output_gpu;
+ float **layers_delta_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;
+#else // CUDNN
+ void* srcTensorDesc, *dstTensorDesc;
+ void* srcTensorDesc16, *dstTensorDesc16;
+ void* dsrcTensorDesc, *ddstTensorDesc;
+ void* dsrcTensorDesc16, *ddstTensorDesc16;
+ void* normTensorDesc, *normDstTensorDesc, *normDstTensorDescF16;
+ void* weightDesc, *weightDesc16;
+ void* dweightDesc, *dweightDesc16;
+ void* convDesc;
+ UNUSED_ENUM_TYPE fw_algo, fw_algo16;
+ UNUSED_ENUM_TYPE bd_algo, bd_algo16;
+ UNUSED_ENUM_TYPE bf_algo, bf_algo16;
+ void* poolingDesc;
+#endif // CUDNN
+//#endif // GPU
+};
+
+
+// network.h
+typedef enum {
+ CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM, SGDR
+} learning_rate_policy;
+
+// network.h
+typedef struct network {
+ int n;
+ int batch;
+ uint64_t *seen;
+ float *badlabels_reject_threshold;
+ float *delta_rolling_max;
+ float *delta_rolling_avg;
+ float *delta_rolling_std;
+ int weights_reject_freq;
+ int equidistant_point;
+ float badlabels_rejection_percentage;
+ float num_sigmas_reject_badlabels;
+ float ema_alpha;
+ int *cur_iteration;
+ float loss_scale;
+ int *t;
+ float epoch;
+ int subdivisions;
+ layer *layers;
+ float *output;
+ learning_rate_policy policy;
+ int benchmark_layers;
+ int *total_bbox;
+ int *rewritten_bbox;
+
+ float learning_rate;
+ float learning_rate_min;
+ float learning_rate_max;
+ int batches_per_cycle;
+ int batches_cycle_mult;
+ float momentum;
+ float decay;
+ float gamma;
+ float scale;
+ float power;
+ int time_steps;
+ int step;
+ int max_batches;
+ int num_boxes;
+ int train_images_num;
+ float *seq_scales;
+ 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)
+ int gaussian_noise;
+ int blur;
+ int mixup;
+ float label_smooth_eps;
+ int resize_step;
+ int attention;
+ int adversarial;
+ float adversarial_lr;
+ float max_chart_loss;
+ int letter_box;
+ int mosaic_bound;
+ int contrastive;
+ int contrastive_jit_flip;
+ int contrastive_color;
+ int unsupervised;
+ float angle;
+ float aspect;
+ float exposure;
+ float saturation;
+ float hue;
+ int random;
+ int track;
+ int augment_speed;
+ int sequential_subdivisions;
+ int init_sequential_subdivisions;
+ int current_subdivision;
+ int try_fix_nan;
+
+ 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_pinned_cpu;
+ int input_pinned_cpu_flag;
+
+ 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;
+
+ void *cuda_graph;
+ void *cuda_graph_exec;
+ int use_cuda_graph;
+ int *cuda_graph_ready;
+
+ float *global_delta_gpu;
+ float *state_delta_gpu;
+ size_t max_delta_gpu_size;
+//#endif // GPU
+ int optimized_memory;
+ int dynamic_minibatch;
+ size_t workspace_size_limit;
+} 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 boxabs {
+ float left, right, top, bot;
+} boxabs;
+
+// box.h
+typedef struct dxrep {
+ float dt, db, dl, dr;
+} dxrep;
+
+// box.h
+typedef struct ious {
+ float iou, giou, diou, ciou;
+ dxrep dx_iou;
+ dxrep dx_giou;
+} ious;
+
+
+// box.h
+typedef struct detection{
+ box bbox;
+ int classes;
+ float *prob;
+ float *mask;
+ float objectness;
+ int sort_class;
+ float *uc; // Gaussian_YOLOv3 - tx,ty,tw,th uncertainty
+ int points; // bit-0 - center, bit-1 - top-left-corner, bit-2 - bottom-right-corner
+ float *embeddings; // embeddings for tracking
+ int embedding_size;
+ float sim;
+ int track_id;
+} detection;
+
+// network.c -batch inference
+typedef struct det_num_pair {
+ int num;
+ detection *dets;
+} det_num_pair, *pdet_num_pair;
+
+// 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 truth_size;
+ int min, max, size;
+ int classes;
+ int background;
+ int scale;
+ int center;
+ int coords;
+ int mini_batch;
+ int track;
+ int augment_speed;
+ int letter_box;
+ int mosaic_bound;
+ int show_imgs;
+ int dontuse_opencv;
+ int contrastive;
+ int contrastive_jit_flip;
+ int contrastive_color;
+ float jitter;
+ float resize;
+ int flip;
+ int gaussian_noise;
+ int blur;
+ int mixup;
+ float label_smooth_eps;
+ 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;
+ int track_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);
+LIB_API void free_network(network net);
+LIB_API void free_network_ptr(network* net);
+
+// 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);
+LIB_API void diounms_sort(detection *dets, int total, int classes, float thresh, NMS_KIND nms_kind, float beta1);
+
+// network.h
+LIB_API float *network_predict(network net, float *input);
+LIB_API float *network_predict_ptr(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 det_num_pair* network_predict_batch(network *net, image im, int batch_size, int w, int h, float thresh, float hier, int *map, int relative, int letter);
+LIB_API void free_detections(detection *dets, int n);
+LIB_API void free_batch_detections(det_num_pair *det_num_pairs, int n);
+LIB_API void fuse_conv_batchnorm(network net);
+LIB_API void calculate_binary_weights(network net);
+LIB_API char *detection_to_json(detection *dets, int nboxes, int classes, char **names, long long int frame_id, char *filename);
+
+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 *network_predict_image_letterbox(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, const int map_points, int letter_box, 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, int mjpeg_port, int show_imgs, int benchmark_layers, char* chart_path);
+LIB_API void test_detector(char *datacfg, char *cfgfile, char *weightfile, char *filename, float thresh,
+ float hier_thresh, int dont_show, int ext_output, int save_labels, char *outfile, int letter_box, int benchmark_layers);
+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 void make_image_red(image im);
+LIB_API image make_attention_image(int img_size, float *original_delta_cpu, float *original_input_cpu, int w, int h, int c);
+LIB_API image resize_image(image im, int w, int h);
+LIB_API void quantize_image(image im);
+LIB_API void copy_image_from_bytes(image im, char *pdata);
+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);
+LIB_API image crop_image(image im, int dx, int dy, int w, int h);
+LIB_API image resize_min(image im, int min);
+
+// layer.h
+LIB_API void free_layer_custom(layer l, int keep_cudnn_desc);
+LIB_API void free_layer(layer l);
+
+// data.c
+LIB_API void free_data(data d);
+LIB_API pthread_t load_data(load_args args);
+LIB_API void free_load_threads(void *ptr);
+LIB_API pthread_t load_data_in_thread(load_args args);
+LIB_API void *load_thread(void *ptr);
+
+// dark_cuda.h
+LIB_API void cuda_pull_array(float *x_gpu, float *x, size_t n);
+LIB_API void cuda_pull_array_async(float *x_gpu, float *x, size_t n);
+LIB_API void cuda_set_device(int n);
+LIB_API void *cuda_get_context();
+
+// 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);
+
+
+// http_stream.h
+LIB_API void delete_json_sender();
+LIB_API void send_json_custom(char const* send_buf, int port, int timeout);
+LIB_API double get_time_point();
+void start_timer();
+void stop_timer();
+double get_time();
+void stop_timer_and_show();
+void stop_timer_and_show_name(char *name);
+void show_total_time();
+
+LIB_API void set_track_id(detection *new_dets, int new_dets_num, float thresh, float sim_thresh, float track_ciou_norm, int deque_size, int dets_for_track, int dets_for_show);
+LIB_API int fill_remaining_id(detection *new_dets, int new_dets_num, int new_track_id, float thresh);
+
+
+// gemm.h
+LIB_API void init_cpu();
+
+#ifdef __cplusplus
+}
+#endif // __cplusplus
+#endif // DARKNET_API
--
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