派生自 Algorithm/baseDetector
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
