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https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
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This commit is contained in:
Joseph Redmon
@@ -1,7 +1,11 @@
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#ifndef DARKNET_API
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#define DARKNET_API
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#include <stdlib.h>
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#include <stdio.h>
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#include <string.h>
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#include <pthread.h>
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#define SECRET_NUM -1234
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extern int gpu_index;
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#ifdef GPU
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@@ -27,6 +31,13 @@ extern int gpu_index;
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#endif
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#endif
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typedef struct{
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int classes;
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char **names;
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} metadata;
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metadata get_metadata(char *file);
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typedef struct{
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int *leaf;
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int n;
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@@ -42,7 +53,7 @@ typedef struct{
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typedef enum{
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LOGISTIC, RELU, RELIE, LINEAR, RAMP, TANH, PLSE, LEAKY, ELU, LOGGY, STAIR, HARDTAN, LHTAN
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}ACTIVATION;
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} ACTIVATION;
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typedef enum {
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CONVOLUTIONAL,
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@@ -255,7 +266,7 @@ struct layer{
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size_t workspace_size;
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#ifdef GPU
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#ifdef GPU
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int *indexes_gpu;
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float *z_gpu;
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@@ -281,8 +292,8 @@ struct layer{
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float * concat_gpu;
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float * concat_delta_gpu;
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float *binary_input_gpu;
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float *binary_weights_gpu;
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float * binary_input_gpu;
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float * binary_weights_gpu;
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float * mean_gpu;
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float * variance_gpu;
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@@ -297,19 +308,22 @@ struct layer{
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float * x_norm_gpu;
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float * weights_gpu;
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float * weight_updates_gpu;
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float * weight_change_gpu;
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float * biases_gpu;
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float * bias_updates_gpu;
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float * bias_change_gpu;
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float * scales_gpu;
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float * scale_updates_gpu;
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float * scale_change_gpu;
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float * output_gpu;
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float * delta_gpu;
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float * rand_gpu;
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float * squared_gpu;
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float * norms_gpu;
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#ifdef CUDNN
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#ifdef CUDNN
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cudnnTensorDescriptor_t srcTensorDesc, dstTensorDesc;
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cudnnTensorDescriptor_t dsrcTensorDesc, ddstTensorDesc;
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cudnnTensorDescriptor_t normTensorDesc;
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@@ -319,8 +333,8 @@ struct layer{
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cudnnConvolutionFwdAlgo_t fw_algo;
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cudnnConvolutionBwdDataAlgo_t bd_algo;
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cudnnConvolutionBwdFilterAlgo_t bf_algo;
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#endif
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#endif
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#endif
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#endif
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};
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void free_layer(layer);
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@@ -383,12 +397,12 @@ typedef struct network{
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int index;
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float *cost;
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#ifdef GPU
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#ifdef GPU
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float *input_gpu;
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float *truth_gpu;
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float *delta_gpu;
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float *output_gpu;
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#endif
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#endif
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} network;
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@@ -403,8 +417,8 @@ typedef struct {
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} augment_args;
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typedef struct {
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int h;
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int w;
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int h;
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int c;
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float *data;
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} image;
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@@ -472,6 +486,7 @@ typedef struct{
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network load_network(char *cfg, char *weights, int clear);
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network *load_network_p(char *cfg, char *weights, int clear);
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load_args get_base_args(network net);
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void free_data(data d);
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@@ -492,47 +507,171 @@ pthread_t load_data(load_args args);
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list *read_data_cfg(char *filename);
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list *read_cfg(char *filename);
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void forward_network(network net);
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void backward_network(network net);
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void update_network(network net);
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void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
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void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
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void scal_cpu(int N, float ALPHA, float *X, int INCX);
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void normalize_cpu(float *x, float *mean, float *variance, int batch, int filters, int spatial);
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int best_3d_shift_r(image a, image b, int min, int max);
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#ifdef GPU
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void axpy_ongpu(int N, float ALPHA, float * X, int INCX, float * Y, int INCY);
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void fill_ongpu(int N, float ALPHA, float * X, int INCX);
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void scal_ongpu(int N, float ALPHA, float * X, int INCX);
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void copy_ongpu(int N, float * X, int INCX, float * Y, int INCY);
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void cuda_set_device(int n);
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void cuda_free(float *x_gpu);
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float *cuda_make_array(float *x, size_t n);
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void cuda_pull_array(float *x_gpu, float *x, size_t n);
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float cuda_mag_array(float *x_gpu, size_t n);
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void cuda_push_array(float *x_gpu, float *x, size_t n);
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void forward_network_gpu(network net);
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void backward_network_gpu(network net);
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void update_network_gpu(network net);
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float train_networks(network *nets, int n, data d, int interval);
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void sync_nets(network *nets, int n, int interval);
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void harmless_update_network_gpu(network net);
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#endif
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void save_image_png(image im, const char *name);
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void get_next_batch(data d, int n, int offset, float *X, float *y);
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void grayscale_image_3c(image im);
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void normalize_image(image p);
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void matrix_to_csv(matrix m);
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float train_network_sgd(network net, data d, int n);
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void rgbgr_image(image im);
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data copy_data(data d);
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data concat_data(data d1, data d2);
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data load_cifar10_data(char *filename);
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float matrix_topk_accuracy(matrix truth, matrix guess, int k);
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void matrix_add_matrix(matrix from, matrix to);
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void scale_matrix(matrix m, float scale);
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matrix csv_to_matrix(char *filename);
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float *network_accuracies(network net, data d, int n);
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float train_network_datum(network net);
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image make_random_image(int w, int h, int c);
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void denormalize_connected_layer(layer l);
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void denormalize_convolutional_layer(layer l);
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void statistics_connected_layer(layer l);
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void rescale_weights(layer l, float scale, float trans);
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void rgbgr_weights(layer l);
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image *get_weights(layer l);
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void demo(char *cfgfile, char *weightfile, float thresh, int cam_index, const char *filename, char **names, int classes, int frame_skip, char *prefix, int avg, float hier_thresh, int w, int h, int fps, int fullscreen);
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void get_detection_boxes(layer l, int w, int h, float thresh, float **probs, box *boxes, int only_objectness);
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char *option_find_str(list *l, char *key, char *def);
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int option_find_int(list *l, char *key, int def);
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network parse_network_cfg(char *filename);
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void save_weights(network net, char *filename);
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void load_weights(network *net, char *filename);
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void save_weights_upto(network net, char *filename, int cutoff);
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void load_weights_upto(network *net, char *filename, int start, int cutoff);
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void zero_objectness(layer l);
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void get_region_boxes(layer l, int w, int h, int netw, int neth, float thresh, float **probs, box *boxes, int only_objectness, int *map, float tree_thresh, int relative);
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void free_network(network net);
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void set_batch_network(network *net, int b);
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image load_image(char *filename, int w, int h, int c);
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image load_image_color(char *filename, int w, int h);
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image make_image(int w, int h, int c);
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image resize_image(image im, int w, int h);
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image letterbox_image(image im, int w, int h);
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image crop_image(image im, int dx, int dy, int w, int h);
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image resize_min(image im, int min);
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image threshold_image(image im, float thresh);
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image mask_to_rgb(image mask);
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int resize_network(network *net, int w, int h);
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void free_matrix(matrix m);
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void test_resize(char *filename);
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void save_image(image p, const char *name);
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void show_image(image p, const char *name);
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image copy_image(image p);
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void draw_box_width(image a, int x1, int y1, int x2, int y2, int w, float r, float g, float b);
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float get_current_rate(network net);
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void composite_3d(char *f1, char *f2, char *out, int delta);
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data load_data_old(char **paths, int n, int m, char **labels, int k, int w, int h);
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int get_current_batch(network net);
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void constrain_image(image im);
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image get_network_image_layer(network net, int i);
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layer get_network_output_layer(network net);
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void top_predictions(network net, int n, int *index);
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void flip_image(image a);
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image float_to_image(int w, int h, int c, float *data);
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void ghost_image(image source, image dest, int dx, int dy);
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float network_accuracy(network net, data d);
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void random_distort_image(image im, float hue, float saturation, float exposure);
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void fill_image(image m, float s);
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image grayscale_image(image im);
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void rotate_image_cw(image im, int times);
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image rotate_image(image m, float rad);
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void visualize_network(network net);
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float box_iou(box a, box b);
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void do_nms(box *boxes, float **probs, int total, int classes, float thresh);
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data load_all_cifar10();
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box_label *read_boxes(char *filename, int *n);
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void draw_detections(image im, int num, float thresh, box *boxes, float **probs, char **names, image **labels, int classes);
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matrix network_predict_data(network net, data test);
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image **load_alphabet();
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image get_network_image(network net);
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float *network_predict(network net, float *input);
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float *network_predict_p(network *net, float *input);
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int network_width(network *net);
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int network_height(network *net);
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float *network_predict_image(network *net, image im);
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char **get_labels(char *filename);
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void do_nms_sort(box *boxes, float **probs, int total, int classes, float thresh);
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void do_nms_obj(box *boxes, float **probs, int total, int classes, float thresh);
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matrix make_matrix(int rows, int cols);
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#ifndef __cplusplus
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#ifdef OPENCV
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image get_image_from_stream(CvCapture *cap);
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#endif
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#endif
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void free_image(image m);
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float train_network(network net, data d);
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pthread_t load_data_in_thread(load_args args);
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list *get_paths(char *filename);
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void hierarchy_predictions(float *predictions, int n, tree *hier, int only_leaves, int stride);
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void change_leaves(tree *t, char *leaf_list);
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int find_int_arg(int argc, char **argv, char *arg, int def);
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float find_float_arg(int argc, char **argv, char *arg, float def);
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int find_arg(int argc, char* argv[], char *arg);
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char *find_char_arg(int argc, char **argv, char *arg, char *def);
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char *basecfg(char *cfgfile);
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void find_replace(char *str, char *orig, char *rep, char *output);
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void free_ptrs(void **ptrs, int n);
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char *fgetl(FILE *fp);
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void strip(char *s);
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float sec(clock_t clocks);
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void **list_to_array(list *l);
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void top_k(float *a, int n, int k, int *index);
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int *read_map(char *filename);
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void error(const char *s);
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int max_index(float *a, int n);
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int sample_array(float *a, int n);
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void free_list(list *l);
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float mse_array(float *a, int n);
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float variance_array(float *a, int n);
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float mag_array(float *a, int n);
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float mean_array(float *a, int n);
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void normalize_array(float *a, int n);
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int *read_intlist(char *s, int *n, int d);
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size_t rand_size_t();
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float rand_normal();
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#include "activation_layer.h"
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#include "activations.h"
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#include "avgpool_layer.h"
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#include "batchnorm_layer.h"
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#include "blas.h"
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#include "box.h"
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#include "classifier.h"
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#include "col2im.h"
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#include "connected_layer.h"
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#include "convolutional_layer.h"
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#include "cost_layer.h"
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#include "crnn_layer.h"
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#include "crop_layer.h"
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#include "cuda.h"
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#include "data.h"
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#include "deconvolutional_layer.h"
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#include "demo.h"
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#include "detection_layer.h"
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#include "dropout_layer.h"
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#include "gemm.h"
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#include "gru_layer.h"
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#include "im2col.h"
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#include "image.h"
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#include "layer.h"
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#include "list.h"
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#include "local_layer.h"
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#include "matrix.h"
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#include "maxpool_layer.h"
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#include "network.h"
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#include "normalization_layer.h"
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#include "option_list.h"
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#include "parser.h"
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#include "region_layer.h"
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#include "reorg_layer.h"
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#include "rnn_layer.h"
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#include "route_layer.h"
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#include "shortcut_layer.h"
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#include "softmax_layer.h"
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#include "stb_image.h"
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#include "stb_image_write.h"
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#include "tree.h"
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#include "utils.h"
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#endif
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