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/network.h | 363 ++++++++++++++++++++++++++-------------------------
1 files changed, 184 insertions(+), 179 deletions(-)
diff --git a/lib/detecter_tools/darknet/network.h b/lib/detecter_tools/darknet/network.h
index 4043558..7661c8e 100644
--- a/lib/detecter_tools/darknet/network.h
+++ b/lib/detecter_tools/darknet/network.h
@@ -1,179 +1,184 @@
-// Oh boy, why am I about to do this....
-#ifndef NETWORK_H
-#define NETWORK_H
-#include "darknet.h"
-
-#include <stdint.h>
-#include "layer.h"
-
-
-#include "image.h"
-#include "data.h"
-#include "tree.h"
-
-#ifdef __cplusplus
-extern "C" {
-#endif
-/*
-typedef enum {
- CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
-} learning_rate_policy;
-
-typedef struct network{
- float *workspace;
- int n;
- int batch;
- uint64_t *seen;
- float epoch;
- int subdivisions;
- float momentum;
- float decay;
- layer *layers;
- int outputs;
- float *output;
- learning_rate_policy policy;
-
- float learning_rate;
- float gamma;
- float scale;
- float power;
- int time_steps;
- int step;
- int max_batches;
- float *scales;
- int *steps;
- int num_steps;
- int burn_in;
- int cudnn_half;
-
- int adam;
- float B1;
- float B2;
- float eps;
-
- int inputs;
- int h, w, c;
- int max_crop;
- int min_crop;
- int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1)
- float angle;
- float aspect;
- float exposure;
- float saturation;
- float hue;
- int small_object;
-
- int gpu_index;
- tree *hierarchy;
-
- #ifdef GPU
- float *input_state_gpu;
-
- float **input_gpu;
- float **truth_gpu;
- float **input16_gpu;
- float **output16_gpu;
- size_t *max_input16_size;
- size_t *max_output16_size;
- int wait_stream;
- #endif
-} network;
-
-
-typedef struct network_state {
- float *truth;
- float *input;
- float *delta;
- float *workspace;
- int train;
- int index;
- network net;
-} network_state;
-*/
-
-#ifdef GPU
-float train_networks(network *nets, int n, data d, int interval);
-void sync_nets(network *nets, int n, int interval);
-float train_network_datum_gpu(network net, float *x, float *y);
-float *network_predict_gpu(network net, float *input);
-float * get_network_output_gpu_layer(network net, int i);
-float * get_network_delta_gpu_layer(network net, int i);
-float *get_network_output_gpu(network net);
-void forward_network_gpu(network net, network_state state);
-void backward_network_gpu(network net, network_state state);
-void update_network_gpu(network net);
-void forward_backward_network_gpu(network net, float *x, float *y);
-#endif
-
-float get_current_seq_subdivisions(network net);
-int get_sequence_value(network net);
-float get_current_rate(network net);
-int get_current_batch(network net);
-int64_t get_current_iteration(network net);
-//void free_network(network net); // darknet.h
-void compare_networks(network n1, network n2, data d);
-char *get_layer_string(LAYER_TYPE a);
-
-network make_network(int n);
-void forward_network(network net, network_state state);
-void backward_network(network net, network_state state);
-void update_network(network net);
-
-float train_network(network net, data d);
-float train_network_waitkey(network net, data d, int wait_key);
-float train_network_batch(network net, data d, int n);
-float train_network_sgd(network net, data d, int n);
-float train_network_datum(network net, float *x, float *y);
-
-matrix network_predict_data(network net, data test);
-//LIB_API float *network_predict(network net, float *input);
-//LIB_API float *network_predict_ptr(network *net, float *input);
-float network_accuracy(network net, data d);
-float *network_accuracies(network net, data d, int n);
-float network_accuracy_multi(network net, data d, int n);
-void top_predictions(network net, int n, int *index);
-float *get_network_output(network net);
-float *get_network_output_layer(network net, int i);
-float *get_network_delta_layer(network net, int i);
-float *get_network_delta(network net);
-int get_network_output_size_layer(network net, int i);
-int get_network_output_size(network net);
-image get_network_image(network net);
-image get_network_image_layer(network net, int i);
-int get_predicted_class_network(network net);
-void print_network(network net);
-void visualize_network(network net);
-int resize_network(network *net, int w, int h);
-void set_batch_network(network *net, int b);
-int get_network_input_size(network net);
-float get_network_cost(network net);
-//LIB_API layer* get_network_layer(network* net, int i);
-//LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
-//LIB_API detection *make_network_boxes(network *net, float thresh, int *num);
-//LIB_API void free_detections(detection *dets, int n);
-//LIB_API void reset_rnn(network *net);
-//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 float *network_predict_image(network *net, image im);
-//LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, 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);
-//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);
-
-int get_network_nuisance(network net);
-int get_network_background(network net);
-//LIB_API void fuse_conv_batchnorm(network net);
-//LIB_API void calculate_binary_weights(network net);
-network combine_train_valid_networks(network net_train, network net_map);
-void copy_weights_net(network net_train, network *net_map);
-void free_network_recurrent_state(network net);
-void randomize_network_recurrent_state(network net);
-void remember_network_recurrent_state(network net);
-void restore_network_recurrent_state(network net);
-
-#ifdef __cplusplus
-}
-#endif
-
-#endif
+// Oh boy, why am I about to do this....
+#ifndef NETWORK_H
+#define NETWORK_H
+#include "darknet.h"
+
+#include <stdint.h>
+#include "layer.h"
+
+
+#include "image.h"
+#include "data.h"
+#include "tree.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+/*
+typedef enum {
+ CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
+} learning_rate_policy;
+
+typedef struct network{
+ float *workspace;
+ int n;
+ int batch;
+ uint64_t *seen;
+ float epoch;
+ int subdivisions;
+ float momentum;
+ float decay;
+ layer *layers;
+ int outputs;
+ float *output;
+ learning_rate_policy policy;
+
+ float learning_rate;
+ float gamma;
+ float scale;
+ float power;
+ int time_steps;
+ int step;
+ int max_batches;
+ float *scales;
+ int *steps;
+ int num_steps;
+ int burn_in;
+ int cudnn_half;
+
+ int adam;
+ float B1;
+ float B2;
+ float eps;
+
+ int inputs;
+ int h, w, c;
+ int max_crop;
+ int min_crop;
+ int flip; // horizontal flip 50% probability augmentaiont for classifier training (default = 1)
+ float angle;
+ float aspect;
+ float exposure;
+ float saturation;
+ float hue;
+ int small_object;
+
+ int gpu_index;
+ tree *hierarchy;
+
+ #ifdef GPU
+ float *input_state_gpu;
+
+ float **input_gpu;
+ float **truth_gpu;
+ float **input16_gpu;
+ float **output16_gpu;
+ size_t *max_input16_size;
+ size_t *max_output16_size;
+ int wait_stream;
+ #endif
+} network;
+
+
+typedef struct network_state {
+ float *truth;
+ float *input;
+ float *delta;
+ float *workspace;
+ int train;
+ int index;
+ network net;
+} network_state;
+*/
+
+#ifdef GPU
+float train_networks(network *nets, int n, data d, int interval);
+void sync_nets(network *nets, int n, int interval);
+float train_network_datum_gpu(network net, float *x, float *y);
+float *network_predict_gpu(network net, float *input);
+float * get_network_output_gpu_layer(network net, int i);
+float * get_network_delta_gpu_layer(network net, int i);
+float *get_network_output_gpu(network net);
+void forward_network_gpu(network net, network_state state);
+void backward_network_gpu(network net, network_state state);
+void update_network_gpu(network net);
+void forward_backward_network_gpu(network net, float *x, float *y);
+#endif
+
+float get_current_seq_subdivisions(network net);
+int get_sequence_value(network net);
+float get_current_rate(network net);
+int get_current_batch(network net);
+int64_t get_current_iteration(network net);
+//void free_network(network net); // darknet.h
+void compare_networks(network n1, network n2, data d);
+char *get_layer_string(LAYER_TYPE a);
+
+network make_network(int n);
+void forward_network(network net, network_state state);
+void backward_network(network net, network_state state);
+void update_network(network net);
+
+float train_network(network net, data d);
+float train_network_waitkey(network net, data d, int wait_key);
+float train_network_batch(network net, data d, int n);
+float train_network_sgd(network net, data d, int n);
+float train_network_datum(network net, float *x, float *y);
+
+matrix network_predict_data(network net, data test);
+//LIB_API float *network_predict(network net, float *input);
+//LIB_API float *network_predict_ptr(network *net, float *input);
+float network_accuracy(network net, data d);
+float *network_accuracies(network net, data d, int n);
+float network_accuracy_multi(network net, data d, int n);
+void top_predictions(network net, int n, int *index);
+float *get_network_output(network net);
+float *get_network_output_layer(network net, int i);
+float *get_network_delta_layer(network net, int i);
+float *get_network_delta(network net);
+int get_network_output_size_layer(network net, int i);
+int get_network_output_size(network net);
+image get_network_image(network net);
+image get_network_image_layer(network net, int i);
+int get_predicted_class_network(network net);
+void print_network(network net);
+void visualize_network(network net);
+int resize_network(network *net, int w, int h);
+void set_batch_network(network *net, int b);
+int get_network_input_size(network net);
+float get_network_cost(network net);
+//LIB_API layer* get_network_layer(network* net, int i);
+//LIB_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
+//LIB_API detection *make_network_boxes(network *net, float thresh, int *num);
+//LIB_API void free_detections(detection *dets, int n);
+//LIB_API void reset_rnn(network *net);
+//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 float *network_predict_image(network *net, image im);
+//LIB_API float validate_detector_map(char *datacfg, char *cfgfile, char *weightfile, float thresh_calc_avg_iou, const float iou_thresh, 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);
+//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);
+
+int get_network_nuisance(network net);
+int get_network_background(network net);
+//LIB_API void fuse_conv_batchnorm(network net);
+//LIB_API void calculate_binary_weights(network net);
+network combine_train_valid_networks(network net_train, network net_map);
+void copy_weights_net(network net_train, network *net_map);
+void free_network_recurrent_state(network net);
+void randomize_network_recurrent_state(network net);
+void remember_network_recurrent_state(network net);
+void restore_network_recurrent_state(network net);
+int is_ema_initialized(network net);
+void ema_update(network net, float ema_alpha);
+void ema_apply(network net);
+void reject_similar_weights(network net, float sim_threshold);
+
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif
--
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