From a723e1c62a27aeb39aaf7fcdeb3beb4e89fba32d Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Wed, 15 Aug 2018 20:52:09 +0000
Subject: [PATCH] Merge pull request #766 from HotChick91/AlexeyAB-mask
---
src/network.h | 125 ++++++++++++++++++++++++++++++++++-------
1 files changed, 102 insertions(+), 23 deletions(-)
diff --git a/src/network.h b/src/network.h
index 66873d2..3b07dbb 100644
--- a/src/network.h
+++ b/src/network.h
@@ -2,63 +2,121 @@
#ifndef NETWORK_H
#define NETWORK_H
+#include <stdint.h>
+#include "layer.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
#include "image.h"
#include "data.h"
+#include "tree.h"
typedef enum {
- CONVOLUTIONAL,
- DECONVOLUTIONAL,
- CONNECTED,
- MAXPOOL,
- SOFTMAX,
- NORMALIZATION,
- DROPOUT,
- FREEWEIGHT,
- CROP,
- COST
-} LAYER_TYPE;
+ CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
+} learning_rate_policy;
-typedef struct {
+typedef struct network{
+ float *workspace;
int n;
int batch;
- int seen;
- float learning_rate;
+ int *seen;
+ float epoch;
+ int subdivisions;
float momentum;
float decay;
- void **layers;
- LAYER_TYPE *types;
+ 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 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_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);
#endif
+float get_current_rate(network net);
+int get_current_batch(network net);
+void free_network(network net);
void compare_networks(network n1, network n2, data d);
char *get_layer_string(LAYER_TYPE a);
-network make_network(int n, int batch);
-void forward_network(network net, float *input, float *truth, int train);
-void backward_network(network net, float *input);
+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_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);
-float *network_predict(network net, float *input);
+YOLODLL_API float *network_predict(network net, float *input);
float network_accuracy(network net, data d);
-float *network_accuracies(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);
@@ -72,11 +130,32 @@
int get_predicted_class_network(network net);
void print_network(network net);
void visualize_network(network net);
-int resize_network(network net, int h, int w, int c);
+int resize_network(network *net, int w, int h);
void set_batch_network(network *net, int b);
-void set_learning_network(network *net, float rate, float momentum, float decay);
int get_network_input_size(network net);
float get_network_cost(network net);
+YOLODLL_API layer* get_network_layer(network* net, int i);
+YOLODLL_API detection *get_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, int *num, int letter);
+YOLODLL_API detection *make_network_boxes(network *net, float thresh, int *num);
+YOLODLL_API void free_detections(detection *dets, int n);
+YOLODLL_API void reset_rnn(network *net);
+YOLODLL_API network *load_network_custom(char *cfg, char *weights, int clear, int batch);
+YOLODLL_API network *load_network(char *cfg, char *weights, int clear);
+YOLODLL_API float *network_predict_image(network *net, image im);
+YOLODLL_API void train_detector(char *datacfg, char *cfgfile, char *weightfile, int *gpus, int ngpus, int clear, int dont_show);
+YOLODLL_API int network_width(network *net);
+YOLODLL_API int network_height(network *net);
+
+YOLODLL_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);
+YOLODLL_API void fuse_conv_batchnorm(network net);
+YOLODLL_API void calculate_binary_weights(network net);
+
+#ifdef __cplusplus
+}
+#endif
#endif
--
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