From 5a47c46b39475fc3581b9819f488b977ea1beca3 Mon Sep 17 00:00:00 2001
From: Edmond Yoo <hj3yoo@uwaterloo.ca>
Date: Sun, 16 Sep 2018 03:11:04 +0000
Subject: [PATCH] Moving files from MTGCardDetector
---
src/network.h | 156 +++++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 141 insertions(+), 15 deletions(-)
diff --git a/src/network.h b/src/network.h
index 10fa6c5..3b07dbb 100644
--- a/src/network.h
+++ b/src/network.h
@@ -2,34 +2,160 @@
#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,
- CONNECTED,
- MAXPOOL
-} LAYER_TYPE;
+ CONSTANT, STEP, EXP, POLY, STEPS, SIG, RANDOM
+} learning_rate_policy;
-typedef struct {
+typedef struct network{
+ float *workspace;
int n;
- void **layers;
- LAYER_TYPE *types;
+ int batch;
+ int *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 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);
-void forward_network(network net, double *input);
-void learn_network(network net, double *input);
-void update_network(network net, double step);
-void train_network_batch(network net, batch b);
-double *get_network_output(network net);
-double *get_network_output_layer(network net, int i);
-double *get_network_delta_layer(network net, int i);
-double *get_network_delta(network net);
+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);
+YOLODLL_API float *network_predict(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);
+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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