From b5936b499abc94c0efffbcc99b5698574b59d860 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 05 Sep 2015 00:52:44 +0000
Subject: [PATCH] lots of stuff

---
 src/darknet.c |  113 +++++++++++++++++++++++++++++++++++++++++++++++++++-----
 1 files changed, 103 insertions(+), 10 deletions(-)

diff --git a/src/darknet.c b/src/darknet.c
index 321b5a9..3709ed1 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -5,15 +5,20 @@
 #include "parser.h"
 #include "utils.h"
 #include "cuda.h"
+#include "blas.h"
 
-#define _GNU_SOURCE
-#include <fenv.h>
+#ifdef OPENCV
+#include "opencv2/highgui/highgui_c.h"
+#endif
 
 extern void run_imagenet(int argc, char **argv);
-extern void run_detection(int argc, char **argv);
+extern void run_yolo(int argc, char **argv);
+extern void run_coco(int argc, char **argv);
 extern void run_writing(int argc, char **argv);
 extern void run_captcha(int argc, char **argv);
 extern void run_nightmare(int argc, char **argv);
+extern void run_dice(int argc, char **argv);
+extern void run_compare(int argc, char **argv);
 
 void change_rate(char *filename, float scale, float add)
 {
@@ -29,18 +34,93 @@
     fclose(fp);
 }
 
+void average(int argc, char *argv[])
+{
+    char *cfgfile = argv[2];
+    char *outfile = argv[3];
+    gpu_index = -1;
+    network net = parse_network_cfg(cfgfile);
+    network sum = parse_network_cfg(cfgfile);
+
+    char *weightfile = argv[4];   
+    load_weights(&sum, weightfile);
+
+    int i, j;
+    int n = argc - 5;
+    for(i = 0; i < n; ++i){
+        weightfile = argv[i+5];   
+        load_weights(&net, weightfile);
+        for(j = 0; j < net.n; ++j){
+            layer l = net.layers[j];
+            layer out = sum.layers[j];
+            if(l.type == CONVOLUTIONAL){
+                int num = l.n*l.c*l.size*l.size;
+                axpy_cpu(l.n, 1, l.biases, 1, out.biases, 1);
+                axpy_cpu(num, 1, l.filters, 1, out.filters, 1);
+            }
+            if(l.type == CONNECTED){
+                axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
+                axpy_cpu(l.outputs*l.inputs, 1, l.weights, 1, out.weights, 1);
+            }
+        }
+    }
+    n = n+1;
+    for(j = 0; j < net.n; ++j){
+        layer l = sum.layers[j];
+        if(l.type == CONVOLUTIONAL){
+            int num = l.n*l.c*l.size*l.size;
+            scal_cpu(l.n, 1./n, l.biases, 1);
+            scal_cpu(num, 1./n, l.filters, 1);
+        }
+        if(l.type == CONNECTED){
+            scal_cpu(l.outputs, 1./n, l.biases, 1);
+            scal_cpu(l.outputs*l.inputs, 1./n, l.weights, 1);
+        }
+    }
+    save_weights(sum, outfile);
+}
+
 void partial(char *cfgfile, char *weightfile, char *outfile, int max)
 {
+    gpu_index = -1;
     network net = parse_network_cfg(cfgfile);
     if(weightfile){
         load_weights_upto(&net, weightfile, max);
     }
-    net.seen = 0;
+    *net.seen = 0;
     save_weights_upto(net, outfile, max);
 }
 
+void stacked(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    net.seen = 0;
+    save_weights_double(net, outfile);
+}
+
 #include "convolutional_layer.h"
-void rgbgr_filters(convolutional_layer l);
+void rescale_net(char *cfgfile, char *weightfile, char *outfile)
+{
+    gpu_index = -1;
+    network net = parse_network_cfg(cfgfile);
+    if(weightfile){
+        load_weights(&net, weightfile);
+    }
+    int i;
+    for(i = 0; i < net.n; ++i){
+        layer l = net.layers[i];
+        if(l.type == CONVOLUTIONAL){
+            rescale_filters(l, 2, -.5);
+            break;
+        }
+    }
+    save_weights(net, outfile);
+}
+
 void rgbgr_net(char *cfgfile, char *weightfile, char *outfile)
 {
     gpu_index = -1;
@@ -66,9 +146,9 @@
         load_weights(&net, weightfile);
     }
     visualize_network(net);
-    #ifdef OPENCV
+#ifdef OPENCV
     cvWaitKey(0);
-    #endif
+#endif
 }
 
 int main(int argc, char **argv)
@@ -87,14 +167,23 @@
     gpu_index = -1;
 #else
     if(gpu_index >= 0){
-        cudaSetDevice(gpu_index);
+        cudaError_t status = cudaSetDevice(gpu_index);
+        check_error(status);
     }
 #endif
 
     if(0==strcmp(argv[1], "imagenet")){
         run_imagenet(argc, argv);
-    } else if (0 == strcmp(argv[1], "detection")){
-        run_detection(argc, argv);
+    } else if (0 == strcmp(argv[1], "average")){
+        average(argc, argv);
+    } else if (0 == strcmp(argv[1], "yolo")){
+        run_yolo(argc, argv);
+    } else if (0 == strcmp(argv[1], "coco")){
+        run_coco(argc, argv);
+    } else if (0 == strcmp(argv[1], "compare")){
+        run_compare(argc, argv);
+    } else if (0 == strcmp(argv[1], "dice")){
+        run_dice(argc, argv);
     } else if (0 == strcmp(argv[1], "writing")){
         run_writing(argc, argv);
     } else if (0 == strcmp(argv[1], "test")){
@@ -107,8 +196,12 @@
         change_rate(argv[2], atof(argv[3]), (argc > 4) ? atof(argv[4]) : 0);
     } else if (0 == strcmp(argv[1], "rgbgr")){
         rgbgr_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "rescale")){
+        rescale_net(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "partial")){
         partial(argv[2], argv[3], argv[4], atoi(argv[5]));
+    } else if (0 == strcmp(argv[1], "stacked")){
+        stacked(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "visualize")){
         visualize(argv[2], (argc > 3) ? argv[3] : 0);
     } else if (0 == strcmp(argv[1], "imtest")){

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