From 481b57a96a9ef29b112caec1bb3e17ffb043ceae Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sun, 25 Sep 2016 06:12:54 +0000
Subject: [PATCH] So I have this new programming paradigm.......

---
 src/darknet.c |   64 ++++++++++++++++++++-----------
 1 files changed, 41 insertions(+), 23 deletions(-)

diff --git a/src/darknet.c b/src/darknet.c
index c367abf..3bc0c6a 100644
--- a/src/darknet.c
+++ b/src/darknet.c
@@ -13,7 +13,6 @@
 #endif
 
 extern void run_voxel(int argc, char **argv);
-extern void run_imagenet(int argc, char **argv);
 extern void run_yolo(int argc, char **argv);
 extern void run_detector(int argc, char **argv);
 extern void run_coco(int argc, char **argv);
@@ -67,7 +66,7 @@
             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);
+                axpy_cpu(num, 1, l.weights, 1, out.weights, 1);
             }
             if(l.type == CONNECTED){
                 axpy_cpu(l.outputs, 1, l.biases, 1, out.biases, 1);
@@ -81,7 +80,7 @@
         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);
+            scal_cpu(num, 1./n, l.weights, 1);
         }
         if(l.type == CONNECTED){
             scal_cpu(l.outputs, 1./n, l.biases, 1);
@@ -137,17 +136,6 @@
     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 rescale_net(char *cfgfile, char *weightfile, char *outfile)
 {
@@ -160,7 +148,7 @@
     for(i = 0; i < net.n; ++i){
         layer l = net.layers[i];
         if(l.type == CONVOLUTIONAL){
-            rescale_filters(l, 2, -.5);
+            rescale_weights(l, 2, -.5);
             break;
         }
     }
@@ -178,7 +166,7 @@
     for(i = 0; i < net.n; ++i){
         layer l = net.layers[i];
         if(l.type == CONVOLUTIONAL){
-            rgbgr_filters(l);
+            rgbgr_weights(l);
             break;
         }
     }
@@ -255,6 +243,39 @@
     save_weights(net, outfile);
 }
 
+void statistics_net(char *cfgfile, char *weightfile)
+{
+    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 == CONNECTED && l.batch_normalize) {
+            printf("Connected Layer %d\n", i);
+            statistics_connected_layer(l);
+        }
+        if (l.type == GRU && l.batch_normalize) {
+            printf("GRU Layer %d\n", i);
+            printf("Input Z\n");
+            statistics_connected_layer(*l.input_z_layer);
+            printf("Input R\n");
+            statistics_connected_layer(*l.input_r_layer);
+            printf("Input H\n");
+            statistics_connected_layer(*l.input_h_layer);
+            printf("State Z\n");
+            statistics_connected_layer(*l.state_z_layer);
+            printf("State R\n");
+            statistics_connected_layer(*l.state_r_layer);
+            printf("State H\n");
+            statistics_connected_layer(*l.state_h_layer);
+        }
+        printf("\n");
+    }
+}
+
 void denormalize_net(char *cfgfile, char *weightfile, char *outfile)
 {
     gpu_index = -1;
@@ -322,14 +343,11 @@
     gpu_index = -1;
 #else
     if(gpu_index >= 0){
-        cudaError_t status = cudaSetDevice(gpu_index);
-        check_error(status);
+        cuda_set_device(gpu_index);
     }
 #endif
 
-    if(0==strcmp(argv[1], "imagenet")){
-        run_imagenet(argc, argv);
-    } else if (0 == strcmp(argv[1], "average")){
+    if (0 == strcmp(argv[1], "average")){
         average(argc, argv);
     } else if (0 == strcmp(argv[1], "yolo")){
         run_yolo(argc, argv);
@@ -377,6 +395,8 @@
         reset_normalize_net(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "denormalize")){
         denormalize_net(argv[2], argv[3], argv[4]);
+    } else if (0 == strcmp(argv[1], "statistics")){
+        statistics_net(argv[2], argv[3]);
     } else if (0 == strcmp(argv[1], "normalize")){
         normalize_net(argv[2], argv[3], argv[4]);
     } else if (0 == strcmp(argv[1], "rescale")){
@@ -389,8 +409,6 @@
         partial(argv[2], argv[3], argv[4], atoi(argv[5]));
     } else if (0 == strcmp(argv[1], "average")){
         average(argc, argv);
-    } 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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