From eef9f8e5bb9fdbe22c6301a56f12e315f9ac64d9 Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Wed, 23 May 2018 15:35:08 +0000
Subject: [PATCH] Minor fixes

---
 src/network.c |   22 ++++++++++++++++------
 1 files changed, 16 insertions(+), 6 deletions(-)

diff --git a/src/network.c b/src/network.c
index 03ad5eb..c0676bf 100644
--- a/src/network.c
+++ b/src/network.c
@@ -30,11 +30,11 @@
 #include "yolo_layer.h"
 #include "parser.h"
 
-network *load_network(char *cfg, char *weights, int clear)
+network *load_network_custom(char *cfg, char *weights, int clear, int batch)
 {
 	printf(" Try to load cfg: %s, weights: %s, clear = %d \n", cfg, weights, clear);
 	network *net = calloc(1, sizeof(network));
-	*net = parse_network_cfg(cfg);
+	*net = parse_network_cfg_custom(cfg, batch);
 	if (weights && weights[0] != 0) {
 		load_weights(net, weights);
 	}
@@ -42,6 +42,11 @@
 	return net;
 }
 
+network *load_network(char *cfg, char *weights, int clear)
+{
+	return load_network_custom(cfg, weights, clear, 0);
+}
+
 int get_current_batch(network net)
 {
     int batch_num = (*net.seen)/(net.batch*net.subdivisions);
@@ -172,7 +177,7 @@
     net.n = n;
     net.layers = calloc(net.n, sizeof(layer));
     net.seen = calloc(1, sizeof(int));
-    #ifdef GPU
+#ifdef GPU
     net.input_gpu = calloc(1, sizeof(float *));
     net.truth_gpu = calloc(1, sizeof(float *));
 
@@ -180,7 +185,7 @@
 	net.output16_gpu = calloc(1, sizeof(float *));
 	net.max_input16_size = calloc(1, sizeof(size_t));
 	net.max_output16_size = calloc(1, sizeof(size_t));
-    #endif
+#endif
     return net;
 }
 
@@ -767,6 +772,11 @@
 		free_layer(net.layers[i]);
 	}
 	free(net.layers);
+
+	free(net.scales);
+	free(net.steps);
+	free(net.seen);
+
 #ifdef GPU
 	if (gpu_index >= 0) cuda_free(net.workspace);
 	else free(net.workspace);
@@ -800,14 +810,14 @@
 				int f;
 				for (f = 0; f < l->n; ++f)
 				{
-					l->biases[f] = l->biases[f] - l->scales[f] * l->rolling_mean[f] / (sqrtf(l->rolling_variance[f]) + .000001f);
+					l->biases[f] = l->biases[f] - (double)l->scales[f] * l->rolling_mean[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
 
 					const size_t filter_size = l->size*l->size*l->c;
 					int i;
 					for (i = 0; i < filter_size; ++i) {
 						int w_index = f*filter_size + i;
 
-						l->weights[w_index] = l->weights[w_index] * l->scales[f] / (sqrtf(l->rolling_variance[f]) + .000001f);
+						l->weights[w_index] = (double)l->weights[w_index] * l->scales[f] / (sqrt((double)l->rolling_variance[f]) + .000001f);
 					}
 				}
 

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