From 8a504c737d2bc3b9b37cb79cb50fbf7eecda07df Mon Sep 17 00:00:00 2001
From: Tino Hager <tino.hager@nager.at>
Date: Wed, 27 Jun 2018 21:56:47 +0000
Subject: [PATCH] repair tabs spaces
---
src/network.c | 43 +++++++++++++++++++++++++++++++++----------
1 files changed, 33 insertions(+), 10 deletions(-)
diff --git a/src/network.c b/src/network.c
index 03ad5eb..d135a29 100644
--- a/src/network.c
+++ b/src/network.c
@@ -28,13 +28,14 @@
#include "route_layer.h"
#include "shortcut_layer.h"
#include "yolo_layer.h"
+#include "upsample_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 +43,11 @@
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 +178,7 @@
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 +186,7 @@
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;
}
@@ -437,8 +443,8 @@
}
#ifdef GPU
if(gpu_index >= 0){
- printf(" try to allocate workspace = %zu * sizeof(float), ", (workspace_size - 1) / sizeof(float) + 1);
- net->workspace = cuda_make_array(0, (workspace_size-1)/sizeof(float)+1);
+ printf(" try to allocate workspace = %zu * sizeof(float), ", workspace_size / sizeof(float) + 1);
+ net->workspace = cuda_make_array(0, workspace_size/sizeof(float) + 1);
printf(" CUDA allocate done! \n");
}else {
free(net->workspace);
@@ -487,6 +493,11 @@
return def;
}
+layer* get_network_layer(network* net, int i)
+{
+ return net->layers + i;
+}
+
image get_network_image(network net)
{
int i;
@@ -576,7 +587,7 @@
box *boxes = calloc(l.w*l.h*l.n, sizeof(box));
float **probs = calloc(l.w*l.h*l.n, sizeof(float *));
int i, j;
- for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float *));
+ for (j = 0; j < l.w*l.h*l.n; ++j) probs[j] = calloc(l.classes, sizeof(float));
get_region_boxes(l, 1, 1, thresh, probs, boxes, 0, map);
for (j = 0; j < l.w*l.h*l.n; ++j) {
dets[j].classes = l.classes;
@@ -596,12 +607,18 @@
void fill_network_boxes(network *net, int w, int h, float thresh, float hier, int *map, int relative, detection *dets, int letter)
{
+ int prev_classes = -1;
int j;
for (j = 0; j < net->n; ++j) {
layer l = net->layers[j];
if (l.type == YOLO) {
int count = get_yolo_detections(l, w, h, net->w, net->h, thresh, map, relative, dets, letter);
dets += count;
+ if (prev_classes < 0) prev_classes = l.classes;
+ else if (prev_classes != l.classes) {
+ printf(" Error: Different [yolo] layers have different number of classes = %d and %d - check your cfg-file! \n",
+ prev_classes, l.classes);
+ }
}
if (l.type == REGION) {
custom_get_region_detections(l, w, h, net->w, net->h, thresh, map, hier, relative, dets, letter);
@@ -634,7 +651,8 @@
float *network_predict_image(network *net, image im)
{
- image imr = letterbox_image(im, net->w, net->h);
+ //image imr = letterbox_image(im, net->w, net->h);
+ image imr = resize_image(im, net->w, net->h);
set_batch_network(net, 1);
float *p = network_predict(*net, imr.data);
free_image(imr);
@@ -767,6 +785,11 @@
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 +823,14 @@
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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