From 989ab8c38a02fa7ea9c25108151736c62e81c972 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 24 Apr 2015 17:27:50 +0000
Subject: [PATCH] IOU loss function
---
src/convolutional_layer.c | 38 +++++++++-----------------------------
1 files changed, 9 insertions(+), 29 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index e20a41c..cd357d3 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -29,7 +29,7 @@
h = convolutional_out_height(layer);
w = convolutional_out_width(layer);
c = layer.n;
- return float_to_image(h,w,c,layer.output);
+ return float_to_image(w,h,c,layer.output);
}
image get_convolutional_delta(convolutional_layer layer)
@@ -38,7 +38,7 @@
h = convolutional_out_height(layer);
w = convolutional_out_width(layer);
c = layer.n;
- return float_to_image(h,w,c,layer.delta);
+ return float_to_image(w,h,c,layer.delta);
}
convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
@@ -61,7 +61,7 @@
layer->biases = calloc(n, sizeof(float));
layer->bias_updates = calloc(n, sizeof(float));
float scale = 1./sqrt(size*size*c);
- for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
+ for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = 2*scale*rand_uniform() - scale;
for(i = 0; i < n; ++i){
layer->biases[i] = scale;
}
@@ -217,42 +217,22 @@
int h = layer.size;
int w = layer.size;
int c = layer.c;
- return float_to_image(h,w,c,layer.filters+i*h*w*c);
+ return float_to_image(w,h,c,layer.filters+i*h*w*c);
}
-image *weighted_sum_filters(convolutional_layer layer, image *prev_filters)
+image *get_filters(convolutional_layer layer)
{
image *filters = calloc(layer.n, sizeof(image));
- int i,j,k,c;
- if(!prev_filters){
- for(i = 0; i < layer.n; ++i){
- filters[i] = copy_image(get_convolutional_filter(layer, i));
- }
- }
- else{
- image base = prev_filters[0];
- for(i = 0; i < layer.n; ++i){
- image filter = get_convolutional_filter(layer, i);
- filters[i] = make_image(base.h, base.w, base.c);
- for(j = 0; j < layer.size; ++j){
- for(k = 0; k < layer.size; ++k){
- for(c = 0; c < layer.c; ++c){
- float weight = get_pixel(filter, j, k, c);
- image prev_filter = copy_image(prev_filters[c]);
- scale_image(prev_filter, weight);
- add_into_image(prev_filter, filters[i], 0,0);
- free_image(prev_filter);
- }
- }
- }
- }
+ int i;
+ for(i = 0; i < layer.n; ++i){
+ filters[i] = copy_image(get_convolutional_filter(layer, i));
}
return filters;
}
image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
{
- image *single_filters = weighted_sum_filters(layer, 0);
+ image *single_filters = get_filters(layer);
show_images(single_filters, layer.n, window);
image delta = get_convolutional_image(layer);
--
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