From 2ea63c0e99a5358eaf38785ea83b9c5923fcc9cd Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 13 Mar 2014 04:57:34 +0000
Subject: [PATCH] Better VOC handling and resizing
---
src/convolutional_layer.c | 79 ++++++++++++++++++++++++++-------------
1 files changed, 53 insertions(+), 26 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 8d8efc1..f7c9c10 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -31,7 +31,7 @@
return float_to_image(h,w,c,layer.delta);
}
-convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
@@ -40,6 +40,7 @@
layer->w = w;
layer->c = c;
layer->n = n;
+ layer->batch = batch;
layer->stride = stride;
layer->size = size;
@@ -56,12 +57,12 @@
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 0;
}
- int out_h = (h-size)/stride + 1;
- int out_w = (w-size)/stride + 1;
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
- layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
- layer->output = calloc(out_h * out_w * n, sizeof(float));
- layer->delta = calloc(out_h * out_w * n, sizeof(float));
+ layer->col_image = calloc(layer->batch*out_h*out_w*size*size*c, sizeof(float));
+ layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
+ layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
layer->activation = activation;
fprintf(stderr, "Convolutional Layer: %d x %d x %d image, %d filters -> %d x %d x %d image\n", h,w,c,n, out_h, out_w, n);
@@ -70,21 +71,39 @@
return layer;
}
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
+{
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
+
+ layer->col_image = realloc(layer->col_image,
+ layer->batch*out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
+ layer->output = realloc(layer->output,
+ layer->batch*out_h * out_w * layer->n*sizeof(float));
+ layer->delta = realloc(layer->delta,
+ layer->batch*out_h * out_w * layer->n*sizeof(float));
+}
+
void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
int i;
int m = layer.n;
int k = layer.size*layer.size*layer.c;
- int n = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
+ int n = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
memset(layer.output, 0, m*n*sizeof(float));
float *a = layer.filters;
float *b = layer.col_image;
float *c = layer.output;
-
- im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
+ for(i = 0; i < layer.batch; ++i){
+ im2col_cpu(in+i*(n/layer.batch), layer.c, layer.h, layer.w, layer.size, layer.stride, b+i*(n/layer.batch));
+ }
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
for(i = 0; i < m*n; ++i){
@@ -97,9 +116,10 @@
void gradient_delta_convolutional_layer(convolutional_layer layer)
{
int i;
- int size = convolutional_out_height(layer)
- *convolutional_out_width(layer)
- *layer.n;
+ int size = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.n*
+ layer.batch;
for(i = 0; i < size; ++i){
layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
@@ -107,15 +127,17 @@
void learn_bias_convolutional_layer(convolutional_layer layer)
{
- int i,j;
+ int i,j,b;
int size = convolutional_out_height(layer)
*convolutional_out_width(layer);
- for(i = 0; i < layer.n; ++i){
- float sum = 0;
- for(j = 0; j < size; ++j){
- sum += layer.delta[j+i*size];
+ for(b = 0; b < layer.batch; ++b){
+ for(i = 0; i < layer.n; ++i){
+ float sum = 0;
+ for(j = 0; j < size; ++j){
+ sum += layer.delta[j+size*(i+b*layer.n)];
+ }
+ layer.bias_updates[i] += sum/size;
}
- layer.bias_updates[i] += sum/size;
}
}
@@ -125,8 +147,9 @@
learn_bias_convolutional_layer(layer);
int m = layer.n;
int n = layer.size*layer.size*layer.c;
- int k = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
+ int k = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
float *a = layer.delta;
float *b = layer.col_image;
@@ -137,10 +160,12 @@
void backward_convolutional_layer(convolutional_layer layer, float *delta)
{
+ int i;
int m = layer.size*layer.size*layer.c;
int k = layer.n;
- int n = ((layer.h-layer.size)/layer.stride + 1)*
- ((layer.w-layer.size)/layer.stride + 1);
+ int n = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
float *a = layer.filters;
float *b = layer.delta;
@@ -150,8 +175,10 @@
memset(c, 0, m*n*sizeof(float));
gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
- memset(delta, 0, layer.h*layer.w*layer.c*sizeof(float));
- col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, delta);
+ memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+ for(i = 0; i < layer.batch; ++i){
+ col2im_cpu(c+i*n/layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, delta+i*n/layer.batch);
+ }
}
void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
@@ -225,7 +252,7 @@
void test_convolutional_layer()
{
- convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
+ convolutional_layer l = *make_convolutional_layer(1,4,4,1,1,3,1,LINEAR);
float input[] = {1,2,3,4,
5,6,7,8,
9,10,11,12,
--
Gitblit v1.10.0