From f7a17f82eb43de864a4f980f235055da9685eef8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 29 Jan 2014 00:28:42 +0000
Subject: [PATCH] Convolutional layers working w/ matrices
---
src/convolutional_layer.c | 90 +++++++++++++++++++++++++++++----------------
1 files changed, 58 insertions(+), 32 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 53eb7bf..cdfe9e1 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -9,7 +9,7 @@
h = layer.out_h;
w = layer.out_w;
c = layer.n;
- return double_to_image(h,w,c,layer.output);
+ return float_to_image(h,w,c,layer.output);
}
image get_convolutional_delta(convolutional_layer layer)
@@ -18,7 +18,7 @@
h = layer.out_h;
w = layer.out_w;
c = layer.n;
- return double_to_image(h,w,c,layer.delta);
+ 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)
@@ -34,14 +34,14 @@
layer->stride = stride;
layer->size = size;
- layer->filters = calloc(c*n*size*size, sizeof(double));
- layer->filter_updates = calloc(c*n*size*size, sizeof(double));
- layer->filter_momentum = calloc(c*n*size*size, sizeof(double));
+ layer->filters = calloc(c*n*size*size, sizeof(float));
+ layer->filter_updates = calloc(c*n*size*size, sizeof(float));
+ layer->filter_momentum = calloc(c*n*size*size, sizeof(float));
- layer->biases = calloc(n, sizeof(double));
- layer->bias_updates = calloc(n, sizeof(double));
- layer->bias_momentum = calloc(n, sizeof(double));
- double scale = 2./(size*size);
+ layer->biases = calloc(n, sizeof(float));
+ layer->bias_updates = calloc(n, sizeof(float));
+ layer->bias_momentum = calloc(n, sizeof(float));
+ float scale = 2./(size*size);
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = rand_normal()*scale;
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
@@ -50,9 +50,9 @@
out_h = (h-size)/stride + 1;
out_w = (w-size)/stride + 1;
- layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(double));
- layer->output = calloc(out_h * out_w * n, sizeof(double));
- layer->delta = calloc(out_h * out_w * n, sizeof(double));
+ 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->activation = activation;
layer->out_h = out_h;
layer->out_w = out_w;
@@ -63,18 +63,18 @@
return layer;
}
-void forward_convolutional_layer(const convolutional_layer layer, double *in)
+void forward_convolutional_layer(const convolutional_layer layer, float *in)
{
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);
- memset(layer.output, 0, m*n*sizeof(double));
+ memset(layer.output, 0, m*n*sizeof(float));
- double *a = layer.filters;
- double *b = layer.col_image;
- double *c = layer.output;
+ 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);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
@@ -94,7 +94,7 @@
int i,j;
int size = layer.out_h*layer.out_w;
for(i = 0; i < layer.n; ++i){
- double sum = 0;
+ float sum = 0;
for(j = 0; j < size; ++j){
sum += layer.delta[j+i*size];
}
@@ -111,14 +111,33 @@
int k = ((layer.h-layer.size)/layer.stride + 1)*
((layer.w-layer.size)/layer.stride + 1);
- double *a = layer.delta;
- double *b = layer.col_image;
- double *c = layer.filter_updates;
+ float *a = layer.delta;
+ float *b = layer.col_image;
+ float *c = layer.filter_updates;
gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
}
-void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
+void backward_convolutional_layer(convolutional_layer layer, float *delta)
+{
+ 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);
+
+ float *a = layer.filters;
+ float *b = layer.delta;
+ float *c = layer.col_image;
+
+
+ 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);
+}
+
+void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
{
int i;
int size = layer.size*layer.size*layer.c*layer.n;
@@ -133,9 +152,9 @@
}
/*
-void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
+void backward_convolutional_layer2(convolutional_layer layer, float *input, float *delta)
{
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
+ image in_delta = float_to_image(layer.h, layer.w, layer.c, delta);
image out_delta = get_convolutional_delta(layer);
int i,j;
for(i = 0; i < layer.n; ++i){
@@ -156,10 +175,10 @@
}
-void learn_convolutional_layer(convolutional_layer layer, double *input)
+void learn_convolutional_layer(convolutional_layer layer, float *input)
{
int i;
- image in_image = double_to_image(layer.h, layer.w, layer.c, input);
+ image in_image = float_to_image(layer.h, layer.w, layer.c, input);
image out_delta = get_convolutional_delta(layer);
gradient_delta_convolutional_layer(layer);
for(i = 0; i < layer.n; ++i){
@@ -168,7 +187,7 @@
}
}
-void update_convolutional_layer(convolutional_layer layer, double step, double momentum, double decay)
+void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
{
int i,j;
for(i = 0; i < layer.n; ++i){
@@ -190,21 +209,28 @@
void test_convolutional_layer()
{
convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
- double input[] = {1,2,3,4,
+ float input[] = {1,2,3,4,
5,6,7,8,
9,10,11,12,
13,14,15,16};
- double filter[] = {.5, 0, .3,
+ float filter[] = {.5, 0, .3,
0 , 1, 0,
.2 , 0, 1};
- double delta[] = {1, 2,
+ float delta[] = {1, 2,
3, 4};
+ float in_delta[] = {.5,1,.3,.6,
+ 5,6,7,8,
+ 9,10,11,12,
+ 13,14,15,16};
l.filters = filter;
forward_convolutional_layer(l, input);
l.delta = delta;
learn_convolutional_layer(l);
- image filter_updates = double_to_image(3,3,1,l.filter_updates);
+ image filter_updates = float_to_image(3,3,1,l.filter_updates);
print_image(filter_updates);
+ printf("Delta:\n");
+ backward_convolutional_layer(l, in_delta);
+ pm(4,4,in_delta);
}
image get_convolutional_filter(convolutional_layer layer, int i)
@@ -212,7 +238,7 @@
int h = layer.size;
int w = layer.size;
int c = layer.c;
- return double_to_image(h,w,c,layer.filters+i*h*w*c);
+ return float_to_image(h,w,c,layer.filters+i*h*w*c);
}
void visualize_convolutional_layer(convolutional_layer layer, char *window)
--
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