From b2b7137b6f185ce2f01664d782a09b08d50d5a07 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 28 Jan 2014 07:16:56 +0000
Subject: [PATCH] About to do something stupid...
---
src/convolutional_layer.c | 195 +++++++++++++++++++++++++++++-------------------
1 files changed, 118 insertions(+), 77 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index ef48120..53eb7bf 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,17 +1,13 @@
#include "convolutional_layer.h"
#include "utils.h"
+#include "mini_blas.h"
#include <stdio.h>
image get_convolutional_image(convolutional_layer layer)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = layer.out_h;
+ w = layer.out_w;
c = layer.n;
return double_to_image(h,w,c,layer.output);
}
@@ -19,13 +15,8 @@
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
- if(layer.edge){
- h = (layer.h-1)/layer.stride + 1;
- w = (layer.w-1)/layer.stride + 1;
- }else{
- h = (layer.h - layer.size)/layer.stride+1;
- w = (layer.h - layer.size)/layer.stride+1;
- }
+ h = layer.out_h;
+ w = layer.out_w;
c = layer.n;
return double_to_image(h,w,c,layer.delta);
}
@@ -34,74 +25,114 @@
{
int i;
int out_h,out_w;
+ size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
layer->h = h;
layer->w = w;
layer->c = c;
layer->n = n;
- layer->edge = 0;
layer->stride = stride;
- layer->kernels = calloc(n, sizeof(image));
- layer->kernel_updates = calloc(n, sizeof(image));
- layer->kernel_momentum = calloc(n, sizeof(image));
+ 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->biases = calloc(n, sizeof(double));
layer->bias_updates = calloc(n, sizeof(double));
layer->bias_momentum = calloc(n, sizeof(double));
double 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;
layer->biases[i] = 0;
- layer->kernels[i] = make_random_kernel(size, c, scale);
- layer->kernel_updates[i] = make_random_kernel(size, c, 0);
- layer->kernel_momentum[i] = make_random_kernel(size, c, 0);
}
- layer->size = 2*(size/2)+1;
- if(layer->edge){
- out_h = (layer->h-1)/layer->stride + 1;
- out_w = (layer->w-1)/layer->stride + 1;
- }else{
- out_h = (layer->h - layer->size)/layer->stride+1;
- out_w = (layer->h - layer->size)/layer->stride+1;
- }
- 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);
+ 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->upsampled = make_image(h,w,n);
layer->activation = activation;
+ layer->out_h = out_h;
+ layer->out_w = out_w;
+
+ 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);
+ srand(0);
return layer;
}
void forward_convolutional_layer(const convolutional_layer layer, double *in)
{
- image input = double_to_image(layer.h, layer.w, layer.c, in);
- image output = get_convolutional_image(layer);
- int i,j;
- for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, output, layer.edge);
- }
- for(i = 0; i < output.c; ++i){
- for(j = 0; j < output.h*output.w; ++j){
- int index = i*output.h*output.w + j;
- output.data[index] += layer.biases[i];
- output.data[index] = activate(output.data[index], layer.activation);
- }
- }
+ 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));
+
+ double *a = layer.filters;
+ double *b = layer.col_image;
+ double *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);
+
}
-void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
+void gradient_delta_convolutional_layer(convolutional_layer layer)
{
int i;
-
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
- image out_delta = get_convolutional_delta(layer);
- zero_image(in_delta);
-
- for(i = 0; i < layer.n; ++i){
- back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
+ for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
+ layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
}
+void learn_bias_convolutional_layer(convolutional_layer layer)
+{
+ int i,j;
+ int size = layer.out_h*layer.out_w;
+ for(i = 0; i < layer.n; ++i){
+ double sum = 0;
+ for(j = 0; j < size; ++j){
+ sum += layer.delta[j+i*size];
+ }
+ layer.bias_updates[i] += sum/size;
+ }
+}
+
+void learn_convolutional_layer(convolutional_layer layer)
+{
+ gradient_delta_convolutional_layer(layer);
+ 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);
+
+ double *a = layer.delta;
+ double *b = layer.col_image;
+ double *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)
+{
+ int i;
+ int size = layer.size*layer.size*layer.c*layer.n;
+ for(i = 0; i < layer.n; ++i){
+ layer.biases[i] += step*layer.bias_updates[i];
+ layer.bias_updates[i] *= momentum;
+ }
+ for(i = 0; i < size; ++i){
+ layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
+ layer.filter_updates[i] *= momentum;
+ }
+}
+/*
+
void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
{
image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
@@ -124,15 +155,6 @@
}
}
-void gradient_delta_convolutional_layer(convolutional_layer layer)
-{
- int i;
- image out_delta = get_convolutional_delta(layer);
- image out_image = get_convolutional_image(layer);
- for(i = 0; i < out_image.h*out_image.w*out_image.c; ++i){
- out_delta.data[i] *= gradient(out_image.data[i], layer.activation);
- }
-}
void learn_convolutional_layer(convolutional_layer layer, double *input)
{
@@ -163,8 +185,37 @@
zero_image(layer.kernel_updates[i]);
}
}
+*/
-void visualize_convolutional_filters(convolutional_layer layer, char *window)
+void test_convolutional_layer()
+{
+ convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
+ double input[] = {1,2,3,4,
+ 5,6,7,8,
+ 9,10,11,12,
+ 13,14,15,16};
+ double filter[] = {.5, 0, .3,
+ 0 , 1, 0,
+ .2 , 0, 1};
+ double delta[] = {1, 2,
+ 3, 4};
+ 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);
+ print_image(filter_updates);
+}
+
+image get_convolutional_filter(convolutional_layer layer, int i)
+{
+ 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);
+}
+
+void visualize_convolutional_layer(convolutional_layer layer, char *window)
{
int color = 1;
int border = 1;
@@ -172,7 +223,7 @@
int size = layer.size;
h = size;
w = (size + border) * layer.n - border;
- c = layer.kernels[0].c;
+ c = layer.c;
if(c != 3 || !color){
h = (h+border)*c - border;
c = 1;
@@ -182,11 +233,13 @@
int i,j;
for(i = 0; i < layer.n; ++i){
int w_offset = i*(size+border);
- image k = layer.kernels[i];
+ image k = get_convolutional_filter(layer, i);
+ //printf("%f ** ", layer.biases[i]);
+ //print_image(k);
image copy = copy_image(k);
normalize_image(copy);
for(j = 0; j < k.c; ++j){
- set_pixel(copy,0,0,j,layer.biases[i]);
+ //set_pixel(copy,0,0,j,layer.biases[i]);
}
if(c == 3 && color){
embed_image(copy, filters, 0, w_offset);
@@ -211,15 +264,3 @@
free_image(filters);
}
-void visualize_convolutional_layer(convolutional_layer layer)
-{
- int i;
- char buff[256];
- for(i = 0; i < layer.n; ++i){
- image k = layer.kernels[i];
- sprintf(buff, "Kernel %d", i);
- if(k.c <= 3) show_image(k, buff);
- else show_image_layers(k, buff);
- }
-}
-
--
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