darknet/src/convolutional_layer.c
2014-02-14 10:26:31 -08:00

299 lines
9.0 KiB
C

#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;
h = layer.out_h;
w = layer.out_w;
c = layer.n;
return float_to_image(h,w,c,layer.output);
}
image get_convolutional_delta(convolutional_layer layer)
{
int h,w,c;
h = layer.out_h;
w = layer.out_w;
c = layer.n;
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)
{
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->stride = stride;
layer->size = size;
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(float));
layer->bias_updates = calloc(n, sizeof(float));
layer->bias_momentum = calloc(n, sizeof(float));
float scale = 1./(size*size*c);
for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
for(i = 0; i < n; ++i){
//layer->biases[i] = rand_normal()*scale + scale;
layer->biases[i] = 0;
}
out_h = (h-size)/stride + 1;
out_w = (w-size)/stride + 1;
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;
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, 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);
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);
gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
for(i = 0; i < m*n; ++i){
layer.output[i] = activate(layer.output[i], layer.activation);
}
//for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
}
void gradient_delta_convolutional_layer(convolutional_layer layer)
{
int i;
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){
float 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);
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 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;
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, float *input, float *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){
rotate_image(layer.kernels[i]);
}
zero_image(in_delta);
upsample_image(out_delta, layer.stride, layer.upsampled);
for(j = 0; j < in_delta.c; ++j){
for(i = 0; i < layer.n; ++i){
two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
}
}
for(i = 0; i < layer.n; ++i){
rotate_image(layer.kernels[i]);
}
}
void learn_convolutional_layer(convolutional_layer layer, float *input)
{
int i;
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){
kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
layer.bias_updates[i] += avg_image_layer(out_delta, i);
}
}
void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
{
int i,j;
for(i = 0; i < layer.n; ++i){
layer.bias_momentum[i] = step*(layer.bias_updates[i])
+ momentum*layer.bias_momentum[i];
layer.biases[i] += layer.bias_momentum[i];
layer.bias_updates[i] = 0;
int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
for(j = 0; j < pixels; ++j){
layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
+ momentum*layer.kernel_momentum[i].data[j];
layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
}
zero_image(layer.kernel_updates[i]);
}
}
*/
void test_convolutional_layer()
{
convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
float input[] = {1,2,3,4,
5,6,7,8,
9,10,11,12,
13,14,15,16};
float filter[] = {.5, 0, .3,
0 , 1, 0,
.2 , 0, 1};
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 = 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)
{
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);
}
void visualize_convolutional_layer(convolutional_layer layer, char *window)
{
int color = 1;
int border = 1;
int h,w,c;
int size = layer.size;
h = size;
w = (size + border) * layer.n - border;
c = layer.c;
if(c != 3 || !color){
h = (h+border)*c - border;
c = 1;
}
image filters = make_image(h,w,c);
int i,j;
for(i = 0; i < layer.n; ++i){
int w_offset = i*(size+border);
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]);
}
if(c == 3 && color){
embed_image(copy, filters, 0, w_offset);
}
else{
for(j = 0; j < k.c; ++j){
int h_offset = j*(size+border);
image layer = get_image_layer(k, j);
embed_image(layer, filters, h_offset, w_offset);
free_image(layer);
}
}
free_image(copy);
}
image delta = get_convolutional_delta(layer);
image dc = collapse_image_layers(delta, 1);
char buff[256];
sprintf(buff, "%s: Delta", window);
show_image(dc, buff);
free_image(dc);
show_image(filters, window);
free_image(filters);
}