From 2c6d4ba1d5cffd26c5c9527175d565a81226e18d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 19 Feb 2014 21:41:44 +0000
Subject: [PATCH] Single image feature extraction for VOC
---
src/convolutional_layer.c | 283 +++++++++++++++++++++++++++++++++++---------------------
1 files changed, 177 insertions(+), 106 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index 45b55b8..8d8efc1 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,110 +1,177 @@
#include "convolutional_layer.h"
#include "utils.h"
+#include "mini_blas.h"
#include <stdio.h>
+int convolutional_out_height(convolutional_layer layer)
+{
+ return (layer.h-layer.size)/layer.stride + 1;
+}
+
+int convolutional_out_width(convolutional_layer layer)
+{
+ return (layer.w-layer.size)/layer.stride + 1;
+}
+
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 = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
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)
{
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 = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
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)
{
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 = 1;
layer->stride = stride;
- layer->kernels = calloc(n, sizeof(image));
- layer->kernel_updates = calloc(n, sizeof(image));
- layer->kernel_momentum = calloc(n, sizeof(image));
- 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->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;
- 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);
- 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);
+ int out_h = (h-size)/stride + 1;
+ int 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;
+ 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);
- }
- }
-}
-
-void backward_convolutional_layer(convolutional_layer layer, double *input, double *delta)
+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);
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
- image out_delta = get_convolutional_delta(layer);
- zero_image(in_delta);
+ memset(layer.output, 0, m*n*sizeof(float));
- for(i = 0; i < layer.n; ++i){
- back_convolve(in_delta, layer.kernels[i], layer.stride, i, out_delta, layer.edge);
+ 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;
+ int size = convolutional_out_height(layer)
+ *convolutional_out_width(layer)
+ *layer.n;
+ for(i = 0; i < size; ++i){
+ layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
}
-void backward_convolutional_layer2(convolutional_layer layer, double *input, double *delta)
+void learn_bias_convolutional_layer(convolutional_layer layer)
{
- image in_delta = double_to_image(layer.h, layer.w, layer.c, delta);
+ int i,j;
+ 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];
+ }
+ 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){
@@ -124,51 +191,74 @@
}
}
-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)
+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){
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);
- //printf("%30.20lf\n", layer.bias_updates[i]);
}
}
-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)
{
- //step = .01;
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.biases[i] = constrain(layer.biases[i],1.);
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];
- //layer.kernels[i].data[j] = constrain(layer.kernels[i].data[j], 1.);
}
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);
+ 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;
@@ -176,7 +266,7 @@
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;
@@ -186,19 +276,13 @@
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);
- /*
- printf("Kernel %d - Bias: %f, Channels:",i,layer.biases[i]);
- for(j = 0; j < k.c; ++j){
- double a = avg_image_layer(k, j);
- printf("%f, ", a);
- }
- printf("\n");
- */
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);
@@ -223,16 +307,3 @@
free_image(filters);
}
-void visualize_convolutional_layer(convolutional_layer layer)
-{
- int i;
- char buff[256];
- //image vis = make_image(layer.n*layer.size, layer.size*layer.kernels[0].c, 3);
- 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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