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 | 324 +++++++++++++++++++++++++++++++++++++++++++++++------
1 files changed, 287 insertions(+), 37 deletions(-)
diff --git a/src/convolutional_layer.c b/src/convolutional_layer.c
index f83622b..f7c9c10 100644
--- a/src/convolutional_layer.c
+++ b/src/convolutional_layer.c
@@ -1,64 +1,215 @@
#include "convolutional_layer.h"
+#include "utils.h"
+#include "mini_blas.h"
+#include <stdio.h>
-double convolution_activation(double x)
+int convolutional_out_height(convolutional_layer layer)
{
- return x*(x>0);
+ return (layer.h-layer.size)/layer.stride + 1;
}
-double convolution_gradient(double x)
+int convolutional_out_width(convolutional_layer layer)
{
- return (x>=0);
+ return (layer.w-layer.size)/layer.stride + 1;
}
-convolutional_layer make_convolutional_layer(int h, int w, int c, int n, int size, int stride)
+image get_convolutional_image(convolutional_layer layer)
+{
+ int h,w,c;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
+ c = layer.n;
+ return float_to_image(h,w,c,layer.output);
+}
+
+image get_convolutional_delta(convolutional_layer layer)
+{
+ int h,w,c;
+ h = convolutional_out_height(layer);
+ w = convolutional_out_width(layer);
+ c = layer.n;
+ return float_to_image(h,w,c,layer.delta);
+}
+
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
{
int i;
- convolutional_layer layer;
- layer.n = n;
- layer.stride = stride;
- layer.kernels = calloc(n, sizeof(image));
- layer.kernel_updates = calloc(n, sizeof(image));
+ 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->batch = batch;
+ 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.kernels[i] = make_random_kernel(size, c);
- layer.kernel_updates[i] = make_random_kernel(size, c);
+ //layer->biases[i] = rand_normal()*scale + scale;
+ layer->biases[i] = 0;
}
- layer.output = make_image((h-1)/stride+1, (w-1)/stride+1, n);
- layer.upsampled = make_image(h,w,n);
+ int out_h = convolutional_out_height(*layer);
+ int out_w = convolutional_out_width(*layer);
+
+ 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);
+ srand(0);
+
return layer;
}
-void run_convolutional_layer(const image input, const convolutional_layer 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;
- for(i = 0; i < layer.n; ++i){
- convolve(input, layer.kernels[i], layer.stride, i, layer.output);
+ int m = layer.n;
+ int k = layer.size*layer.size*layer.c;
+ 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;
+ 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));
}
- for(i = 0; i < input.h*input.w*input.c; ++i){
- input.data[i] = convolution_activation(input.data[i]);
+ 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*
+ layer.batch;
+ for(i = 0; i < size; ++i){
+ layer.delta[i] *= gradient(layer.output[i], layer.activation);
}
}
-void backpropagate_layer(image input, convolutional_layer layer)
+void learn_bias_convolutional_layer(convolutional_layer layer)
{
- int i;
- zero_image(input);
- for(i = 0; i < layer.n; ++i){
- back_convolve(input, layer.kernels[i], layer.stride, i, layer.output);
+ int i,j,b;
+ int size = convolutional_out_height(layer)
+ *convolutional_out_width(layer);
+ 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;
+ }
}
}
-void backpropagate_layer_convolve(image input, convolutional_layer layer)
+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 = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
+
+ 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 i;
+ int m = layer.size*layer.size*layer.c;
+ int k = layer.n;
+ int n = convolutional_out_height(layer)*
+ convolutional_out_width(layer)*
+ layer.batch;
+
+ 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.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)
+{
+ 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(input);
- upsample_image(layer.output, layer.stride, layer.upsampled);
- for(j = 0; j < input.c; ++j){
+ 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, input, j);
+ two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
}
}
@@ -67,20 +218,119 @@
}
}
-void error_convolutional_layer(image input, convolutional_layer layer)
+
+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(input, layer.kernel_updates[i], layer.stride, i, layer.output);
+ 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);
}
- image old_input = copy_image(input);
- zero_image(input);
+}
+
+void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
+{
+ int i,j;
for(i = 0; i < layer.n; ++i){
- back_convolve(input, layer.kernels[i], layer.stride, i, layer.output);
+ 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]);
}
- for(i = 0; i < input.h*input.w*input.c; ++i){
- input.data[i] = input.data[i]*convolution_gradient(input.data[i]);
+}
+*/
+
+void test_convolutional_layer()
+{
+ 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,
+ 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;
}
- free_image(old_input);
+
+ 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);
}
--
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