mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
299 lines
9.0 KiB
C
299 lines
9.0 KiB
C
#include "convolutional_layer.h"
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#include "utils.h"
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#include "mini_blas.h"
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#include <stdio.h>
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image get_convolutional_image(convolutional_layer layer)
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{
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int h,w,c;
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h = layer.out_h;
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w = layer.out_w;
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c = layer.n;
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return float_to_image(h,w,c,layer.output);
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}
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image get_convolutional_delta(convolutional_layer layer)
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{
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int h,w,c;
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h = layer.out_h;
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w = layer.out_w;
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c = layer.n;
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return float_to_image(h,w,c,layer.delta);
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}
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convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
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{
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int i;
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int out_h,out_w;
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size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
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convolutional_layer *layer = calloc(1, sizeof(convolutional_layer));
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layer->h = h;
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layer->w = w;
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layer->c = c;
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layer->n = n;
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layer->stride = stride;
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layer->size = size;
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layer->filters = calloc(c*n*size*size, sizeof(float));
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layer->filter_updates = calloc(c*n*size*size, sizeof(float));
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layer->filter_momentum = calloc(c*n*size*size, sizeof(float));
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layer->biases = calloc(n, sizeof(float));
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layer->bias_updates = calloc(n, sizeof(float));
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layer->bias_momentum = calloc(n, sizeof(float));
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float scale = 1./(size*size*c);
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*(rand_uniform());
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for(i = 0; i < n; ++i){
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//layer->biases[i] = rand_normal()*scale + scale;
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layer->biases[i] = 0;
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}
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out_h = (h-size)/stride + 1;
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out_w = (w-size)/stride + 1;
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layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
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layer->output = calloc(out_h * out_w * n, sizeof(float));
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layer->delta = calloc(out_h * out_w * n, sizeof(float));
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layer->activation = activation;
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layer->out_h = out_h;
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layer->out_w = out_w;
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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);
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srand(0);
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return layer;
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}
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void forward_convolutional_layer(const convolutional_layer layer, float *in)
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{
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int i;
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int m = layer.n;
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int k = layer.size*layer.size*layer.c;
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int n = ((layer.h-layer.size)/layer.stride + 1)*
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((layer.w-layer.size)/layer.stride + 1);
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memset(layer.output, 0, m*n*sizeof(float));
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float *a = layer.filters;
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float *b = layer.col_image;
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float *c = layer.output;
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im2col_cpu(in, layer.c, layer.h, layer.w, layer.size, layer.stride, b);
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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for(i = 0; i < m*n; ++i){
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layer.output[i] = activate(layer.output[i], layer.activation);
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}
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//for(i = 0; i < m*n; ++i) if(i%(m*n/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
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}
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void gradient_delta_convolutional_layer(convolutional_layer layer)
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{
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int i;
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for(i = 0; i < layer.out_h*layer.out_w*layer.n; ++i){
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layer.delta[i] *= gradient(layer.output[i], layer.activation);
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}
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}
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void learn_bias_convolutional_layer(convolutional_layer layer)
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{
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int i,j;
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int size = layer.out_h*layer.out_w;
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for(i = 0; i < layer.n; ++i){
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float sum = 0;
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for(j = 0; j < size; ++j){
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sum += layer.delta[j+i*size];
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}
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layer.bias_updates[i] += sum/size;
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}
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}
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void learn_convolutional_layer(convolutional_layer layer)
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{
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gradient_delta_convolutional_layer(layer);
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learn_bias_convolutional_layer(layer);
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int m = layer.n;
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int n = layer.size*layer.size*layer.c;
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int k = ((layer.h-layer.size)/layer.stride + 1)*
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((layer.w-layer.size)/layer.stride + 1);
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float *a = layer.delta;
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float *b = layer.col_image;
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float *c = layer.filter_updates;
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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}
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void backward_convolutional_layer(convolutional_layer layer, float *delta)
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{
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int m = layer.size*layer.size*layer.c;
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int k = layer.n;
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int n = ((layer.h-layer.size)/layer.stride + 1)*
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((layer.w-layer.size)/layer.stride + 1);
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float *a = layer.filters;
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float *b = layer.delta;
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float *c = layer.col_image;
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memset(c, 0, m*n*sizeof(float));
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gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
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memset(delta, 0, layer.h*layer.w*layer.c*sizeof(float));
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col2im_cpu(c, layer.c, layer.h, layer.w, layer.size, layer.stride, delta);
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}
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void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
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{
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int i;
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int size = layer.size*layer.size*layer.c*layer.n;
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for(i = 0; i < layer.n; ++i){
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layer.biases[i] += step*layer.bias_updates[i];
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layer.bias_updates[i] *= momentum;
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}
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for(i = 0; i < size; ++i){
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layer.filters[i] += step*(layer.filter_updates[i] - decay*layer.filters[i]);
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layer.filter_updates[i] *= momentum;
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}
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}
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/*
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void backward_convolutional_layer2(convolutional_layer layer, float *input, float *delta)
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{
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image in_delta = float_to_image(layer.h, layer.w, layer.c, delta);
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image out_delta = get_convolutional_delta(layer);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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rotate_image(layer.kernels[i]);
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}
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zero_image(in_delta);
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upsample_image(out_delta, layer.stride, layer.upsampled);
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for(j = 0; j < in_delta.c; ++j){
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for(i = 0; i < layer.n; ++i){
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two_d_convolve(layer.upsampled, i, layer.kernels[i], j, 1, in_delta, j, layer.edge);
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}
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}
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for(i = 0; i < layer.n; ++i){
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rotate_image(layer.kernels[i]);
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}
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}
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void learn_convolutional_layer(convolutional_layer layer, float *input)
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{
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int i;
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image in_image = float_to_image(layer.h, layer.w, layer.c, input);
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image out_delta = get_convolutional_delta(layer);
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gradient_delta_convolutional_layer(layer);
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for(i = 0; i < layer.n; ++i){
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kernel_update(in_image, layer.kernel_updates[i], layer.stride, i, out_delta, layer.edge);
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layer.bias_updates[i] += avg_image_layer(out_delta, i);
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}
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}
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void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
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{
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int i,j;
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for(i = 0; i < layer.n; ++i){
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layer.bias_momentum[i] = step*(layer.bias_updates[i])
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+ momentum*layer.bias_momentum[i];
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layer.biases[i] += layer.bias_momentum[i];
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layer.bias_updates[i] = 0;
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int pixels = layer.kernels[i].h*layer.kernels[i].w*layer.kernels[i].c;
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for(j = 0; j < pixels; ++j){
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layer.kernel_momentum[i].data[j] = step*(layer.kernel_updates[i].data[j] - decay*layer.kernels[i].data[j])
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+ momentum*layer.kernel_momentum[i].data[j];
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layer.kernels[i].data[j] += layer.kernel_momentum[i].data[j];
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}
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zero_image(layer.kernel_updates[i]);
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}
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}
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*/
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void test_convolutional_layer()
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{
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convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
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float input[] = {1,2,3,4,
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5,6,7,8,
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9,10,11,12,
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13,14,15,16};
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float filter[] = {.5, 0, .3,
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0 , 1, 0,
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.2 , 0, 1};
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float delta[] = {1, 2,
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3, 4};
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float in_delta[] = {.5,1,.3,.6,
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5,6,7,8,
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9,10,11,12,
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13,14,15,16};
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l.filters = filter;
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forward_convolutional_layer(l, input);
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l.delta = delta;
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learn_convolutional_layer(l);
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image filter_updates = float_to_image(3,3,1,l.filter_updates);
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print_image(filter_updates);
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printf("Delta:\n");
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backward_convolutional_layer(l, in_delta);
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pm(4,4,in_delta);
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}
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image get_convolutional_filter(convolutional_layer layer, int i)
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{
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int h = layer.size;
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int w = layer.size;
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int c = layer.c;
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return float_to_image(h,w,c,layer.filters+i*h*w*c);
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}
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void visualize_convolutional_layer(convolutional_layer layer, char *window)
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{
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int color = 1;
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int border = 1;
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int h,w,c;
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int size = layer.size;
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h = size;
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w = (size + border) * layer.n - border;
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c = layer.c;
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if(c != 3 || !color){
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h = (h+border)*c - border;
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c = 1;
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}
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image filters = make_image(h,w,c);
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int i,j;
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for(i = 0; i < layer.n; ++i){
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int w_offset = i*(size+border);
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image k = get_convolutional_filter(layer, i);
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//printf("%f ** ", layer.biases[i]);
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//print_image(k);
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image copy = copy_image(k);
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normalize_image(copy);
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for(j = 0; j < k.c; ++j){
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//set_pixel(copy,0,0,j,layer.biases[i]);
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}
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if(c == 3 && color){
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embed_image(copy, filters, 0, w_offset);
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}
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else{
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for(j = 0; j < k.c; ++j){
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int h_offset = j*(size+border);
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image layer = get_image_layer(k, j);
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embed_image(layer, filters, h_offset, w_offset);
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free_image(layer);
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}
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}
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free_image(copy);
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}
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image delta = get_convolutional_delta(layer);
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image dc = collapse_image_layers(delta, 1);
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char buff[256];
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sprintf(buff, "%s: Delta", window);
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show_image(dc, buff);
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free_image(dc);
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show_image(filters, window);
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free_image(filters);
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}
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