#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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#include <time.h>
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int convolutional_out_height(convolutional_layer layer)
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{
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int h = layer.h;
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if (!layer.pad) h -= layer.size;
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else h -= 1;
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return h/layer.stride + 1;
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}
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int convolutional_out_width(convolutional_layer layer)
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{
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int w = layer.w;
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if (!layer.pad) w -= layer.size;
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else w -= 1;
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return w/layer.stride + 1;
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}
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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 = convolutional_out_height(layer);
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w = convolutional_out_width(layer);
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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 = convolutional_out_height(layer);
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w = convolutional_out_width(layer);
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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 batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, float learning_rate, float momentum, float decay)
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{
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int i;
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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->learning_rate = learning_rate;
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layer->momentum = momentum;
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layer->decay = decay;
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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->batch = batch;
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layer->stride = stride;
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layer->size = size;
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layer->pad = pad;
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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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scale = .05;
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for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*2*(rand_uniform()-.5);
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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] = .5;
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}
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int out_h = convolutional_out_height(*layer);
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int out_w = convolutional_out_width(*layer);
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layer->col_image = calloc(layer->batch*out_h*out_w*size*size*c, sizeof(float));
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layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
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layer->delta = calloc(layer->batch*out_h * out_w * n, sizeof(float));
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#ifdef GPU
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layer->filters_cl = cl_make_array(layer->filters, c*n*size*size);
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layer->filter_updates_cl = cl_make_array(layer->filter_updates, c*n*size*size);
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layer->filter_momentum_cl = cl_make_array(layer->filter_momentum, c*n*size*size);
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layer->biases_cl = cl_make_array(layer->biases, n);
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layer->bias_updates_cl = cl_make_array(layer->bias_updates, n);
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layer->bias_momentum_cl = cl_make_array(layer->bias_momentum, n);
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layer->col_image_cl = cl_make_array(layer->col_image, layer->batch*out_h*out_w*size*size*c);
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layer->delta_cl = cl_make_array(layer->delta, layer->batch*out_h*out_w*n);
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layer->output_cl = cl_make_array(layer->output, layer->batch*out_h*out_w*n);
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#endif
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layer->activation = activation;
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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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return layer;
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}
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void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
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{
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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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int out_h = convolutional_out_height(*layer);
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int out_w = convolutional_out_width(*layer);
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layer->col_image = realloc(layer->col_image,
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layer->batch*out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
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layer->output = realloc(layer->output,
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layer->batch*out_h * out_w * layer->n*sizeof(float));
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layer->delta = realloc(layer->delta,
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layer->batch*out_h * out_w * layer->n*sizeof(float));
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}
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void bias_output(const convolutional_layer layer)
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{
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int i,j,b;
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int out_h = convolutional_out_height(layer);
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int out_w = convolutional_out_width(layer);
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for(b = 0; b < layer.batch; ++b){
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for(i = 0; i < layer.n; ++i){
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for(j = 0; j < out_h*out_w; ++j){
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layer.output[(b*layer.n + i)*out_h*out_w + j] = layer.biases[i];
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}
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}
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}
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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 out_h = convolutional_out_height(layer);
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int out_w = convolutional_out_width(layer);
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int i;
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bias_output(layer);
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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 = out_h*out_w;
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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.batch, layer.c, layer.h, layer.w,
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layer.size, layer.stride, layer.pad, b);
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for(i = 0; i < layer.batch; ++i){
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gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
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b += k*n;
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c += n*m;
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}
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activate_array(layer.output, m*n*layer.batch, layer.activation);
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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,b;
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int size = convolutional_out_height(layer)
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*convolutional_out_width(layer);
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for(b = 0; b < layer.batch; ++b){
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for(i = 0; i < layer.n; ++i){
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layer.bias_updates[i] += sum_array(layer.delta+size*(i+b*layer.n), size);
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}
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}
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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 i;
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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 = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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gradient_array(layer.output, m*k*layer.batch, layer.activation, layer.delta);
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learn_bias_convolutional_layer(layer);
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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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for(i = 0; i < layer.batch; ++i){
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gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
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a += m*k;
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b += k*n;
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}
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if(delta){
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m = layer.size*layer.size*layer.c;
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k = layer.n;
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n = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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a = layer.filters;
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b = layer.delta;
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c = layer.col_image;
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for(i = 0; i < layer.batch; ++i){
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gemm(1,0,m,n,k,1,a,m,b,n,0,c,n);
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b += k*n;
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c += m*n;
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}
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memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
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col2im_cpu(layer.col_image, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta);
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}
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}
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void update_convolutional_layer(convolutional_layer layer)
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{
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int size = layer.size*layer.size*layer.c*layer.n;
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axpy_cpu(layer.n, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
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scal_cpu(layer.n,layer.momentum, layer.bias_updates, 1);
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scal_cpu(size, 1.-layer.learning_rate*layer.decay, layer.filters, 1);
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axpy_cpu(size, layer.learning_rate, layer.filter_updates, 1, layer.filters, 1);
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scal_cpu(size, layer.momentum, layer.filter_updates, 1);
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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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image *weighted_sum_filters(convolutional_layer layer, image *prev_filters)
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{
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image *filters = calloc(layer.n, sizeof(image));
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int i,j,k,c;
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if(!prev_filters){
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for(i = 0; i < layer.n; ++i){
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filters[i] = copy_image(get_convolutional_filter(layer, i));
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}
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}
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else{
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image base = prev_filters[0];
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for(i = 0; i < layer.n; ++i){
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image filter = get_convolutional_filter(layer, i);
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filters[i] = make_image(base.h, base.w, base.c);
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for(j = 0; j < layer.size; ++j){
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for(k = 0; k < layer.size; ++k){
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for(c = 0; c < layer.c; ++c){
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float weight = get_pixel(filter, j, k, c);
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image prev_filter = copy_image(prev_filters[c]);
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scale_image(prev_filter, weight);
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add_into_image(prev_filter, filters[i], 0,0);
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free_image(prev_filter);
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}
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}
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}
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}
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}
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return filters;
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}
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image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
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{
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image *single_filters = weighted_sum_filters(layer, 0);
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show_images(single_filters, layer.n, window);
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image delta = get_convolutional_image(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: Output", window);
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//show_image(dc, buff);
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//save_image(dc, buff);
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free_image(dc);
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return single_filters;
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}
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#ifdef GPU
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cl_kernel get_convolutional_learn_bias_kernel()
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{
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static int init = 0;
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static cl_kernel kernel;
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if(!init){
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kernel = get_kernel("src/convolutional_layer.cl", "learn_bias", 0);
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init = 1;
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}
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return kernel;
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}
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void learn_bias_convolutional_layer_ongpu(convolutional_layer layer)
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{
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int size = convolutional_out_height(layer) * convolutional_out_width(layer);
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cl_setup();
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cl_kernel kernel = get_convolutional_learn_bias_kernel();
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cl_command_queue queue = cl.queue;
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cl_uint i = 0;
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.batch), (void*) &layer.batch);
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
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cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.delta_cl), (void*) &layer.delta_cl);
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.bias_updates_cl), (void*) &layer.bias_updates_cl);
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check_error(cl);
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const size_t global_size[] = {layer.n};
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clEnqueueNDRangeKernel(queue, kernel, 1, 0, global_size, 0, 0, 0, 0);
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check_error(cl);
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}
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cl_kernel get_convolutional_bias_kernel()
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{
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static int init = 0;
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static cl_kernel kernel;
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if(!init){
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kernel = get_kernel("src/convolutional_layer.cl", "bias", 0);
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init = 1;
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}
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return kernel;
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}
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void bias_output_gpu(const convolutional_layer layer)
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{
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int out_h = convolutional_out_height(layer);
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int out_w = convolutional_out_width(layer);
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int size = out_h*out_w;
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cl_setup();
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cl_kernel kernel = get_convolutional_bias_kernel();
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cl_command_queue queue = cl.queue;
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cl_uint i = 0;
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.n), (void*) &layer.n);
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cl.error = clSetKernelArg(kernel, i++, sizeof(size), (void*) &size);
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.biases_cl), (void*) &layer.biases_cl);
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cl.error = clSetKernelArg(kernel, i++, sizeof(layer.output_cl), (void*) &layer.output_cl);
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check_error(cl);
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const size_t global_size[] = {layer.batch, layer.n*size};
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clEnqueueNDRangeKernel(queue, kernel, 2, 0, global_size, 0, 0, 0, 0);
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check_error(cl);
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}
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//#define TIMEIT
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void forward_convolutional_layer_gpu(convolutional_layer layer, cl_mem 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 = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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bias_output_gpu(layer);
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#ifdef TIMEIT
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clock_t time = clock();
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printf("Forward\n");
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#endif
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im2col_ongpu(in, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, layer.col_image_cl);
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#ifdef TIMEIT
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clFinish(cl.queue);
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printf("Im2col %f\n", sec(clock()-time));
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time = clock();
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#endif
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for(i = 0; i < layer.batch; ++i){
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cl_mem a = layer.filters_cl;
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cl_mem b = cl_sub_array(layer.col_image_cl, i*k*n, k*n);
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cl_mem c = cl_sub_array(layer.output_cl, i*m*n, m*n);
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gemm_ongpu(0,0,m,n,k,1.,a,k,b,n,1.,c,n);
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clReleaseMemObject(b);
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clReleaseMemObject(c);
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}
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#ifdef TIMEIT
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clFinish(cl.queue);
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printf("Gemm %f\n", sec(clock()-time));
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#endif
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activate_array_ongpu(layer.output_cl, m*n*layer.batch, layer.activation);
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#ifdef TIMEIT
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cl_read_array(layer.output_cl, layer.output, m*n*layer.batch);
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#endif
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}
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void backward_convolutional_layer_gpu(convolutional_layer layer, cl_mem delta_cl)
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{
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int i;
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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 = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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gradient_array_ongpu(layer.output_cl, m*k*layer.batch, layer.activation, layer.delta_cl);
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learn_bias_convolutional_layer_ongpu(layer);
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for(i = 0; i < layer.batch; ++i){
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cl_mem a = cl_sub_array(layer.delta_cl,i*m*k, m*k);
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cl_mem b = cl_sub_array(layer.col_image_cl,i*k*n, k*n);
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cl_mem c = layer.filter_updates_cl;
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gemm_ongpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
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clReleaseMemObject(a);
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clReleaseMemObject(b);
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}
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//cl_read_array(layer.delta_cl, layer.delta, m*k*layer.batch);
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if(delta_cl){
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m = layer.size*layer.size*layer.c;
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k = layer.n;
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n = convolutional_out_height(layer)*
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convolutional_out_width(layer);
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for(i = 0; i < layer.batch; ++i){
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cl_mem a = layer.filters_cl;
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cl_mem b = cl_sub_array(layer.delta_cl, i*k*n, k*n);
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cl_mem c = cl_sub_array(layer.col_image_cl, i*m*n, m*n);
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gemm_ongpu(1,0,m,n,k,1,a,m,b,n,0,c,n);
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clReleaseMemObject(b);
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clReleaseMemObject(c);
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}
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scal_ongpu(layer.batch*layer.h*layer.w*layer.c,0,delta_cl, 1);
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col2im_ongpu(layer.col_image_cl, layer.batch, layer.c, layer.h, layer.w, layer.size, layer.stride, layer.pad, delta_cl);
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}
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}
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void pull_convolutional_layer(convolutional_layer layer)
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{
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cl_read_array(layer.filters_cl, layer.filters, layer.c*layer.n*layer.size*layer.size);
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cl_read_array(layer.biases_cl, layer.biases, layer.n);
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}
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void update_convolutional_layer_gpu(convolutional_layer layer)
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{
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int size = layer.size*layer.size*layer.c*layer.n;
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axpy_ongpu(layer.n, layer.learning_rate, layer.bias_updates_cl, 1, layer.biases_cl, 1);
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scal_ongpu(layer.n,layer.momentum, layer.bias_updates_cl, 1);
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scal_ongpu(size, 1.-layer.learning_rate*layer.decay, layer.filters_cl, 1);
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axpy_ongpu(size, layer.learning_rate, layer.filter_updates_cl, 1, layer.filters_cl, 1);
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scal_ongpu(size, layer.momentum, layer.filter_updates_cl, 1);
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pull_convolutional_layer(layer);
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}
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#endif
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