darknet/src/convolutional_layer.c

#include "convolutional_layer.h"
#include "utils.h"
#include "im2col.h"
#include "col2im.h"
#include "blas.h"
#include "gemm.h"
#include <stdio.h>
#include <time.h>

void swap_binary(convolutional_layer *l)
{
    float *swap = l->filters;
    l->filters = l->binary_filters;
    l->binary_filters = swap;

    #ifdef GPU
    swap = l->filters_gpu;
    l->filters_gpu = l->binary_filters_gpu;
    l->binary_filters_gpu = swap;
    #endif
}

void binarize_filters2(float *filters, int n, int size, char *binary, float *scales)
{
    int i, k, f;
    for(f = 0; f < n; ++f){
        float mean = 0;
        for(i = 0; i < size; ++i){
            mean += fabs(filters[f*size + i]);
        }
        mean = mean / size;
        scales[f] = mean;
        for(i = 0; i < size/8; ++i){
            binary[f*size + i] = (filters[f*size + i] > 0) ? 1 : 0;
            for(k = 0; k < 8; ++k){
            }
        }
    }
}

void binarize_filters(float *filters, int n, int size, float *binary)
{
    int i, f;
    for(f = 0; f < n; ++f){
        float mean = 0;
        for(i = 0; i < size; ++i){
            mean += fabs(filters[f*size + i]);
        }
        mean = mean / size;
        for(i = 0; i < size; ++i){
            binary[f*size + i] = (filters[f*size + i] > 0) ? mean : -mean;
        }
    }
}

int convolutional_out_height(convolutional_layer l)
{
    int h = l.h;
    if (!l.pad) h -= l.size;
    else h -= 1;
    return h/l.stride + 1;
}

int convolutional_out_width(convolutional_layer l)
{
    int w = l.w;
    if (!l.pad) w -= l.size;
    else w -= 1;
    return w/l.stride + 1;
}

image get_convolutional_image(convolutional_layer l)
{
    int h,w,c;
    h = convolutional_out_height(l);
    w = convolutional_out_width(l);
    c = l.n;
    return float_to_image(w,h,c,l.output);
}

image get_convolutional_delta(convolutional_layer l)
{
    int h,w,c;
    h = convolutional_out_height(l);
    w = convolutional_out_width(l);
    c = l.n;
    return float_to_image(w,h,c,l.delta);
}

void backward_scale_cpu(float *x_norm, float *delta, int batch, int n, int size, float *scale_updates)
{
    int i,b,f;
    for(f = 0; f < n; ++f){
        float sum = 0;
        for(b = 0; b < batch; ++b){
            for(i = 0; i < size; ++i){
                int index = i + size*(f + n*b);
                sum += delta[index] * x_norm[index];
            }
        }
        scale_updates[f] += sum;
    }
}

void mean_delta_cpu(float *delta, float *variance, int batch, int filters, int spatial, float *mean_delta)
{

    int i,j,k;
    for(i = 0; i < filters; ++i){
        mean_delta[i] = 0;
        for (j = 0; j < batch; ++j) {
            for (k = 0; k < spatial; ++k) {
                int index = j*filters*spatial + i*spatial + k;
                mean_delta[i] += delta[index];
            }
        }
        mean_delta[i] *= (-1./sqrt(variance[i] + .00001f));
    }
}
void  variance_delta_cpu(float *x, float *delta, float *mean, float *variance, int batch, int filters, int spatial, float *variance_delta)
{

    int i,j,k;
    for(i = 0; i < filters; ++i){
        variance_delta[i] = 0;
        for(j = 0; j < batch; ++j){
            for(k = 0; k < spatial; ++k){
                int index = j*filters*spatial + i*spatial + k;
                variance_delta[i] += delta[index]*(x[index] - mean[i]);
            }
        }
        variance_delta[i] *= -.5 * pow(variance[i] + .00001f, (float)(-3./2.));
    }
}
void normalize_delta_cpu(float *x, float *mean, float *variance, float *mean_delta, float *variance_delta, int batch, int filters, int spatial, float *delta)
{
    int f, j, k;
    for(j = 0; j < batch; ++j){
        for(f = 0; f < filters; ++f){
            for(k = 0; k < spatial; ++k){
                int index = j*filters*spatial + f*spatial + k;
                delta[index] = delta[index] * 1./(sqrt(variance[f]) + .00001f) + variance_delta[f] * 2. * (x[index] - mean[f]) / (spatial * batch) + mean_delta[f]/(spatial*batch);
            }
        }
    }
}

convolutional_layer make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation, int batch_normalize, int binary)
{
    int i;
    convolutional_layer l = {0};
    l.type = CONVOLUTIONAL;

    l.h = h;
    l.w = w;
    l.c = c;
    l.n = n;
    l.binary = binary;
    l.batch = batch;
    l.stride = stride;
    l.size = size;
    l.pad = pad;
    l.batch_normalize = batch_normalize;

    l.filters = calloc(c*n*size*size, sizeof(float));
    l.filter_updates = calloc(c*n*size*size, sizeof(float));

    l.biases = calloc(n, sizeof(float));
    l.bias_updates = calloc(n, sizeof(float));

    // float scale = 1./sqrt(size*size*c);
    float scale = sqrt(2./(size*size*c));
    for(i = 0; i < c*n*size*size; ++i) l.filters[i] = scale*rand_uniform(-1, 1);
    int out_h = convolutional_out_height(l);
    int out_w = convolutional_out_width(l);
    l.out_h = out_h;
    l.out_w = out_w;
    l.out_c = n;
    l.outputs = l.out_h * l.out_w * l.out_c;
    l.inputs = l.w * l.h * l.c;

    l.col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
    l.output = calloc(l.batch*out_h * out_w * n, sizeof(float));
    l.delta  = calloc(l.batch*out_h * out_w * n, sizeof(float));

    if(binary){
        l.binary_filters = calloc(c*n*size*size, sizeof(float));
        l.cfilters = calloc(c*n*size*size, sizeof(char));
        l.scales = calloc(n, sizeof(float));
    }

    if(batch_normalize){
        l.scales = calloc(n, sizeof(float));
        l.scale_updates = calloc(n, sizeof(float));
        for(i = 0; i < n; ++i){
            l.scales[i] = 1;
        }

        l.mean = calloc(n, sizeof(float));
        l.variance = calloc(n, sizeof(float));

        l.rolling_mean = calloc(n, sizeof(float));
        l.rolling_variance = calloc(n, sizeof(float));
    }

#ifdef GPU
    l.filters_gpu = cuda_make_array(l.filters, c*n*size*size);
    l.filter_updates_gpu = cuda_make_array(l.filter_updates, c*n*size*size);

    l.biases_gpu = cuda_make_array(l.biases, n);
    l.bias_updates_gpu = cuda_make_array(l.bias_updates, n);

    l.scales_gpu = cuda_make_array(l.scales, n);
    l.scale_updates_gpu = cuda_make_array(l.scale_updates, n);

    l.col_image_gpu = cuda_make_array(l.col_image, out_h*out_w*size*size*c);
    l.delta_gpu = cuda_make_array(l.delta, l.batch*out_h*out_w*n);
    l.output_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);

    if(binary){
        l.binary_filters_gpu = cuda_make_array(l.filters, c*n*size*size);
    }

    if(batch_normalize){
        l.mean_gpu = cuda_make_array(l.mean, n);
        l.variance_gpu = cuda_make_array(l.variance, n);

        l.rolling_mean_gpu = cuda_make_array(l.mean, n);
        l.rolling_variance_gpu = cuda_make_array(l.variance, n);

        l.mean_delta_gpu = cuda_make_array(l.mean, n);
        l.variance_delta_gpu = cuda_make_array(l.variance, n);

        l.x_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
        l.x_norm_gpu = cuda_make_array(l.output, l.batch*out_h*out_w*n);
    }
#endif
    l.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);

    return l;
}

void denormalize_convolutional_layer(convolutional_layer l)
{
    int i, j;
    for(i = 0; i < l.n; ++i){
        float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .00001);
        for(j = 0; j < l.c*l.size*l.size; ++j){
            l.filters[i*l.c*l.size*l.size + j] *= scale;
        }
        l.biases[i] -= l.rolling_mean[i] * scale;
    }
}

void test_convolutional_layer()
{
    convolutional_layer l = make_convolutional_layer(1, 5, 5, 3, 2, 5, 2, 1, LEAKY, 1, 0);
    l.batch_normalize = 1;
    float data[] = {1,1,1,1,1,
        1,1,1,1,1,
        1,1,1,1,1,
        1,1,1,1,1,
        1,1,1,1,1,
        2,2,2,2,2,
        2,2,2,2,2,
        2,2,2,2,2,
        2,2,2,2,2,
        2,2,2,2,2,
        3,3,3,3,3,
        3,3,3,3,3,
        3,3,3,3,3,
        3,3,3,3,3,
        3,3,3,3,3};
    network_state state = {0};
    state.input = data;
    forward_convolutional_layer(l, state);
}

void resize_convolutional_layer(convolutional_layer *l, int w, int h)
{
    l->w = w;
    l->h = h;
    int out_w = convolutional_out_width(*l);
    int out_h = convolutional_out_height(*l);

    l->out_w = out_w;
    l->out_h = out_h;

    l->outputs = l->out_h * l->out_w * l->out_c;
    l->inputs = l->w * l->h * l->c;

    l->col_image = realloc(l->col_image,
            out_h*out_w*l->size*l->size*l->c*sizeof(float));
    l->output = realloc(l->output,
            l->batch*out_h * out_w * l->n*sizeof(float));
    l->delta  = realloc(l->delta,
            l->batch*out_h * out_w * l->n*sizeof(float));

#ifdef GPU
    cuda_free(l->col_image_gpu);
    cuda_free(l->delta_gpu);
    cuda_free(l->output_gpu);

    l->col_image_gpu = cuda_make_array(l->col_image, out_h*out_w*l->size*l->size*l->c);
    l->delta_gpu =     cuda_make_array(l->delta, l->batch*out_h*out_w*l->n);
    l->output_gpu =    cuda_make_array(l->output, l->batch*out_h*out_w*l->n);
#endif
}

void add_bias(float *output, float *biases, int batch, int n, int size)
{
    int i,j,b;
    for(b = 0; b < batch; ++b){
        for(i = 0; i < n; ++i){
            for(j = 0; j < size; ++j){
                output[(b*n + i)*size + j] += biases[i];
            }
        }
    }
}

void scale_bias(float *output, float *scales, int batch, int n, int size)
{
    int i,j,b;
    for(b = 0; b < batch; ++b){
        for(i = 0; i < n; ++i){
            for(j = 0; j < size; ++j){
                output[(b*n + i)*size + j] *= scales[i];
            }
        }
    }
}

void backward_bias(float *bias_updates, float *delta, int batch, int n, int size)
{
    int i,b;
    for(b = 0; b < batch; ++b){
        for(i = 0; i < n; ++i){
            bias_updates[i] += sum_array(delta+size*(i+b*n), size);
        }
    }
}

void forward_convolutional_layer(convolutional_layer l, network_state state)
{
    int out_h = convolutional_out_height(l);
    int out_w = convolutional_out_width(l);
    int i;

    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
    /*
    if(l.binary){
        binarize_filters(l.filters, l.n, l.c*l.size*l.size, l.binary_filters);
        binarize_filters2(l.filters, l.n, l.c*l.size*l.size, l.cfilters, l.scales);
        swap_binary(&l);
    }
    */

    if(l.binary){
        int m = l.n;
        int k = l.size*l.size*l.c;
        int n = out_h*out_w;

        char  *a = l.cfilters;
        float *b = l.col_image;
        float *c = l.output;

        for(i = 0; i < l.batch; ++i){
            im2col_cpu(state.input, l.c, l.h, l.w, 
                    l.size, l.stride, l.pad, b);
            gemm_bin(m,n,k,1,a,k,b,n,c,n);
            c += n*m;
            state.input += l.c*l.h*l.w;
        }
        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
        add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);
        activate_array(l.output, m*n*l.batch, l.activation);
        return;
    }

    int m = l.n;
    int k = l.size*l.size*l.c;
    int n = out_h*out_w;

    float *a = l.filters;
    float *b = l.col_image;
    float *c = l.output;

    for(i = 0; i < l.batch; ++i){
        im2col_cpu(state.input, l.c, l.h, l.w, 
                l.size, l.stride, l.pad, b);
        gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
        c += n*m;
        state.input += l.c*l.h*l.w;
    }

    if(l.batch_normalize){
        if(state.train){
            mean_cpu(l.output, l.batch, l.n, l.out_h*l.out_w, l.mean);   
            variance_cpu(l.output, l.mean, l.batch, l.n, l.out_h*l.out_w, l.variance);   
            normalize_cpu(l.output, l.mean, l.variance, l.batch, l.n, l.out_h*l.out_w);   
        } else {
            normalize_cpu(l.output, l.rolling_mean, l.rolling_variance, l.batch, l.n, l.out_h*l.out_w);
        }
        scale_bias(l.output, l.scales, l.batch, l.n, out_h*out_w);
    }
    add_bias(l.output, l.biases, l.batch, l.n, out_h*out_w);

    activate_array(l.output, m*n*l.batch, l.activation);
}

void backward_convolutional_layer(convolutional_layer l, network_state state)
{
    int i;
    int m = l.n;
    int n = l.size*l.size*l.c;
    int k = convolutional_out_height(l)*
        convolutional_out_width(l);

    gradient_array(l.output, m*k*l.batch, l.activation, l.delta);
    backward_bias(l.bias_updates, l.delta, l.batch, l.n, k);

    for(i = 0; i < l.batch; ++i){
        float *a = l.delta + i*m*k;
        float *b = l.col_image;
        float *c = l.filter_updates;

        float *im = state.input+i*l.c*l.h*l.w;

        im2col_cpu(im, l.c, l.h, l.w, 
                l.size, l.stride, l.pad, b);
        gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);

        if(state.delta){
            a = l.filters;
            b = l.delta + i*m*k;
            c = l.col_image;

            gemm(1,0,n,k,m,1,a,n,b,k,0,c,k);

            col2im_cpu(l.col_image, l.c,  l.h,  l.w,  l.size,  l.stride, l.pad, state.delta+i*l.c*l.h*l.w);
        }
    }
}

void update_convolutional_layer(convolutional_layer l, int batch, float learning_rate, float momentum, float decay)
{
    int size = l.size*l.size*l.c*l.n;
    axpy_cpu(l.n, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
    scal_cpu(l.n, momentum, l.bias_updates, 1);

    axpy_cpu(size, -decay*batch, l.filters, 1, l.filter_updates, 1);
    axpy_cpu(size, learning_rate/batch, l.filter_updates, 1, l.filters, 1);
    scal_cpu(size, momentum, l.filter_updates, 1);
}


image get_convolutional_filter(convolutional_layer l, int i)
{
    int h = l.size;
    int w = l.size;
    int c = l.c;
    return float_to_image(w,h,c,l.filters+i*h*w*c);
}

void rgbgr_filters(convolutional_layer l)
{
    int i;
    for(i = 0; i < l.n; ++i){
        image im = get_convolutional_filter(l, i);
        if (im.c == 3) {
            rgbgr_image(im);
        }
    }
}

void rescale_filters(convolutional_layer l, float scale, float trans)
{
    int i;
    for(i = 0; i < l.n; ++i){
        image im = get_convolutional_filter(l, i);
        if (im.c == 3) {
            scale_image(im, scale);
            float sum = sum_array(im.data, im.w*im.h*im.c);
            l.biases[i] += sum*trans;
        }
    }
}

image *get_filters(convolutional_layer l)
{
    image *filters = calloc(l.n, sizeof(image));
    int i;
    for(i = 0; i < l.n; ++i){
        filters[i] = copy_image(get_convolutional_filter(l, i));
        //normalize_image(filters[i]);
    }
    return filters;
}

image *visualize_convolutional_layer(convolutional_layer l, char *window, image *prev_filters)
{
    image *single_filters = get_filters(l);
    show_images(single_filters, l.n, window);

    image delta = get_convolutional_image(l);
    image dc = collapse_image_layers(delta, 1);
    char buff[256];
    sprintf(buff, "%s: Output", window);
    //show_image(dc, buff);
    //save_image(dc, buff);
    free_image(dc);
    return single_filters;
}