level adjustment for images

Joseph Redmon

2015-04-15 d97331b88ff3d50035b1e22c9d0eb671b61227e3

 src/convolutional_layer.c

@@ -29,7 +29,7 @@
    h = convolutional_out_height(layer);
    w = convolutional_out_width(layer);
    c = layer.n;
    return float_to_image(h,w,c,layer.output);
    return float_to_image(w,h,c,layer.output);
}

image get_convolutional_delta(convolutional_layer layer)
@@ -38,7 +38,7 @@
    h = convolutional_out_height(layer);
    w = convolutional_out_width(layer);
    c = layer.n;
    return float_to_image(h,w,c,layer.delta);
    return float_to_image(w,h,c,layer.delta);
}

convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, int pad, ACTIVATION activation)
@@ -61,7 +61,7 @@
    layer->biases = calloc(n, sizeof(float));
    layer->bias_updates = calloc(n, sizeof(float));
    float scale = 1./sqrt(size*size*c);
    for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = scale*rand_normal();
    for(i = 0; i < c*n*size*size; ++i) layer->filters[i] = 2*scale*rand_uniform() - scale;
    for(i = 0; i < n; ++i){
        layer->biases[i] = scale;
    }
@@ -217,42 +217,22 @@
    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);
    return float_to_image(w,h,c,layer.filters+i*h*w*c);
}

image *weighted_sum_filters(convolutional_layer layer, image *prev_filters)
image *get_filters(convolutional_layer layer)
{
    image *filters = calloc(layer.n, sizeof(image));
    int i,j,k,c;
    if(!prev_filters){
        for(i = 0; i < layer.n; ++i){
            filters[i] = copy_image(get_convolutional_filter(layer, i));
        }
    }
    else{
        image base = prev_filters[0];
        for(i = 0; i < layer.n; ++i){
            image filter = get_convolutional_filter(layer, i);
            filters[i] = make_image(base.h, base.w, base.c);
            for(j = 0; j < layer.size; ++j){
                for(k = 0; k < layer.size; ++k){
                    for(c = 0; c < layer.c; ++c){
                        float weight = get_pixel(filter, j, k, c);
                        image prev_filter = copy_image(prev_filters[c]);
                        scale_image(prev_filter, weight);
                        add_into_image(prev_filter, filters[i], 0,0);
                        free_image(prev_filter);
                    }
                }
            }
        }
    int i;
    for(i = 0; i < layer.n; ++i){
        filters[i] = copy_image(get_convolutional_filter(layer, i));
    }
    return filters;
}

image *visualize_convolutional_layer(convolutional_layer layer, char *window, image *prev_filters)
{
    image *single_filters = weighted_sum_filters(layer, 0);
    image *single_filters = get_filters(layer);
    show_images(single_filters, layer.n, window);

    image delta = get_convolutional_image(layer);