Visualizations?

Joseph Redmon

2014-04-17 738cd4c2d7abcf62b85b759a5765b08380ee90e8

 src/network.c

@@ -8,6 +8,7 @@
#include "convolutional_layer.h"
//#include "old_conv.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "softmax_layer.h"

network make_network(int n, int batch)
@@ -40,6 +41,17 @@
    fprintf(fp, "data=");
    for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
    for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
    /*
    int j,k;
    for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
    for(i = 0; i < l->n; ++i){
        for(j = l->c-1; j >= 0; --j){
            for(k = 0; k < l->size*l->size; ++k){
                fprintf(fp, "%g,", l->filters[i*(l->c*l->size*l->size)+j*l->size*l->size+k]);
            }
        }
    }
    */
    fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
@@ -68,6 +80,20 @@
    fprintf(fp, "stride=%d\n\n", l->stride);
}

void print_normalization_cfg(FILE *fp, normalization_layer *l, int first)
{
    fprintf(fp, "[localresponsenormalization]\n");
    if(first) fprintf(fp,   "batch=%d\n"
            "height=%d\n"
            "width=%d\n"
            "channels=%d\n",
            l->batch,l->h, l->w, l->c);
    fprintf(fp, "size=%d\n"
                "alpha=%g\n"
                "beta=%g\n"
                "kappa=%g\n\n", l->size, l->alpha, l->beta, l->kappa);
}

void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
    fprintf(fp, "[softmax]\n");
@@ -88,6 +114,8 @@
            print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
        else if(net.types[i] == MAXPOOL)
            print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
        else if(net.types[i] == NORMALIZATION)
            print_normalization_cfg(fp, (normalization_layer *)net.layers[i], i==0);
        else if(net.types[i] == SOFTMAX)
            print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
    }
@@ -118,6 +146,11 @@
            forward_maxpool_layer(layer, input);
            input = layer.output;
        }
        else if(net.types[i] == NORMALIZATION){
            normalization_layer layer = *(normalization_layer *)net.layers[i];
            forward_normalization_layer(layer, input);
            input = layer.output;
        }
    }
}

@@ -135,6 +168,9 @@
        else if(net.types[i] == SOFTMAX){
            //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
        }
        else if(net.types[i] == NORMALIZATION){
            //maxpool_layer layer = *(maxpool_layer *)net.layers[i];
        }
        else if(net.types[i] == CONNECTED){
            connected_layer layer = *(connected_layer *)net.layers[i];
            update_connected_layer(layer, step, momentum, decay);
@@ -156,6 +192,9 @@
    } else if(net.types[i] == CONNECTED){
        connected_layer layer = *(connected_layer *)net.layers[i];
        return layer.output;
    } else if(net.types[i] == NORMALIZATION){
        normalization_layer layer = *(normalization_layer *)net.layers[i];
        return layer.output;
    }
    return 0;
}
@@ -233,6 +272,10 @@
            maxpool_layer layer = *(maxpool_layer *)net.layers[i];
            if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
        }
        else if(net.types[i] == NORMALIZATION){
            normalization_layer layer = *(normalization_layer *)net.layers[i];
            if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
        }
        else if(net.types[i] == SOFTMAX){
            softmax_layer layer = *(softmax_layer *)net.layers[i];
            if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
@@ -381,16 +424,21 @@
            h = output.h;
            w = output.w;
            c = output.c;
        }
        else if(net.types[i] == MAXPOOL){
        }else if(net.types[i] == MAXPOOL){
            maxpool_layer *layer = (maxpool_layer *)net.layers[i];
            resize_maxpool_layer(layer, h, w, c);
            image output = get_maxpool_image(*layer);
            h = output.h;
            w = output.w;
            c = output.c;
        }
        else{
        }else if(net.types[i] == NORMALIZATION){
            normalization_layer *layer = (normalization_layer *)net.layers[i];
            resize_normalization_layer(layer, h, w, c);
            image output = get_normalization_image(*layer);
            h = output.h;
            w = output.w;
            c = output.c;
        }else{
            error("Cannot resize this type of layer");
        }
    }
@@ -413,6 +461,10 @@
        maxpool_layer layer = *(maxpool_layer *)net.layers[i];
        return get_maxpool_image(layer);
    }
    else if(net.types[i] == NORMALIZATION){
        normalization_layer layer = *(normalization_layer *)net.layers[i];
        return get_normalization_image(layer);
    }
    return make_empty_image(0,0,0);
}

@@ -437,6 +489,10 @@
            convolutional_layer layer = *(convolutional_layer *)net.layers[i];
            prev = visualize_convolutional_layer(layer, buff, prev);
        }
        if(net.types[i] == NORMALIZATION){
            normalization_layer layer = *(normalization_layer *)net.layers[i];
            visualize_normalization_layer(layer, buff);
        }
    } 
}