Visualizations?

Makefile

@@ -1,20 +1,21 @@
 CC=gcc
 COMMON=-Wall `pkg-config --cflags opencv`
 UNAME = $(shell uname)
+OPTS=-O3
 ifeq ($(UNAME), Darwin)
 COMMON+= -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
 LDFLAGS= -framework OpenCL
 else
-COMMON+= -march=native -flto
+OPTS+= -march=native -flto
 LDFLAGS= -lOpenCL
 endif
-CFLAGS= $(COMMON) -Ofast
+CFLAGS= $(COMMON) $(OPTS)
 #CFLAGS= $(COMMON) -O0 -g 
 LDFLAGS+=`pkg-config --libs opencv` -lm
 VPATH=./src/
 EXEC=cnn
 
-OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o opencl.o gpu_gemm.o cpu_gemm.o
+OBJ=network.o image.o tests.o connected_layer.o maxpool_layer.o activations.o list.o option_list.o parser.o utils.o data.o matrix.o softmax_layer.o mini_blas.o convolutional_layer.o opencl.o gpu_gemm.o cpu_gemm.o normalization_layer.o
 
 all: $(EXEC)
 

src/convolutional_layer.c

@@ -320,11 +320,12 @@ image *visualize_convolutional_layer(convolutional_layer layer, char *window, im
     image *single_filters = weighted_sum_filters(layer, 0);
     show_images(single_filters, layer.n, window);
 
-    image delta = get_convolutional_delta(layer);
+    image delta = get_convolutional_image(layer);
     image dc = collapse_image_layers(delta, 1);
     char buff[256];
-    sprintf(buff, "%s: Delta", window);
-    //show_image(dc, buff);
+    sprintf(buff, "%s: Output", window);
+    show_image(dc, buff);
+    save_image(dc, buff);
     free_image(dc);
     return single_filters;
 }

src/image.c

@@ -264,7 +264,7 @@ void add_into_image(image src, image dest, int h, int w)
     }
 }
 
-void add_scalar_image(image m, float s)
+void translate_image(image m, float s)
 {
     int i;
     for(i = 0; i < m.h*m.w*m.c; ++i) m.data[i] += s;
@@ -645,15 +645,49 @@ void print_image(image m)
     for(i =0 ; i < m.h*m.w*m.c; ++i) printf("%lf, ", m.data[i]);
     printf("\n");
 }
+image collapse_images_vert(image *ims, int n)
+{
+    int color = 1;
+    int border = 1;
+    int h,w,c;
+    w = ims[0].w;
+    h = (ims[0].h + border) * n - border;
+    c = ims[0].c;
+    if(c != 3 || !color){
+        w = (w+border)*c - border;
+        c = 1;
+    }
 
-image collapse_images(image *ims, int n)
+    image filters = make_image(h,w,c);
+    int i,j;
+    for(i = 0; i < n; ++i){
+        int h_offset = i*(ims[0].h+border);
+        image copy = copy_image(ims[i]);
+        //normalize_image(copy);
+        if(c == 3 && color){
+            embed_image(copy, filters, h_offset, 0);
+        }
+        else{
+            for(j = 0; j < copy.c; ++j){
+                int w_offset = j*(ims[0].w+border);
+                image layer = get_image_layer(copy, j);
+                embed_image(layer, filters, h_offset, w_offset);
+                free_image(layer);
+            }
+        }
+        free_image(copy);
+    }
+    return filters;
+} 
+
+image collapse_images_horz(image *ims, int n)
 {
     int color = 1;
     int border = 1;
     int h,w,c;
     int size = ims[0].h;
     h = size;
-    w = (size + border) * n - border;
+    w = (ims[0].w + border) * n - border;
     c = ims[0].c;
     if(c != 3 || !color){
         h = (h+border)*c - border;
@@ -665,7 +699,7 @@ image collapse_images(image *ims, int n)
     for(i = 0; i < n; ++i){
         int w_offset = i*(size+border);
         image copy = copy_image(ims[i]);
-        normalize_image(copy);
+        //normalize_image(copy);
         if(c == 3 && color){
             embed_image(copy, filters, 0, w_offset);
         }
@@ -684,11 +718,49 @@ image collapse_images(image *ims, int n)
 
 void show_images(image *ims, int n, char *window)
 {
-    image m = collapse_images(ims, n);
+    image m = collapse_images_vert(ims, n);
+    save_image(m, window);
     show_image(m, window);
     free_image(m);
 }
 
+image grid_images(image **ims, int h, int w)
+{
+    int i;
+    image *rows = calloc(h, sizeof(image));
+    for(i = 0; i < h; ++i){
+        rows[i] = collapse_images_horz(ims[i], w);
+    }
+    image out = collapse_images_vert(rows, h);
+    for(i = 0; i < h; ++i){
+        free_image(rows[i]);
+    }
+    free(rows);
+    return out;
+}
+
+void test_grid()
+{
+    int i,j;
+    int num = 3;
+    int topk = 3;
+    image **vizs = calloc(num, sizeof(image*));
+    for(i = 0; i < num; ++i){
+        vizs[i] = calloc(topk, sizeof(image));
+        for(j = 0; j < topk; ++j) vizs[i][j] = make_image(3,3,3);
+    }
+    image grid = grid_images(vizs, num, topk);
+    save_image(grid, "Test Grid");
+    free_image(grid);
+}
+
+void show_images_grid(image **ims, int h, int w, char *window)
+{
+    image out = grid_images(ims, h, w);
+    show_image(out, window);
+    free_image(out);
+}
+
 void free_image(image m)
 {
     free(m.data);

src/image.h

@@ -1,6 +1,7 @@
 #ifndef IMAGE_H
 #define IMAGE_H
 
+
 #include "opencv2/highgui/highgui_c.h"
 #include "opencv2/imgproc/imgproc_c.h"
 typedef struct {
@@ -12,7 +13,7 @@ typedef struct {
 
 image image_distance(image a, image b);
 void scale_image(image m, float s);
-void add_scalar_image(image m, float s);
+void translate_image(image m, float s);
 void normalize_image(image p);
 void z_normalize_image(image p);
 void threshold_image(image p, float t);
@@ -23,6 +24,8 @@ float avg_image_layer(image m, int l);
 void embed_image(image source, image dest, int h, int w);
 void add_into_image(image src, image dest, int h, int w);
 image collapse_image_layers(image source, int border);
+image collapse_images_horz(image *ims, int n);
+image collapse_images_vert(image *ims, int n);
 image get_sub_image(image m, int h, int w, int dh, int dw);
 
 void show_image(image p, char *name);
@@ -30,6 +33,9 @@ void save_image(image p, char *name);
 void show_images(image *ims, int n, char *window);
 void show_image_layers(image p, char *name);
 void show_image_collapsed(image p, char *name);
+void show_images_grid(image **ims, int h, int w, char *window);
+void test_grid();
+image grid_images(image **ims, int h, int w);
 void print_image(image m);
 
 image make_image(int h, int w, int c);

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 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
     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)
@@ -48,9 +60,9 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
     fprintf(fp, "[connected]\n");
     if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
     fprintf(fp, "output=%d\n"
-                "activation=%s\n",
-                l->outputs,
-                get_activation_string(l->activation));
+            "activation=%s\n",
+            l->outputs,
+            get_activation_string(l->activation));
     fprintf(fp, "data=");
     for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
     for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
@@ -61,13 +73,27 @@ void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
 {
     fprintf(fp, "[maxpool]\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);
+            "height=%d\n"
+            "width=%d\n"
+            "channels=%d\n",
+            l->batch,l->h, l->w, l->c);
     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 @@ void save_network(network net, char *filename)
             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 @@ void forward_network(network net, float *input)
             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 @@ void update_network(network net, float step, float momentum, float decay)
         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 @@ float *get_network_output_layer(network net, int i)
     } 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 @@ float backward_network(network net, float *input, float *truth)
             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);
@@ -272,7 +315,7 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
             error += err;
             ++pos;
         }
-        
+
 
         //printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
         //if((i+1)%10 == 0){
@@ -341,34 +384,34 @@ int get_network_output_size_layer(network net, int i)
 }
 
 /*
-int resize_network(network net, int h, int w, int c)
-{
-    int i;
-    for (i = 0; i < net.n; ++i){
-        if(net.types[i] == CONVOLUTIONAL){
-            convolutional_layer *layer = (convolutional_layer *)net.layers[i];
-            layer->h = h;
-            layer->w = w;
-            layer->c = c;
-            image output = get_convolutional_image(*layer);
-            h = output.h;
-            w = output.w;
-            c = output.c;
-        }
-        else if(net.types[i] == MAXPOOL){
-            maxpool_layer *layer = (maxpool_layer *)net.layers[i];
-            layer->h = h;
-            layer->w = w;
-            layer->c = c;
-            image output = get_maxpool_image(*layer);
-            h = output.h;
-            w = output.w;
-            c = output.c;
-        }
-    }
-    return 0;
-}
-*/
+   int resize_network(network net, int h, int w, int c)
+   {
+   int i;
+   for (i = 0; i < net.n; ++i){
+   if(net.types[i] == CONVOLUTIONAL){
+   convolutional_layer *layer = (convolutional_layer *)net.layers[i];
+   layer->h = h;
+   layer->w = w;
+   layer->c = c;
+   image output = get_convolutional_image(*layer);
+   h = output.h;
+   w = output.w;
+   c = output.c;
+   }
+   else if(net.types[i] == MAXPOOL){
+   maxpool_layer *layer = (maxpool_layer *)net.layers[i];
+   layer->h = h;
+   layer->w = w;
+   layer->c = c;
+   image output = get_maxpool_image(*layer);
+   h = output.h;
+   w = output.w;
+   c = output.c;
+   }
+   }
+   return 0;
+   }
+ */
 
 int resize_network(network net, int h, int w, int c)
 {
@@ -381,16 +424,21 @@ int resize_network(network net, int h, int w, int c)
             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 @@ image get_network_image_layer(network net, int i)
         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 @@ void visualize_network(network net)
             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);
+        }
     } 
 }
 

src/network.h

@@ -9,7 +9,8 @@ typedef enum {
     CONVOLUTIONAL,
     CONNECTED,
     MAXPOOL,
-    SOFTMAX
+    SOFTMAX,
+    NORMALIZATION
 } LAYER_TYPE;
 
 typedef struct {

src/normalization_layer.c

@@ -0,0 +1,96 @@
+#include "normalization_layer.h"
+#include <stdio.h>
+
+image get_normalization_image(normalization_layer layer)
+{
+    int h = layer.h;
+    int w = layer.w;
+    int c = layer.c;
+    return float_to_image(h,w,c,layer.output);
+}
+
+image get_normalization_delta(normalization_layer layer)
+{
+    int h = layer.h;
+    int w = layer.w;
+    int c = layer.c;
+    return float_to_image(h,w,c,layer.delta);
+}
+
+normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa)
+{
+    fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", h,w,c,size);
+    normalization_layer *layer = calloc(1, sizeof(normalization_layer));
+    layer->batch = batch;
+    layer->h = h;
+    layer->w = w;
+    layer->c = c;
+    layer->kappa = kappa;
+    layer->size = size;
+    layer->alpha = alpha;
+    layer->beta = beta;
+    layer->output = calloc(h * w * c * batch, sizeof(float));
+    layer->delta = calloc(h * w * c * batch, sizeof(float));
+    layer->sums = calloc(h*w, sizeof(float));
+    return layer;
+}
+
+void resize_normalization_layer(normalization_layer *layer, int h, int w, int c)
+{
+    layer->h = h;
+    layer->w = w;
+    layer->c = c;
+    layer->output = realloc(layer->output, h * w * c * layer->batch * sizeof(float));
+    layer->delta = realloc(layer->delta, h * w * c * layer->batch * sizeof(float));
+    layer->sums = realloc(layer->sums, h*w * sizeof(float));
+}
+
+void add_square_array(float *src, float *dest, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        dest[i] += src[i]*src[i];
+    }
+}
+void sub_square_array(float *src, float *dest, int n)
+{
+    int i;
+    for(i = 0; i < n; ++i){
+        dest[i] -= src[i]*src[i];
+    }
+}
+
+void forward_normalization_layer(const normalization_layer layer, float *in)
+{
+    int i,j,k;
+    memset(layer.sums, 0, layer.h*layer.w*sizeof(float));
+    int imsize = layer.h*layer.w;
+    for(j = 0; j < layer.size/2; ++j){
+        if(j < layer.c) add_square_array(in+j*imsize, layer.sums, imsize);
+    }
+    for(k = 0; k < layer.c; ++k){
+        int next = k+layer.size/2;
+        int prev = k-layer.size/2-1;
+        if(next < layer.c) add_square_array(in+next*imsize, layer.sums, imsize);
+        if(prev > 0)        sub_square_array(in+prev*imsize, layer.sums, imsize);
+        for(i = 0; i < imsize; ++i){
+            layer.output[k*imsize + i] = in[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
+        }
+    }
+}
+
+void backward_normalization_layer(const normalization_layer layer, float *in, float *delta)
+{
+    //TODO!
+}
+
+void visualize_normalization_layer(normalization_layer layer, char *window)
+{
+    image delta = get_normalization_image(layer);
+    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);
+}

src/normalization_layer.h

@@ -0,0 +1,26 @@
+#ifndef NORMALIZATION_LAYER_H
+#define NORMALIZATION_LAYER_H
+
+#include "image.h"
+
+typedef struct {
+    int batch;
+    int h,w,c;
+    int size;
+    float alpha;
+    float beta;
+    float kappa;
+    float *delta;
+    float *output;
+    float *sums;
+} normalization_layer;
+
+image get_normalization_image(normalization_layer layer);
+normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa);
+void resize_normalization_layer(normalization_layer *layer, int h, int w, int c);
+void forward_normalization_layer(const normalization_layer layer, float *in);
+void backward_normalization_layer(const normalization_layer layer, float *in, float *delta);
+void visualize_normalization_layer(normalization_layer layer, char *window);
+
+#endif
+

src/parser.c

@@ -7,6 +7,7 @@
 #include "convolutional_layer.h"
 #include "connected_layer.h"
 #include "maxpool_layer.h"
+#include "normalization_layer.h"
 #include "softmax_layer.h"
 #include "list.h"
 #include "option_list.h"
@@ -21,6 +22,7 @@ int is_convolutional(section *s);
 int is_connected(section *s);
 int is_maxpool(section *s);
 int is_softmax(section *s);
+int is_normalization(section *s);
 list *read_cfg(char *filename);
 
 void free_section(section *s)
@@ -152,6 +154,30 @@ maxpool_layer *parse_maxpool(list *options, network net, int count)
     return layer;
 }
 
+normalization_layer *parse_normalization(list *options, network net, int count)
+{
+    int h,w,c;
+    int size = option_find_int(options, "size",1);
+    float alpha = option_find_float(options, "alpha", 0.);
+    float beta = option_find_float(options, "beta", 1.);
+    float kappa = option_find_float(options, "kappa", 1.);
+    if(count == 0){
+        h = option_find_int(options, "height",1);
+        w = option_find_int(options, "width",1);
+        c = option_find_int(options, "channels",1);
+        net.batch = option_find_int(options, "batch",1);
+    }else{
+        image m =  get_network_image_layer(net, count-1);
+        h = m.h;
+        w = m.w;
+        c = m.c;
+        if(h == 0) error("Layer before convolutional layer must output image.");
+    }
+    normalization_layer *layer = make_normalization_layer(net.batch,h,w,c,size, alpha, beta, kappa);
+    option_unused(options);
+    return layer;
+}
+
 network parse_network_cfg(char *filename)
 {
     list *sections = read_cfg(filename);
@@ -182,6 +208,11 @@ network parse_network_cfg(char *filename)
             net.types[count] = MAXPOOL;
             net.layers[count] = layer;
             net.batch = layer->batch;
+        }else if(is_normalization(s)){
+            normalization_layer *layer = parse_normalization(options, net, count);
+            net.types[count] = NORMALIZATION;
+            net.layers[count] = layer;
+            net.batch = layer->batch;
         }else{
             fprintf(stderr, "Type not recognized: %s\n", s->type);
         }
@@ -216,6 +247,11 @@ int is_softmax(section *s)
     return (strcmp(s->type, "[soft]")==0
             || strcmp(s->type, "[softmax]")==0);
 }
+int is_normalization(section *s)
+{
+    return (strcmp(s->type, "[lrnorm]")==0
+            || strcmp(s->type, "[localresponsenormalization]")==0);
+}
 
 int read_option(char *s, list *options)
 {

src/tests.c

@@ -1,4 +1,5 @@
 #include "connected_layer.h"
+
 //#include "old_conv.h"
 #include "convolutional_layer.h"
 #include "maxpool_layer.h"
@@ -223,7 +224,7 @@ void train_full()
 
 void test_visualize()
 {
-    network net = parse_network_cfg("cfg/imagenet.cfg");
+    network net = parse_network_cfg("cfg/voc_imagenet.cfg");
     srand(2222222);
     visualize_network(net);
     cvWaitKey(0);
@@ -445,6 +446,12 @@ void test_im2row()
     }
 }
 
+void flip_network()
+{
+    network net = parse_network_cfg("cfg/voc_imagenet_orig.cfg");
+    save_network(net, "cfg/voc_imagenet_rev.cfg");
+}
+
 void train_VOC()
 {
     network net = parse_network_cfg("cfg/voc_start.cfg");
@@ -498,6 +505,7 @@ image features_output_size(network net, IplImage *src, int outh, int outw)
     IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
     cvResize(src, sized, CV_INTER_LINEAR);
     image im = ipl_to_image(sized);
+    normalize_array(im.data, im.h*im.w*im.c);
     resize_network(net, im.h, im.w, im.c);
     forward_network(net, im.data);
     image out = get_network_image_layer(net, 6);
@@ -523,6 +531,69 @@ void features_VOC_image_size(char *image_path, int h, int w)
     free_image(out);
     cvReleaseImage(&src);
 }
+void visualize_imagenet_topk(char *filename)
+{
+    int i,j,k,l;
+    int topk = 10;
+    network net = parse_network_cfg("cfg/voc_imagenet.cfg");
+    list *plist = get_paths(filename);
+    node *n = plist->front;
+    int h = voc_size(1), w = voc_size(1);
+    int num = get_network_image(net).c;
+    image **vizs = calloc(num, sizeof(image*));
+    float **score = calloc(num, sizeof(float *));
+    for(i = 0; i < num; ++i){
+        vizs[i] = calloc(topk, sizeof(image));
+        for(j = 0; j < topk; ++j) vizs[i][j] = make_image(h,w,3);
+        score[i] = calloc(topk, sizeof(float));
+    }
+
+    while(n){
+        char *image_path = (char *)n->val;
+        image im = load_image(image_path, 0, 0);
+        n = n->next;
+        if(im.h < 200 || im.w < 200) continue;
+        printf("Processing %dx%d image\n", im.h, im.w);
+        resize_network(net, im.h, im.w, im.c);
+        //scale_image(im, 1./255);
+        translate_image(im, -144);
+        forward_network(net, im.data);
+        image out = get_network_image(net);
+
+        int dh = (im.h - h)/h;
+        int dw = (im.w - w)/w;
+        for(i = 0; i < out.h; ++i){
+            for(j = 0; j < out.w; ++j){
+                image sub = get_sub_image(im, dh*i, dw*j, h, w);
+                for(k = 0; k < out.c; ++k){
+                    float val = get_pixel(out, i, j, k);
+                    //printf("%f, ", val);
+                    image sub_c = copy_image(sub);
+                    for(l = 0; l < topk; ++l){
+                        if(val > score[k][l]){
+                            float swap = score[k][l];
+                            score[k][l] = val;
+                            val = swap;
+
+                            image swapi = vizs[k][l];
+                            vizs[k][l] = sub_c;
+                            sub_c = swapi;
+                        }
+                    }
+                    free_image(sub_c);
+                }
+                free_image(sub);
+            }
+        }
+        free_image(im);
+        //printf("\n");
+        image grid = grid_images(vizs, num, topk);
+        show_image(grid, "IMAGENET Visualization");
+        save_image(grid, "IMAGENET Grid");
+        free_image(grid);
+    }
+    //cvWaitKey(0);
+}
 
 void visualize_imagenet_features(char *filename)
 {
@@ -566,6 +637,20 @@ void visualize_imagenet_features(char *filename)
     cvWaitKey(0);
 }
 
+void visualize_cat()
+{
+    network net = parse_network_cfg("cfg/voc_imagenet.cfg");
+    image im = load_image("data/cat.png", 0, 0);
+    printf("Processing %dx%d image\n", im.h, im.w);
+    resize_network(net, im.h, im.w, im.c);
+    forward_network(net, im.data);
+    
+    image out = get_network_image(net);
+    visualize_network(net);
+    cvWaitKey(1000);
+    cvWaitKey(0);
+}
+
 void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
 {
     int i,j;
@@ -693,7 +778,10 @@ int main(int argc, char *argv[])
     //features_VOC_image(argv[1], argv[2], argv[3]);
     //features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3]));
     //visualize_imagenet_features("data/assira/train.list");
-    visualize_imagenet_features("data/VOC2011.list");
+    visualize_imagenet_topk("data/VOC2011.list");
+    //visualize_cat();
+    //flip_network();
+    //test_visualize();
     fprintf(stderr, "Success!\n");
     //test_random_preprocess();
     //test_random_classify();