Better VOC handling and resizing

Makefile

@@ -4,9 +4,9 @@ UNAME = $(shell uname)
 ifeq ($(UNAME), Darwin)
 COMMON += -isystem /usr/local/Cellar/opencv/2.4.6.1/include/opencv -isystem /usr/local/Cellar/opencv/2.4.6.1/include
 else
-COMMON += -march=native
+COMMON += -march=native -flto
 endif
-CFLAGS= $(COMMON) -Ofast -flto
+CFLAGS= $(COMMON) -Ofast
 #CFLAGS= $(COMMON) -O0 -g 
 LDFLAGS=`pkg-config --libs opencv` -lm
 VPATH=./src/

src/connected_layer.c

@@ -7,16 +7,17 @@
 #include <stdlib.h>
 #include <string.h>
 
-connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activation)
+connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation)
 {
     fprintf(stderr, "Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
     int i;
     connected_layer *layer = calloc(1, sizeof(connected_layer));
     layer->inputs = inputs;
     layer->outputs = outputs;
+    layer->batch=batch;
 
-    layer->output = calloc(outputs, sizeof(float*));
-    layer->delta = calloc(outputs, sizeof(float*));
+    layer->output = calloc(batch*outputs, sizeof(float*));
+    layer->delta = calloc(batch*outputs, sizeof(float*));
 
     layer->weight_updates = calloc(inputs*outputs, sizeof(float));
     layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
@@ -78,14 +79,14 @@ void forward_connected_layer(connected_layer layer, float *input)
 {
     int i;
     memcpy(layer.output, layer.biases, layer.outputs*sizeof(float));
-    int m = 1;
+    int m = layer.batch;
     int k = layer.inputs;
     int n = layer.outputs;
     float *a = input;
     float *b = layer.weights;
     float *c = layer.output;
     gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
-    for(i = 0; i < layer.outputs; ++i){
+    for(i = 0; i < layer.outputs*layer.batch; ++i){
         layer.output[i] = activate(layer.output[i], layer.activation);
     }
     //for(i = 0; i < layer.outputs; ++i) if(i%(layer.outputs/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
@@ -94,12 +95,12 @@ void forward_connected_layer(connected_layer layer, float *input)
 void learn_connected_layer(connected_layer layer, float *input)
 {
     int i;
-    for(i = 0; i < layer.outputs; ++i){
+    for(i = 0; i < layer.outputs*layer.batch; ++i){
         layer.delta[i] *= gradient(layer.output[i], layer.activation);
-        layer.bias_updates[i] += layer.delta[i];
+        layer.bias_updates[i%layer.batch] += layer.delta[i]/layer.batch;
     }
     int m = layer.inputs;
-    int k = 1;
+    int k = layer.batch;
     int n = layer.outputs;
     float *a = input;
     float *b = layer.delta;
@@ -113,7 +114,7 @@ void backward_connected_layer(connected_layer layer, float *input, float *delta)
 
     int m = layer.inputs;
     int k = layer.outputs;
-    int n = 1;
+    int n = layer.batch;
 
     float *a = layer.weights;
     float *b = layer.delta;

src/connected_layer.h

@@ -4,6 +4,7 @@
 #include "activations.h"
 
 typedef struct{
+    int batch;
     int inputs;
     int outputs;
     float *weights;
@@ -25,7 +26,7 @@ typedef struct{
 
 } connected_layer;
 
-connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activation);
+connected_layer *make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation);
 
 void forward_connected_layer(connected_layer layer, float *input);
 void backward_connected_layer(connected_layer layer, float *input, float *delta);

src/convolutional_layer.c

@@ -31,7 +31,7 @@ image get_convolutional_delta(convolutional_layer layer)
     return float_to_image(h,w,c,layer.delta);
 }
 
-convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation)
 {
     int i;
     size = 2*(size/2)+1; //HA! And you thought you'd use an even sized filter...
@@ -40,6 +40,7 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
     layer->w = w;
     layer->c = c;
     layer->n = n;
+    layer->batch = batch;
     layer->stride = stride;
     layer->size = size;
 
@@ -56,12 +57,12 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
         //layer->biases[i] = rand_normal()*scale + scale;
         layer->biases[i] = 0;
     }
-    int out_h = (h-size)/stride + 1;
-    int out_w = (w-size)/stride + 1;
+    int out_h = convolutional_out_height(*layer);
+    int out_w = convolutional_out_width(*layer);
 
-    layer->col_image = calloc(out_h*out_w*size*size*c, sizeof(float));
-    layer->output = calloc(out_h * out_w * n, sizeof(float));
-    layer->delta  = calloc(out_h * out_w * n, sizeof(float));
+    layer->col_image = calloc(layer->batch*out_h*out_w*size*size*c, sizeof(float));
+    layer->output = calloc(layer->batch*out_h * out_w * n, sizeof(float));
+    layer->delta  = calloc(layer->batch*out_h * out_w * n, sizeof(float));
     layer->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);
@@ -70,21 +71,39 @@ convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int si
     return layer;
 }
 
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c)
+{
+    layer->h = h;
+    layer->w = w;
+    layer->c = c;
+    int out_h = convolutional_out_height(*layer);
+    int out_w = convolutional_out_width(*layer);
+
+    layer->col_image = realloc(layer->col_image,
+                                layer->batch*out_h*out_w*layer->size*layer->size*layer->c*sizeof(float));
+    layer->output = realloc(layer->output,
+                                layer->batch*out_h * out_w * layer->n*sizeof(float));
+    layer->delta  = realloc(layer->delta,
+                                layer->batch*out_h * out_w * layer->n*sizeof(float));
+}
+
 void forward_convolutional_layer(const convolutional_layer layer, float *in)
 {
     int i;
     int m = layer.n;
     int k = layer.size*layer.size*layer.c;
-    int n = ((layer.h-layer.size)/layer.stride + 1)*
-            ((layer.w-layer.size)/layer.stride + 1);
+    int n = convolutional_out_height(layer)*
+            convolutional_out_width(layer)*
+            layer.batch;
 
     memset(layer.output, 0, m*n*sizeof(float));
 
     float *a = layer.filters;
     float *b = layer.col_image;
     float *c = layer.output;
-
-    im2col_cpu(in,  layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, b);
+    for(i = 0; i < layer.batch; ++i){
+        im2col_cpu(in+i*(n/layer.batch),  layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, b+i*(n/layer.batch));
+    }
     gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
 
     for(i = 0; i < m*n; ++i){
@@ -97,9 +116,10 @@ void forward_convolutional_layer(const convolutional_layer layer, float *in)
 void gradient_delta_convolutional_layer(convolutional_layer layer)
 {
     int i;
-    int size = convolutional_out_height(layer)
-                *convolutional_out_width(layer)
-                *layer.n;
+    int size = convolutional_out_height(layer)*
+                convolutional_out_width(layer)*
+                layer.n*
+                layer.batch;
     for(i = 0; i < size; ++i){
         layer.delta[i] *= gradient(layer.output[i], layer.activation);
     }
@@ -107,15 +127,17 @@ void gradient_delta_convolutional_layer(convolutional_layer layer)
 
 void learn_bias_convolutional_layer(convolutional_layer layer)
 {
-    int i,j;
+    int i,j,b;
     int size = convolutional_out_height(layer)
                 *convolutional_out_width(layer);
-    for(i = 0; i < layer.n; ++i){
-        float sum = 0;
-        for(j = 0; j < size; ++j){
-            sum += layer.delta[j+i*size];
+    for(b = 0; b < layer.batch; ++b){
+        for(i = 0; i < layer.n; ++i){
+            float sum = 0;
+            for(j = 0; j < size; ++j){
+                sum += layer.delta[j+size*(i+b*layer.n)];
+            }
+            layer.bias_updates[i] += sum/size;
         }
-        layer.bias_updates[i] += sum/size;
     }
 }
 
@@ -125,8 +147,9 @@ void learn_convolutional_layer(convolutional_layer layer)
     learn_bias_convolutional_layer(layer);
     int m = layer.n;
     int n = layer.size*layer.size*layer.c;
-    int k = ((layer.h-layer.size)/layer.stride + 1)*
-            ((layer.w-layer.size)/layer.stride + 1);
+    int k = convolutional_out_height(layer)*
+            convolutional_out_width(layer)*
+            layer.batch;
 
     float *a = layer.delta;
     float *b = layer.col_image;
@@ -137,10 +160,12 @@ void learn_convolutional_layer(convolutional_layer layer)
 
 void backward_convolutional_layer(convolutional_layer layer, float *delta)
 {
+    int i;
     int m = layer.size*layer.size*layer.c;
     int k = layer.n;
-    int n = ((layer.h-layer.size)/layer.stride + 1)*
-            ((layer.w-layer.size)/layer.stride + 1);
+    int n = convolutional_out_height(layer)*
+            convolutional_out_width(layer)*
+            layer.batch;
 
     float *a = layer.filters;
     float *b = layer.delta;
@@ -150,8 +175,10 @@ void backward_convolutional_layer(convolutional_layer layer, float *delta)
     memset(c, 0, m*n*sizeof(float));
     gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
 
-    memset(delta, 0, layer.h*layer.w*layer.c*sizeof(float));
-    col2im_cpu(c,  layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, delta);
+    memset(delta, 0, layer.batch*layer.h*layer.w*layer.c*sizeof(float));
+    for(i = 0; i < layer.batch; ++i){
+        col2im_cpu(c+i*n/layer.batch,  layer.c,  layer.h,  layer.w,  layer.size,  layer.stride, delta+i*n/layer.batch);
+    }
 }
 
 void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay)
@@ -225,7 +252,7 @@ void update_convolutional_layer(convolutional_layer layer, float step, float mom
 
 void test_convolutional_layer()
 {
-    convolutional_layer l = *make_convolutional_layer(4,4,1,1,3,1,LINEAR);
+    convolutional_layer l = *make_convolutional_layer(1,4,4,1,1,3,1,LINEAR);
     float input[] =    {1,2,3,4,
                         5,6,7,8,
                         9,10,11,12,

src/convolutional_layer.h

@@ -5,6 +5,7 @@
 #include "activations.h"
 
 typedef struct {
+    int batch;
     int h,w,c;
     int n;
     int size;
@@ -24,7 +25,8 @@ typedef struct {
     ACTIVATION activation;
 } convolutional_layer;
 
-convolutional_layer *make_convolutional_layer(int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
+convolutional_layer *make_convolutional_layer(int batch, int h, int w, int c, int n, int size, int stride, ACTIVATION activation);
+void resize_convolutional_layer(convolutional_layer *layer, int h, int w, int c);
 void forward_convolutional_layer(const convolutional_layer layer, float *in);
 void learn_convolutional_layer(convolutional_layer layer);
 void update_convolutional_layer(convolutional_layer layer, float step, float momentum, float decay);

src/data.c

@@ -119,6 +119,30 @@ data load_categorical_data_csv(char *filename, int target, int k)
     return d;
 }
 
+data load_cifar10_data(char *filename)
+{
+    data d;
+    d.shallow = 0;
+    unsigned long i,j;
+    matrix X = make_matrix(10000, 3072);
+    matrix y = make_matrix(10000, 10);
+    d.X = X;
+    d.y = y;
+
+    FILE *fp = fopen(filename, "rb");
+    for(i = 0; i < 10000; ++i){
+        unsigned char bytes[3073];
+        fread(bytes, 1, 3073, fp);
+        int class = bytes[0];
+        y.vals[i][class] = 1;
+        for(j = 0; j < X.cols; ++j){
+            X.vals[i][j] = (double)bytes[j+1];
+        }
+    }
+    fclose(fp);
+    return d;
+}
+
 void randomize_data(data d)
 {
     int i;

src/data.h

@@ -17,6 +17,7 @@ data load_data_image_pathfile_part(char *filename, int part, int total,
                                     char **labels, int k, int h, int w);
 data load_data_image_pathfile_random(char *filename, int n, char **labels, 
                                         int k, int h, int w);
+data load_cifar10_data(char *filename);
 list *get_paths(char *filename);
 data load_categorical_data_csv(char *filename, int target, int k);
 void normalize_data_rows(data d);

src/image.c

@@ -136,7 +136,7 @@ void show_image(image p, char *name)
         }
     }
     free_image(copy);
-    if(disp->height < 500 || disp->width < 500){
+    if(disp->height < 500 || disp->width < 500 || disp->height > 1000){
         int w = 1500;
         int h = w*p.h/p.w;
         if(h > 1000){

src/maxpool_layer.c

@@ -17,10 +17,12 @@ image get_maxpool_delta(maxpool_layer layer)
     return float_to_image(h,w,c,layer.delta);
 }
 
-maxpool_layer *make_maxpool_layer(int h, int w, int c, int stride)
+maxpool_layer *make_maxpool_layer(int batch, int h, int w, int c, int stride)
 {
+    c = c*batch;
     fprintf(stderr, "Maxpool Layer: %d x %d x %d image, %d stride\n", h,w,c,stride);
     maxpool_layer *layer = calloc(1, sizeof(maxpool_layer));
+    layer->batch = batch;
     layer->h = h;
     layer->w = w;
     layer->c = c;
@@ -30,6 +32,15 @@ maxpool_layer *make_maxpool_layer(int h, int w, int c, int stride)
     return layer;
 }
 
+void resize_maxpool_layer(maxpool_layer *layer, int h, int w, int c)
+{
+    layer->h = h;
+    layer->w = w;
+    layer->c = c;
+    layer->output = realloc(layer->output, ((h-1)/layer->stride+1) * ((w-1)/layer->stride+1) * c * sizeof(float));
+    layer->delta = realloc(layer->delta, ((h-1)/layer->stride+1) * ((w-1)/layer->stride+1) * c * sizeof(float));
+}
+
 void forward_maxpool_layer(const maxpool_layer layer, float *in)
 {
     image input = float_to_image(layer.h, layer.w, layer.c, in);

src/maxpool_layer.h

@@ -4,6 +4,7 @@
 #include "image.h"
 
 typedef struct {
+    int batch;
     int h,w,c;
     int stride;
     float *delta;
@@ -11,7 +12,8 @@ typedef struct {
 } maxpool_layer;
 
 image get_maxpool_image(maxpool_layer layer);
-maxpool_layer *make_maxpool_layer(int h, int w, int c, int stride);
+maxpool_layer *make_maxpool_layer(int batch, int h, int w, int c, int stride);
+void resize_maxpool_layer(maxpool_layer *layer, int h, int w, int c);
 void forward_maxpool_layer(const maxpool_layer layer, float *in);
 void backward_maxpool_layer(const maxpool_layer layer, float *in, float *delta);
 

src/network.c

@@ -10,10 +10,11 @@
 #include "maxpool_layer.h"
 #include "softmax_layer.h"
 
-network make_network(int n)
+network make_network(int n, int batch)
 {
     network net;
     net.n = n;
+    net.batch = batch;
     net.layers = calloc(net.n, sizeof(void *));
     net.types = calloc(net.n, sizeof(LAYER_TYPE));
     net.outputs = 0;
@@ -25,10 +26,11 @@ void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
 {
     int i;
     fprintf(fp, "[convolutional]\n");
-    if(first) fprintf(fp,   "height=%d\n"
+    if(first) fprintf(fp,   "batch=%d\n"
+                            "height=%d\n"
                             "width=%d\n"
                             "channels=%d\n",
-                            l->h, l->w, l->c);
+                            l->batch,l->h, l->w, l->c);
     fprintf(fp, "filters=%d\n"
                 "size=%d\n"
                 "stride=%d\n"
@@ -44,7 +46,7 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
 {
     int i;
     fprintf(fp, "[connected]\n");
-    if(first) fprintf(fp, "input=%d\n", l->inputs);
+    if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
     fprintf(fp, "output=%d\n"
                 "activation=%s\n",
                 l->outputs,
@@ -58,17 +60,18 @@ void print_connected_cfg(FILE *fp, connected_layer *l, int first)
 void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
 {
     fprintf(fp, "[maxpool]\n");
-    if(first) fprintf(fp,   "height=%d\n"
+    if(first) fprintf(fp,   "batch=%d\n"
+                            "height=%d\n"
                             "width=%d\n"
                             "channels=%d\n",
-                            l->h, l->w, l->c);
+                            l->batch,l->h, l->w, l->c);
     fprintf(fp, "stride=%d\n\n", l->stride);
 }
 
 void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
 {
     fprintf(fp, "[softmax]\n");
-    if(first) fprintf(fp, "input=%d\n", l->inputs);
+    if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
     fprintf(fp, "\n");
 }
 
@@ -191,11 +194,11 @@ float calculate_error_network(network net, float *truth)
     float *out = get_network_output(net);
     int i, k = get_network_output_size(net);
     for(i = 0; i < k; ++i){
-        printf("%f, ", out[i]);
+        //printf("%f, ", out[i]);
         delta[i] = truth[i] - out[i];
         sum += delta[i]*delta[i];
     }
-    printf("\n");
+    //printf("\n");
     return sum;
 }
 
@@ -258,19 +261,26 @@ float train_network_sgd(network net, data d, int n, float step, float momentum,f
     int i;
     float error = 0;
     int correct = 0;
+    int pos = 0;
     for(i = 0; i < n; ++i){
         int index = rand()%d.X.rows;
-        error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+        float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
         float *y = d.y.vals[index];
         int class = get_predicted_class_network(net);
         correct += (y[class]?1:0);
+        if(y[1]){
+            error += err;
+            ++pos;
+        }
+        
+
         //printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
         //if((i+1)%10 == 0){
         //    printf("%d: %f\n", (i+1), (float)correct/(i+1));
         //}
     }
-    printf("Accuracy: %f\n",(float) correct/n);
-    return error/n;
+    //printf("Accuracy: %f\n",(float) correct/n);
+    return error/pos;
 }
 float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
 {
@@ -304,7 +314,7 @@ void train_network(network net, data d, float step, float momentum, float decay)
     }
     visualize_network(net);
     cvWaitKey(100);
-    printf("Accuracy: %f\n", (float)correct/d.X.rows);
+    fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
 }
 
 int get_network_output_size_layer(network net, int i)
@@ -330,7 +340,8 @@ int get_network_output_size_layer(network net, int i)
     return 0;
 }
 
-int reset_network_size(network net, int h, int w, int c)
+/*
+int resize_network(network net, int h, int w, int c)
 {
     int i;
     for (i = 0; i < net.n; ++i){
@@ -357,6 +368,34 @@ int reset_network_size(network net, int h, int w, int 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];
+            resize_convolutional_layer(layer, h, w, 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];
+            resize_maxpool_layer(layer, h, w, c);
+            image output = get_maxpool_image(*layer);
+            h = output.h;
+            w = output.w;
+            c = output.c;
+        }
+        else{
+            error("Cannot resize this type of layer");
+        }
+    }
+    return 0;
+}
 
 int get_network_output_size(network net)
 {

src/network.h

@@ -14,13 +14,14 @@ typedef enum {
 
 typedef struct {
     int n;
+    int batch;
     void **layers;
     LAYER_TYPE *types;
     int outputs;
     float *output;
 } network;
 
-network make_network(int n);
+network make_network(int n, int batch);
 void forward_network(network net, float *input);
 float backward_network(network net, float *input, float *truth);
 void update_network(network net, float step, float momentum, float decay);
@@ -41,7 +42,7 @@ int get_predicted_class_network(network net);
 void print_network(network net);
 void visualize_network(network net);
 void save_network(network net, char *filename);
-int reset_network_size(network net, int h, int w, int c);
+int resize_network(network net, int h, int w, int c);
 
 #endif
 

src/parser.c

@@ -52,6 +52,7 @@ convolutional_layer *parse_convolutional(list *options, network net, int count)
         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;
@@ -59,7 +60,7 @@ convolutional_layer *parse_convolutional(list *options, network net, int count)
         c = m.c;
         if(h == 0) error("Layer before convolutional layer must output image.");
     }
-    convolutional_layer *layer = make_convolutional_layer(h,w,c,n,size,stride, activation);
+    convolutional_layer *layer = make_convolutional_layer(net.batch,h,w,c,n,size,stride, activation);
     char *data = option_find_str(options, "data", 0);
     if(data){
         char *curr = data;
@@ -90,10 +91,11 @@ connected_layer *parse_connected(list *options, network net, int count)
     ACTIVATION activation = get_activation(activation_s);
     if(count == 0){
         input = option_find_int(options, "input",1);
+        net.batch = option_find_int(options, "batch",1);
     }else{
         input =  get_network_output_size_layer(net, count-1);
     }
-    connected_layer *layer = make_connected_layer(input, output, activation);
+    connected_layer *layer = make_connected_layer(net.batch, input, output, activation);
     char *data = option_find_str(options, "data", 0);
     if(data){
         char *curr = data;
@@ -120,10 +122,11 @@ softmax_layer *parse_softmax(list *options, network net, int count)
     int input;
     if(count == 0){
         input = option_find_int(options, "input",1);
+        net.batch = option_find_int(options, "batch",1);
     }else{
         input =  get_network_output_size_layer(net, count-1);
     }
-    softmax_layer *layer = make_softmax_layer(input);
+    softmax_layer *layer = make_softmax_layer(net.batch, input);
     option_unused(options);
     return layer;
 }
@@ -136,6 +139,7 @@ maxpool_layer *parse_maxpool(list *options, network net, int count)
         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;
@@ -143,7 +147,7 @@ maxpool_layer *parse_maxpool(list *options, network net, int count)
         c = m.c;
         if(h == 0) error("Layer before convolutional layer must output image.");
     }
-    maxpool_layer *layer = make_maxpool_layer(h,w,c,stride);
+    maxpool_layer *layer = make_maxpool_layer(net.batch,h,w,c,stride);
     option_unused(options);
     return layer;
 }
@@ -151,7 +155,7 @@ maxpool_layer *parse_maxpool(list *options, network net, int count)
 network parse_network_cfg(char *filename)
 {
     list *sections = read_cfg(filename);
-    network net = make_network(sections->size);
+    network net = make_network(sections->size, 0);
 
     node *n = sections->front;
     int count = 0;
@@ -162,18 +166,22 @@ network parse_network_cfg(char *filename)
             convolutional_layer *layer = parse_convolutional(options, net, count);
             net.types[count] = CONVOLUTIONAL;
             net.layers[count] = layer;
+            net.batch = layer->batch;
         }else if(is_connected(s)){
             connected_layer *layer = parse_connected(options, net, count);
             net.types[count] = CONNECTED;
             net.layers[count] = layer;
+            net.batch = layer->batch;
         }else if(is_softmax(s)){
             softmax_layer *layer = parse_softmax(options, net, count);
             net.types[count] = SOFTMAX;
             net.layers[count] = layer;
+            net.batch = layer->batch;
         }else if(is_maxpool(s)){
             maxpool_layer *layer = parse_maxpool(options, net, count);
             net.types[count] = MAXPOOL;
             net.layers[count] = layer;
+            net.batch = layer->batch;
         }else{
             fprintf(stderr, "Type not recognized: %s\n", s->type);
         }

src/softmax_layer.c

@@ -3,13 +3,14 @@
 #include <stdlib.h>
 #include <stdio.h>
 
-softmax_layer *make_softmax_layer(int inputs)
+softmax_layer *make_softmax_layer(int batch, int inputs)
 {
     fprintf(stderr, "Softmax Layer: %d inputs\n", inputs);
     softmax_layer *layer = calloc(1, sizeof(softmax_layer));
+    layer->batch = batch;
     layer->inputs = inputs;
-    layer->output = calloc(inputs, sizeof(float));
-    layer->delta = calloc(inputs, sizeof(float));
+    layer->output = calloc(inputs*batch, sizeof(float));
+    layer->delta = calloc(inputs*batch, sizeof(float));
     return layer;
 }
 
@@ -28,28 +29,30 @@ void forward_softmax_layer(const softmax_layer layer, float *input)
 */
 void forward_softmax_layer(const softmax_layer layer, float *input)
 {
-    int i;
-    float sum = 0;
-    float largest = 0;
-    for(i = 0; i < layer.inputs; ++i){
-        if(input[i] > largest) largest = input[i];
-    }
-    for(i = 0; i < layer.inputs; ++i){
-        sum += exp(input[i]-largest);
-        //printf("%f, ", input[i]);
-    }
-    //printf("\n");
-    if(sum) sum = largest+log(sum);
-    else sum = largest-100;
-    for(i = 0; i < layer.inputs; ++i){
-        layer.output[i] = exp(input[i]-sum);
+    int i,b;
+    for(b = 0; b < layer.batch; ++b){
+        float sum = 0;
+        float largest = 0;
+        for(i = 0; i < layer.inputs; ++i){
+            if(input[i+b*layer.inputs] > largest) largest = input[i+b*layer.inputs];
+        }
+        for(i = 0; i < layer.inputs; ++i){
+            sum += exp(input[i+b*layer.inputs]-largest);
+            //printf("%f, ", input[i]);
+        }
+        //printf("\n");
+        if(sum) sum = largest+log(sum);
+        else sum = largest-100;
+        for(i = 0; i < layer.inputs; ++i){
+            layer.output[i+b*layer.inputs] = exp(input[i+b*layer.inputs]-sum);
+        }
     }
 }
 
 void backward_softmax_layer(const softmax_layer layer, float *input, float *delta)
 {
     int i;
-    for(i = 0; i < layer.inputs; ++i){
+    for(i = 0; i < layer.inputs*layer.batch; ++i){
         delta[i] = layer.delta[i];
     }
 }

src/softmax_layer.h

@@ -3,11 +3,12 @@
 
 typedef struct {
     int inputs;
+    int batch;
     float *delta;
     float *output;
 } softmax_layer;
 
-softmax_layer *make_softmax_layer(int inputs);
+softmax_layer *make_softmax_layer(int batch, int inputs);
 void forward_softmax_layer(const softmax_layer layer, float *input);
 void backward_softmax_layer(const softmax_layer layer, float *input, float *delta);
 

src/tests.c

@@ -77,7 +77,7 @@ void verify_convolutional_layer()
     int size = 3;
     float eps = .00000001;
     image test = make_random_image(5,5, 1);
-    convolutional_layer layer = *make_convolutional_layer(test.h,test.w,test.c, n, size, stride, RELU);
+    convolutional_layer layer = *make_convolutional_layer(1,test.h,test.w,test.c, n, size, stride, RELU);
     image out = get_convolutional_image(layer);
     float **jacobian = calloc(test.h*test.w*test.c, sizeof(float));
     
@@ -200,7 +200,7 @@ void train_full()
     while(1){
         i += 1000;
         data train = load_data_image_pathfile_random("images/assira/train.list", 1000, labels, 2, 256, 256);
-        image im = float_to_image(256, 256, 3,train.X.vals[0]);
+        //image im = float_to_image(256, 256, 3,train.X.vals[0]);
         //visualize_network(net);
         //cvWaitKey(100);
         //show_image(im, "input");
@@ -247,30 +247,75 @@ void test_full()
     fclose(fp);
 }
 
+void test_cifar10()
+{
+    data test = load_cifar10_data("images/cifar10/test_batch.bin");
+    scale_data_rows(test, 1./255);
+    network net = parse_network_cfg("cfg/cifar10.cfg");
+    int count = 0;
+    float lr = .000005;
+    float momentum = .99;
+    float decay = 0.001;
+    decay = 0;
+    int batch = 10000;
+    while(++count <= 10000){
+        char buff[256];
+        sprintf(buff, "images/cifar10/data_batch_%d.bin", rand()%5+1);
+        data train = load_cifar10_data(buff);
+        scale_data_rows(train, 1./255);
+        train_network_sgd(net, train, batch, lr, momentum, decay);
+        //printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+        
+        float test_acc = network_accuracy(net, test);
+        printf("%5f %5f\n",(double)count*batch/train.X.rows/5, 1-test_acc);
+        free_data(train);
+    }
+
+}
+
+void test_vince()
+{
+    network net = parse_network_cfg("cfg/vince.cfg");
+    data train = load_categorical_data_csv("images/vince.txt", 144, 2);
+    normalize_data_rows(train);
+
+    int count = 0;
+    float lr = .00005;
+    float momentum = .9;
+    float decay = 0.0001;
+    decay = 0;
+    int batch = 10000;
+    while(++count <= 10000){
+        float loss = train_network_sgd(net, train, batch, lr, momentum, decay);
+        printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+    }
+}
+
 void test_nist()
 {
     srand(444444);
     srand(888888);
-    network net = parse_network_cfg("nist.cfg");
+    network net = parse_network_cfg("cfg/nist_basic.cfg");
     data train = load_categorical_data_csv("mnist/mnist_train.csv", 0, 10);
     data test = load_categorical_data_csv("mnist/mnist_test.csv",0,10);
     normalize_data_rows(train);
     normalize_data_rows(test);
     //randomize_data(train);
     int count = 0;
-    float lr = .0005;
+    float lr = .00005;
     float momentum = .9;
-    float decay = 0.001;
-    clock_t start = clock(), end;
-    while(++count <= 100){
-        //visualize_network(net);
-        float loss = train_network_sgd(net, train, 1000, lr, momentum, decay);
-        printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*100, loss, lr, momentum, decay);
-        end = clock();
-        printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
-        start=end;
-        //cvWaitKey(100);
-        //lr /= 2; 
+    float decay = 0.0001;
+    decay = 0;
+    //clock_t start = clock(), end;
+    int batch = 10000;
+    while(++count <= 10000){
+        float loss = train_network_sgd(net, train, batch, lr, momentum, decay);
+        printf("%5f %5f\n",(double)count*batch/train.X.rows, loss);
+        //printf("%5d Training Loss: %lf, Params: %f %f %f, ",count*1000, loss, lr, momentum, decay);
+        //end = clock();
+        //printf("Time: %lf seconds\n", (float)(end-start)/CLOCKS_PER_SEC);
+        //start=end;
+        /*
         if(count%5 == 0){
             float train_acc = network_accuracy(net, train);
             fprintf(stderr, "\nTRAIN: %f\n", train_acc);
@@ -279,6 +324,7 @@ void test_nist()
             printf("%d, %f, %f\n", count, train_acc, test_acc);
             //lr *= .5;
         }
+        */
     }
 }
 
@@ -439,91 +485,35 @@ image features_output_size(network net, IplImage *src, int outh, int outw)
 {
     int h = voc_size(outh);
     int w = voc_size(outw);
-    printf("%d %d\n", h, w);
+    fprintf(stderr, "%d %d\n", h, w);
 
     IplImage *sized = cvCreateImage(cvSize(w,h), src->depth, src->nChannels);
     cvResize(src, sized, CV_INTER_LINEAR);
     image im = ipl_to_image(sized);
-    reset_network_size(net, 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);
-    //printf("%d %d\n%d %d\n", outh, out.h, outw, out.w);
     free_image(im);
     cvReleaseImage(&sized);
     return copy_image(out);
 }
 
-void features_VOC(int part, int total)
+void features_VOC_image_size(char *image_path, int h, int w)
 {
-    int i,j, count = 0;
+    int j;
     network net = parse_network_cfg("cfg/voc_imagenet.cfg");
-    char *path_file = "images/VOC2012/all_paths.txt";
-    char *out_dir = "voc_features/";
-    list *paths = get_paths(path_file);
-    node *n = paths->front;
-    int size = paths->size;
-    for(count = 0; count < part*size/total; ++count) n = n->next;
-    while(n && count++ < (part+1)*size/total){
-        char *path = (char *)n->val;
-        char buff[1024];
-        sprintf(buff, "%s%s.txt",out_dir, path);
-        printf("%s\n", path);
-        FILE *fp = fopen(buff, "w");
-        if(fp == 0) file_error(buff);
+    fprintf(stderr, "%s\n", image_path);
 
-        IplImage* src = 0;
-        if( (src = cvLoadImage(path,-1)) == 0 )
-        {
-            printf("Cannot load file image %s\n", path);
-            exit(0);
-        }
-        int w = src->width;
-        int h = src->height;
-        int sbin = 8;
-        int interval = 10;
-        double scale = pow(2., 1./interval);
-        int m = (w<h)?w:h;
-        int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale));
-        image *ims = calloc(max_scale+interval, sizeof(image));
-
-        for(i = 0; i < interval; ++i){
-            double factor = 1./pow(scale, i);
-            double ih =  round(h*factor);
-            double iw =  round(w*factor);
-            int ex_h = round(ih/4.) - 2;
-            int ex_w = round(iw/4.) - 2;
-            ims[i] = features_output_size(net, src, ex_h, ex_w);
-
-            ih =  round(h*factor);
-            iw =  round(w*factor);
-            ex_h = round(ih/8.) - 2;
-            ex_w = round(iw/8.) - 2;
-            ims[i+interval] = features_output_size(net, src, ex_h, ex_w);
-            for(j = i+interval; j < max_scale; j += interval){
-                factor /= 2.;
-                ih =  round(h*factor);
-                iw =  round(w*factor);
-                ex_h = round(ih/8.) - 2;
-                ex_w = round(iw/8.) - 2;
-                ims[j+interval] = features_output_size(net, src, ex_h, ex_w);
-            }
-        }
-        for(i = 0; i < max_scale+interval; ++i){
-            image out = ims[i];
-            //printf("%d, %d\n", out.h, out.w);
-            fprintf(fp, "%d, %d, %d\n",out.c, out.h, out.w);
-            for(j = 0; j < out.c*out.h*out.w; ++j){
-                if(j != 0)fprintf(fp, ",");
-                fprintf(fp, "%g", out.data[j]);
-            }
-            fprintf(fp, "\n");
-            free_image(out);
-        }
-        free(ims);
-        fclose(fp);
-        cvReleaseImage(&src);
-        n = n->next;
+    IplImage* src = 0;
+    if( (src = cvLoadImage(image_path,-1)) == 0 ) file_error(image_path);
+    image out = features_output_size(net, src, h, w);
+    for(j = 0; j < out.c*out.h*out.w; ++j){
+        if(j != 0) printf(",");
+        printf("%g", out.data[j]);
     }
+    printf("\n");
+    free_image(out);
+    cvReleaseImage(&src);
 }
 
 void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
@@ -531,9 +521,9 @@ void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
     int i,j;
     network net = parse_network_cfg("cfg/voc_imagenet.cfg");
     char image_path[1024];
-    sprintf(image_path, "%s%s",image_dir, image_file);
+    sprintf(image_path, "%s/%s",image_dir, image_file);
     char out_path[1024];
-    sprintf(out_path, "%s%s.txt",out_dir, image_file);
+    sprintf(out_path, "%s/%s.txt",out_dir, image_file);
     printf("%s\n", image_file);
     FILE *fp = fopen(out_path, "w");
     if(fp == 0) file_error(out_path);
@@ -543,10 +533,11 @@ void features_VOC_image(char *image_file, char *image_dir, char *out_dir)
     int w = src->width;
     int h = src->height;
     int sbin = 8;
-    int interval = 10;
+    int interval = 4;
     double scale = pow(2., 1./interval);
     int m = (w<h)?w:h;
     int max_scale = 1+floor((double)log((double)m/(5.*sbin))/log(scale));
+    if(max_scale < interval) error("max_scale must be >= interval");
     image *ims = calloc(max_scale+interval, sizeof(image));
 
     for(i = 0; i < interval; ++i){
@@ -642,10 +633,13 @@ int main(int argc, char *argv[])
     //test_split();
     //test_ensemble();
     //test_nist();
+    //test_cifar10();
+    //test_vince();
     //test_full();
     //train_VOC();
-    features_VOC_image(argv[1], argv[2], argv[3]);
-    printf("Success!\n");
+    //features_VOC_image(argv[1], argv[2], argv[3]);
+    features_VOC_image_size(argv[1], atoi(argv[2]), atoi(argv[3]));
+    fprintf(stderr, "Success!\n");
     //test_random_preprocess();
     //test_random_classify();
     //test_parser();