some fixes

src/blas.c

@@ -92,6 +92,12 @@ void scal_cpu(int N, float ALPHA, float *X, int INCX)
     for(i = 0; i < N; ++i) X[i*INCX] *= ALPHA;
 }
 
+void fill_cpu(int N, float ALPHA, float *X, int INCX)
+{
+    int i;
+    for(i = 0; i < N; ++i) X[i*INCX] = ALPHA;
+}
+
 void copy_cpu(int N, float *X, int INCX, float *Y, int INCY)
 {
     int i;

src/blas.h

@@ -13,6 +13,7 @@ void mul_cpu(int N, float *X, int INCX, float *Y, int INCY);
 void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY);
 void copy_cpu(int N, float *X, int INCX, float *Y, int INCY);
 void scal_cpu(int N, float ALPHA, float *X, int INCX);
+void fill_cpu(int N, float ALPHA, float * X, int INCX);
 float dot_cpu(int N, float *X, int INCX, float *Y, int INCY);
 void test_gpu_blas();
 void shortcut_cpu(float *out, int w, int h, int c, int batch, int sample, float *add, int stride, int c2);

src/classifier.c

@@ -143,7 +143,7 @@ void validate_classifier(char *datacfg, char *filename, char *weightfile)
     clock_t time;
     float avg_acc = 0;
     float avg_topk = 0;
-    int splits = 50;
+    int splits = m/1000;
     int num = (i+1)*m/splits - i*m/splits;
 
     data val, buffer;
@@ -201,7 +201,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
     int i = 0;
     char **names = get_labels(name_list);
     clock_t time;
-    int indexes[10];
+    int *indexes = calloc(top, sizeof(int));
     char buff[256];
     char *input = buff;
     while(1){
@@ -214,7 +214,7 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
             if(!input) return;
             strtok(input, "\n");
         }
-        image im = load_image_color(input, 256, 256);
+        image im = load_image_color(input, net.w, net.h);
         float *X = im.data;
         time=clock();
         float *predictions = network_predict(net, X);
@@ -229,10 +229,10 @@ void predict_classifier(char *datacfg, char *cfgfile, char *weightfile, char *fi
     }
 }
 
-void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filename, int target_layer)
+void test_classifier(char *datacfg, char *cfgfile, char *weightfile, int target_layer)
 {
     int curr = 0;
-    network net = parse_network_cfg(filename);
+    network net = parse_network_cfg(cfgfile);
     if(weightfile){
         load_weights(&net, weightfile);
     }
@@ -241,10 +241,8 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filen
     list *options = read_data_cfg(datacfg);
 
     char *test_list = option_find_str(options, "test", "data/test.list");
-    char *label_list = option_find_str(options, "labels", "data/labels.list");
     int classes = option_find_int(options, "classes", 2);
 
-    char **labels = get_labels(label_list);
     list *plist = get_paths(test_list);
 
     char **paths = (char **)list_to_array(plist);
@@ -262,7 +260,7 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filen
     args.classes = classes;
     args.n = net.batch;
     args.m = 0;
-    args.labels = labels;
+    args.labels = 0;
     args.d = &buffer;
     args.type = CLASSIFICATION_DATA;
 
@@ -283,13 +281,17 @@ void test_classifier(char *datacfg, char *cfgfile, char *weightfile, char *filen
         time=clock();
         matrix pred = network_predict_data(net, val);
         
-        int i;
+        int i, j;
         if (target_layer >= 0){
             //layer l = net.layers[target_layer];
         }
 
-        for(i = 0; i < val.X.rows; ++i){
-
+        for(i = 0; i < pred.rows; ++i){
+            printf("%s", paths[curr-net.batch+i]);
+            for(j = 0; j < pred.cols; ++j){
+                printf("\t%g", pred.vals[i][j]);
+            }
+            printf("\n");
         }
 
         free_matrix(pred);
@@ -315,7 +317,7 @@ void run_classifier(int argc, char **argv)
     int layer = layer_s ? atoi(layer_s) : -1;
     if(0==strcmp(argv[2], "predict")) predict_classifier(data, cfg, weights, filename);
     else if(0==strcmp(argv[2], "train")) train_classifier(data, cfg, weights);
-    else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights,filename, layer);
+    else if(0==strcmp(argv[2], "test")) test_classifier(data, cfg, weights, layer);
     else if(0==strcmp(argv[2], "valid")) validate_classifier(data, cfg, weights);
 }
 

src/convolutional_layer.c

@@ -194,13 +194,25 @@ void resize_convolutional_layer(convolutional_layer *l, int w, int h)
 #endif
 }
 
-void bias_output(float *output, float *biases, int batch, int n, int size)
+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];
+                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];
             }
         }
     }
@@ -222,7 +234,7 @@ void forward_convolutional_layer(const convolutional_layer l, network_state stat
     int out_w = convolutional_out_width(l);
     int i;
 
-    bias_output(l.output, l.biases, l.batch, l.n, out_h*out_w);
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
 
     int m = l.n;
     int k = l.size*l.size*l.c;
@@ -241,10 +253,16 @@ void forward_convolutional_layer(const convolutional_layer l, network_state stat
     }
 
     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);
 }

src/convolutional_layer.h

@@ -31,7 +31,7 @@ image *visualize_convolutional_layer(convolutional_layer layer, char *window, im
 
 void backward_convolutional_layer(convolutional_layer layer, network_state state);
 
-void bias_output(float *output, float *biases, int batch, int n, int size);
+void add_bias(float *output, float *biases, int batch, int n, int size);
 void backward_bias(float *bias_updates, float *delta, int batch, int n, int size);
 
 image get_convolutional_image(convolutional_layer layer);

src/deconvolutional_layer.c

@@ -134,7 +134,7 @@ void forward_deconvolutional_layer(const deconvolutional_layer l, network_state
     int n = l.h*l.w;
     int k = l.c;
 
-    bias_output(l.output, l.biases, l.batch, l.n, size);
+    fill_cpu(l.outputs*l.batch, 0, l.output, 1);
 
     for(i = 0; i < l.batch; ++i){
         float *a = l.filters;
@@ -145,6 +145,7 @@ void forward_deconvolutional_layer(const deconvolutional_layer l, network_state
 
         col2im_cpu(c, l.n, out_h, out_w, l.size, l.stride, 0, l.output+i*l.n*size);
     }
+    add_bias(l.output, l.biases, l.batch, l.n, size);
     activate_array(l.output, l.batch*l.n*size, l.activation);
 }