stuff changed probably

src/data.c

@@ -137,18 +137,20 @@ void fill_truth_detection(char *path, float *truth, int classes, int height, int
         if(j < 0) j = 0;
         if(j >= num_height) j = num_height-1;
         
-        float dw = (x - i*box_width)/box_width;
-        float dh = (y - j*box_height)/box_height;
+        float dw = constrain(0,1, (x - i*box_width)/box_width );
+        float dh = constrain(0,1, (y - j*box_height)/box_height );
+        float th = constrain(0,1, h*(height+jitter)/height );
+        float tw = constrain(0,1, w*(width+jitter)/width );
 
         int index = (i+j*num_width)*(4+classes+background);
-        if(truth[index+classes+background]) continue;
+        if(truth[index+classes+background+2]) continue;
         if(background) truth[index++] = 0;
         truth[index+id] = 1;
         index += classes;
         truth[index++] = dh;
         truth[index++] = dw;
-        truth[index++] = h*(height+jitter)/height;
-        truth[index++] = w*(width+jitter)/width;
+        truth[index++] = th;
+        truth[index++] = tw;
     }
     free(boxes);
 }

src/detection.c

@@ -50,7 +50,7 @@ void train_detection(char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    net.seen = 0;
+    //net.seen = 0;
     printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     int imgs = 128;
     srand(time(0));
@@ -63,7 +63,7 @@ void train_detection(char *cfgfile, char *weightfile)
     int im_dim = 512;
     int jitter = 64;
     int classes = 20;
-    int background = 0;
+    int background = 1;
     pthread_t load_thread = load_data_detection_thread(imgs, paths, plist->size, classes, im_dim, im_dim, 7, 7, jitter, background, &buffer);
     clock_t time;
     while(1){
@@ -109,8 +109,9 @@ void validate_detection(char *cfgfile, char *weightfile)
     char **paths = (char **)list_to_array(plist);
     int im_size = 448;
     int classes = 20;
-    int background = 0;
-    int num_output = 7*7*(4+classes+background);
+    int background = 1;
+    int nuisance = 0;
+    int num_output = 7*7*(4+classes+background+nuisance);
 
     int m = plist->size;
     int i = 0;
@@ -134,17 +135,19 @@ void validate_detection(char *cfgfile, char *weightfile)
         matrix pred = network_predict_data(net, val);
         int j, k, class;
         for(j = 0; j < pred.rows; ++j){
-            for(k = 0; k < pred.cols; k += classes+4+background){
+            for(k = 0; k < pred.cols; k += classes+4+background+nuisance){
+                float scale = 1.;
+                if(nuisance) scale = pred.vals[j][k];
                 for(class = 0; class < classes; ++class){
-                    int index = (k)/(classes+4+background); 
+                    int index = (k)/(classes+4+background+nuisance); 
                     int r = index/7;
                     int c = index%7;
-                    int ci = k+classes+background;
+                    int ci = k+classes+background+nuisance;
                     float y = (r + pred.vals[j][ci + 0])/7.;
                     float x = (c + pred.vals[j][ci + 1])/7.;
                     float h = pred.vals[j][ci + 2];
                     float w = pred.vals[j][ci + 3];
-                    printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, pred.vals[j][k+class+background], y, x, h, w);
+                    printf("%d %d %f %f %f %f %f\n", (i-1)*m/splits + j, class, scale*pred.vals[j][k+class+background+nuisance], y, x, h, w);
                 }
             }
         }

src/detection_layer.c

@@ -16,7 +16,7 @@ int get_detection_layer_output_size(detection_layer layer)
     return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
 }
 
-detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background)
+detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
 {
     detection_layer *layer = calloc(1, sizeof(detection_layer));
     
@@ -25,6 +25,7 @@ detection_layer *make_detection_layer(int batch, int inputs, int classes, int co
     layer->classes = classes;
     layer->coords = coords;
     layer->rescore = rescore;
+    layer->nuisance = nuisance;
     layer->background = background;
     int outputs = get_detection_layer_output_size(*layer);
     layer->output = calloc(batch*outputs, sizeof(float));
@@ -72,12 +73,18 @@ void forward_detection_layer(const detection_layer layer, network_state state)
         int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
         float scale = 1;
         if(layer.rescore) scale = state.input[in_i++];
-        if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
+        else if(layer.nuisance){
+            layer.output[out_i++] = 1-state.input[in_i++];
+            scale = mask;
+        }
+        else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
 
         for(j = 0; j < layer.classes; ++j){
             layer.output[out_i++] = scale*state.input[in_i++];
         }
-        if(layer.background){
+        if(layer.nuisance){
+            
+        }else if(layer.background){
             softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
             activate_array(state.input+in_i, layer.coords, LOGISTIC);
         }
@@ -85,6 +92,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
             layer.output[out_i++] = mask*state.input[in_i++];
         }
     }
+    /*
     if(layer.background || 1){
         for(i = 0; i < layer.batch*locations; ++i){
             int index = i*(layer.classes+layer.coords+layer.background);
@@ -95,6 +103,7 @@ void forward_detection_layer(const detection_layer layer, network_state state)
             }
         }
     }
+    */
 }
 
 void backward_detection_layer(const detection_layer layer, network_state state)
@@ -107,13 +116,15 @@ void backward_detection_layer(const detection_layer layer, network_state state)
         float scale = 1;
         float latent_delta = 0;
         if(layer.rescore) scale = state.input[in_i++];
-        if(layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
+        else if (layer.nuisance)   state.delta[in_i++] = -layer.delta[out_i++];
+        else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
         for(j = 0; j < layer.classes; ++j){
             latent_delta += state.input[in_i]*layer.delta[out_i];
             state.delta[in_i++] = scale*layer.delta[out_i++];
         }
 
-        if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
+        if (layer.nuisance) ;
+        else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
         for(j = 0; j < layer.coords; ++j){
             state.delta[in_i++] = layer.delta[out_i++];
         }

src/detection_layer.h

@@ -10,6 +10,7 @@ typedef struct {
     int coords;
     int background;
     int rescore;
+    int nuisance;
     float *output;
     float *delta;
     #ifdef GPU
@@ -18,7 +19,7 @@ typedef struct {
     #endif
 } detection_layer;
 
-detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background);
+detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance);
 void forward_detection_layer(const detection_layer layer, network_state state);
 void backward_detection_layer(const detection_layer layer, network_state state);
 int get_detection_layer_output_size(detection_layer layer);

src/parser.c

@@ -165,8 +165,9 @@ detection_layer *parse_detection(list *options, size_params params)
     int coords = option_find_int(options, "coords", 1);
     int classes = option_find_int(options, "classes", 1);
     int rescore = option_find_int(options, "rescore", 1);
+    int nuisance = option_find_int(options, "nuisance", 0);
     int background = option_find_int(options, "background", 1);
-    detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background);
+    detection_layer *layer = make_detection_layer(params.batch, params.inputs, classes, coords, rescore, background, nuisance);
     option_unused(options);
     return layer;
 }
@@ -550,7 +551,7 @@ void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
 void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
 {
     fprintf(fp, "[detection]\n");
-    fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\n", l->classes, l->coords, l->rescore);
+    fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance);
     fprintf(fp, "\n");
 }
 

src/utils.c

@@ -276,10 +276,10 @@ float variance_array(float *a, int n)
     return variance;
 }
 
-float constrain(float a, float max)
+float constrain(float min, float max, float a)
 {
-    if(a > abs(max)) return abs(max);
-    if(a < -abs(max)) return -abs(max);
+    if (a < min) return min;
+    if (a > max) return max;
     return a;
 }
 

src/utils.h

@@ -26,7 +26,7 @@ void normalize_array(float *a, int n);
 void scale_array(float *a, int n, float s);
 void translate_array(float *a, int n, float s);
 int max_index(float *a, int n);
-float constrain(float a, float max);
+float constrain(float min, float max, float a);
 float mse_array(float *a, int n);
 float rand_normal();
 float rand_uniform();