stuff and things

src/layer.h

@@ -67,6 +67,7 @@ struct layer{
     int size;
     int side;
     int stride;
+    int reverse;
     int pad;
     int sqrt;
     int flip;
@@ -118,6 +119,7 @@ struct layer{
     int bias_match;
     int random;
     float thresh;
+    int classfix;
 
     int dontload;
     int dontloadscales;

src/parser.c

@@ -268,6 +268,7 @@ layer parse_region(list *options, size_params params)
     l.rescore = option_find_int_quiet(options, "rescore",0);
 
     l.thresh = option_find_float(options, "thresh", .5);
+    l.classfix = option_find_int_quiet(options, "classfix", 0);
 
     l.coord_scale = option_find_float(options, "coord_scale", 1);
     l.object_scale = option_find_float(options, "object_scale", 1);
@@ -357,6 +358,7 @@ crop_layer parse_crop(list *options, size_params params)
 layer parse_reorg(list *options, size_params params)
 {
     int stride = option_find_int(options, "stride",1);
+    int reverse = option_find_int_quiet(options, "reverse",0);
 
     int batch,h,w,c;
     h = params.h;
@@ -365,7 +367,7 @@ layer parse_reorg(list *options, size_params params)
     batch=params.batch;
     if(!(h && w && c)) error("Layer before reorg layer must output image.");
 
-    layer layer = make_reorg_layer(batch,w,h,c,stride);
+    layer layer = make_reorg_layer(batch,w,h,c,stride,reverse);
     return layer;
 }
 

src/region_layer.c

@@ -89,6 +89,31 @@ float delta_region_box(box truth, float *x, float *biases, int n, int index, int
     return iou;
 }
 
+void delta_region_class(float *output, float *delta, int index, int class, int classes, tree *hier, float scale, float *avg_cat)
+{
+    int i, n;
+    if(hier){
+        float pred = 1;
+        while(class >= 0){
+            pred *= output[index + class];
+            int g = hier->group[class];
+            int offset = hier->group_offset[g];
+            for(i = 0; i < hier->group_size[g]; ++i){
+                delta[index + offset + i] = scale * (0 - output[index + offset + i]);
+            }
+            delta[index + class] = scale * (1 - output[index + class]);
+
+            class = hier->parent[class];
+        }
+        *avg_cat += pred;
+    } else {
+        for(n = 0; n < classes; ++n){
+            delta[index + n] = scale * (((n == class)?1 : 0) - output[index + n]);
+            if(n == class) *avg_cat += output[index + n];
+        }
+    }
+}
+
 float logit(float x)
 {
     return log(x/(1.-x));
@@ -125,6 +150,7 @@ void forward_region_layer(const region_layer l, network_state state)
     float avg_obj = 0;
     float avg_anyobj = 0;
     int count = 0;
+    int class_count = 0;
     *(l.cost) = 0;
     for (b = 0; b < l.batch; ++b) {
         for (j = 0; j < l.h; ++j) {
@@ -133,15 +159,28 @@ void forward_region_layer(const region_layer l, network_state state)
                     int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs;
                     box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h);
                     float best_iou = 0;
+                    int best_class = -1;
                     for(t = 0; t < 30; ++t){
                         box truth = float_to_box(state.truth + t*5 + b*l.truths);
                         if(!truth.x) break;
                         float iou = box_iou(pred, truth);
-                        if (iou > best_iou) best_iou = iou;
+                        if (iou > best_iou) {
+                            best_class = state.truth[t*5 + b*l.truths + 4];
+                            best_iou = iou;
+                        }
                     }
                     avg_anyobj += l.output[index + 4];
                     l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
-                    if(best_iou > l.thresh) l.delta[index + 4] = 0;
+                    if(l.classfix == -1) l.delta[index + 4] = l.noobject_scale * ((best_iou - l.output[index + 4]) * logistic_gradient(l.output[index + 4]));
+                    else{
+                        if (best_iou > l.thresh) {
+                            l.delta[index + 4] = 0;
+                            if(l.classfix > 0){
+                                delta_region_class(l.output, l.delta, index + 5, best_class, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat);
+                                ++class_count;
+                            }
+                        }
+                    }
 
                     if(*(state.net.seen) < 12800){
                         box truth = {0};
@@ -205,35 +244,15 @@ void forward_region_layer(const region_layer l, network_state state)
 
             int class = state.truth[t*5 + b*l.truths + 4];
             if (l.map) class = l.map[class];
-            if(l.softmax_tree){
+            delta_region_class(l.output, l.delta, best_index + 5, class, l.classes, l.softmax_tree, l.class_scale, &avg_cat);
-                float pred = 1;
-                while(class >= 0){
-                    pred *= l.output[best_index + 5 + class];
-                    int g = l.softmax_tree->group[class];
-                    int i;
-                    int offset = l.softmax_tree->group_offset[g];
-                    for(i = 0; i < l.softmax_tree->group_size[g]; ++i){
-                        int index = best_index + 5 + offset + i;
-                        l.delta[index] = l.class_scale * (0 - l.output[index]);
-                    }
-                    l.delta[best_index + 5 + class] = l.class_scale * (1 - l.output[best_index + 5 + class]);
-
-                    class = l.softmax_tree->parent[class];
-                }
-                avg_cat += pred;
-            } else {
-                for(n = 0; n < l.classes; ++n){
-                    l.delta[best_index + 5 + n] = l.class_scale * (((n == class)?1 : 0) - l.output[best_index + 5 + n]);
-                    if(n == class) avg_cat += l.output[best_index + 5 + n];
-                }
-            }
             ++count;
+            ++class_count;
         }
     }
     //printf("\n");
     reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0);
     *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2);
-    printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f,  count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
+    printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f,  count: %d\n", avg_iou/count, avg_cat/class_count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count);
 }
 
 void backward_region_layer(const region_layer l, network_state state)
@@ -245,7 +264,6 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b
 {
     int i,j,n;
     float *predictions = l.output;
-    //int per_cell = 5*num+classes;
     for (i = 0; i < l.w*l.h; ++i){
         int row = i / l.w;
         int col = i % l.w;
@@ -253,6 +271,7 @@ void get_region_boxes(layer l, int w, int h, float thresh, float **probs, box *b
             int index = i*l.n + n;
             int p_index = index * (l.classes + 5) + 4;
             float scale = predictions[p_index];
+            if(l.classfix == -1 && scale < .5) scale = 0;
             int box_index = index * (l.classes + 5);
             boxes[index] = get_region_box(predictions, l.biases, n, box_index, col, row, l.w, l.h);
             boxes[index].x *= w;

src/reorg_layer.c

@@ -4,7 +4,7 @@
 #include <stdio.h>
 
 
-layer make_reorg_layer(int batch, int h, int w, int c, int stride)
+layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse)
 {
     layer l = {0};
     l.type = REORG;
@@ -13,9 +13,15 @@ layer make_reorg_layer(int batch, int h, int w, int c, int stride)
     l.h = h;
     l.w = w;
     l.c = c;
+    if(reverse){
         l.out_w = w*stride;
         l.out_h = h*stride;
         l.out_c = c/(stride*stride);
+    }else{
+        l.out_w = w/stride;
+        l.out_h = h/stride;
+        l.out_c = c*(stride*stride);
+    }
     fprintf(stderr, "Reorg Layer: %d x %d x %d image -> %d x %d x %d image, \n", w,h,c,l.out_w, l.out_h, l.out_c);
     l.outputs = l.out_h * l.out_w * l.out_c;
     l.inputs = h*w*c;
@@ -25,13 +31,13 @@ layer make_reorg_layer(int batch, int h, int w, int c, int stride)
 
     l.forward = forward_reorg_layer;
     l.backward = backward_reorg_layer;
-    #ifdef GPU
+#ifdef GPU
     l.forward_gpu = forward_reorg_layer_gpu;
     l.backward_gpu = backward_reorg_layer_gpu;
 
     l.output_gpu  = cuda_make_array(l.output, output_size);
     l.delta_gpu   = cuda_make_array(l.delta, output_size);
-    #endif
+#endif
     return l;
 }
 
@@ -52,12 +58,12 @@ void resize_reorg_layer(layer *l, int w, int h)
     l->output = realloc(l->output, output_size * sizeof(float));
     l->delta = realloc(l->delta, output_size * sizeof(float));
 
-    #ifdef GPU
+#ifdef GPU
     cuda_free(l->output_gpu);
     cuda_free(l->delta_gpu);
     l->output_gpu  = cuda_make_array(l->output, output_size);
     l->delta_gpu   = cuda_make_array(l->delta,  output_size);
-    #endif
+#endif
 }
 
 void forward_reorg_layer(const layer l, network_state state)
@@ -107,11 +113,19 @@ void backward_reorg_layer(const layer l, network_state state)
 #ifdef GPU
 void forward_reorg_layer_gpu(layer l, network_state state)
 {
+    if(l.reverse){
         reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 1, l.output_gpu);
+    }else {
+        reorg_ongpu(state.input, l.w, l.h, l.c, l.batch, l.stride, 0, l.output_gpu);
+    }
 }
 
 void backward_reorg_layer_gpu(layer l, network_state state)
 {
+    if(l.reverse){
         reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 0, state.delta);
+    }else{
+        reorg_ongpu(l.delta_gpu, l.w, l.h, l.c, l.batch, l.stride, 1, state.delta);
+    }
 }
 #endif

src/reorg_layer.h

@@ -6,7 +6,7 @@
 #include "layer.h"
 #include "network.h"
 
-layer make_reorg_layer(int batch, int h, int w, int c, int stride);
+layer make_reorg_layer(int batch, int h, int w, int c, int stride, int reverse);
 void resize_reorg_layer(layer *l, int w, int h);
 void forward_reorg_layer(const layer l, network_state state);
 void backward_reorg_layer(const layer l, network_state state);