yolo working w/ regions

Makefile

@@ -34,7 +34,7 @@ CFLAGS+= -DGPU
 LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
 endif
 
-OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o yoloplus.o
+OBJ=gemm.o utils.o cuda.o deconvolutional_layer.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o imagenet.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o region_layer.o layer.o compare.o swag.o
 ifeq ($(GPU), 1) 
 OBJ+=convolutional_kernels.o deconvolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o softmax_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
 endif

cfg/darknet.cfg

@@ -1,15 +1,17 @@
 [net]
-batch=128
+batch=256
 subdivisions=1
 height=256
 width=256
 channels=3
 momentum=0.9
 decay=0.0005
+
 learning_rate=0.01
-policy=poly
+policy=step
-power=.5
+scale=.1
-max_batches=600000
+step=100000
+max_batches=400000
 
 [crop]
 crop_height=224

src/coco.c

@@ -111,20 +111,6 @@ void train_coco(char *cfgfile, char *weightfile)
         avg_loss = avg_loss*.9 + loss*.1;
 
         printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
-        if((i-1)*imgs <= N && i*imgs > N){
-            fprintf(stderr, "First stage done\n");
-            net.learning_rate *= 10;
-            char buff[256];
-            sprintf(buff, "%s/%s_first_stage.weights", backup_directory, base);
-            save_weights(net, buff);
-        }
-
-        if((i-1)*imgs <= 80*N && i*imgs > N*80){
-            fprintf(stderr, "Second stage done.\n");
-            char buff[256];
-            sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
-            save_weights(net, buff);
-        }
         if(i%1000==0){
             char buff[256];
             sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);

src/compare.c

@@ -175,8 +175,8 @@ int bbox_comparator(const void *a, const void *b)
     image im1 = load_image_color(box1.filename, net.w, net.h);
     image im2 = load_image_color(box2.filename, net.w, net.h);
     float *X  = calloc(net.w*net.h*net.c, sizeof(float));
-    memcpy(X,                   im1.data, im1.w*im1.h*im1.c);
+    memcpy(X,                   im1.data, im1.w*im1.h*im1.c*sizeof(float));
-    memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c);
+    memcpy(X+im1.w*im1.h*im1.c, im2.data, im2.w*im2.h*im2.c*sizeof(float));
     float *predictions = network_predict(net, X);
     
     free_image(im1);

src/darknet.c

@@ -13,7 +13,7 @@
 
 extern void run_imagenet(int argc, char **argv);
 extern void run_yolo(int argc, char **argv);
-extern void run_yoloplus(int argc, char **argv);
+extern void run_swag(int argc, char **argv);
 extern void run_coco(int argc, char **argv);
 extern void run_writing(int argc, char **argv);
 extern void run_captcha(int argc, char **argv);
@@ -179,8 +179,8 @@ int main(int argc, char **argv)
         average(argc, argv);
     } else if (0 == strcmp(argv[1], "yolo")){
         run_yolo(argc, argv);
-    } else if (0 == strcmp(argv[1], "yoloplus")){
+    } else if (0 == strcmp(argv[1], "swag")){
-        run_yoloplus(argc, argv);
+        run_swag(argc, argv);
     } else if (0 == strcmp(argv[1], "coco")){
         run_coco(argc, argv);
     } else if (0 == strcmp(argv[1], "compare")){

src/data.c

@@ -176,8 +176,10 @@ void fill_truth_region(char *path, float *truth, int classes, int num_boxes, int
         int index = (col+row*num_boxes)*(5+classes);
         if (truth[index]) continue;
         truth[index++] = 1;
-        if (classes) truth[index+id] = 1;
+
+        if (id < classes) truth[index+id] = 1;
         index += classes;
+
         truth[index++] = x;
         truth[index++] = y;
         truth[index++] = w;

src/layer.h

@@ -30,6 +30,7 @@ typedef struct {
     int batch;
     int inputs;
     int outputs;
+    int truths;
     int h,w,c;
     int out_h, out_w, out_c;
     int n;
@@ -40,10 +41,12 @@ typedef struct {
     int pad;
     int crop_width;
     int crop_height;
+    int sqrt;
     int flip;
     float angle;
     float saturation;
     float exposure;
+    int softmax;
     int classes;
     int coords;
     int background;

src/network.c

@@ -48,7 +48,7 @@ float get_current_rate(network net)
         case POLY:
             return net.learning_rate * pow(1 - (float)batch_num / net.max_batches, net.power);
         case SIG:
-            return net.learning_rate * (1/(1+exp(net.gamma*(batch_num - net.step))));
+            return net.learning_rate * (1./(1.+exp(net.gamma*(batch_num - net.step))));
         default:
             fprintf(stderr, "Policy is weird!\n");
             return net.learning_rate;

src/network_kernels.cu

@@ -134,6 +134,7 @@ float train_network_datum_gpu(network net, float *x, float *y)
     network_state state;
     int x_size = get_network_input_size(net)*net.batch;
     int y_size = get_network_output_size(net)*net.batch;
+    if(net.layers[net.n-1].type == REGION) y_size = net.layers[net.n-1].truths*net.batch;
     if(!*net.input_gpu){
         *net.input_gpu = cuda_make_array(x, x_size);
         *net.truth_gpu = cuda_make_array(y, y_size);

src/parser.c

@@ -182,6 +182,10 @@ region_layer parse_region(list *options, size_params params)
     int num = option_find_int(options, "num", 1);
     int side = option_find_int(options, "side", 7);
     region_layer layer = make_region_layer(params.batch, params.inputs, num, side, classes, coords, rescore);
+    int softmax = option_find_int(options, "softmax", 0);
+    int sqrt = option_find_int(options, "sqrt", 0);
+    layer.softmax = softmax;
+    layer.sqrt = sqrt;
     return layer;
 }
 

src/region_layer.c

@@ -22,15 +22,15 @@ region_layer make_region_layer(int batch, int inputs, int n, int side, int class
     l.coords = coords;
     l.rescore = rescore;
     l.side = side;
-    assert(side*side*l.coords*l.n == inputs);
+    assert(side*side*((1 + l.coords)*l.n + l.classes) == inputs);
     l.cost = calloc(1, sizeof(float));
-    int outputs = l.n*5*side*side;
+    l.outputs = l.inputs;
-    l.outputs = outputs;
+    l.truths = l.side*l.side*(1+l.coords+l.classes);
-    l.output = calloc(batch*outputs, sizeof(float));
+    l.output = calloc(batch*l.outputs, sizeof(float));
-    l.delta = calloc(batch*inputs, sizeof(float));
+    l.delta = calloc(batch*l.outputs, sizeof(float));
 #ifdef GPU
-    l.output_gpu = cuda_make_array(l.output, batch*outputs);
+    l.output_gpu = cuda_make_array(l.output, batch*l.outputs);
-    l.delta_gpu = cuda_make_array(l.delta, batch*inputs);
+    l.delta_gpu = cuda_make_array(l.delta, batch*l.outputs);
 #endif
 
     fprintf(stderr, "Region Layer\n");
@@ -43,64 +43,69 @@ void forward_region_layer(const region_layer l, network_state state)
 {
     int locations = l.side*l.side;
     int i,j;
+    memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float));
     for(i = 0; i < l.batch*locations; ++i){
-        for(j = 0; j < l.n; ++j){
+        int index = i*((1+l.coords)*l.n + l.classes);
-            int in_index =  i*l.n*l.coords + j*l.coords;
+        if(l.softmax){
-            int out_index = i*l.n*5 + j*5;
+            activate_array(l.output + index, l.n*(1+l.coords), LOGISTIC);
-
+            int offset = l.n*(1+l.coords);
-            float prob =  state.input[in_index+0];
+            softmax_array(l.output + index + offset, l.classes,
-            float x =     state.input[in_index+1];
+                    l.output + index + offset);
-            float y =     state.input[in_index+2];
-            float w =     state.input[in_index+3];
-            float h =     state.input[in_index+4];
-            /*
-            float min_w = state.input[in_index+5];
-            float max_w = state.input[in_index+6];
-            float min_h = state.input[in_index+7];
-            float max_h = state.input[in_index+8];
-            */
-
-            l.output[out_index+0] = prob;
-            l.output[out_index+1] = x;
-            l.output[out_index+2] = y;
-            l.output[out_index+3] = w;
-            l.output[out_index+4] = h;
-
         }
     }
     if(state.train){
         float avg_iou = 0;
+        float avg_cat = 0;
+        float avg_obj = 0;
+        float avg_anyobj = 0;
         int count = 0;
         *(l.cost) = 0;
         int size = l.inputs * l.batch;
         memset(l.delta, 0, size * sizeof(float));
         for (i = 0; i < l.batch*locations; ++i) {
-
+            int index = i*((1+l.coords)*l.n + l.classes);
             for(j = 0; j < l.n; ++j){
-                int in_index = i*l.n*l.coords + j*l.coords;
+                int prob_index = index + j*(1 + l.coords);
-                l.delta[in_index+0] = .1*(0-state.input[in_index+0]);
+                l.delta[prob_index] = (1./l.n)*(0-l.output[prob_index]);
+                if(l.softmax){
+                    l.delta[prob_index] = 1./(l.n*l.side)*(0-l.output[prob_index]);
+                }
+                *(l.cost) += (1./l.n)*pow(l.output[prob_index], 2);
+                //printf("%f\n", l.output[prob_index]);
+                avg_anyobj += l.output[prob_index];
             }
 
-            int truth_index = i*5;
+            int truth_index = i*(1 + l.coords + l.classes);
             int best_index = -1;
             float best_iou = 0;
             float best_rmse = 4;
 
             int bg = !state.truth[truth_index];
-            if(bg) continue;
+            if(bg) {
+                continue;
+            }
 
-            box truth = {state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3], state.truth[truth_index+4]};
+            int class_index = index + l.n*(1+l.coords);
+            for(j = 0; j < l.classes; ++j) {
+                l.delta[class_index+j] = state.truth[truth_index+1+j] - l.output[class_index+j];
+                *(l.cost) += pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2);
+                if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j];
+            }
+            truth_index += l.classes + 1;
+            box truth = {state.truth[truth_index+0], state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3]};
             truth.x /= l.side;
             truth.y /= l.side;
 
             for(j = 0; j < l.n; ++j){
-                int out_index = i*l.n*5 + j*5;
+                int out_index = index + j*(1+l.coords);
                 box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
 
-                //printf("\n%f %f %f %f %f\n", l.output[out_index+0], out.x, out.y, out.w, out.h);
-
                 out.x /= l.side;
                 out.y /= l.side;
+                if (l.sqrt){
+                    out.w = out.w*out.w;
+                    out.h = out.h*out.h;
+                }
 
                 float iou  = box_iou(out, truth);
                 float rmse = box_rmse(out, truth);
@@ -116,46 +121,41 @@ void forward_region_layer(const region_layer l, network_state state)
                     }
                 }
             }
-            printf("%d", best_index);
+            //printf("%d", best_index);
-            //int out_index = i*l.n*5 + best_index*5;
+            int in_index = index + best_index*(1+l.coords);
-            //box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]};
+            *(l.cost) -= pow(l.output[in_index], 2);
-            int in_index =  i*l.n*l.coords + best_index*l.coords;
+            *(l.cost) += pow(1-l.output[in_index], 2);
-
+            avg_obj += l.output[in_index];
-            l.delta[in_index+0] = (1-state.input[in_index+0]);
+            l.delta[in_index+0] = (1.-l.output[in_index]);
-            l.delta[in_index+1] = state.truth[truth_index+1] - state.input[in_index+1];
+            if(l.softmax){
-            l.delta[in_index+2] = state.truth[truth_index+2] - state.input[in_index+2];
+                l.delta[in_index+0] = 5*(1.-l.output[in_index]);
-            l.delta[in_index+3] = state.truth[truth_index+3] - state.input[in_index+3];
+            }
-            l.delta[in_index+4] = state.truth[truth_index+4] - state.input[in_index+4];
+            //printf("%f\n", l.output[in_index]);
-            /*
-            l.delta[in_index+5] = 0 - state.input[in_index+5];
-            l.delta[in_index+6] = 1 - state.input[in_index+6];
-            l.delta[in_index+7] = 0 - state.input[in_index+7];
-            l.delta[in_index+8] = 1 - state.input[in_index+8];
-            */
-
-            /*
-            float x =     state.input[in_index+1];
-            float y =     state.input[in_index+2];
-            float w =     state.input[in_index+3];
-            float h =     state.input[in_index+4];
-            float min_w = state.input[in_index+5];
-            float max_w = state.input[in_index+6];
-            float min_h = state.input[in_index+7];
-            float max_h = state.input[in_index+8];
-            */
 
+            l.delta[in_index+1] = 5*(state.truth[truth_index+0] - l.output[in_index+1]);
+            l.delta[in_index+2] = 5*(state.truth[truth_index+1] - l.output[in_index+2]);
+            if(l.sqrt){
+                l.delta[in_index+3] = 5*(sqrt(state.truth[truth_index+2]) - l.output[in_index+3]);
+                l.delta[in_index+4] = 5*(sqrt(state.truth[truth_index+3]) - l.output[in_index+4]);
+            }else{
+                l.delta[in_index+3] = 5*(state.truth[truth_index+2] - l.output[in_index+3]);
+                l.delta[in_index+4] = 5*(state.truth[truth_index+3] - l.output[in_index+4]);
+            }
 
+            *(l.cost) += pow(1-best_iou, 2);
             avg_iou += best_iou;
             ++count;
+            if(l.softmax){
+                gradient_array(l.output + index, l.n*(1+l.coords), LOGISTIC, l.delta + index);
             }
-        printf("\nAvg IOU: %f %d\n", avg_iou/count, count);
+        }
+        printf("Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count);
     }
 }
 
 void backward_region_layer(const region_layer l, network_state state)
 {
     axpy_cpu(l.batch*l.inputs, 1, l.delta, 1, state.delta, 1);
-    //copy_cpu(l.batch*l.inputs, l.delta, 1, state.delta, 1);
 }
 
 #ifdef GPU
@@ -165,8 +165,9 @@ void forward_region_layer_gpu(const region_layer l, network_state state)
     float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
     float *truth_cpu = 0;
     if(state.truth){
-        truth_cpu = calloc(l.batch*l.outputs, sizeof(float));
+        int num_truth = l.batch*l.side*l.side*(1+l.coords+l.classes);
-        cuda_pull_array(state.truth, truth_cpu, l.batch*l.outputs);
+        truth_cpu = calloc(num_truth, sizeof(float));
+        cuda_pull_array(state.truth, truth_cpu, num_truth);
     }
     cuda_pull_array(state.input, in_cpu, l.batch*l.inputs);
     network_state cpu_state;

src/swag.c

@@ -11,7 +11,7 @@
 
 char *voc_names[] = {"aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
 
-void draw_yoloplus(image im, float *box, int side, int objectness, char *label, float thresh)
+void draw_swag(image im, float *box, int side, int objectness, char *label, float thresh)
 {
     int classes = 20;
     int elems = 4+classes+objectness;
@@ -52,7 +52,7 @@ void draw_yoloplus(image im, float *box, int side, int objectness, char *label,
     show_image(im, label);
 }
 
-void train_yoloplus(char *cfgfile, char *weightfile)
+void train_swag(char *cfgfile, char *weightfile)
 {
     char *train_images = "/home/pjreddie/data/voc/test/train.txt";
     char *backup_directory = "/home/pjreddie/backup/";
@@ -65,23 +65,20 @@ void train_yoloplus(char *cfgfile, char *weightfile)
     if(weightfile){
         load_weights(&net, weightfile);
     }
-    detection_layer layer = get_network_detection_layer(net);
+    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
-    int imgs = 128;
+    int imgs = net.batch*net.subdivisions;
     int i = *net.seen/imgs;
-
-    char **paths;
-    list *plist = get_paths(train_images);
-    int N = plist->size;
-    paths = (char **)list_to_array(plist);
-
-    if(i*imgs > N*120){
-        net.layers[net.n-1].rescore = 1;
-    }
     data train, buffer;
 
-    int classes = layer.classes;
+
-    int background = layer.objectness;
+    layer l = net.layers[net.n - 1];
-    int side = sqrt(get_detection_layer_locations(layer));
+
+    int side = l.side;
+    int classes = l.classes;
+
+    list *plist = get_paths(train_images);
+    int N = plist->size;
+    char **paths = (char **)list_to_array(plist);
 
     load_args args = {0};
     args.w = net.w;
@@ -91,12 +88,12 @@ void train_yoloplus(char *cfgfile, char *weightfile)
     args.m = plist->size;
     args.classes = classes;
     args.num_boxes = side;
-    args.background = background;
     args.d = &buffer;
-    args.type = DETECTION_DATA;
+    args.type = REGION_DATA;
 
     pthread_t load_thread = load_data_in_thread(args);
     clock_t time;
+    //while(i*imgs < N*120){
     while(get_current_batch(net) < net.max_batches){
         i += 1;
         time=clock();
@@ -105,36 +102,21 @@ void train_yoloplus(char *cfgfile, char *weightfile)
         load_thread = load_data_in_thread(args);
 
         printf("Loaded: %lf seconds\n", sec(clock()-time));
+
+/*
+        image im = float_to_image(net.w, net.h, 3, train.X.vals[113]);
+        image copy = copy_image(im);
+        draw_swag(copy, train.y.vals[113], 7, "truth");
+        cvWaitKey(0);
+        free_image(copy);
+        */
+
         time=clock();
         float loss = train_network(net, train);
         if (avg_loss < 0) avg_loss = loss;
         avg_loss = avg_loss*.9 + loss*.1;
 
-        printf("%d: %f, %f avg, %lf seconds, %f rate, %d images, epoch: %f\n", get_current_batch(net), loss, avg_loss, sec(clock()-time), get_current_rate(net), *net.seen, (float)*net.seen/N);
+        printf("%d: %f, %f avg, %lf seconds, %d images\n", i, loss, avg_loss, sec(clock()-time), i*imgs);
-
-        if((i-1)*imgs <= 80*N && i*imgs > N*80){
-            fprintf(stderr, "Second stage done.\n");
-            char buff[256];
-            sprintf(buff, "%s/%s_second_stage.weights", backup_directory, base);
-            save_weights(net, buff);
-            net.layers[net.n-1].joint = 1;
-            net.layers[net.n-1].objectness = 0;
-            background = 0;
-
-            pthread_join(load_thread, 0);
-            free_data(buffer);
-            args.background = background;
-            load_thread = load_data_in_thread(args);
-        }
-
-        if((i-1)*imgs <= 120*N && i*imgs > N*120){
-            fprintf(stderr, "Third stage done.\n");
-            char buff[256];
-            sprintf(buff, "%s/%s_final.weights", backup_directory, base);
-            net.layers[net.n-1].rescore = 1;
-            save_weights(net, buff);
-        }
-
         if(i%1000==0){
             char buff[256];
             sprintf(buff, "%s/%s_%d.weights", backup_directory, base, i);
@@ -143,36 +125,38 @@ void train_yoloplus(char *cfgfile, char *weightfile)
         free_data(train);
     }
     char buff[256];
-    sprintf(buff, "%s/%s_rescore.weights", backup_directory, base);
+    sprintf(buff, "%s/%s_final.weights", backup_directory, base);
     save_weights(net, buff);
 }
 
-void convert_yoloplus_detections(float *predictions, int classes, int objectness, int background, int num_boxes, int w, int h, float thresh, float **probs, box *boxes)
+void convert_swag_detections(float *predictions, int classes, int num, int square, int side, int w, int h, float thresh, float **probs, box *boxes)
 {
-    int i,j;
+    int i,j,n;
-    int per_box = 4+classes+(background || objectness);
+    int per_cell = 5*num+classes;
-    for (i = 0; i < num_boxes*num_boxes; ++i){
+    for (i = 0; i < side*side; ++i){
-        float scale = 1;
+        int row = i / side;
-        if(objectness) scale = 1-predictions[i*per_box];
+        int col = i % side;
-        int offset = i*per_box+(background||objectness);
+        for(n = 0; n < num; ++n){
+            int offset = i*per_cell + 5*n;
+            float scale = predictions[offset];
+            int index = i*num + n;
+            boxes[index].x = (predictions[offset + 1] + col) / side * w;
+            boxes[index].y = (predictions[offset + 2] + row) / side * h;
+            boxes[index].w = pow(predictions[offset + 3], (square?2:1)) * w;
+            boxes[index].h = pow(predictions[offset + 4], (square?2:1)) * h;
             for(j = 0; j < classes; ++j){
+                offset = i*per_cell + 5*num;
                 float prob = scale*predictions[offset+j];
-            probs[i][j] = (prob > thresh) ? prob : 0;
+                probs[index][j] = (prob > thresh) ? prob : 0;
+            }
         }
-        int row = i / num_boxes;
-        int col = i % num_boxes;
-        offset += classes;
-        boxes[i].x = (predictions[offset + 0] + col) / num_boxes * w;
-        boxes[i].y = (predictions[offset + 1] + row) / num_boxes * h;
-        boxes[i].w = pow(predictions[offset + 2], 2) * w;
-        boxes[i].h = pow(predictions[offset + 3], 2) * h;
     }
 }
 
-void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs, int num_boxes, int classes, int w, int h)
+void print_swag_detections(FILE **fps, char *id, box *boxes, float **probs, int total, int classes, int w, int h)
 {
     int i, j;
-    for(i = 0; i < num_boxes*num_boxes; ++i){
+    for(i = 0; i < total; ++i){
         float xmin = boxes[i].x - boxes[i].w/2.;
         float xmax = boxes[i].x + boxes[i].w/2.;
         float ymin = boxes[i].y - boxes[i].h/2.;
@@ -190,14 +174,13 @@ void print_yoloplus_detections(FILE **fps, char *id, box *boxes, float **probs,
     }
 }
 
-void validate_yoloplus(char *cfgfile, char *weightfile)
+void validate_swag(char *cfgfile, char *weightfile)
 {
     network net = parse_network_cfg(cfgfile);
     if(weightfile){
         load_weights(&net, weightfile);
     }
     set_batch_network(&net, 1);
-    detection_layer layer = get_network_detection_layer(net);
     fprintf(stderr, "Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
     srand(time(0));
 
@@ -205,10 +188,10 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
     list *plist = get_paths("/home/pjreddie/data/voc/test/2007_test.txt");
     char **paths = (char **)list_to_array(plist);
 
-    int classes = layer.classes;
+    layer l = net.layers[net.n-1];
-    int objectness = layer.objectness;
+    int classes = l.classes;
-    int background = layer.background;
+    int square = l.sqrt;
-    int num_boxes = sqrt(get_detection_layer_locations(layer));
+    int side = l.side;
 
     int j;
     FILE **fps = calloc(classes, sizeof(FILE *));
@@ -217,9 +200,9 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
         snprintf(buff, 1024, "%s%s.txt", base, voc_names[j]);
         fps[j] = fopen(buff, "w");
     }
-    box *boxes = calloc(num_boxes*num_boxes, sizeof(box));
+    box *boxes = calloc(side*side*l.n, sizeof(box));
-    float **probs = calloc(num_boxes*num_boxes, sizeof(float *));
+    float **probs = calloc(side*side*l.n, sizeof(float *));
-    for(j = 0; j < num_boxes*num_boxes; ++j) probs[j] = calloc(classes, sizeof(float *));
+    for(j = 0; j < side*side*l.n; ++j) probs[j] = calloc(classes, sizeof(float *));
 
     int m = plist->size;
     int i=0;
@@ -268,9 +251,9 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
             float *predictions = network_predict(net, X);
             int w = val[t].w;
             int h = val[t].h;
-            convert_yoloplus_detections(predictions, classes, objectness, background, num_boxes, w, h, thresh, probs, boxes);
+            convert_swag_detections(predictions, classes, l.n, square, side, w, h, thresh, probs, boxes);
-            if (nms) do_nms(boxes, probs, num_boxes*num_boxes, classes, iou_thresh);
+            if (nms) do_nms(boxes, probs, side*side*l.n, classes, iou_thresh);
-            print_yoloplus_detections(fps, id, boxes, probs, num_boxes, classes, w, h);
+            print_swag_detections(fps, id, boxes, probs, side*side*l.n, classes, w, h);
             free(id);
             free_image(val[t]);
             free_image(val_resized[t]);
@@ -279,7 +262,7 @@ void validate_yoloplus(char *cfgfile, char *weightfile)
     fprintf(stderr, "Total Detection Time: %f Seconds\n", (double)(time(0) - start));
 }
 
-void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh)
+void test_swag(char *cfgfile, char *weightfile, char *filename, float thresh)
 {
 
     network net = parse_network_cfg(cfgfile);
@@ -306,7 +289,7 @@ void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh
         time=clock();
         float *predictions = network_predict(net, X);
         printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
-        draw_yoloplus(im, predictions, 7, layer.objectness, "predictions", thresh);
+        draw_swag(im, predictions, 7, layer.objectness, "predictions", thresh);
         free_image(im);
         free_image(sized);
 #ifdef OPENCV
@@ -317,7 +300,7 @@ void test_yoloplus(char *cfgfile, char *weightfile, char *filename, float thresh
     }
 }
 
-void run_yoloplus(int argc, char **argv)
+void run_swag(int argc, char **argv)
 {
     float thresh = find_float_arg(argc, argv, "-thresh", .2);
     if(argc < 4){
@@ -328,7 +311,7 @@ void run_yoloplus(int argc, char **argv)
     char *cfg = argv[3];
     char *weights = (argc > 4) ? argv[4] : 0;
     char *filename = (argc > 5) ? argv[5]: 0;
-    if(0==strcmp(argv[2], "test")) test_yoloplus(cfg, weights, filename, thresh);
+    if(0==strcmp(argv[2], "test")) test_swag(cfg, weights, filename, thresh);
-    else if(0==strcmp(argv[2], "train")) train_yoloplus(cfg, weights);
+    else if(0==strcmp(argv[2], "train")) train_swag(cfg, weights);
-    else if(0==strcmp(argv[2], "valid")) validate_yoloplus(cfg, weights);
+    else if(0==strcmp(argv[2], "valid")) validate_swag(cfg, weights);
 }