updates

utils/gcp.sh

@@ -45,11 +45,14 @@ wget https://storage.googleapis.com/ultralytics/yolov3/best_v1_0.pt -O weights/b
 
 # Debug/Development
 sudo rm -rf yolov3
-# git clone https://github.com/ultralytics/yolov3  # master
-git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3  # branch
+git clone https://github.com/ultralytics/yolov3  # master
+# git clone -b hyperparameter_search --depth 1 https://github.com/ultralytics/yolov3 hyperparameter_search  # branch
 cp -r weights yolov3
 cp -r cocoapi/PythonAPI/pycocotools yolov3
 cd yolov3
 
-#git pull https://github.com/ultralytics/yolov3 map_update  # branch
-python3 test.py --img-size 320
+git pull https://github.com/ultralytics/yolov3 #hyperparameter_search  # branch
+python3 train.py --data-cfg data/coco_1cls.data
+python3 train.py --data-cfg data/coco_1img.data
+
+

utils/utils.py

@@ -295,12 +295,11 @@ def build_targets(model, targets):
 
     txy, twh, tcls, indices = [], [], [], []
     for i, layer in enumerate(get_yolo_layers(model)):
-        nG = model.module_list[layer][0].nG  # grid size
-        anchor_vec = model.module_list[layer][0].anchor_vec
+        layer = model.module_list[layer][0]
 
         # iou of targets-anchors
-        gwh = targets[:, 4:6] * nG
-        iou = [wh_iou(x, gwh) for x in anchor_vec]
+        gwh = targets[:, 4:6] * layer.nG
+        iou = [wh_iou(x, gwh) for x in layer.anchor_vec]
         iou, a = torch.stack(iou, 0).max(0)  # best iou and anchor
 
         # reject below threshold ious (OPTIONAL, increases P, lowers R)
@@ -313,7 +312,7 @@ def build_targets(model, targets):
 
         # Indices
         b, c = t[:, :2].long().t()  # target image, class
-        gxy = t[:, 2:4] * nG
+        gxy = t[:, 2:4] * layer.nG
         gi, gj = gxy.long().t()  # grid_i, grid_j
         indices.append((b, a, gj, gi))
 
@@ -321,11 +320,12 @@ def build_targets(model, targets):
         txy.append(gxy - gxy.floor())
 
         # Width and height
-        twh.append(torch.log(gwh / anchor_vec[a]))  # yolo method
+        twh.append(torch.log(gwh / layer.anchor_vec[a]))  # yolo method
         # twh.append(torch.sqrt(gwh / anchor_vec[a]) / 2)  # power method
 
         # Class
         tcls.append(c)
+        assert c.max() <= layer.nC, 'Target classes exceed model classes'
 
     return txy, twh, tcls, indices