updates

utils/utils.py

@@ -364,6 +364,11 @@ class FocalLoss(nn.Module):
             return loss
 
 
+def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
+    # return positive, negative label smoothing BCE targets
+    return 1.0 - 0.5 * eps, 0.5 * eps
+
+
 def compute_loss(p, targets, model):  # predictions, targets, model
     ft = torch.cuda.FloatTensor if p[0].is_cuda else torch.Tensor
     lcls, lbox, lobj = ft([0]), ft([0]), ft([0])
@@ -379,12 +384,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     CE = nn.CrossEntropyLoss(reduction=red)  # weight=model.class_weights
 
     # class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-    smooth = False
-    if smooth:
-        e = 0.1  #  class label smoothing epsilon
-        cp, cn = 1.0 - e, e / (model.nc - 0.99)  # class positive and negative labels
-    else:
-        cp, cn = 1.0, 0.0
+    cp, cn = smooth_BCE(eps=0.0)
 
     if 'F' in arc:  # add focal loss
         g = h['fl_gamma']
@@ -656,6 +656,7 @@ def print_model_biases(model):
     print('\nModel Bias Summary: %8s%18s%18s%18s' % ('layer', 'regression', 'objectness', 'classification'))
     multi_gpu = type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
     for l in model.yolo_layers:  # print pretrained biases
+        try:
             if multi_gpu:
                 na = model.module.module_list[l].na  # number of anchors
                 b = model.module.module_list[l - 1][0].bias.view(na, -1)  # bias 3x85
@@ -665,6 +666,8 @@ def print_model_biases(model):
             print(' ' * 20 + '%8g %18s%18s%18s' % (l, '%5.2f+/-%-5.2f' % (b[:, :4].mean(), b[:, :4].std()),
                                                    '%5.2f+/-%-5.2f' % (b[:, 4].mean(), b[:, 4].std()),
                                                    '%5.2f+/-%-5.2f' % (b[:, 5:].mean(), b[:, 5:].std())))
+        except:
+            pass
 
 
 def strip_optimizer(f='weights/last.pt'):  # from utils.utils import *; strip_optimizer()