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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2020-03-08 16:15:41 -07:00
parent 1d43b2a55a
commit 4a90221e79
1 changed files with 8 additions and 7 deletions
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15
utils/utils.py
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@ -378,6 +378,14 @@ def compute_loss(p, targets, model): # predictions, targets, model
BCE = nn.BCEWithLogitsLoss(reduction=red)
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
if 'F' in arc: # add focal loss
g = h['fl_gamma']
BCEcls, BCEobj, BCE, CE = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g), FocalLoss(BCE, g), FocalLoss(CE, g)
@ -405,13 +413,6 @@ def compute_loss(p, targets, model): # predictions, targets, model
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) # giou ratio
if 'default' in arc and model.nc > 1: # cls loss (only if multiple classes)
smooth = False # class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
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
t = torch.zeros_like(ps[:, 5:]) + cn # targets
t[range(nb), tcls[i]] = cp
lcls += BCEcls(ps[:, 5:], t) # BCE