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car-detection-bayes
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focal and obj loss speed/stability

This commit is contained in:
Glenn Jocher 2020-03-16 21:40:57 -07:00
parent 448c4a6e1f
commit b3adc896f9
1 changed files with 2 additions and 2 deletions
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4
utils/utils.py
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@ -357,7 +357,7 @@ class FocalLoss(nn.Module):
p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
modulating_factor = (1.0 - p_t) ** self.gamma
loss = alpha_factor * modulating_factor * loss
loss *= alpha_factor * modulating_factor
if self.reduction == 'mean':
return loss.mean()
@ -411,7 +411,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
pbox = torch.cat((pxy, pwh), 1) # predicted box
giou = bbox_iou(pbox.t(), tbox[i], x1y1x2y2=False, GIoU=True) # giou computation
lbox += (1.0 - giou).sum() if red == 'sum' else (1.0 - giou).mean() # giou loss
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().type(tobj.dtype) # giou ratio
tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * giou.detach().clamp(0).type(tobj.dtype) # giou ratio
if model.nc > 1: # cls loss (only if multiple classes)
t = torch.full_like(ps[:, 5:], cn) # targets