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from ..custom_types import *
def occupancy_bce(predict: T, winding_gt: T, ignore: Optional[T] = None, *args) -> T:
if winding_gt.dtype is not torch.bool:
winding_gt = winding_gt.gt(0)
labels = winding_gt.flatten().float()
predict = predict.flatten()
if ignore is not None:
ignore = (~ignore).flatten().float()
loss = nnf.binary_cross_entropy_with_logits(predict, labels, weight=ignore)
return loss
def reg_z_loss(z: T) -> T:
norms = z.norm(2, 1)
loss = norms.mean()
return loss
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