python 修饰器@

def weighted_loss(loss_func):
	"""Create a weighted version of a given loss function.
	To use this decorator, the loss function must have the signature like
	`loss_func(pred, target, **kwargs)`. The function only needs to compute
	element-wise loss without any reduction. This decorator will add weight
	and reduction arguments to the function. The decorated function will have
	the signature like `loss_func(pred, target, weight=None, reduction='mean',
	avg_factor=None, **kwargs)`.
	:Example:
	>>> import torch
	>>> @weighted_loss
	>>> def l1_loss(pred, target):
	>>> return (pred - target).abs()
	>>> pred = torch.Tensor([0, 2, 3])
	>>> target = torch.Tensor([1, 1, 1])
	>>> weight = torch.Tensor([1, 0, 1])
	>>> l1_loss(pred, target)
	tensor(1.3333)
	>>> l1_loss(pred, target, weight)
	tensor(1.)
	>>> l1_loss(pred, target, reduction='none')
	tensor([1., 1., 2.])
	>>> l1_loss(pred, target, weight, avg_factor=2)
	tensor(1.5000)
	"""
	@functools.wraps(loss_func)
	def wrapper(pred,
	target,
	weight=None,
	reduction='mean',
	avg_factor=None,
	**kwargs):
	# get element-wise loss
	loss = loss_func(pred, target, **kwargs)
	loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
	return loss
	return wrapper