Function weighted_loss

embodiedscan/models/losses/reduce_loss.py:68–125

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 arg

(loss_func: Callable)

def weighted_loss(loss_func: Callable) -> Callable:
    """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: Tensor,
                target: Tensor,
                weight: Optional[Tensor] = None,
                reduction: str = 'mean',
                avg_factor: Optional[int] = None,
                **kwargs) -> Tensor:
        """
        Args:
            pred (Tensor): The prediction.
            target (Tensor): Target bboxes.
            weight (Optional[Tensor], optional): The weight of loss for each
                prediction. Defaults to None.
            reduction (str, optional): Options are "none", "mean" and "sum".
                Defaults to 'mean'.
            avg_factor (Optional[int], optional): Average factor that is used
                to average the loss. Defaults to None.

        Returns:
            Tensor: Loss tensor.
        """
        # get element-wise loss
        loss = loss_func(pred, target, **kwargs)
        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
        return loss

    return wrapper