Class FocalLossCost

embodiedscan/models/losses/match_cost.py:117–210

FocalLossCost. Args: alpha (Union[float, int]): focal_loss alpha. Defaults to 0.25. gamma (Union[float, int]): focal_loss gamma. Defaults to 2. eps (float): Defaults to 1e-12. binary_input (bool): Whether the input is binary. Currently, binary_input =

@TASK_UTILS.register_module()
class FocalLossCost(BaseMatchCost):
    """FocalLossCost.

    Args:
        alpha (Union[float, int]): focal_loss alpha. Defaults to 0.25.
        gamma (Union[float, int]): focal_loss gamma. Defaults to 2.
        eps (float): Defaults to 1e-12.
        binary_input (bool): Whether the input is binary. Currently,
            binary_input = True is for masks input, binary_input = False
            is for label input. Defaults to False.
        weight (Union[float, int]): Cost weight. Defaults to 1.
    """

    def __init__(self,
                 alpha: Union[float, int] = 0.25,
                 gamma: Union[float, int] = 2,
                 eps: float = 1e-12,
                 binary_input: bool = False,
                 weight: Union[float, int] = 1.) -> None:
        super().__init__(weight=weight)
        self.alpha = alpha
        self.gamma = gamma
        self.eps = eps
        self.binary_input = binary_input

    def _focal_loss_cost(self, cls_pred: Tensor, gt_labels: Tensor) -> Tensor:
        """
        Args:
            cls_pred (Tensor): Predicted classification logits, shape
                (num_queries, num_class).
            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).

        Returns:
            torch.Tensor: cls_cost value with weight
        """
        cls_pred = cls_pred.sigmoid()
        neg_cost = -(1 - cls_pred + self.eps).log() * (
            1 - self.alpha) * cls_pred.pow(self.gamma)
        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
            1 - cls_pred).pow(self.gamma)

        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
        return cls_cost * self.weight

    def _mask_focal_loss_cost(self, cls_pred, gt_labels) -> Tensor:
        """
        Args:
            cls_pred (Tensor): Predicted classification logits.
                in shape (num_queries, d1, ..., dn), dtype=torch.float32.
            gt_labels (Tensor): Ground truth in shape (num_gt, d1, ..., dn),
                dtype=torch.long. Labels should be binary.

        Returns:
            Tensor: Focal cost matrix with weight in shape\
                (num_queries, num_gt).
        """
        cls_pred = cls_pred.flatten(1)
        gt_labels = gt_labels.flatten(1).float()