def binary_cross_entropy(pred,
                         label,
                         weight=None,
                         reduction='mean',
                         avg_factor=None,
                         class_weight=None,
                         ignore_index=255):
    """Calculate the binary CrossEntropy loss.

    Args:
        pred (torch.Tensor): The prediction with shape (N, 1).
        label (torch.Tensor): The learning label of the prediction.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        reduction (str, optional): The method used to reduce the loss.
            Options are "none", "mean" and "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
        class_weight (list[float], optional): The weight for each class.
        ignore_index (int | None): The label index to be ignored. Default: 255

    Returns:
        torch.Tensor: The calculated loss
    """
    if pred.dim() != label.dim():
        assert (pred.dim() == 2 and label.dim() == 1) or (
                pred.dim() == 4 and label.dim() == 3), \
            'Only pred shape [N, C], label shape [N] or pred shape [N, C, ' \
            'H, W], label shape [N, H, W] are supported'
        label, weight = _expand_onehot_labels(label, weight, pred.shape,
                                              ignore_index)

    # weighted element-wise losses
    if weight is not None:
        weight = weight.float()
    loss = F.binary_cross_entropy_with_logits(
        pred, label.float(), pos_weight=class_weight, reduction='none')
    # do the reduction for the weighted loss
    loss = weight_reduce_loss(
        loss, weight, reduction=reduction, avg_factor=avg_factor)

    return loss
Calculate the binary CrossEntropy loss.

    Args:
        pred (torch.Tensor): The prediction with shape (N, 1).
        label (torch.Tensor): The learning label of the prediction.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        reduction (str, optional): The method used to reduce the loss.
            Options are "none", "mean" and "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
        class_weight (list[float], optional): The weight for each class.
        ignore_index (int | None): The label index to be ignored. Default: 255

    Returns:
        torch.Tensor: The calculated loss
    
ControlNet-v1-1-nightly/annotator/uniformer/mmseg/models/losses/cross_entropy_loss.py