def mask_cross_entropy(pred,
                       target,
                       label,
                       reduction='mean',
                       avg_factor=None,
                       class_weight=None,
                       ignore_index=None,
                       **kwargs):
    """Calculate the CrossEntropy loss for masks.

    Args:
        pred (torch.Tensor): The prediction with shape (N, C, *), C is the
            number of classes. The trailing * indicates arbitrary shape.
        target (torch.Tensor): The learning label of the prediction.
        label (torch.Tensor): ``label`` indicates the class label of the mask
            corresponding object. This will be used to select the mask in the
            of the class which the object belongs to when the mask prediction
            if not class-agnostic.
        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 (None): Placeholder, to be consistent with other loss.
            Default: None.

    Returns:
        torch.Tensor: The calculated loss

    Example:
        >>> N, C = 3, 11
        >>> H, W = 2, 2
        >>> pred = torch.randn(N, C, H, W) * 1000
        >>> target = torch.rand(N, H, W)
        >>> label = torch.randint(0, C, size=(N,))
        >>> reduction = 'mean'
        >>> avg_factor = None
        >>> class_weights = None
        >>> loss = mask_cross_entropy(pred, target, label, reduction,
        >>>                           avg_factor, class_weights)
        >>> assert loss.shape == (1,)
    """
    assert ignore_index is None, 'BCE loss does not support ignore_index'
    # TODO: handle these two reserved arguments
    assert reduction == 'mean' and avg_factor is None
    num_rois = pred.size()[0]
    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
    pred_slice = pred[inds, label].squeeze(1)
    return F.binary_cross_entropy_with_logits(
        pred_slice, target, weight=class_weight, reduction='mean')[None]
Calculate the CrossEntropy loss for masks.

Args:
pred (torch.Tensor): The prediction with shape (N, C, *), C is the
number of classes. The trailing * indicates arbitrary shape.
target (torch.Tensor): The learning label of the prediction.
label (torch.Tensor): ``label`` indicates the class label of the mask
corresponding object. This will be used to select the mask in the
of the class which the object belongs to when the mask prediction
if not class-agnostic.
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 (None): Placeholder, to be consistent with other loss.
Default: None.

Returns:
torch.Tensor: The calculated loss

Example:
>>> N, C = 3, 11
>>> H, W = 2, 2
>>> pred = torch.randn(N, C, H, W) * 1000
>>> target = torch.rand(N, H, W)
>>> label = torch.randint(0, C, size=(N,))
>>> reduction = 'mean'
>>> avg_factor = None
>>> class_weights = None
>>> loss = mask_cross_entropy(pred, target, label, reduction,
>>>                           avg_factor, class_weights)
>>> assert loss.shape == (1,)
third_parts/mmdet/models/losses/cross_entropy_loss.py