def py_sigmoid_focal_loss(pred,
                          target,
                          weight=None,
                          gamma=2.0,
                          alpha=0.25,
                          reduction='mean',
                          avg_factor=None):
    """PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.

    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the
            number of classes
        target (torch.Tensor): The learning label of the prediction.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 2.0.
        alpha (float, optional): A balanced form for Focal Loss.
            Defaults to 0.25.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """
    pred_sigmoid = pred.sigmoid()
    target = target.type_as(pred)
    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
    focal_weight = (alpha * target + (1 - alpha) *
                    (1 - target)) * pt.pow(gamma)
    loss = F.binary_cross_entropy_with_logits(
        pred, target, reduction='none') * focal_weight
    if weight is not None:
        if weight.shape != loss.shape:
            if weight.size(0) == loss.size(0):
                # For most cases, weight is of shape (num_priors, ),
                #  which means it does not have the second axis num_class
                weight = weight.view(-1, 1)
            else:
                # Sometimes, weight per anchor per class is also needed. e.g.
                #  in FSAF. But it may be flattened of shape
                #  (num_priors x num_class, ), while loss is still of shape
                #  (num_priors, num_class).
                assert weight.numel() == loss.numel()
                weight = weight.view(loss.size(0), -1)
        assert weight.ndim == loss.ndim
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss
PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.

    Args:
        pred (torch.Tensor): The prediction with shape (N, C), C is the
            number of classes
        target (torch.Tensor): The learning label of the prediction.
        weight (torch.Tensor, optional): Sample-wise loss weight.
        gamma (float, optional): The gamma for calculating the modulating
            factor. Defaults to 2.0.
        alpha (float, optional): A balanced form for Focal Loss.
            Defaults to 0.25.
        reduction (str, optional): The method used to reduce the loss into
            a scalar. Defaults to 'mean'.
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    
EVA-01/seg/mmseg_custom/models/losses/focal_loss.py