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Python ops.sigmoid_focal_loss方法代码示例

本文整理汇总了Python中mmdet.ops.sigmoid_focal_loss方法的典型用法代码示例。如果您正苦于以下问题:Python ops.sigmoid_focal_loss方法的具体用法?Python ops.sigmoid_focal_loss怎么用?Python ops.sigmoid_focal_loss使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在mmdet.ops的用法示例。


在下文中一共展示了ops.sigmoid_focal_loss方法的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: sigmoid_focal_loss

# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import sigmoid_focal_loss [as 别名]
def sigmoid_focal_loss(pred,
                       target,
                       weight=None,
                       gamma=2.0,
                       alpha=0.25,
                       reduction='mean',
                       avg_factor=None):
    # Function.apply does not accept keyword arguments, so the decorator
    # "weighted_loss" is not applicable
    loss = _sigmoid_focal_loss(pred, target, gamma, alpha)
    # TODO: find a proper way to handle the shape of weight
    if weight is not None:
        weight = weight.view(-1, 1)
    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
    return loss 
开发者ID:xvjiarui,项目名称:GCNet,代码行数:17,代码来源:focal_loss.py

示例2: sigmoid_focal_loss

# 需要导入模块: from mmdet import ops [as 别名]
# 或者: from mmdet.ops import sigmoid_focal_loss [as 别名]
def sigmoid_focal_loss(pred,
                       target,
                       weight=None,
                       gamma=2.0,
                       alpha=0.25,
                       reduction='mean',
                       avg_factor=None):
    r"""A warpper of cuda version `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'. Options are "none", "mean" and "sum".
        avg_factor (int, optional): Average factor that is used to average
            the loss. Defaults to None.
    """
    # Function.apply does not accept keyword arguments, so the decorator
    # "weighted_loss" is not applicable
    loss = _sigmoid_focal_loss(pred, target, gamma, alpha)
    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 
开发者ID:open-mmlab,项目名称:mmdetection,代码行数:45,代码来源:focal_loss.py


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