sigmoid_focal_loss¶
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torchvision.ops.sigmoid_focal_loss(inputs: torch.Tensor, targets: torch.Tensor, alpha: float = 0.25, gamma: float = 2, reduction: str = 'none')[source]¶ Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py . Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
- Parameters
inputs – A float tensor of arbitrary shape. The predictions for each example.
targets – A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class).
alpha – (optional) Weighting factor in range (0,1) to balance positive vs negative examples or -1 for ignore. Default = 0.25
gamma – Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
reduction – ‘none’ | ‘mean’ | ‘sum’ ‘none’: No reduction will be applied to the output. ‘mean’: The output will be averaged. ‘sum’: The output will be summed.
- Returns
Loss tensor with the reduction option applied.
