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AsymmetricLoss

class mmcls.models.AsymmetricLoss(gamma_pos=0.0, gamma_neg=4.0, clip=0.05, reduction='mean', loss_weight=1.0, use_sigmoid=True, eps=1e-08)[source]

asymmetric loss.

Parameters
  • gamma_pos (float) – positive focusing parameter. Defaults to 0.0.

  • gamma_neg (float) – Negative focusing parameter. We usually set gamma_neg > gamma_pos. Defaults to 4.0.

  • clip (float, optional) – Probability margin. Defaults to 0.05.

  • reduction (str) – The method used to reduce the loss into a scalar.

  • loss_weight (float) – Weight of loss. Defaults to 1.0.

  • use_sigmoid (bool) – Whether the prediction uses sigmoid instead of softmax. Defaults to True.

  • eps (float) – The minimum value of the argument of logarithm. Defaults to 1e-8.

forward(pred, target, weight=None, avg_factor=None, reduction_override=None)[source]

asymmetric loss.

Parameters
  • pred (torch.Tensor) – The prediction with shape (N, *).

  • target (torch.Tensor) – The ground truth label of the prediction with shape (N, *), N or (N,1).

  • weight (torch.Tensor, optional) – Sample-wise loss weight with shape (N, *). Defaults to None.

  • avg_factor (int, optional) – Average factor that is used to average the loss. Defaults to None.

  • reduction_override (str, optional) – The method used to reduce the loss into a scalar. Options are “none”, “mean” and “sum”. Defaults to None.

Returns

Loss.

Return type

torch.Tensor

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