Note

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SeesawLoss¶

class mmcls.models.SeesawLoss(use_sigmoid=False, p=0.8, q=2.0, num_classes=1000, eps=0.01, reduction='mean', loss_weight=1.0)[source]¶

Implementation of seesaw loss.

Refers to Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)

Parameters

use_sigmoid (bool) – Whether the prediction uses sigmoid of softmax. Only False is supported. Defaults to False.
p (float) – The p in the mitigation factor. Defaults to 0.8.
q (float) – The q in the compenstation factor. Defaults to 2.0.
num_classes (int) – The number of classes. Default to 1000 for the ImageNet dataset.
eps (float) – The minimal value of divisor to smooth the computation of compensation factor, default to 1e-2.
reduction (str) – The method that reduces the loss to a scalar. Options are “none”, “mean” and “sum”. Default to “mean”.
loss_weight (float) – The weight of the loss. Defaults to 1.0

forward(cls_score, labels, weight=None, avg_factor=None, reduction_override=None)[source]¶

Forward function.

Parameters

cls_score (torch.Tensor) – The prediction with shape (N, C).
labels (torch.Tensor) – The learning label of the prediction.
weight (torch.Tensor, optional) – Sample-wise loss weight.
avg_factor (int, optional) – Average factor that is used to average the loss. Defaults to None.
reduction (str, optional) – The method used to reduce the loss. Options are “none”, “mean” and “sum”.

Returns

The calculated loss

Return type

torch.Tensor