diffengine.models.losses.debias_estimation_loss

Module Contents

Classes

DeBiasEstimationLoss

DeBias Estimation loss.
class diffengine.models.losses.debias_estimation_loss.DeBiasEstimationLoss(loss_weight=1.0, reduction='mean', loss_name='debias_estimation')[source]

Bases: diffengine.models.losses.base.BaseLoss

DeBias Estimation loss.

https://arxiv.org/abs/2310.08442

Args:

loss_weight (float): Weight of this loss item.

Defaults to 1..

reduction: (str): The reduction method for the loss.

Defaults to ‘mean’.

loss_name (str, optional): Name of the loss item. If you want this loss

item to be included into the backward graph, loss_ must be the prefix of the name. Defaults to ‘l2’.

property use_snr: bool[source]

Whether or not this loss uses SNR.

Return type:

bool

forward(pred, gt, timesteps, alphas_cumprod, prediction_type, weight=None)[source]

Forward function.

Args:

pred (torch.Tensor): The predicted tensor. gt (torch.Tensor): The ground truth tensor. timesteps (torch.Tensor): The timestep tensor. alphas_cumprod (torch.Tensor): The alphas_cumprod from the

scheduler.

prediction_type (str): The prediction type from scheduler. weight (torch.Tensor | None, optional): The loss weight.

Defaults to None.

Returns:

torch.Tensor: loss

Parameters:

  • pred (torch.Tensor) –

  • gt (torch.Tensor) –

  • timesteps (torch.Tensor) –

  • alphas_cumprod (torch.Tensor) –

  • prediction_type (str) –

  • weight (torch.Tensor | None) –

Return type:

torch.Tensor

Parameters:

  • loss_weight (float) –

  • reduction (str) –

  • loss_name (str) –