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3e936b2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | from abc import ABC, abstractmethod
import torch
class DenoisingLoss(ABC):
@abstractmethod
def __call__(self, x: torch.Tensor, x_pred: torch.Tensor, noise: torch.Tensor, noise_pred: torch.Tensor, alphas_cumprod: torch.Tensor, timestep: torch.Tensor, gradient_mask: torch.Tensor=None, **kwargs) -> torch.Tensor:
pass
class X0PredLoss(DenoisingLoss):
def __call__(self, x: torch.Tensor, x_pred: torch.Tensor, noise: torch.Tensor, noise_pred: torch.Tensor, alphas_cumprod: torch.Tensor, timestep: torch.Tensor, gradient_mask: torch.Tensor=None, **kwargs) -> torch.Tensor:
err = (x - x_pred) ** 2
if gradient_mask is not None:
return err[gradient_mask].mean()
return err.mean()
class VPredLoss(DenoisingLoss):
def __call__(self, x: torch.Tensor, x_pred: torch.Tensor, noise: torch.Tensor, noise_pred: torch.Tensor, alphas_cumprod: torch.Tensor, timestep: torch.Tensor, gradient_mask: torch.Tensor=None, **kwargs) -> torch.Tensor:
weights = 1 / (1 - alphas_cumprod[timestep].reshape(*timestep.shape, 1, 1, 1))
err = weights * (x - x_pred) ** 2
if gradient_mask is not None:
return err[gradient_mask].mean()
return err.mean()
class NoisePredLoss(DenoisingLoss):
def __call__(self, x: torch.Tensor, x_pred: torch.Tensor, noise: torch.Tensor, noise_pred: torch.Tensor, alphas_cumprod: torch.Tensor, timestep: torch.Tensor, gradient_mask: torch.Tensor=None, **kwargs) -> torch.Tensor:
err = (noise - noise_pred) ** 2
if gradient_mask is not None:
return err[gradient_mask].mean()
return err.mean()
class FlowPredLoss(DenoisingLoss):
def __call__(self, x: torch.Tensor, x_pred: torch.Tensor, noise: torch.Tensor, noise_pred: torch.Tensor, alphas_cumprod: torch.Tensor, timestep: torch.Tensor, gradient_mask: torch.Tensor=None, **kwargs) -> torch.Tensor:
err = (kwargs['flow_pred'] - (noise - x)) ** 2
if gradient_mask is not None:
return err[gradient_mask].mean()
return err.mean()
NAME_TO_CLASS = {'x0': X0PredLoss, 'v': VPredLoss, 'noise': NoisePredLoss, 'flow': FlowPredLoss}
def get_denoising_loss(loss_type: str) -> DenoisingLoss:
return NAME_TO_CLASS[loss_type]
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