import torch
import torch.nn as nn


class AdaptiveWingLoss(nn.Module):
    def __init__(
        self,
        omega: float = 14,
        theta: float = 0.5,
        eps: float = 1.0,
        alpha: float = 2.1,
    ) -> None:
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.eps = eps
        self.alpha = alpha

    def forward(
        self,
        pred: torch.Tensor,
        target: torch.Tensor,
        mask: torch.Tensor = None,
    ) -> torch.Tensor:
        delta = torch.abs(pred - target)
        if mask is not None: delta = mask * delta

        delta1 = delta[delta < self.theta]
        delta2 = delta[delta >= self.theta]

        pred1 = pred[delta < self.theta]
        pred2 = pred[delta >= self.theta]

        loss1 = self.omega * torch.log(
            1 + (delta1 / self.omega) ** (self.alpha - pred1)
        )

        A = (
            self.omega 
            * (1 / (1 + (self.theta / self.eps) ** (self.alpha - pred2)))
            * (self.alpha - pred2)
            * ((self.theta / self.eps) ** (self.alpha - pred2 - 1))
            * (1 / self.eps)
        )
        C = self.theta * A - self.omega * torch.log(
            1 + torch.pow(self.theta / self.eps, self.alpha - pred2)
        )
        loss2 = A * delta2 - C

        return (
            (torch.sum(loss1) + torch.sum(loss2)) / (len(loss1) + len(loss2))
        )
f85830c074899ad602233a3d3da991a65d912e54
<mask token>


class AdaptiveWingLoss(nn.Module):
    <mask token>
    <mask token>

<mask token>


class AdaptiveWingLoss(nn.Module):

    def __init__(self, omega: float=14, theta: float=0.5, eps: float=1.0,
        alpha: float=2.1) ->None:
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.eps = eps
        self.alpha = alpha
    <mask token>

<mask token>


class AdaptiveWingLoss(nn.Module):

    def __init__(self, omega: float=14, theta: float=0.5, eps: float=1.0,
        alpha: float=2.1) ->None:
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.eps = eps
        self.alpha = alpha

    def forward(self, pred: torch.Tensor, target: torch.Tensor, mask: torch
        .Tensor=None) ->torch.Tensor:
        delta = torch.abs(pred - target)
        if mask is not None:
            delta = mask * delta
        delta1 = delta[delta < self.theta]
        delta2 = delta[delta >= self.theta]
        pred1 = pred[delta < self.theta]
        pred2 = pred[delta >= self.theta]
        loss1 = self.omega * torch.log(1 + (delta1 / self.omega) ** (self.
            alpha - pred1))
        A = self.omega * (1 / (1 + (self.theta / self.eps) ** (self.alpha -
            pred2))) * (self.alpha - pred2) * (self.theta / self.eps) ** (
            self.alpha - pred2 - 1) * (1 / self.eps)
        C = self.theta * A - self.omega * torch.log(1 + torch.pow(self.
            theta / self.eps, self.alpha - pred2))
        loss2 = A * delta2 - C
        return (torch.sum(loss1) + torch.sum(loss2)) / (len(loss1) + len(loss2)
            )

import torch
import torch.nn as nn


class AdaptiveWingLoss(nn.Module):

    def __init__(self, omega: float=14, theta: float=0.5, eps: float=1.0,
        alpha: float=2.1) ->None:
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.eps = eps
        self.alpha = alpha

    def forward(self, pred: torch.Tensor, target: torch.Tensor, mask: torch
        .Tensor=None) ->torch.Tensor:
        delta = torch.abs(pred - target)
        if mask is not None:
            delta = mask * delta
        delta1 = delta[delta < self.theta]
        delta2 = delta[delta >= self.theta]
        pred1 = pred[delta < self.theta]
        pred2 = pred[delta >= self.theta]
        loss1 = self.omega * torch.log(1 + (delta1 / self.omega) ** (self.
            alpha - pred1))
        A = self.omega * (1 / (1 + (self.theta / self.eps) ** (self.alpha -
            pred2))) * (self.alpha - pred2) * (self.theta / self.eps) ** (
            self.alpha - pred2 - 1) * (1 / self.eps)
        C = self.theta * A - self.omega * torch.log(1 + torch.pow(self.
            theta / self.eps, self.alpha - pred2))
        loss2 = A * delta2 - C
        return (torch.sum(loss1) + torch.sum(loss2)) / (len(loss1) + len(loss2)
            )

import torch
import torch.nn as nn


class AdaptiveWingLoss(nn.Module):
    def __init__(
        self,
        omega: float = 14,
        theta: float = 0.5,
        eps: float = 1.0,
        alpha: float = 2.1,
    ) -> None:
        super(AdaptiveWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.eps = eps
        self.alpha = alpha

    def forward(
        self,
        pred: torch.Tensor,
        target: torch.Tensor,
        mask: torch.Tensor = None,
    ) -> torch.Tensor:
        delta = torch.abs(pred - target)
        if mask is not None: delta = mask * delta

        delta1 = delta[delta < self.theta]
        delta2 = delta[delta >= self.theta]

        pred1 = pred[delta < self.theta]
        pred2 = pred[delta >= self.theta]

        loss1 = self.omega * torch.log(
            1 + (delta1 / self.omega) ** (self.alpha - pred1)
        )

        A = (
            self.omega 
            * (1 / (1 + (self.theta / self.eps) ** (self.alpha - pred2)))
            * (self.alpha - pred2)
            * ((self.theta / self.eps) ** (self.alpha - pred2 - 1))
            * (1 / self.eps)
        )
        C = self.theta * A - self.omega * torch.log(
            1 + torch.pow(self.theta / self.eps, self.alpha - pred2)
        )
        loss2 = A * delta2 - C

        return (
            (torch.sum(loss1) + torch.sum(loss2)) / (len(loss1) + len(loss2))
        )