wing-loss pytorch

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# -*- coding: utf-8 -*-
# @Time    : 2019/9/9 
# @Author  : Elliott Zheng  
# @Email   : [email protected]

import math
import torch
from torch import nn


#torch.log  and math.log is e based
class WingLoss(nn.Module):
    def __init__(self, omega=10, epsilon=2):
        super(WingLoss, self).__init__()
        self.omega = omega
        self.epsilon = epsilon

    def forward(self, pred, target):
        y = target
        y_hat = pred
        delta_y = (y - y_hat).abs()
        delta_y1 = delta_y[delta_y < self.omega]
        delta_y2 = delta_y[delta_y >= self.omega]
        loss1 = self.omega * torch.log(1 + delta_y1 / self.epsilon)
        C = self.omega - self.omega * math.log(1 + self.omega / self.epsilon)
        loss2 = delta_y2 - C
        return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2))


if __name__ == "__main__":
    loss_func = WingLoss()
    y = torch.ones(2, 68, 64, 64)
    y_hat = torch.zeros(2, 68, 64, 64)
    y_hat.requires_grad_(True)
    loss = loss_func(y_hat, y)
    loss.backward()
    print(loss)