external/landmark_detection/lib/loss/awingLoss.py

import torch
import torch.nn as nn
import torch.nn.functional as F


class AWingLoss(nn.Module):
    def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1, use_weight_map=True):
        super(AWingLoss, self).__init__()
        self.omega = omega
        self.theta = theta
        self.epsilon = epsilon
        self.alpha = alpha
        self.use_weight_map = use_weight_map

    def __repr__(self):
        return "AWingLoss()"

    def generate_weight_map(self, heatmap, k_size=3, w=10):
        dilate = F.max_pool2d(heatmap, kernel_size=k_size, stride=1, padding=1)
        weight_map = torch.where(dilate < 0.2, torch.zeros_like(heatmap), torch.ones_like(heatmap))
        return w * weight_map + 1

    def forward(self, output, groundtruth):
        """
        input:  b x n x h x w
        output: b x n x h x w => 1
        """
        delta = (output - groundtruth).abs()
        A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - groundtruth))) * (self.alpha - groundtruth) * \
            (torch.pow(self.theta / self.epsilon, self.alpha - groundtruth - 1)) * (1 / self.epsilon)
        C = self.theta * A - self.omega * \
            torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - groundtruth))
        loss = torch.where(delta < self.theta, 
               self.omega * torch.log(1 + torch.pow(delta / self.epsilon, self.alpha - groundtruth)), 
               (A * delta - C))
        if self.use_weight_map:
            weight = self.generate_weight_map(groundtruth)
            loss = loss * weight
        return loss.mean()