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()