Update loss.py

loss/loss.py

@@ -38,36 +38,3 @@ def wing_loss(landmarks, labels, w=10., epsilon=2.):
 def got_total_wing_loss(output,crop_landmarks):
     loss = wing_loss(output, crop_landmarks)
     return loss
-
-'''
-AdaptiveWingLoss
-'''
-
-class AdaptiveWingLoss(nn.Module):
-    def __init__(self, omega=14, theta=0.5, epsilon=1, alpha=2.1):
-        super(AdaptiveWingLoss, self).__init__()
-        self.omega = omega
-        self.theta = theta
-        self.epsilon = epsilon
-        self.alpha = alpha
-
-    def forward(self, pred, target):
-        '''
-        :param pred: BxNxHxH
-        :param target: BxNxHxH
-        :return:
-        '''
-
-        y = target
-        y_hat = pred
-        delta_y = (y - y_hat).abs()
-        delta_y1 = delta_y[delta_y < self.theta]
-        delta_y2 = delta_y[delta_y >= self.theta]
-        y1 = y[delta_y < self.theta]
-        y2 = y[delta_y >= self.theta]
-        loss1 = self.omega * torch.log(1 + torch.pow(delta_y1 / self.omega, self.alpha - y1))
-        A = self.omega * (1 / (1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))) * (self.alpha - y2) * (
-            torch.pow(self.theta / self.epsilon, self.alpha - y2 - 1)) * (1 / self.epsilon)
-        C = self.theta * A - self.omega * torch.log(1 + torch.pow(self.theta / self.epsilon, self.alpha - y2))
-        loss2 = A * delta_y2 - C
-        return (loss1.sum() + loss2.sum()) / (len(loss1) + len(loss2))