From da7ef08933ca4f239cb3d2b49bdeec5b1568ad4e Mon Sep 17 00:00:00 2001
From: "Eric.Lee2021" <305141918@qq.com>
Date: Tue, 11 May 2021 11:38:51 +0800
Subject: [PATCH] Update loss.py

---
 loss/loss.py | 33 ---------------------------------
 1 file changed, 33 deletions(-)

diff --git a/loss/loss.py b/loss/loss.py
index 620db1d..d191153 100644
--- a/loss/loss.py
+++ b/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))
-- 
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