Restructure of original evidential loss + renaming

mlguess/torch/regression_losses.py

@@ -7,46 +7,46 @@
 tol = torch.finfo(torch.float32).eps
 
 
-def nig_nll(y, gamma, v, alpha, beta):
-    """Implements Normal Inverse Gamma-Negative Log Likelihood for
-       Deep Evidential Regression
+# original loss used in Amini paper
 
-    Reference: https://www.mit.edu/~amini/pubs/pdf/deep-evidential-regression.pdf
-    Source: https://github.com/hxu296/torch-evidental-deep-learning
-    """
-    two_blambda = 2 * beta * (1 + v) + tol
-    nll = 0.5 * torch.log(np.pi / (v + tol)) \
-          - alpha * torch.log(two_blambda + tol) \
-          + (alpha + 0.5) * torch.log(v * (y - gamma) ** 2 + two_blambda + tol) \
-          + torch.lgamma(alpha) \
-          - torch.lgamma(alpha + 0.5)
-
-    return nll
 
+class EvidentialRegressionLoss:
+    def __init__(self, coef=1.0):
+        self.coef = coef
 
-def nig_reg(y, gamma, v, alpha):
-    """Implements Normal Inverse Gamma Regularizer for Deep Evidential
-       Regression
+    def normal_inverse_gamma_nll(self, y, gamma, v, alpha, beta):
+        """Implements Normal Inverse Gamma-Negative Log Likelihood for
+        Deep Evidential Regression
 
-    Reference: https://www.mit.edu/~amini/pubs/pdf/deep-evidential-regression.pdf
-    Source: https://github.com/hxu296/torch-evidental-deep-learning
-    """
-    error = F.l1_loss(y, gamma, reduction="none")
-    evi = 2 * v + alpha
-    return error * evi
+        Reference: https://www.mit.edu/~amini/pubs/pdf/deep-evidential-regression.pdf
+        Source: https://github.com/hxu296/torch-evidental-deep-learning
+        """
+        two_blambda = 2 * beta * (1 + v) + tol
+        nll = 0.5 * torch.log(np.pi / (v + tol)) \
+            - alpha * torch.log(two_blambda + tol) \
+            + (alpha + 0.5) * torch.log(v * (y - gamma) ** 2 + two_blambda + tol) \
+            + torch.lgamma(alpha) \
+            - torch.lgamma(alpha + 0.5)
 
+        return nll
 
-def evidential_regression_loss(y, pred, coef=1.0):
-    """Implements Evidential Regression Loss for Deep Evidential
-       Regression
+    def normal_inverse_gamma_reg(self, y, gamma, v, alpha, beta):
+        """Implements Normal Inverse Gamma Regularizer for Deep Evidential
+        Regression
 
-    Reference: https://www.mit.edu/~amini/pubs/pdf/deep-evidential-regression.pdf
-    Source: https://github.com/hxu296/torch-evidental-deep-learning
-    """
-    gamma, v, alpha, beta = pred
-    loss_nll = nig_nll(y, gamma, v, alpha, beta)
-    loss_reg = nig_reg(y, gamma, v, alpha, beta)
-    return loss_nll.mean() + coef * loss_reg.mean()
+        Reference: https://www.mit.edu/~amini/pubs/pdf/deep-evidential-regression.pdf
+        Source: https://github.com/hxu296/torch-evidental-deep-learning
+        """
+        error = F.l1_loss(y, gamma, reduction="none")
+        evi = 2 * v + alpha
+        return error * evi
+
+    def __call__(self, y, pred):
+        """Calculate the Evidential Regression Loss"""
+        gamma, v, alpha, beta = pred
+        loss_nll = self.normal_inverse_gamma_nll(y, gamma, v, alpha, beta)
+        loss_reg = self.normal_inverse_gamma_reg(y, gamma, v, alpha, beta)
+        return loss_nll.mean() + self.coef * loss_reg.mean()
 
 
 # code below based off https://github.com/deargen/MT-ENet