From 7b6fa4e72e8b15500bd04ed1b1781efc2f8d6fe3 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Wed, 20 Oct 2021 11:32:07 +0200
Subject: [PATCH] Implemented median reduction.
---
climax/core/loss.py | 10 ++++------
1 file changed, 4 insertions(+), 6 deletions(-)
diff --git a/climax/core/loss.py b/climax/core/loss.py
index c2cc811..2147c5b 100644
--- a/climax/core/loss.py
+++ b/climax/core/loss.py
@@ -26,6 +26,8 @@ class NaNLoss(_Loss):
return tensor.mean()
elif self.reduction == 'sum':
return tensor.sum()
+ elif self.reduction == 'median':
+ return tensor.median()
class MSELoss(NaNLoss):
@@ -133,13 +135,9 @@ class BernoulliWeibullLoss(BernoulliLoss):
# clip probabilities to (0, 1)
p_pred = torch.sigmoid(y_pred[:, 0, ...].squeeze()[mask])
- # clip scale to (0, +infinity)
- scale = torch.exp(y_pred[:, 2, ...].squeeze()[mask][~mask_p])
-
- # clip shape to (0, 10)
- # NOTE: in general shape in (0, +infinity), clipping is required for
- # numerical stability
+ # clip shape and scale to (0, +infinity)
shape = torch.exp(y_pred[:, 1, ...].squeeze()[mask][~mask_p])
+ scale = torch.exp(y_pred[:, 2, ...].squeeze()[mask][~mask_p])
# negative log-likelihood function of Bernoulli-Weibull distribution
loss = torch.zeros_like(y_true)
--
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