From 186b21eae78eff29280a75cff51d0e9fc3c41980 Mon Sep 17 00:00:00 2001
From: Devashish Lohani <devashish.lohani@univ-lyon2.fr>
Date: Wed, 19 Oct 2022 13:27:57 +0000
Subject: [PATCH] Upload New File
---
models/loss.py | 30 ++++++++++++++++++++++++++++++
1 file changed, 30 insertions(+)
create mode 100644 models/loss.py
diff --git a/models/loss.py b/models/loss.py
new file mode 100644
index 0000000..cea2ecb
--- /dev/null
+++ b/models/loss.py
@@ -0,0 +1,30 @@
+import torch
+
+def get_memory_loss(memory_att):
+ """The memory attribute should be with size [batch_size, memory_dim, reduced_time_dim, f_h, f_w]
+ loss = \sum_{t=1}^{reduced_time_dim} (-mem) * (mem + 1e-12).log()
+ averaged on each pixel and each batch
+ 2. average over batch_size * fh * fw
+ """
+ s = memory_att.shape
+ memory_att = (-memory_att) * (memory_att + 1e-12).log() # [batch_size, memory_dim, time, fh, fw]
+ memory_att = memory_att.sum() / (s[0] * s[-2] * s[-1])
+ return memory_att
+
+
+def get_unormalized_data(x_input, x_recons, mean, std):
+ x_input = x_input.mul(std).add(mean)
+ x_recons = x_recons.mul(std).add(mean)
+ return x_input, x_recons
+
+
+def get_reconstruction_loss(x_input, x_recons, mean=0.5, std=0.5):
+ """Calculates the reconstruction loss between x_input and x_recons
+ x_input: [batch_size, ch, time, imh, imw]
+ x_recons: [batch_size, ch, time, imh, imw]
+ """
+ batch_size, ch, time_dimension, imh, imw = x_input.shape
+ x_input, x_recons = get_unormalized_data(x_input, x_recons, mean, std)
+ recons_loss = (x_input - x_recons) ** 2
+ recons_loss = recons_loss.sum().sqrt()/(batch_size * imh * imw)
+ return recons_loss
\ No newline at end of file
--
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