From 6ba3b73af0efb7f277de4351a8385655ddbf6ffe Mon Sep 17 00:00:00 2001
From: hua-zi <83271073+hua-zi@users.noreply.github.com>
Date: Tue, 9 Jul 2024 18:17:36 +0800
Subject: [PATCH] Create loss_utils.py

ModuleNotFoundError: No module named 'utils.loss_utils'

The code is missing the loss_utils.py
---
 utils/loss_utils.py | 64 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 utils/loss_utils.py

diff --git a/utils/loss_utils.py b/utils/loss_utils.py
new file mode 100644
index 0000000..9defc23
--- /dev/null
+++ b/utils/loss_utils.py
@@ -0,0 +1,64 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+from math import exp
+
+def l1_loss(network_output, gt):
+    return torch.abs((network_output - gt)).mean()
+
+def l2_loss(network_output, gt):
+    return ((network_output - gt) ** 2).mean()
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+def ssim(img1, img2, window_size=11, size_average=True):
+    channel = img1.size(-3)
+    window = create_window(window_size, channel)
+
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+
+    return _ssim(img1, img2, window, window_size, channel, size_average)
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+