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# 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
def mse(img1, img2):
return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
@torch.no_grad()
def psnr(img1, img2, mask=None):
if mask is not None:
img1 = img1.flatten(1)
img2 = img2.flatten(1)
mask = mask.flatten(1).repeat(3,1)
mask = torch.where(mask!=0,True,False)
img1 = img1[mask]
img2 = img2[mask]
mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
else:
mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
psnr = 20 * torch.log10(1.0 / torch.sqrt(mse.float()))
if mask is not None:
if torch.isinf(psnr).any():
print(mse.mean(),psnr.mean())
psnr = 20 * torch.log10(1.0 / torch.sqrt(mse.float()))
psnr = psnr[~torch.isinf(psnr)]
return psnr
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