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Running
on
Zero
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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
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp
from lpipsPyTorch import lpips as lpips_fn
from lpipsPyTorch.modules.lpips import LPIPS
_lpips = None
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)
def lpips(img1, img2):
global _lpips
if _lpips is None:
_lpips = LPIPS("vgg", "0.1").to("cuda")
return _lpips(img1, img2).mean()
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