mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-29 23:42:25 +00:00
65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
#
|
|
# 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)
|
|
|