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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
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 _ncc(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)
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
sigma1 = torch.sqrt(sigma1_sq + 1e-4)
sigma2 = torch.sqrt(sigma2_sq + 1e-4)
image1_norm = (img1 - mu1) / (sigma1 + 1e-8)
image2_norm = (img2 - mu2) / (sigma2 + 1e-8)
ncc = F.conv2d((image1_norm * image2_norm), window, padding=0, groups=channel)
return torch.mean(ncc, dim=2)
# def _ncc(pred, gt, window, channel):
# ntotpx, nviews, nc, h, w = pred.shape
# flat_pred = pred.view(-1, nc, h, w)
# mu1 = F.conv2d(flat_pred, window, padding=0, groups=channel).view(ntotpx, nviews, nc)
# mu2 = F.conv2d(gt, window, padding=0, groups=channel).view(ntotpx, nc)
# mu1_sq = mu1.pow(2)
# mu2_sq = mu2.pow(2).unsqueeze(1) # (ntotpx, 1, nc)
# sigma1_sq = F.conv2d(flat_pred * flat_pred, window, padding=0, groups=channel).view(ntotpx, nviews, nc) - mu1_sq
# sigma2_sq = F.conv2d(gt * gt, window, padding=0, groups=channel).view(ntotpx, 1, 3) - mu2_sq
# sigma1 = torch.sqrt(sigma1_sq + 1e-4)
# sigma2 = torch.sqrt(sigma2_sq + 1e-4)
# pred_norm = (pred - mu1[:, :, :, None, None]) / (sigma1[:, :, :, None, None] + 1e-8) # [ntotpx, nviews, nc, h, w]
# gt_norm = (gt[:, None, :, :, :] - mu2[:, None, :, None, None]) / (
# sigma2[:, :, :, None, None] + 1e-8) # ntotpx, nc, h, w
# ncc = F.conv2d((pred_norm * gt_norm).view(-1, nc, h, w), window, padding=0, groups=channel).view(
# ntotpx, nviews, nc)
# return torch.mean(ncc, dim=2)
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