mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2025-06-26 18:18:11 +00:00
223 lines
7.9 KiB
Python
223 lines
7.9 KiB
Python
#
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# Copyright (C) 2023, Inria
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# GRAPHDECO research group, https://team.inria.fr/graphdeco
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# All rights reserved.
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#
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# This software is free for non-commercial, research and evaluation use
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# under the terms of the LICENSE.md file.
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#
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# For inquiries contact george.drettakis@inria.fr
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#
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.autograd import Variable
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from math import exp
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try:
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from diff_gaussian_rasterization._C import fusedssim, fusedssim_backward
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except:
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pass
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import math
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C1 = 0.01 ** 2
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C2 = 0.03 ** 2
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class FusedSSIMMap(torch.autograd.Function):
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@staticmethod
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def forward(ctx, C1, C2, img1, img2):
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ssim_map = fusedssim(C1, C2, img1, img2)
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ctx.save_for_backward(img1.detach(), img2)
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ctx.C1 = C1
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ctx.C2 = C2
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return ssim_map
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@staticmethod
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def backward(ctx, opt_grad):
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img1, img2 = ctx.saved_tensors
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C1, C2 = ctx.C1, ctx.C2
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grad = fusedssim_backward(C1, C2, img1, img2, opt_grad)
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return None, None, grad, None
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def l1_loss(network_output, gt):
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return torch.abs((network_output - gt)).mean()
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def l2_loss(network_output, gt):
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return ((network_output - gt) ** 2).mean()
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def gaussian(window_size, sigma):
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gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
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return gauss / gauss.sum()
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def create_window(window_size, channel):
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_1D_window = gaussian(window_size, 1.5).unsqueeze(1)
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_2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
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window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
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return window
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def ssim(img1, img2, window_size=11, size_average=True):
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channel = img1.size(-3)
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window = create_window(window_size, channel)
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if img1.is_cuda:
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window = window.cuda(img1.get_device())
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window = window.type_as(img1)
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return _ssim(img1, img2, window, window_size, channel, size_average)
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def _ssim(img1, img2, window, window_size, channel, size_average=True):
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mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
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mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
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mu1_sq = mu1.pow(2)
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mu2_sq = mu2.pow(2)
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mu1_mu2 = mu1 * mu2
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sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
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sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
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sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
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C1 = 0.01 ** 2
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C2 = 0.03 ** 2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
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if size_average:
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return ssim_map.mean()
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else:
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return ssim_map.mean(1).mean(1).mean(1)
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def fast_ssim(img1, img2):
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ssim_map = FusedSSIMMap.apply(C1, C2, img1, img2)
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return ssim_map.mean()
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def build_gaussian_kernel(kernel_size=5, sigma=1.0, channels=3):
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# Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
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x_coord = torch.arange(kernel_size)
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x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
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y_grid = x_grid.t()
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xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
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mean = (kernel_size - 1)/2.
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variance = sigma**2.
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# Calculate the 2-dimensional gaussian kernel
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gaussian_kernel = (1./(2.*math.pi*variance)) * \
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torch.exp(-torch.sum((xy_grid - mean)**2., dim=-1) / \
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(2*variance))
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# Make sure sum of values in gaussian kernel equals 1.
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gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
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# Reshape to 2D depthwise convolutional weight
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gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
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gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1)
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return gaussian_kernel
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def gaussian_blur(x, kernel):
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"""Apply gaussian blur to input tensor"""
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padding = (kernel.shape[-1] - 1) // 2
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return F.conv2d(x, kernel, padding=padding, groups=x.shape[1])
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def create_laplacian_pyramid(image, max_levels=4):
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"""Create Laplacian pyramid for an image"""
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pyramids = []
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current = image
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kernel = build_gaussian_kernel().to(image.device)
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for _ in range(max_levels):
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# Blur and downsample
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blurred = gaussian_blur(current, kernel)
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down = F.interpolate(blurred, scale_factor=0.5, mode='bilinear', align_corners=False)
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# Upsample and subtract
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up = F.interpolate(down, size=current.shape[2:], mode='bilinear', align_corners=False)
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laplace = current - up
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pyramids.append(laplace)
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current = down
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pyramids.append(current) # Add the final residual
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return pyramids
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def laplacian_pyramid_loss(pred, target, max_levels=4, weights=None):
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"""Compute Laplacian Pyramid Loss between predicted and target images"""
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if weights is None:
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weights = [1.0] * (max_levels + 1)
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pred_pyramids = create_laplacian_pyramid(pred, max_levels)
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target_pyramids = create_laplacian_pyramid(target, max_levels)
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loss = 0
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for pred_lap, target_lap, weight in zip(pred_pyramids, target_pyramids, weights):
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loss += weight * torch.abs(pred_lap - target_lap).mean()
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return loss
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class LaplacianPyramidLoss(torch.nn.Module):
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def __init__(self, max_levels=4, channels=3, kernel_size=5, sigma=1.0):
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super().__init__()
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self.max_levels = max_levels
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self.kernel = build_gaussian_kernel(kernel_size, sigma, channels)
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def forward(self, pred, target, weights=None):
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if weights is None:
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weights = [1.0] * (self.max_levels + 1)
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# Move kernel to the same device as input
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kernel = self.kernel.to(pred.device)
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pred_pyramids = self.create_laplacian_pyramid(pred, kernel)
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target_pyramids = self.create_laplacian_pyramid(target, kernel)
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loss = 0
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for pred_lap, target_lap, weight in zip(pred_pyramids, target_pyramids, weights):
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loss += weight * torch.abs(pred_lap - target_lap).mean()
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return loss
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@staticmethod
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def create_laplacian_pyramid(image, kernel, max_levels=4):
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pyramids = []
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current = image
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for _ in range(max_levels):
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# Apply Gaussian blur before downsampling to prevent aliasing
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blurred = gaussian_blur(current, kernel)
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down = F.interpolate(blurred, scale_factor=0.5, mode='bilinear', align_corners=False)
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# Upsample and subtract from the original image
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up = F.interpolate(down, size=current.shape[2:], mode='bilinear', align_corners=False)
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laplace = current - gaussian_blur(up, kernel) # Apply blur to upsampled image
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pyramids.append(laplace)
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current = down
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pyramids.append(current) # Add the final residual
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return pyramids
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class InvDepthSmoothnessLoss(nn.Module):
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def __init__(self, alpha=10):
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super(InvDepthSmoothnessLoss, self).__init__()
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self.alpha = alpha # 엣지 가중치 강도를 조절하는 하이퍼파라미터
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def forward(self, inv_depth, image):
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# 역깊이 맵의 그래디언트 계산
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dx_inv_depth = torch.abs(inv_depth[:, :, :-1] - inv_depth[:, :, 1:])
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dy_inv_depth = torch.abs(inv_depth[:, :-1, :] - inv_depth[:, 1:, :])
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# 이미지의 그래디언트 계산
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dx_image = torch.mean(torch.abs(image[:, :, :-1] - image[:, :, 1:]), 1, keepdim=True)
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dy_image = torch.mean(torch.abs(image[:, :-1, :] - image[:, 1:, :]), 1, keepdim=True)
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# 이미지 그래디언트에 기반한 가중치 계산
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weight_x = torch.exp(-self.alpha * dx_image)
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weight_y = torch.exp(-self.alpha * dy_image)
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# Smoothness loss 계산
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smoothness_x = dx_inv_depth * weight_x
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smoothness_y = dy_inv_depth * weight_y
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return torch.mean(smoothness_x) + torch.mean(smoothness_y) |