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Zero
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# Code is copied from the gaussian-splatting/utils/loss_utils.py
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from math import exp
def l1_loss(network_output, gt, mean=True):
return torch.abs((network_output - gt)).mean() if mean else torch.abs((network_output - gt))
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, mask = None):
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, mask)
def _ssim(img1, img2, window, window_size, channel, size_average=True, mask = None):
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 mask is not None:
ssim_map = ssim_map * mask
if size_average:
return ssim_map.mean()
else:
return ssim_map.mean(1).mean(1).mean(1)
def mse(img1, img2):
return (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
def psnr(img1, img2):
"""
Computes the Peak Signal-to-Noise Ratio (PSNR) between two single images. NOT BATCHED!
Args:
img1 (torch.Tensor): The first image tensor, with pixel values scaled between 0 and 1.
Shape should be (channels, height, width).
img2 (torch.Tensor): The second image tensor with the same shape as img1, used for comparison.
Returns:
torch.Tensor: A scalar tensor containing the PSNR value in decibels (dB).
"""
mse = (((img1 - img2)) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
return 20 * torch.log10(1.0 / torch.sqrt(mse))
def tv_loss(image):
"""
Computes the total variation (TV) loss for an image of shape [3, H, W].
Args:
image (torch.Tensor): Input image of shape [3, H, W]
Returns:
torch.Tensor: Scalar value representing the total variation loss.
"""
# Ensure the image has the correct dimensions
assert image.ndim == 3 and image.shape[0] == 3, "Input must be of shape [3, H, W]"
# Compute the difference between adjacent pixels in the x-direction (width)
diff_x = torch.abs(image[:, :, 1:] - image[:, :, :-1])
# Compute the difference between adjacent pixels in the y-direction (height)
diff_y = torch.abs(image[:, 1:, :] - image[:, :-1, :])
# Sum the total variation in both directions
tv_loss_value = torch.mean(diff_x) + torch.mean(diff_y)
return tv_loss_value |