Source code for kornia.losses.psnr

import torch
import torch.nn as nn
from torch.nn.functional import mse_loss as mse


[docs]def psnr(input: torch.Tensor, target: torch.Tensor, max_val: float) -> torch.Tensor: r"""Creates a function that calculates the PSNR between 2 images. PSNR is Peek Signal to Noise Ratio, which is similar to mean squared error. Given an m x n image, the PSNR is: .. math:: \text{PSNR} = 10 \log_{10} \bigg(\frac{\text{MAX}_I^2}{MSE(I,T)}\bigg) where .. math:: \text{MSE}(I,T) = \frac{1}{mn}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1} [I(i,j) - T(i,j)]^2 and :math:`\text{MAX}_I` is the maximum possible input value (e.g for floating point images :math:`\text{MAX}_I=1`). Args: input (torch.Tensor): the input image with arbitrary shape :math:`(*)`. labels (torch.Tensor): the labels image with arbitrary shape :math:`(*)`. max_val (float): The maximum value in the input tensor. Return: torch.Tensor: the computed loss as a scalar. Examples: >>> ones = torch.ones(1) >>> psnr(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(20.0000) Reference: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Definition """ if not isinstance(input, torch.Tensor): raise TypeError(f"Expected torch.Tensor but got {type(target)}.") if not isinstance(target, torch.Tensor): raise TypeError(f"Expected torch.Tensor but got {type(input)}.") if input.shape != target.shape: raise TypeError(f"Expected tensors of equal shapes, but got {input.shape} and {target.shape}") return 10. * torch.log10(max_val ** 2 / mse(input, target, reduction='mean'))
[docs]def psnr_loss(input: torch.Tensor, target: torch.Tensor, max_val: float) -> torch.Tensor: r"""Function that computes the PSNR loss. The loss is computed as follows: .. math:: \text{loss} = -\text{psnr(x, y)} See :meth:`~kornia.losses.psnr` for details abut PSNR. Args: input (torch.Tensor): the input image with shape :math:`(*)`. labels (torch.Tensor): the labels image with shape :math:`(*)`. max_val (float): The maximum value in the input tensor. Return: torch.Tensor: the computed loss as a scalar. Examples: >>> ones = torch.ones(1) >>> psnr_loss(ones, 1.2 * ones, 2.) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(-20.0000) """ return -1. * psnr(input, target, max_val)
[docs]class PSNRLoss(nn.Module): r"""Creates a criterion that calculates the PSNR loss. The loss is computed as follows: .. math:: \text{loss} = -\text{psnr(x, y)} See :meth:`~kornia.losses.psnr` for details abut PSNR. Shape: - Input: arbitrary dimensional tensor :math:`(*)`. - Target: arbitrary dimensional tensor :math:`(*)` same shape as input. - Output: a scalar. Examples: >>> ones = torch.ones(1) >>> criterion = PSNRLoss(2.) >>> criterion(ones, 1.2 * ones) # 10 * log(4/((1.2-1)**2)) / log(10) tensor(-20.0000) """ def __init__(self, max_val: float) -> None: super(PSNRLoss, self).__init__() self.max_val: float = max_val def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: return psnr_loss(input, target, self.max_val)