Source code for monai.losses.ssim_loss

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
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from __future__ import annotations

from import Sequence

import torch
from torch.nn.modules.loss import _Loss

from monai.metrics.regression import KernelType, SSIMMetric
from monai.utils import LossReduction, ensure_tuple_rep

[docs] class SSIMLoss(_Loss): """ Compute the loss function based on the Structural Similarity Index Measure (SSIM) Metric. For more info, visit SSIM reference paper: Wang, Zhou, et al. "Image quality assessment: from error visibility to structural similarity." IEEE transactions on image processing 13.4 (2004): 600-612. """
[docs] def __init__( self, spatial_dims: int, data_range: float = 1.0, kernel_type: KernelType | str = KernelType.GAUSSIAN, win_size: int | Sequence[int] = 11, kernel_sigma: float | Sequence[float] = 1.5, k1: float = 0.01, k2: float = 0.03, reduction: LossReduction | str = LossReduction.MEAN, ): """ Args: spatial_dims: number of spatial dimensions of the input images. data_range: value range of input images. (usually 1.0 or 255) kernel_type: type of kernel, can be "gaussian" or "uniform". win_size: window size of kernel kernel_sigma: standard deviation for Gaussian kernel. k1: stability constant used in the luminance denominator k2: stability constant used in the contrast denominator reduction: {``"none"``, ``"mean"``, ``"sum"``} Specifies the reduction to apply to the output. Defaults to ``"mean"``. - ``"none"``: no reduction will be applied. - ``"mean"``: the sum of the output will be divided by the number of elements in the output. - ``"sum"``: the output will be summed. """ super().__init__(reduction=LossReduction(reduction).value) self.spatial_dims = spatial_dims self._data_range = data_range self.kernel_type = kernel_type if not isinstance(win_size, Sequence): win_size = ensure_tuple_rep(win_size, spatial_dims) self.kernel_size = win_size if not isinstance(kernel_sigma, Sequence): kernel_sigma = ensure_tuple_rep(kernel_sigma, spatial_dims) self.kernel_sigma = kernel_sigma self.k1 = k1 self.k2 = k2 self.ssim_metric = SSIMMetric( spatial_dims=self.spatial_dims, data_range=self._data_range, kernel_type=self.kernel_type, win_size=self.kernel_size, kernel_sigma=self.kernel_sigma, k1=self.k1, k2=self.k2, )
@property def data_range(self) -> float: return self._data_range @data_range.setter def data_range(self, value: float) -> None: self._data_range = value self.ssim_metric.data_range = value
[docs] def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: """ Args: input: batch of predicted images with shape (batch_size, channels, spatial_dim1, spatial_dim2[, spatial_dim3]) target: batch of target images with shape (batch_size, channels, spatial_dim1, spatial_dim2[, spatial_dim3]) Returns: 1 minus the ssim index (recall this is meant to be a loss function) Example: .. code-block:: python import torch # 2D data x = torch.ones([1,1,10,10])/2 y = torch.ones([1,1,10,10])/2 print(1-SSIMLoss(spatial_dims=2)(x,y)) # pseudo-3D data x = torch.ones([1,5,10,10])/2 # 5 could represent number of slices y = torch.ones([1,5,10,10])/2 print(1-SSIMLoss(spatial_dims=2)(x,y)) # 3D data x = torch.ones([1,1,10,10,10])/2 y = torch.ones([1,1,10,10,10])/2 print(1-SSIMLoss(spatial_dims=3)(x,y)) """ ssim_value = self.ssim_metric._compute_tensor(input, target).view(-1, 1) loss: torch.Tensor = 1 - ssim_value if self.reduction == LossReduction.MEAN.value: loss = torch.mean(loss) # the batch average elif self.reduction == LossReduction.SUM.value: loss = torch.sum(loss) # sum over the batch return loss