Source code for monai.losses.spectral_loss

# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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from __future__ import annotations

import torch
import torch.nn.functional as F
from torch.fft import fftn
from torch.nn.modules.loss import _Loss

from monai.utils import LossReduction

[docs] class JukeboxLoss(_Loss): """ Calculate spectral component based on the magnitude of Fast Fourier Transform (FFT). Based on: Dhariwal, et al. 'Jukebox: A generative model for music.' Args: spatial_dims: number of spatial dimensions. fft_signal_size: signal size in the transformed dimensions. See torch.fft.fftn() for more information. fft_norm: {``"forward"``, ``"backward"``, ``"ortho"``} Specifies the normalization mode in the fft. See torch.fft.fftn() for more information. 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. """ def __init__( self, spatial_dims: int, fft_signal_size: tuple[int] | None = None, fft_norm: str = "ortho", reduction: LossReduction | str = LossReduction.MEAN, ) -> None: super().__init__(reduction=LossReduction(reduction).value) self.spatial_dims = spatial_dims self.fft_signal_size = fft_signal_size self.fft_dim = tuple(range(1, spatial_dims + 2)) self.fft_norm = fft_norm
[docs] def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor: input_amplitude = self._get_fft_amplitude(target) target_amplitude = self._get_fft_amplitude(input) # Compute distance between amplitude of frequency components # See Section 3.3 from loss = F.mse_loss(target_amplitude, input_amplitude, reduction="none") if self.reduction == LossReduction.MEAN.value: loss = loss.mean() elif self.reduction == LossReduction.SUM.value: loss = loss.sum() elif self.reduction == LossReduction.NONE.value: pass return loss
def _get_fft_amplitude(self, images: torch.Tensor) -> torch.Tensor: """ Calculate the amplitude of the fourier transformations representation of the images Args: images: Images that are to undergo fftn Returns: fourier transformation amplitude """ img_fft = fftn(images, s=self.fft_signal_size, dim=self.fft_dim, norm=self.fft_norm) amplitude = torch.sqrt(torch.real(img_fft) ** 2 + torch.imag(img_fft) ** 2) return amplitude