# Source code for monai.losses.deform

```
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import torch
from torch.nn.modules.loss import _Loss
from monai.utils import LossReduction
def spatial_gradient(x: torch.Tensor, dim: int) -> torch.Tensor:
"""
Calculate gradients on single dimension of a tensor using central finite difference.
It moves the tensor along the dimension to calculate the approximate gradient
dx[i] = (x[i+1] - x[i-1]) / 2.
Adapted from:
DeepReg (https://github.com/DeepRegNet/DeepReg)
Args:
x: the shape should be BCH(WD).
dim: dimension to calculate gradient along.
Returns:
gradient_dx: the shape should be BCH(WD)
"""
slice_1 = slice(1, -1)
slice_2_s = slice(2, None)
slice_2_e = slice(None, -2)
slice_all = slice(None)
slicing_s, slicing_e = [slice_all, slice_all], [slice_all, slice_all]
while len(slicing_s) < x.ndim:
slicing_s = slicing_s + [slice_1]
slicing_e = slicing_e + [slice_1]
slicing_s[dim] = slice_2_s
slicing_e[dim] = slice_2_e
return (x[slicing_s] - x[slicing_e]) / 2.0
[docs]
class BendingEnergyLoss(_Loss):
"""
Calculate the bending energy based on second-order differentiation of ``pred`` using central finite difference.
For more information,
see https://github.com/Project-MONAI/tutorials/blob/main/modules/bending_energy_diffusion_loss_notes.ipynb.
Adapted from:
DeepReg (https://github.com/DeepRegNet/DeepReg)
"""
[docs]
def __init__(self, normalize: bool = False, reduction: LossReduction | str = LossReduction.MEAN) -> None:
"""
Args:
normalize:
Whether to divide out spatial sizes in order to make the computation roughly
invariant to image scale (i.e. vector field sampling resolution). Defaults to False.
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.normalize = normalize
[docs]
def forward(self, pred: torch.Tensor) -> torch.Tensor:
"""
Args:
pred: the shape should be BCH(WD)
Raises:
ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"].
ValueError: When ``pred`` is not 3-d, 4-d or 5-d.
ValueError: When any spatial dimension of ``pred`` has size less than or equal to 4.
ValueError: When the number of channels of ``pred`` does not match the number of spatial dimensions.
"""
if pred.ndim not in [3, 4, 5]:
raise ValueError(f"Expecting 3-d, 4-d or 5-d pred, instead got pred of shape {pred.shape}")
for i in range(pred.ndim - 2):
if pred.shape[-i - 1] <= 4:
raise ValueError(f"All spatial dimensions must be > 4, got spatial dimensions {pred.shape[2:]}")
if pred.shape[1] != pred.ndim - 2:
raise ValueError(
f"Number of vector components, i.e. number of channels of the input DDF, {pred.shape[1]}, "
f"does not match number of spatial dimensions, {pred.ndim - 2}"
)
# first order gradient
first_order_gradient = [spatial_gradient(pred, dim) for dim in range(2, pred.ndim)]
# spatial dimensions in a shape suited for broadcasting below
if self.normalize:
spatial_dims = torch.tensor(pred.shape, device=pred.device)[2:].reshape((1, -1) + (pred.ndim - 2) * (1,))
energy = torch.tensor(0)
for dim_1, g in enumerate(first_order_gradient):
dim_1 += 2
if self.normalize:
g *= pred.shape[dim_1] / spatial_dims
energy = energy + (spatial_gradient(g, dim_1) * pred.shape[dim_1]) ** 2
else:
energy = energy + spatial_gradient(g, dim_1) ** 2
for dim_2 in range(dim_1 + 1, pred.ndim):
if self.normalize:
energy = energy + 2 * (spatial_gradient(g, dim_2) * pred.shape[dim_2]) ** 2
else:
energy = energy + 2 * spatial_gradient(g, dim_2) ** 2
if self.reduction == LossReduction.MEAN.value:
energy = torch.mean(energy) # the batch and channel average
elif self.reduction == LossReduction.SUM.value:
energy = torch.sum(energy) # sum over the batch and channel dims
elif self.reduction != LossReduction.NONE.value:
raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')
return energy
[docs]
class DiffusionLoss(_Loss):
"""
Calculate the diffusion based on first-order differentiation of ``pred`` using central finite difference.
For the original paper, please refer to
VoxelMorph: A Learning Framework for Deformable Medical Image Registration,
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
IEEE TMI: Transactions on Medical Imaging. 2019. eprint arXiv:1809.05231.
For more information,
see https://github.com/Project-MONAI/tutorials/blob/main/modules/bending_energy_diffusion_loss_notes.ipynb.
Adapted from:
VoxelMorph (https://github.com/voxelmorph/voxelmorph)
"""
[docs]
def __init__(self, normalize: bool = False, reduction: LossReduction | str = LossReduction.MEAN) -> None:
"""
Args:
normalize:
Whether to divide out spatial sizes in order to make the computation roughly
invariant to image scale (i.e. vector field sampling resolution). Defaults to False.
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.normalize = normalize
[docs]
def forward(self, pred: torch.Tensor) -> torch.Tensor:
"""
Args:
pred:
Predicted dense displacement field (DDF) with shape BCH[WD],
where C is the number of spatial dimensions.
Note that diffusion loss can only be calculated
when the sizes of the DDF along all spatial dimensions are greater than 2.
Raises:
ValueError: When ``self.reduction`` is not one of ["mean", "sum", "none"].
ValueError: When ``pred`` is not 3-d, 4-d or 5-d.
ValueError: When any spatial dimension of ``pred`` has size less than or equal to 2.
ValueError: When the number of channels of ``pred`` does not match the number of spatial dimensions.
"""
if pred.ndim not in [3, 4, 5]:
raise ValueError(f"Expecting 3-d, 4-d or 5-d pred, instead got pred of shape {pred.shape}")
for i in range(pred.ndim - 2):
if pred.shape[-i - 1] <= 2:
raise ValueError(f"All spatial dimensions must be > 2, got spatial dimensions {pred.shape[2:]}")
if pred.shape[1] != pred.ndim - 2:
raise ValueError(
f"Number of vector components, i.e. number of channels of the input DDF, {pred.shape[1]}, "
f"does not match number of spatial dimensions, {pred.ndim - 2}"
)
# first order gradient
first_order_gradient = [spatial_gradient(pred, dim) for dim in range(2, pred.ndim)]
# spatial dimensions in a shape suited for broadcasting below
if self.normalize:
spatial_dims = torch.tensor(pred.shape, device=pred.device)[2:].reshape((1, -1) + (pred.ndim - 2) * (1,))
diffusion = torch.tensor(0)
for dim_1, g in enumerate(first_order_gradient):
dim_1 += 2
if self.normalize:
# We divide the partial derivative for each vector component at each voxel by the spatial size
# corresponding to that component relative to the spatial size of the vector component with respect
# to which the partial derivative is taken.
g *= pred.shape[dim_1] / spatial_dims
diffusion = diffusion + g**2
if self.reduction == LossReduction.MEAN.value:
diffusion = torch.mean(diffusion) # the batch and channel average
elif self.reduction == LossReduction.SUM.value:
diffusion = torch.sum(diffusion) # sum over the batch and channel dims
elif self.reduction != LossReduction.NONE.value:
raise ValueError(f'Unsupported reduction: {self.reduction}, available options are ["mean", "sum", "none"].')
return diffusion
```