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7380 mention demo in bending energy and diffusion docstrings #7381

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Jan 10, 2024
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7380 mention demo in bending energy and diffusion docstrings #7381

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d901aa5
mention demo in docstring, fix raise errors in bending energy loss
kvttt Jan 9, 2024
972a0eb
fix typo
kvttt Jan 9, 2024
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16 changes: 13 additions & 3 deletions monai/losses/deform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,10 @@ def spatial_gradient(x: torch.Tensor, dim: int) -> torch.Tensor:

class BendingEnergyLoss(_Loss):
"""
Calculate the bending energy based on second-order differentiation of pred using central finite difference.
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)
Expand Down Expand Up @@ -75,6 +78,9 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor:

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]:
Expand All @@ -84,7 +90,8 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor:
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, {pred.shape[1]}, does not match number of spatial dimensions, {pred.ndim-2}"
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
Expand Down Expand Up @@ -120,12 +127,15 @@ def forward(self, pred: torch.Tensor) -> torch.Tensor:

class DiffusionLoss(_Loss):
"""
Calculate the diffusion based on first-order differentiation of pred using central finite difference.
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)
"""
Expand Down
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