Skip to content

Adjust execution order of activation and masking in MaskedDiceLoss #8704

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
ytl0623 wants to merge 1 commit into
base: dev
Choose a base branch
from
Open

Adjust execution order of activation and masking in MaskedDiceLoss #8704

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
30 changes: 29 additions & 1 deletion monai/losses/dice.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -244,7 +244,22 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
Args follow :py:class:`monai.losses.DiceLoss`.
"""
super().__init__(*args, **kwargs)
self.spatial_weighted = MaskedLoss(loss=super().forward)
self.dice = DiceLoss(
include_background=self.include_background,
to_onehot_y=self.to_onehot_y,
sigmoid=False,
softmax=False,
other_act=None,
squared_pred=self.squared_pred,
jaccard=self.jaccard,
reduction=self.reduction,
smooth_nr=self.smooth_nr,
smooth_dr=self.smooth_dr,
batch=self.batch,
weight=self.class_weight,
soft_label=self.soft_label,
)
self.spatial_weighted = MaskedLoss(loss=self.dice.forward)

def forward(self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor | None = None) -> torch.Tensor:
"""
Expand All @@ -253,6 +268,19 @@ def forward(self, input: torch.Tensor, target: torch.Tensor, mask: torch.Tensor
target: the shape should be BNH[WD].
mask: the shape should B1H[WD] or 11H[WD].
"""

if self.sigmoid:
input = torch.sigmoid(input)

n_pred_ch = input.shape[1]
if self.softmax:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `softmax=True` ignored.")
else:
input = torch.softmax(input, 1)

if self.other_act is not None:
input = self.other_act(input)
return self.spatial_weighted(input=input, target=target, mask=mask) # type: ignore[no-any-return]


Expand Down
6 changes: 3 additions & 3 deletions tests/losses/test_masked_dice_loss.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,7 +27,7 @@
"target": torch.tensor([[[[1.0, 0.0], [1.0, 1.0]]]]),
"mask": torch.tensor([[[[0.0, 0.0], [1.0, 1.0]]]]),
},
0.500,
0.333333,
],
[ # shape: (2, 1, 2, 2), (2, 1, 2, 2)
{"include_background": True, "sigmoid": True, "smooth_nr": 1e-4, "smooth_dr": 1e-4},
Expand All @@ -36,7 +36,7 @@
"target": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 0.0], [1.0, 0.0]]]]),
"mask": torch.tensor([[[[1.0, 1.0], [1.0, 1.0]]], [[[1.0, 1.0], [0.0, 0.0]]]]),
},
0.422969,
0.301128,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{"include_background": False, "to_onehot_y": True, "smooth_nr": 0, "smooth_dr": 0},
Expand All @@ -54,7 +54,7 @@
"target": torch.tensor([[[1.0, 0.0, 0.0]], [[1.0, 1.0, 0.0]]]),
"mask": torch.tensor([[[1.0, 1.0, 0.0]]]),
},
0.47033,
0.579184,
],
[ # shape: (2, 2, 3), (2, 1, 3)
{
Expand Down
Loading