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Merged
wyli merged 3 commits into from
May 6, 2022
Merged

Update dice.py #4234

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6f16197
Update dice.py
ryancinsight May 5, 2022
be14db4
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 5, 2022
4018a99
[MONAI] python code formatting
monai-bot May 6, 2022
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28 changes: 19 additions & 9 deletions monai/losses/dice.py
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Original file line number Diff line number Diff line change
Expand Up @@ -796,8 +796,6 @@ def __init__(
"""
super().__init__()
self.dice = DiceLoss(
include_background=include_background,
to_onehot_y=to_onehot_y,
sigmoid=sigmoid,
softmax=softmax,
other_act=other_act,
Expand All @@ -808,19 +806,15 @@ def __init__(
smooth_dr=smooth_dr,
batch=batch,
)
self.focal = FocalLoss(
include_background=include_background,
to_onehot_y=to_onehot_y,
gamma=gamma,
weight=focal_weight,
reduction=reduction,
)
self.focal = FocalLoss(gamma=gamma, weight=focal_weight, reduction=reduction)
if lambda_dice < 0.0:
raise ValueError("lambda_dice should be no less than 0.0.")
if lambda_focal < 0.0:
raise ValueError("lambda_focal should be no less than 0.0.")
self.lambda_dice = lambda_dice
self.lambda_focal = lambda_focal
self.to_onehot_y = to_onehot_y
self.include_background = include_background

def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Expand All @@ -837,6 +831,22 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
if len(input.shape) != len(target.shape):
raise ValueError("the number of dimensions for input and target should be the same.")

n_pred_ch = input.shape[1]

if self.to_onehot_y:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
else:
target = one_hot(target, num_classes=n_pred_ch)

if not self.include_background:
if n_pred_ch == 1:
warnings.warn("single channel prediction, `include_background=False` ignored.")
else:
# if skipping background, removing first channel
target = target[:, 1:]
input = input[:, 1:]

dice_loss = self.dice(input, target)
focal_loss = self.focal(input, target)
total_loss: torch.Tensor = self.lambda_dice * dice_loss + self.lambda_focal * focal_loss
Expand Down