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Refactor self.weight in DiceLoss and FocalLoss #7158

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Merged
wyli merged 4 commits into from
Oct 24, 2023
Merged

Refactor self.weight in DiceLoss and FocalLoss #7158

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42aa088
fix #7065
KumoLiu Oct 24, 2023
5b4fe29
minor fix
KumoLiu Oct 24, 2023
8713554
fix mypy
KumoLiu Oct 24, 2023
843131f
fix ci
KumoLiu Oct 24, 2023
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14 changes: 7 additions & 7 deletions monai/losses/dice.py
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Original file line number Diff line number Diff line change
Expand Up @@ -111,8 +111,9 @@ def __init__(
self.smooth_nr = float(smooth_nr)
self.smooth_dr = float(smooth_dr)
self.batch = batch
self.weight = weight
self.register_buffer("class_weight", torch.ones(1))
weight = torch.as_tensor(weight) if weight is not None else None
self.register_buffer("class_weight", weight)
self.class_weight: None | torch.Tensor

def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Expand Down Expand Up @@ -189,13 +190,12 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:

f: torch.Tensor = 1.0 - (2.0 * intersection + self.smooth_nr) / (denominator + self.smooth_dr)

if self.weight is not None and target.shape[1] != 1:
num_of_classes = target.shape[1]
if self.class_weight is not None and num_of_classes != 1:
# make sure the lengths of weights are equal to the number of classes
num_of_classes = target.shape[1]
if isinstance(self.weight, (float, int)):
self.class_weight = torch.as_tensor([self.weight] * num_of_classes)
if self.class_weight.ndim == 0:
self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
else:
self.class_weight = torch.as_tensor(self.weight)
if self.class_weight.shape[0] != num_of_classes:
raise ValueError(
"""the length of the `weight` sequence should be the same as the number of classes.
Expand Down
13 changes: 7 additions & 6 deletions monai/losses/focal_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,7 +113,9 @@ def __init__(
self.alpha = alpha
self.weight = weight
self.use_softmax = use_softmax
self.register_buffer("class_weight", torch.ones(1))
weight = torch.as_tensor(weight) if weight is not None else None
self.register_buffer("class_weight", weight)
self.class_weight: None | torch.Tensor

def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
"""
Expand Down Expand Up @@ -162,13 +164,12 @@ def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
else:
loss = sigmoid_focal_loss(input, target, self.gamma, self.alpha)

if self.weight is not None:
num_of_classes = target.shape[1]
if self.class_weight is not None and num_of_classes != 1:
# make sure the lengths of weights are equal to the number of classes
num_of_classes = target.shape[1]
if isinstance(self.weight, (float, int)):
self.class_weight = torch.as_tensor([self.weight] * num_of_classes)
if self.class_weight.ndim == 0:
self.class_weight = torch.as_tensor([self.class_weight] * num_of_classes)
else:
self.class_weight = torch.as_tensor(self.weight)
if self.class_weight.shape[0] != num_of_classes:
raise ValueError(
"""the length of the `weight` sequence should be the same as the number of classes.
Expand Down