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import torch.nn as nn
from . import base
from . import functional as F
from ..base.modules import Activation
class JaccardLoss(base.Loss):
def __init__(self, eps=1.0, activation=None, ignore_channels=None, **kwargs):
super().__init__(**kwargs)
self.eps = eps
self.activation = Activation(activation)
self.ignore_channels = ignore_channels
def forward(self, y_pr, y_gt):
y_pr = self.activation(y_pr)
return 1 - F.jaccard(
y_pr,
y_gt,
eps=self.eps,
threshold=None,
ignore_channels=self.ignore_channels,
)
class DiceLoss(base.Loss):
def __init__(
self, eps=1.0, beta=1.0, activation=None, ignore_channels=None, **kwargs
):
super().__init__(**kwargs)
self.eps = eps
self.beta = beta
self.activation = Activation(activation)
self.ignore_channels = ignore_channels
def forward(self, y_pr, y_gt):
y_pr = self.activation(y_pr)
return 1 - F.f_score(
y_pr,
y_gt,
beta=self.beta,
eps=self.eps,
threshold=None,
ignore_channels=self.ignore_channels,
)
class L1Loss(nn.L1Loss, base.Loss):
pass
class MSELoss(nn.MSELoss, base.Loss):
pass
class CrossEntropyLoss(nn.CrossEntropyLoss, base.Loss):
pass
class NLLLoss(nn.NLLLoss, base.Loss):
pass
class BCELoss(nn.BCELoss, base.Loss):
pass
class BCEWithLogitsLoss(nn.BCEWithLogitsLoss, base.Loss):
pass
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