minerva.losses.dice
Attributes
Classes
Initialize the DiceLoss class. |
Module Contents
- minerva.losses.dice.BINARY_MODE = 'binary'
- class minerva.losses.dice.DiceLoss(mode, classes=None, log_loss=False, from_logits=True, smooth=0.0, ignore_index=None, eps=1e-07)
Bases:
torch.nn.modules.loss._LossInitialize the DiceLoss class.
Parameters
- modestr
Loss mode. Valid options are ‘binary’, ‘multiclass’, or ‘multilabel’.
- classesOptional[List[int]], optional
List of classes that contribute in loss computation. By default, all channels are included. By default None
- log_lossbool, optional
If True, loss is computed as - log(dice_coeff). If False, loss is computed as 1 - dice_coeff, by default False
- from_logitsbool, optional
If True, assumes input is raw logits. If False, assumes input is probabilities., by default True
- smoothfloat, optional
Smoothness constant for dice coefficient (a), by default 0.0
- ignore_indexOptional[int], optional
Label that indicates ignored pixels (does not contribute to loss), by default None
- epsfloat, optional
A small epsilon for numerical stability to avoid zero division error (denominator will be always greater or equal to eps), by default 1e-7
Raises
- AssertionError
If the mode is not one of ‘binary’, ‘multiclass’, or ‘multilabel’ and classes are being masked with mode=’binary’.
- aggregate_loss(loss)
- compute_score(output, target, smooth=0.0, eps=1e-07, dims=None)
- Return type:
torch.Tensor
- forward(y_pred, y_true)
- Parameters:
y_pred (torch.Tensor)
y_true (torch.Tensor)
- Return type:
torch.Tensor
- Parameters:
mode (str)
classes (Optional[List[int]])
log_loss (bool)
from_logits (bool)
smooth (float)
ignore_index (Optional[int])
eps (float)
- minerva.losses.dice.MULTICLASS_MODE = 'multiclass'
- minerva.losses.dice.MULTILABEL_MODE = 'multilabel'