ncalab.losses

Classes

DiceScore	Pytorch Module that computes the Dice overlap score between two images.
DiceLoss	Base class for all neural network modules.
DiceBCELoss	Combination of Dice and BCE Loss between two images.
FocalLoss	Base class for all neural network modules.

Module Contents

class ncalab.losses.DiceScore

Bases: torch.nn.Module

Pytorch Module that computes the Dice overlap score between two images.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) → torch.Tensor

Parameters:

• x (torch.Tensor) – Reference Input

• y (torch.Tensor) – Other Input

• smooth (float) – Smooting factor, defaults to 1.0

Returns:

Dice score

Return type:

torch.Tensor

class ncalab.losses.DiceLoss

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

dicescore

forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) → torch.Tensor

class ncalab.losses.DiceBCELoss

Bases: torch.nn.Module

Combination of Dice and BCE Loss between two images.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

dicescore

forward(x: torch.Tensor, y: torch.Tensor, smooth: float = 1.0) → torch.Tensor

Parameters:

• x (torch.Tensor) – Reference Input

• y (torch.Tensor) – Other Input

• smooth (float) – Smooting factor, defaults to 1.0

Returns:

Dice score

Return type:

torch.Tensor

class ncalab.losses.FocalLoss(weight=None, gamma=2, device='cpu')

Bases: torch.nn.modules.loss._WeightedLoss

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing them to be nested in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will also have their parameters converted when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initialize internal Module state, shared by both nn.Module and ScriptModule.

gamma = 2

weight = None

device = 'cpu'

ce_loss

forward(_input, _target)