dacapo.experiments.tasks.losses.hot_distance_loss

Classes

HotDistanceLoss

A class used to represent the Hot Distance Loss function. This class inherits from the Loss class. The Hot Distance Loss

Module Contents

class dacapo.experiments.tasks.losses.hot_distance_loss.HotDistanceLoss

A class used to represent the Hot Distance Loss function. This class inherits from the Loss class. The Hot Distance Loss function is used for predicting hot and distance maps at the same time. The first half of the channels are the hot maps, the second half are the distance maps. The loss is the sum of the BCELoss for the hot maps and the MSELoss for the distance maps. The model should predict twice the number of channels as the target.

hot_loss: The Binary Cross Entropy Loss function.

distance_loss: The Mean Square Error Loss function.

compute(prediction, target, weight) → torch.Tensor: Function to compute the Hot Distance Loss for the provided prediction and target, with respect to the weight.

split(x) → Tuple[torch.Tensor, torch.Tensor]: Function to split the input tensor into two tensors.

Note

This class is abstract. Subclasses must implement the abstract methods. Once created, the values of its attributes cannot be changed.

compute(prediction, target, weight)

Function to compute the Hot Distance Loss for the provided prediction and target, with respect to the weight.

Parameters:

prediction – torch.Tensor The predicted tensor.
target – torch.Tensor The target tensor.
weight – torch.Tensor The weight tensor.

Returns:

torch.Tensor: The computed Hot Distance Loss tensor.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> loss = HotDistanceLoss()
>>> prediction = torch.tensor([1.0, 2.0, 3.0])
>>> target = torch.tensor([1.0, 2.0, 3.0])
>>> weight = torch.tensor([1.0, 1.0, 1.0])
>>> loss.compute(prediction, target, weight)
tensor(0.)

Note

This method must be implemented in the subclass. It should return the computed Hot Distance Loss tensor.

hot_loss(prediction, target, weight)

The Binary Cross Entropy Loss function. This function computes the BCELoss for the hot maps.

Parameters:

prediction – torch.Tensor The predicted tensor.
target – torch.Tensor The target tensor.
weight – torch.Tensor The weight tensor.

Returns:

torch.Tensor: The computed BCELoss tensor.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> loss = HotDistanceLoss()
>>> prediction = torch.tensor([1.0, 2.0, 3.0])
>>> target = torch.tensor([1.0, 2.0, 3.0])
>>> weight = torch.tensor([1.0, 1.0, 1.0])
>>> loss.hot_loss(prediction, target, weight)
tensor(0.)

Note

This method must be implemented in the subclass. It should return the computed BCELoss tensor.

distance_loss(prediction, target, weight)

The Mean Square Error Loss function. This function computes the MSELoss for the distance maps.

Parameters:

prediction – torch.Tensor The predicted tensor.
target – torch.Tensor The target tensor.
weight – torch.Tensor The weight tensor.

Returns:

torch.Tensor: The computed MSELoss tensor.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> loss = HotDistanceLoss()
>>> prediction = torch.tensor([1.0, 2.0, 3.0])
>>> target = torch.tensor([1.0, 2.0, 3.0])
>>> weight = torch.tensor([1.0, 1.0, 1.0])
>>> loss.distance_loss(prediction, target, weight)
tensor(0.)

Note

This method must be implemented in the subclass. It should return the computed MSELoss tensor.

split(x)

Function to split the input tensor into two tensors.

Parameters:

x – torch.Tensor The input tensor.

Returns:

Tuple[torch.Tensor, torch.Tensor]: The two split tensors.

Raises:

NotImplementedError – If the method is not implemented in the subclass.

Examples

>>> loss = HotDistanceLoss()
>>> x = torch.tensor([1.0, 2.0, 3.0])
>>> loss.split(x)
(tensor([1.0]), tensor([2.0]))

Note

This method must be implemented in the subclass. It should return the two split tensors.