# Source code for catalyst.dl.utils.criterion.focal

```
import torch
import torch.nn.functional as F
[docs]def sigmoid_focal_loss(
outputs: torch.Tensor,
targets: torch.Tensor,
gamma: float = 2.0,
alpha: float = 0.25,
reduction: str = "mean"
):
"""
Compute binary focal loss between target and output logits.
Source https://github.com/BloodAxe/pytorch-toolbelt
See :class:`~pytorch_toolbelt.losses` for details.
Args:
outputs: Tensor of arbitrary shape
targets: Tensor of the same shape as input
reduction (string, optional):
Specifies the reduction to apply to the output:
"none" | "mean" | "sum" | "batchwise_mean".
"none": no reduction will be applied,
"mean": the sum of the output will be divided by the number of
elements in the output,
"sum": the output will be summed.
See https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/loss/losses.py # noqa: E501
"""
targets = targets.type(outputs.type())
logpt = -F.binary_cross_entropy_with_logits(
outputs, targets, reduction="none"
)
pt = torch.exp(logpt)
# compute the loss
loss = -((1 - pt).pow(gamma)) * logpt
if alpha is not None:
loss = loss * (alpha * targets + (1 - alpha) * (1 - targets))
if reduction == "mean":
loss = loss.mean()
if reduction == "sum":
loss = loss.sum()
if reduction == "batchwise_mean":
loss = loss.sum(0)
return loss
[docs]def reduced_focal_loss(
outputs: torch.Tensor,
targets: torch.Tensor,
threshold: float = 0.5,
gamma: float = 2.0,
reduction="mean"
):
"""
Compute reduced focal loss between target and output logits.
Source https://github.com/BloodAxe/pytorch-toolbelt
See :class:`~pytorch_toolbelt.losses` for details.
Args:
outputs: Tensor of arbitrary shape
targets: Tensor of the same shape as input
reduction (string, optional):
Specifies the reduction to apply to the output:
"none" | "mean" | "sum" | "batchwise_mean".
"none": no reduction will be applied,
"mean": the sum of the output will be divided by the number of
elements in the output,
"sum": the output will be summed.
Note: :attr:`size_average` and :attr:`reduce`
are in the process of being deprecated,
and in the meantime, specifying either of those two args
will override :attr:`reduction`.
"batchwise_mean" computes mean loss per sample in batch.
Default: "mean"
See https://arxiv.org/abs/1903.01347
"""
targets = targets.type(outputs.type())
logpt = -F.binary_cross_entropy_with_logits(
outputs, targets, reduction="none"
)
pt = torch.exp(logpt)
# compute the loss
focal_reduction = ((1. - pt) / threshold).pow(gamma)
focal_reduction[pt < threshold] = 1
loss = -focal_reduction * logpt
if reduction == "mean":
loss = loss.mean()
if reduction == "sum":
loss = loss.sum()
if reduction == "batchwise_mean":
loss = loss.sum(0)
return loss
__all__ = ["sigmoid_focal_loss", "reduced_focal_loss"]
```