# Source code for catalyst.metrics.functional._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.
Args:
outputs: tensor of arbitrary shape
targets: tensor of the same shape as input
gamma: gamma for focal loss
alpha: alpha for focal loss
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.
Returns:
computed loss
Source: https://github.com/BloodAxe/pytorch-toolbelt
"""
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",
) -> torch.Tensor:
"""Compute reduced focal loss between target and output logits.
It has been proposed in `Reduced Focal Loss\: 1st Place Solution to xView
object detection in Satellite Imagery`_ paper.
.. note::
``size_average`` and ``reduce`` params are in the process of being
deprecated, and in the meantime, specifying either of those two args
will override ``reduction``.
Source: https://github.com/BloodAxe/pytorch-toolbelt
.. _Reduced Focal Loss\: 1st Place Solution to xView object detection
in Satellite Imagery: https://arxiv.org/abs/1903.01347
Args:
outputs: tensor of arbitrary shape
targets: tensor of the same shape as input
threshold: threshold for focal reduction
gamma: gamma for focal reduction
reduction: 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.
``"batchwise_mean"`` computes mean loss per sample in batch.
Default: "mean"
Returns: # noqa: DAR201
torch.Tensor: computed loss
"""
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 = ((1.0 - 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"]
```