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import torch
import torch.nn.functional as F
from ..utils import _log_api_usage_once
def sigmoid_focal_loss(
inputs: torch.Tensor,
targets: torch.Tensor,
alpha: float = 0.25,
gamma: float = 2,
reduction: str = "none",
) -> torch.Tensor:
"""
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
Args:
inputs (Tensor): A float tensor of arbitrary shape.
The predictions for each example.
targets (Tensor): A float tensor with the same shape as inputs. Stores the binary
classification label for each element in inputs
(0 for the negative class and 1 for the positive class).
alpha (float): Weighting factor in range [0, 1] to balance
positive vs negative examples or -1 for ignore. Default: ``0.25``.
gamma (float): Exponent of the modulating factor (1 - p_t) to
balance easy vs hard examples. Default: ``2``.
reduction (string): ``'none'`` | ``'mean'`` | ``'sum'``
``'none'``: No reduction will be applied to the output.
``'mean'``: The output will be averaged.
``'sum'``: The output will be summed. Default: ``'none'``.
Returns:
Loss tensor with the reduction option applied.
"""
# Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py
if not (0 <= alpha <= 1) and alpha != -1:
raise ValueError(f"Invalid alpha value: {alpha}. alpha must be in the range [0,1] or -1 for ignore.")
if not torch.jit.is_scripting() and not torch.jit.is_tracing():
_log_api_usage_once(sigmoid_focal_loss)
p = torch.sigmoid(inputs)
ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
p_t = p * targets + (1 - p) * (1 - targets)
loss = ce_loss * ((1 - p_t) ** gamma)
if alpha >= 0:
alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
loss = alpha_t * loss
# Check reduction option and return loss accordingly
if reduction == "none":
pass
elif reduction == "mean":
loss = loss.mean()
elif reduction == "sum":
loss = loss.sum()
else:
raise ValueError(
f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'"
)
return loss
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