FocalLoss

class torch_ecg.models.loss.FocalLoss(gamma: float = 2.0, weight: Tensor | None = None, class_weight: Tensor | None = None, size_average: bool | None = None, reduce: bool | None = None, reduction: str = 'mean', multi_label: bool = True, **kwargs: Any)

Bases: _WeightedLoss

Focal loss class.

The focal loss is proposed in [1], and this implementation is based on [2], [3], and [4]. The focal loss is computed as follows:

\[\operatorname{FL}(p_t) = -\alpha_t (1 - p_t)^{\gamma} \, \log(p_t)\]

Where:

• \(p_t\) is the model’s estimated probability for each class.

Parameters:

• gamma (float, default 2.0) – The gamma parameter of focal loss.

• weight (torch.Tensor, optional) – If multi_label is True, is a manual rescaling weight given to the loss of each batch element, of size batch_size; if multi_label is False, is a weight for each class, of size n_classes.

• class_weight (torch.Tensor, optional) – The class weight, of shape (1, n_classes).

• size_average (bool, optional) – Not used, to keep in accordance with PyTorch native loss.

• reduce (bool, optional) – Not used, to keep in accordance with PyTorch native loss.

• reduction ({"none", "mean", "sum"}, optional) – Specifies the reduction to apply to the output, by default “mean”.

• multi_label (bool, default True) – If True, the loss is computed for multi-label classification.

References

[1]

Lin, Tsung-Yi, et al. “Focal loss for dense object detection.” Proceedings of the IEEE international conference on computer vision. 2017.

[2]

https://github.com/kornia/kornia/blob/master/kornia/losses/focal.py

[3]

https://github.com/clcarwin/focal_loss_pytorch/blob/master/focalloss.py

[4]

https://discuss.pytorch.org/t/is-this-a-correct-implementation-for-focal-loss-in-pytorch/43327

forward(input: Tensor, target: Tensor) → Tensor

Forward pass.

Parameters:

• input (torch.Tensor) – The predicted value tensor (before sigmoid), of shape (batch_size, n_classes).

• target (torch.Tensor) – Multi-label binarized vector of shape (batch_size, n_classes), or single label binarized vector of shape (batch_size,).

Returns:

The focal loss.

Return type:

torch.Tensor