Mixup#

class torch_ecg.augmenters.Mixup(fs: int | None = None, alpha: Real = 0.5, beta: Real | None = None, prob: float = 0.5, inplace: bool = True, **kwargs: Any)[source]#

Bases: Augmenter

Mixup augmentor.

Mixup is a data augmentation technique originally proposed in [1]. The PDF file of the paper can be found on arXiv [2]. The official implementation is provided in [3]. This technique was designed for image classification tasks, but it is also widely used for ECG tasks.

Parameters:

  • fs (int, optional) – Sampling frequency of the ECGs to be augmented.

  • alpha (numbers.Real, default 0.5) – alpha parameter of the Beta distribution used in Mixup.

  • beta (numbers.Real, optional) – beta parameter of the Beta distribution used in Mixup, defaults to alpha.

  • prob (float, default 0.5) – Probability of applying Mixup.

  • inplace (bool, default True) – If True, ECG signal tensors will be modified inplace.

  • **kwargs (dict, optional) – Additional keyword arguments, not used.

Examples

mixup = Mixup(alpha=0.3, beta=0.6, prob=0.7)
sig = torch.randn(32, 12, 5000)
label = torch.randint(0, 2, (32, 26), dtype=torch.float32)
sig, label = mixup(sig, label)

References

extra_repr_keys() → List[str][source]#

Extra keys for __repr__() and __str__().

forward(sig: Tensor, label: Tensor, *extra_tensors: Sequence[Tensor], **kwargs: Any) → Tuple[Tensor, ...][source]#

Forward method of the Mixup augmenter.

Parameters:

  • sig (torch.Tensor) – Batched ECGs to be augmented, of shape (batch, lead, siglen).

  • label (torch.Tensor) – Label tensor of the ECGs.

  • extra_tensors (Sequence[torch.Tensor], optional) – Not used, but kept for consistency with other augmenters.

  • **kwargs (dict, optional) – Not used, but kept for consistency with other augmenters.

Returns:

  • sig (torch.Tensor,) – The augmented ECGs.

  • label (torch.Tensor) – The augmented labels.

  • extra_tensors (Sequence[torch.Tensor], optional) – Unchanged extra tensors.