RandomRenormalize¶
- class torch_ecg.augmenters.RandomRenormalize(mean: Iterable[Real] = [-0.05, 0.1], std: Iterable[Real] = [0.08, 0.32], per_channel: bool = False, prob: float = 0.5, inplace: bool = True, **kwargs: Any)[source]¶
Bases:
Augmenter
Randomly re-normalize the ECG tensor, using the Z-score normalization method.
- Parameters:
mean (array_like, default
[-0.05, 0.1]
) – Range of mean value of the re-normalized signal, of shape(2,)
; or range of mean values for each lead of the re-normalized signal, of shape(lead, 2)
.std (array_like, default
[0.08, 0.32]
) – Range of standard deviation of the re-normalized signal, of shape(2,)
; or range of standard deviations for each lead of the re-normalized signal, of shape(lead, 2)
.per_channel (bool, default False) – If True, re-normalization will be done per channel.
prob (float, default 0.5) – Probability of applying the random re-normalization augmenter.
inplace (bool, default True) – Whether to apply the random re-normalization augmenter in-place.
kwargs (dict, optional) – Additional keyword arguments.
Examples
rrn = RandomRenormalize() sig = torch.randn(32, 12, 5000) sig, _ = rrn(sig, None)
- forward(sig: Tensor, label: Tensor | None, *extra_tensors: Sequence[Tensor], **kwargs: Any) Tuple[Tensor, ...] [source]¶
Forward function of the RandomRenormalize augmenter.
- Parameters:
sig (torch.Tensor) – The input ECG tensor, of shape
(batch, lead, siglen)
.label (torch.Tensor, optional) – The input ECG label tensor. Not used, but kept for compatibility with other augmenters.
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 randomly re-normalized ECG tensor.
label (torch.Tensor) – The label tensor of the augmented ECGs, unchanged.
extra_tensors (Sequence[torch.Tensor], optional,) – Unchanged extra tensors.