ToTensor¶
- class torcheeg.transforms.ToTensor(apply_to_baseline: bool = False)[source][source]¶
Convert a
numpy.ndarray
to tensor. Different fromtorchvision
, tensors are returned without scaling.from torcheeg import transforms t = transforms.ToTensor() t(eeg=np.random.randn(32, 128))['eeg'].shape >>> (32, 128)
- Parameters:
apply_to_baseline (bool) – Whether to apply the transform to the baseline signal. (default:
False
)
- __call__(*args, eeg: ndarray, baseline: ndarray | None = None, **kwargs) Dict[str, Tensor] [source][source]¶
- Parameters:
eeg (np.ndarray) – The input EEG signals.
baseline (np.ndarray, optional) – The corresponding baseline signal, if apply_to_baseline is set to True and baseline is passed, the baseline signal will be transformed with the same way as the experimental signal.
- Returns:
If baseline is passed and apply_to_baseline is set to True, then {‘eeg’: …, ‘baseline’: …}, else {‘eeg’: …}. The output is represented by
torch.Tensor
.- Return type:
dict