Resize¶
- class torcheeg.transforms.Resize(size: Union[Sequence[int], int], interpolation: str = 'bilinear', apply_to_baseline: bool = False)[source][source]¶
Use an interpolation algorithm to scale a grid-like EEG signal at the spatial dimension.
transform = ToTensor(size=(64, 64)) transform(eeg=torch.randn(128, 9, 9))['eeg'].shape >>> (128, 64, 64)
- Parameters
size (tuple) – The output spatial size.
interpolation (str) – The interpolation algorithm used for upsampling, can be nearest, linear, bilinear, bicubic, trilinear, and area. (defualt:
'nearest')apply_to_baseline – (bool): Whether to act on the baseline signal at the same time, if the baseline is passed in when calling. (defualt:
False)
- __call__(*args, eeg: Tensor, baseline: Optional[Tensor] = None, **kwargs) Dict[str, Tensor][source][source]¶
- Parameters
eeg (torch.Tensor) – The input EEG signal in shape of [height of grid, width of grid, number of data points].
baseline (torch.Tensor, 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
The scaled EEG signal at the saptial dimension.
- Return type
torch.Tensor[new height of grid, new width of grid, number of sub-bands]
