Function pad_list_data_collate

monai/data/utils.py:644–667

Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`. Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest tensor in each dimension. This transform is useful if some of the applied transforms gener

(batch: Sequence, method: str = Method.SYMMETRIC, mode: str = NumpyPadMode.CONSTANT, **kwargs)

def pad_list_data_collate(batch: Sequence, method: str = Method.SYMMETRIC, mode: str = NumpyPadMode.CONSTANT, **kwargs):
    """
    Function version of :py:class:`monai.transforms.croppad.batch.PadListDataCollate`.

    Same as MONAI's ``list_data_collate``, except any tensors are centrally padded to match the shape of the biggest
    tensor in each dimension. This transform is useful if some of the applied transforms generate batch data of
    different sizes.

    This can be used on both list and dictionary data.
    Note that in the case of the dictionary data, this decollate function may add the transform information of
    `PadListDataCollate` to the list of invertible transforms if input batch have different spatial shape, so need to
    call static method: `monai.transforms.croppad.batch.PadListDataCollate.inverse` before inverting other transforms.

    Args:
        batch: batch of data to pad-collate
        method: padding method (see :py:class:`monai.transforms.SpatialPad`)
        mode: padding mode (see :py:class:`monai.transforms.SpatialPad`)
        kwargs: other arguments for the `np.pad` or `torch.pad` function.
            note that `np.pad` treats channel dimension as the first dimension.

    """
    from monai.transforms.croppad.batch import PadListDataCollate  # needs to be here to avoid circular import

    return PadListDataCollate(method=method, mode=mode, **kwargs)(batch)


def no_collation(x):