Function convert_to_tensor_complex

monai/apps/reconstruction/complex_utils.py:27–99

Convert complex-valued data to a 2-channel PyTorch tensor. The real and imaginary parts are stacked along the last dimension. This function relies on 'monai.utils.type_conversion.convert_to_tensor' Args: data: input data can be PyTorch Tensor, numpy array, list, int, and fl

(
    data: NdarrayOrTensor | list | int | float,
    dtype: torch.dtype | None = None,
    device: torch.device | None = None,
    wrap_sequence: bool = True,
    track_meta: bool = False,
)

def convert_to_tensor_complex(
    data: NdarrayOrTensor | list | int | float,
    dtype: torch.dtype | None = None,
    device: torch.device | None = None,
    wrap_sequence: bool = True,
    track_meta: bool = False,
) -> Tensor:
    """
    Convert complex-valued data to a 2-channel PyTorch tensor.
    The real and imaginary parts are stacked along the last dimension.
    This function relies on 'monai.utils.type_conversion.convert_to_tensor'

    Args:
        data: input data can be PyTorch Tensor, numpy array, list, int, and float.
            will convert Tensor, Numpy array, float, int, bool to Tensor, strings and objects keep the original.
            for list, convert every item to a Tensor if applicable.
        dtype: target data type to when converting to Tensor.
        device: target device to put the converted Tensor data.
        wrap_sequence: if `False`, then lists will recursively call this function.
            E.g., `[1, 2]` -> `[tensor(1), tensor(2)]`. If `True`, then `[1, 2]` -> `tensor([1, 2])`.
        track_meta: whether to track the meta information, if `True`, will convert to `MetaTensor`.
            default to `False`.

    Returns:
        PyTorch version of the data

    Example:
        .. code-block:: python

            import numpy as np
            data = np.array([ [1+1j, 1-1j], [2+2j, 2-2j] ])
            # the following line prints (2,2)
            print(data.shape)
            # the following line prints torch.Size([2, 2, 2])
            print(convert_to_tensor_complex(data).shape)
    """
    # if data is not complex, just turn it into a tensor
    if isinstance(data, Tensor):
        if not torch.is_complex(data):
            converted_data: Tensor = convert_to_tensor(
                data, dtype=dtype, device=device, wrap_sequence=wrap_sequence, track_meta=track_meta
            )
            return converted_data
    else:
        if not np.iscomplexobj(data):
            converted_data = convert_to_tensor(
                data, dtype=dtype, device=device, wrap_sequence=wrap_sequence, track_meta=track_meta
            )
            return converted_data

    # if data is complex, turn its stacked version into a tensor
    if isinstance(data, torch.Tensor):
        data = torch.stack([data.real, data.imag], dim=-1)

    elif isinstance(data, np.ndarray):
        if re.search(r"[SaUO]", data.dtype.str) is None:
            # numpy array with 0 dims is also sequence iterable,
            # `ascontiguousarray` will add 1 dim if img has no dim, so we only apply on data with dims