Function create_grid

monai/transforms/utils.py:761–790

compute a `spatial_size` mesh. - when ``homogeneous=True``, the output shape is (N+1, dim_size_1, dim_size_2, ..., dim_size_N) - when ``homogeneous=False``, the output shape is (N, dim_size_1, dim_size_2, ..., dim_size_N) Args: spatial_size: spatial size of the gri

(
    spatial_size: Sequence[int],
    spacing: Sequence[float] | None = None,
    homogeneous: bool = True,
    dtype: DtypeLike | torch.dtype = float,
    device: torch.device | None = None,
    backend=TransformBackends.NUMPY,
)

def create_grid(
    spatial_size: Sequence[int],
    spacing: Sequence[float] | None = None,
    homogeneous: bool = True,
    dtype: DtypeLike | torch.dtype = float,
    device: torch.device | None = None,
    backend=TransformBackends.NUMPY,
) -> NdarrayOrTensor:
    """
    compute a `spatial_size` mesh.

        - when ``homogeneous=True``, the output shape is (N+1, dim_size_1, dim_size_2, ..., dim_size_N)
        - when ``homogeneous=False``, the output shape is (N, dim_size_1, dim_size_2, ..., dim_size_N)

    Args:
        spatial_size: spatial size of the grid.
        spacing: same len as ``spatial_size``, defaults to 1.0 (dense grid).
        homogeneous: whether to make homogeneous coordinates.
        dtype: output grid data type, defaults to `float`.
        device: device to compute and store the output (when the backend is "torch").
        backend: APIs to use, ``numpy`` or ``torch``.

    """
    _backend = look_up_option(backend, TransformBackends)
    _dtype = dtype or float
    if _backend == TransformBackends.NUMPY:
        return _create_grid_numpy(spatial_size, spacing, homogeneous, _dtype)  # type: ignore
    if _backend == TransformBackends.TORCH:
        return _create_grid_torch(spatial_size, spacing, homogeneous, _dtype, device)  # type: ignore
    raise ValueError(f"backend {backend} is not supported")


def _create_grid_numpy(