Class SlidingWindowInferer

monai/inferers/inferer.py:446–605

Sliding window method for model inference, with `sw_batch_size` windows for every model.forward(). Usage example can be found in the :py:class:`monai.inferers.Inferer` base class. Args: roi_size: the window size to execute SlidingWindow evaluation. If it has non

class SlidingWindowInferer(Inferer):
    """
    Sliding window method for model inference,
    with `sw_batch_size` windows for every model.forward().
    Usage example can be found in the :py:class:`monai.inferers.Inferer` base class.

    Args:
        roi_size: the window size to execute SlidingWindow evaluation.
            If it has non-positive components, the corresponding `inputs` size will be used.
            if the components of the `roi_size` are non-positive values, the transform will use the
            corresponding components of img size. For example, `roi_size=(32, -1)` will be adapted
            to `(32, 64)` if the second spatial dimension size of img is `64`.
        sw_batch_size: the batch size to run window slices.
        overlap: Amount of overlap between scans along each spatial dimension, defaults to ``0.25``.
        mode: {``"constant"``, ``"gaussian"``}
            How to blend output of overlapping windows. Defaults to ``"constant"``.

            - ``"constant``": gives equal weight to all predictions.
            - ``"gaussian``": gives less weight to predictions on edges of windows.

        sigma_scale: the standard deviation coefficient of the Gaussian window when `mode` is ``"gaussian"``.
            Default: 0.125. Actual window sigma is ``sigma_scale`` * ``dim_size``.
            When sigma_scale is a sequence of floats, the values denote sigma_scale at the corresponding
            spatial dimensions.
        padding_mode: {``"constant"``, ``"reflect"``, ``"replicate"``, ``"circular"``}
            Padding mode when ``roi_size`` is larger than inputs. Defaults to ``"constant"``
            See also: https://pytorch.org/docs/stable/generated/torch.nn.functional.pad.html
        cval: fill value for 'constant' padding mode. Default: 0
        sw_device: device for the window data.
            By default the device (and accordingly the memory) of the `inputs` is used.
            Normally `sw_device` should be consistent with the device where `predictor` is defined.
        device: device for the stitched output prediction.
            By default the device (and accordingly the memory) of the `inputs` is used. If for example
            set to device=torch.device('cpu') the gpu memory consumption is less and independent of the
            `inputs` and `roi_size`. Output is on the `device`.
        progress: whether to print a tqdm progress bar.
        cache_roi_weight_map: whether to precompute the ROI weight map.
        cpu_thresh: when provided, dynamically switch to stitching on cpu (to save gpu memory)
            when input image volume is larger than this threshold (in pixels/voxels).
            Otherwise use ``"device"``. Thus, the output may end-up on either cpu or gpu.
        buffer_steps: the number of sliding window iterations along the ``buffer_dim``
            to be buffered on ``sw_device`` before writing to ``device``.
            (Typically, ``sw_device`` is ``cuda`` and ``device`` is ``cpu``.)
            default is None, no buffering. For the buffer dim, when spatial size is divisible by buffer_steps*roi_size,
            (i.e. no overlapping among the buffers) non_blocking copy may be automatically enabled for efficiency.
        buffer_dim: the spatial dimension along which the buffers are created.
            0 indicates the first spatial dimension. Default is -1, the last spatial dimension.
        with_coord: whether to pass the window coordinates to ``network``. Defaults to False.
            If True, the ``network``'s 2nd input argument should accept the window coordinates.

    Note:
        ``sw_batch_size`` denotes the max number of windows per network inference iteration,
        not the batch size of inputs.

    """

    def __init__(
        self,