def IoU(cm, ignore_index=None):
    """Calculates Intersection over Union

    Args:
        cm (ConfusionMatrix): instance of confusion matrix metric
        ignore_index (int, optional): index to ignore, e.g. background index

    Returns:
        MetricsLambda

    Examples:

    .. code-block:: python

        train_evaluator = ...

        cm = ConfusionMatrix(num_classes=num_classes)
        IoU(cm, ignore_index=0).attach(train_evaluator, 'IoU')

        state = train_evaluator.run(train_dataset)
        # state.metrics['IoU'] -> tensor of shape (num_classes - 1, )

    """
    if not isinstance(cm, ConfusionMatrix):
        raise TypeError("Argument cm should be instance of ConfusionMatrix, but given {}".format(type(cm)))

    if ignore_index is not None:
        if not (isinstance(ignore_index, numbers.Integral) and 0 <= ignore_index < cm.num_classes):
            raise ValueError("ignore_index should be non-negative integer, but given {}".format(ignore_index))

    # Increase floating point precision
    cm = cm.type(torch.float64)
    iou = cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag() + 1e-15)
    if ignore_index is not None:

        def ignore_index_fn(iou_vector):
            if ignore_index >= len(iou_vector):
                raise ValueError("ignore_index {} is larger than the length of IoU vector {}"
                                 .format(ignore_index, len(iou_vector)))
            indices = list(range(len(iou_vector)))
            indices.remove(ignore_index)
            return iou_vector[indices]

        return MetricsLambda(ignore_index_fn, iou)
    else:
        return iou
Calculates Intersection over Union

    Args:
        cm (ConfusionMatrix): instance of confusion matrix metric
        ignore_index (int, optional): index to ignore, e.g. background index

    Returns:
        MetricsLambda

    Examples:

    .. code-block:: python

        train_evaluator = ...

        cm = ConfusionMatrix(num_classes=num_classes)
        IoU(cm, ignore_index=0).attach(train_evaluator, 'IoU')

        state = train_evaluator.run(train_dataset)
        # state.metrics['IoU'] -> tensor of shape (num_classes - 1, )