def check_points_inside_bboxes(points,
                               bboxes,
                               center_radius_tensor=None,
                               eps=1e-9,
                               sm_use=False):
    r"""
    Args:
        points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors
        bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format
        center_radius_tensor (Tensor, float32): shape [L, 1]. Default: None.
        eps (float): Default: 1e-9
    Returns:
        is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected
    """
    points = points.unsqueeze([0, 1])
    x, y = points.chunk(2, axis=-1)
    xmin, ymin, xmax, ymax = bboxes.unsqueeze(2).chunk(4, axis=-1)
    # check whether `points` is in `bboxes`
    l = x - xmin
    t = y - ymin
    r = xmax - x
    b = ymax - y
    delta_ltrb = paddle.concat([l, t, r, b], axis=-1)
    is_in_bboxes = (delta_ltrb.min(axis=-1) > eps)
    if center_radius_tensor is not None:
        # check whether `points` is in `center_radius`
        center_radius_tensor = center_radius_tensor.unsqueeze([0, 1])
        cx = (xmin + xmax) * 0.5
        cy = (ymin + ymax) * 0.5
        l = x - (cx - center_radius_tensor)
        t = y - (cy - center_radius_tensor)
        r = (cx + center_radius_tensor) - x
        b = (cy + center_radius_tensor) - y
        delta_ltrb_c = paddle.concat([l, t, r, b], axis=-1)
        is_in_center = (delta_ltrb_c.min(axis=-1) > eps)
        if sm_use:
            return is_in_bboxes.astype(bboxes.dtype), is_in_center.astype(
                bboxes.dtype)
        else:
            return (paddle.logical_and(is_in_bboxes, is_in_center),
                    paddle.logical_or(is_in_bboxes, is_in_center))

    return is_in_bboxes.astype(bboxes.dtype)

    Args:
        points (Tensor, float32): shape[L, 2], "xy" format, L: num_anchors
        bboxes (Tensor, float32): shape[B, n, 4], "xmin, ymin, xmax, ymax" format
        center_radius_tensor (Tensor, float32): shape [L, 1]. Default: None.
        eps (float): Default: 1e-9
    Returns:
        is_in_bboxes (Tensor, float32): shape[B, n, L], value=1. means selected
    
ppdet/modeling/assigners/utils.py