def points_in_boxes_all(points, boxes):
    """Find all boxes in which each point is (CUDA).

    Args:
        points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate
        boxes (torch.Tensor): [B, T, 7],
            num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz],
            (x, y, z) is the bottom center.

    Returns:
        box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0.
    """
    assert boxes.shape[0] == points.shape[0], \
        'Points and boxes should have the same batch size, ' \
        f'but got {boxes.shape[0]} and {boxes.shape[0]}'
    assert boxes.shape[2] == 7, \
        'boxes dimension should be 7, ' \
        f'but got unexpected shape {boxes.shape[2]}'
    assert points.shape[2] == 3, \
        'points dimension should be 3, ' \
        f'but got unexpected shape {points.shape[2]}'
    batch_size, num_points, _ = points.shape
    num_boxes = boxes.shape[1]

    box_idxs_of_pts = points.new_zeros((batch_size, num_points, num_boxes),
                                       dtype=torch.int).fill_(0)

    # Same reason as line 25-32
    points_device = points.get_device()
    assert points_device == boxes.get_device(), \
        'Points and boxes should be put on the same device'
    if torch.cuda.current_device() != points_device:
        torch.cuda.set_device(points_device)

    ext_module.points_in_boxes_all_forward(boxes.contiguous(),
                                           points.contiguous(),
                                           box_idxs_of_pts)

    return box_idxs_of_pts
Find all boxes in which each point is (CUDA).

    Args:
        points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR/DEPTH coordinate
        boxes (torch.Tensor): [B, T, 7],
            num_valid_boxes <= T, [x, y, z, x_size, y_size, z_size, rz],
            (x, y, z) is the bottom center.

    Returns:
        box_idxs_of_pts (torch.Tensor): (B, M, T), default background = 0.
    
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/ops/points_in_boxes.py