def distribute_fpn_proposals(fpn_rois,
                             min_level,
                             max_level,
                             refer_level,
                             refer_scale,
                             pixel_offset=False,
                             rois_num=None,
                             name=None):
    r"""
    
    **This op only takes LoDTensor as input.** In Feature Pyramid Networks 
    (FPN) models, it is needed to distribute all proposals into different FPN 
    level, with respect to scale of the proposals, the referring scale and the 
    referring level. Besides, to restore the order of proposals, we return an 
    array which indicates the original index of rois in current proposals. 
    To compute FPN level for each roi, the formula is given as follows:
    
    .. math::

        roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}

        level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)

    where BBoxArea is a function to compute the area of each roi.

    Args:

        fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is 
            float32 or float64. The input fpn_rois.
        min_level(int32): The lowest level of FPN layer where the proposals come 
            from.
        max_level(int32): The highest level of FPN layer where the proposals
            come from.
        refer_level(int32): The referring level of FPN layer with specified scale.
        refer_scale(int32): The referring scale of FPN layer with specified level.
        rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. 
            The shape is [B] and data type is int32. B is the number of images.
            If it is not None then return a list of 1-D Tensor. Each element 
            is the output RoIs' number of each image on the corresponding level
            and the shape is [B]. None by default.
        name(str, optional): For detailed information, please refer 
            to :ref:`api_guide_Name`. Usually name is no need to set and 
            None by default. 

    Returns:
        Tuple:

        multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] 
        and data type of float32 and float64. The length is 
        max_level-min_level+1. The proposals in each FPN level.

        restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is 
        the number of total rois. The data type is int32. It is
        used to restore the order of fpn_rois.

        rois_num_per_level(List): A list of 1-D Tensor and each Tensor is 
        the RoIs' number in each image on the corresponding level. The shape 
        is [B] and data type of int32. B is the number of images


    Examples:
        .. code-block:: python

            import paddle
            from ppdet.modeling import ops
            paddle.enable_static()
            fpn_rois = paddle.static.data(
                name='data', shape=[None, 4], dtype='float32', lod_level=1)
            multi_rois, restore_ind = ops.distribute_fpn_proposals(
                fpn_rois=fpn_rois,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224)
    """
    num_lvl = max_level - min_level + 1

    if in_dynamic_mode():
        assert rois_num is not None, "rois_num should not be None in dygraph mode."
        attrs = ('min_level', min_level, 'max_level', max_level, 'refer_level',
                 refer_level, 'refer_scale', refer_scale, 'pixel_offset',
                 pixel_offset)
        multi_rois, restore_ind, rois_num_per_level = C_ops.distribute_fpn_proposals(
            fpn_rois, rois_num, num_lvl, num_lvl, *attrs)

        return multi_rois, restore_ind, rois_num_per_level

    else:
        check_variable_and_dtype(fpn_rois, 'fpn_rois', ['float32', 'float64'],
                                 'distribute_fpn_proposals')
        helper = LayerHelper('distribute_fpn_proposals', **locals())
        dtype = helper.input_dtype('fpn_rois')
        multi_rois = [
            helper.create_variable_for_type_inference(dtype)
            for i in range(num_lvl)
        ]

        restore_ind = helper.create_variable_for_type_inference(dtype='int32')

        inputs = {'FpnRois': fpn_rois}
        outputs = {
            'MultiFpnRois': multi_rois,
            'RestoreIndex': restore_ind,
        }

        if rois_num is not None:
            inputs['RoisNum'] = rois_num
            rois_num_per_level = [
                helper.create_variable_for_type_inference(dtype='int32')
                for i in range(num_lvl)
            ]
            outputs['MultiLevelRoIsNum'] = rois_num_per_level
        else:
            rois_num_per_level = None

        helper.append_op(
            type='distribute_fpn_proposals',
            inputs=inputs,
            outputs=outputs,
            attrs={
                'min_level': min_level,
                'max_level': max_level,
                'refer_level': refer_level,
                'refer_scale': refer_scale,
                'pixel_offset': pixel_offset
            })
        return multi_rois, restore_ind, rois_num_per_level

    
    **This op only takes LoDTensor as input.** In Feature Pyramid Networks 
    (FPN) models, it is needed to distribute all proposals into different FPN 
    level, with respect to scale of the proposals, the referring scale and the 
    referring level. Besides, to restore the order of proposals, we return an 
    array which indicates the original index of rois in current proposals. 
    To compute FPN level for each roi, the formula is given as follows:
    
    .. math::

        roi\_scale &= \sqrt{BBoxArea(fpn\_roi)}

        level = floor(&\log(\\frac{roi\_scale}{refer\_scale}) + refer\_level)

    where BBoxArea is a function to compute the area of each roi.

    Args:

        fpn_rois(Variable): 2-D Tensor with shape [N, 4] and data type is 
            float32 or float64. The input fpn_rois.
        min_level(int32): The lowest level of FPN layer where the proposals come 
            from.
        max_level(int32): The highest level of FPN layer where the proposals
            come from.
        refer_level(int32): The referring level of FPN layer with specified scale.
        refer_scale(int32): The referring scale of FPN layer with specified level.
        rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. 
            The shape is [B] and data type is int32. B is the number of images.
            If it is not None then return a list of 1-D Tensor. Each element 
            is the output RoIs' number of each image on the corresponding level
            and the shape is [B]. None by default.
        name(str, optional): For detailed information, please refer 
            to :ref:`api_guide_Name`. Usually name is no need to set and 
            None by default. 

    Returns:
        Tuple:

        multi_rois(List) : A list of 2-D LoDTensor with shape [M, 4] 
        and data type of float32 and float64. The length is 
        max_level-min_level+1. The proposals in each FPN level.

        restore_ind(Variable): A 2-D Tensor with shape [N, 1], N is 
        the number of total rois. The data type is int32. It is
        used to restore the order of fpn_rois.

        rois_num_per_level(List): A list of 1-D Tensor and each Tensor is 
        the RoIs' number in each image on the corresponding level. The shape 
        is [B] and data type of int32. B is the number of images


    Examples:
        .. code-block:: python

            import paddle
            from ppdet.modeling import ops
            paddle.enable_static()
            fpn_rois = paddle.static.data(
                name='data', shape=[None, 4], dtype='float32', lod_level=1)
            multi_rois, restore_ind = ops.distribute_fpn_proposals(
                fpn_rois=fpn_rois,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224)
    
ppdet/modeling/ops.py