def prior_box(input,
              image,
              min_sizes,
              max_sizes=None,
              aspect_ratios=[1.],
              variance=[0.1, 0.1, 0.2, 0.2],
              flip=False,
              clip=False,
              steps=[0.0, 0.0],
              offset=0.5,
              min_max_aspect_ratios_order=False,
              name=None):
    """

    This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
    Each position of the input produce N prior boxes, N is determined by
    the count of min_sizes, max_sizes and aspect_ratios, The size of the
    box is in range(min_size, max_size) interval, which is generated in
    sequence according to the aspect_ratios.

    Parameters:
       input(Tensor): 4-D tensor(NCHW), the data type should be float32 or float64.
       image(Tensor): 4-D tensor(NCHW), the input image data of PriorBoxOp,
            the data type should be float32 or float64.
       min_sizes(list|tuple|float): the min sizes of generated prior boxes.
       max_sizes(list|tuple|None): the max sizes of generated prior boxes.
            Default: None.
       aspect_ratios(list|tuple|float): the aspect ratios of generated
            prior boxes. Default: [1.].
       variance(list|tuple): the variances to be encoded in prior boxes.
            Default:[0.1, 0.1, 0.2, 0.2].
       flip(bool): Whether to flip aspect ratios. Default:False.
       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
       step(list|tuple): Prior boxes step across width and height, If
            step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
            height or weight of the input will be automatically calculated.
            Default: [0., 0.]
       offset(float): Prior boxes center offset. Default: 0.5
       min_max_aspect_ratios_order(bool): If set True, the output prior box is
            in order of [min, max, aspect_ratios], which is consistent with
            Caffe. Please note, this order affects the weights order of
            convolution layer followed by and does not affect the final
            detection results. Default: False.
       name(str, optional): The default value is None.  Normally there is no need for 
            user to set this property. For more information, please refer to :ref:`api_guide_Name`

    Returns:
        Tuple: A tuple with two Variable (boxes, variances)

        boxes(Tensor): the output prior boxes of PriorBox.
        4-D tensor, the layout is [H, W, num_priors, 4].
        H is the height of input, W is the width of input,
        num_priors is the total box count of each position of input.

        variances(Tensor): the expanded variances of PriorBox.
        4-D tensor, the layput is [H, W, num_priors, 4].
        H is the height of input, W is the width of input
        num_priors is the total box count of each position of input

    Examples:
        .. code-block:: python

        import paddle
        from ppdet.modeling import ops

        paddle.enable_static()
        input = paddle.static.data(name="input", shape=[None,3,6,9])
        image = paddle.static.data(name="image", shape=[None,3,9,12])
        box, var = ops.prior_box(
                    input=input,
                    image=image,
                    min_sizes=[100.],
                    clip=True,
                    flip=True)
    """
    return paddle.vision.ops.prior_box(
        input,
        image,
        min_sizes,
        max_sizes,
        aspect_ratios,
        variance,
        flip,
        clip,
        steps,
        offset,
        min_max_aspect_ratios_order,
        name,
    )


    This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
    Each position of the input produce N prior boxes, N is determined by
    the count of min_sizes, max_sizes and aspect_ratios, The size of the
    box is in range(min_size, max_size) interval, which is generated in
    sequence according to the aspect_ratios.

    Parameters:
       input(Tensor): 4-D tensor(NCHW), the data type should be float32 or float64.
       image(Tensor): 4-D tensor(NCHW), the input image data of PriorBoxOp,
            the data type should be float32 or float64.
       min_sizes(list|tuple|float): the min sizes of generated prior boxes.
       max_sizes(list|tuple|None): the max sizes of generated prior boxes.
            Default: None.
       aspect_ratios(list|tuple|float): the aspect ratios of generated
            prior boxes. Default: [1.].
       variance(list|tuple): the variances to be encoded in prior boxes.
            Default:[0.1, 0.1, 0.2, 0.2].
       flip(bool): Whether to flip aspect ratios. Default:False.
       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
       step(list|tuple): Prior boxes step across width and height, If
            step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
            height or weight of the input will be automatically calculated.
            Default: [0., 0.]
       offset(float): Prior boxes center offset. Default: 0.5
       min_max_aspect_ratios_order(bool): If set True, the output prior box is
            in order of [min, max, aspect_ratios], which is consistent with
            Caffe. Please note, this order affects the weights order of
            convolution layer followed by and does not affect the final
            detection results. Default: False.
       name(str, optional): The default value is None.  Normally there is no need for 
            user to set this property. For more information, please refer to :ref:`api_guide_Name`

    Returns:
        Tuple: A tuple with two Variable (boxes, variances)

        boxes(Tensor): the output prior boxes of PriorBox.
        4-D tensor, the layout is [H, W, num_priors, 4].
        H is the height of input, W is the width of input,
        num_priors is the total box count of each position of input.

        variances(Tensor): the expanded variances of PriorBox.
        4-D tensor, the layput is [H, W, num_priors, 4].
        H is the height of input, W is the width of input
        num_priors is the total box count of each position of input

    Examples:
        .. code-block:: python

        import paddle
        from ppdet.modeling import ops

        paddle.enable_static()
        input = paddle.static.data(name="input", shape=[None,3,6,9])
        image = paddle.static.data(name="image", shape=[None,3,9,12])
        box, var = ops.prior_box(
                    input=input,
                    image=image,
                    min_sizes=[100.],
                    clip=True,
                    flip=True)
    
ppdet/modeling/ops.py