def anchor_generator(input,
                     anchor_sizes=None,
                     aspect_ratios=None,
                     variance=[0.1, 0.1, 0.2, 0.2],
                     stride=None,
                     offset=0.5):
    """
    **Anchor generator operator**
    Generate anchors for Faster RCNN algorithm.
    Each position of the input produce N anchors, N =
    size(anchor_sizes) * size(aspect_ratios). The order of generated anchors
    is firstly aspect_ratios loop then anchor_sizes loop.
    Args:
       input(Variable): 4-D Tensor with shape [N,C,H,W]. The input feature map.
       anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated
          anchors, given in absolute pixels e.g. [64., 128., 256., 512.].
          For instance, the anchor size of 64 means the area of this anchor 
          equals to 64**2. None by default.
       aspect_ratios(float32|list|tuple, optional): The height / width ratios 
           of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.
       variance(list|tuple, optional): The variances to be used in box 
           regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by 
           default.
       stride(list|tuple, optional): The anchors stride across width and height.
           The data type is float32. e.g. [16.0, 16.0]. None by default.
       offset(float32, optional): Prior boxes center offset. 0.5 by default.
    Returns:
        Tuple:
        Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4].
        H is the height of input, W is the width of input,
        num_anchors is the box count of each position. 
        Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
 
        Variances(Variable): The expanded variances of anchors
        with a layout of [H, W, num_priors, 4].
        H is the height of input, W is the width of input
        num_anchors is the box count of each position.
        Each variance is in (xcenter, ycenter, w, h) format.
    Examples:
        .. code-block:: python
            import paddle.fluid as fluid
            conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
            anchor, var = fluid.layers.anchor_generator(
                input=conv1,
                anchor_sizes=[64, 128, 256, 512],
                aspect_ratios=[0.5, 1.0, 2.0],
                variance=[0.1, 0.1, 0.2, 0.2],
                stride=[16.0, 16.0],
                offset=0.5)
    """

    def _is_list_or_tuple_(data):
        return (isinstance(data, list) or isinstance(data, tuple))

    if not _is_list_or_tuple_(anchor_sizes):
        anchor_sizes = [anchor_sizes]
    if not _is_list_or_tuple_(aspect_ratios):
        aspect_ratios = [aspect_ratios]
    if not (_is_list_or_tuple_(stride) and len(stride) == 2):
        raise ValueError('stride should be a list or tuple ',
                         'with length 2, (stride_width, stride_height).')

    anchor_sizes = list(map(float, anchor_sizes))
    aspect_ratios = list(map(float, aspect_ratios))
    stride = list(map(float, stride))

    if in_dynamic_mode():
        attrs = ('anchor_sizes', anchor_sizes, 'aspect_ratios', aspect_ratios,
                 'variances', variance, 'stride', stride, 'offset', offset)
        anchor, var = C_ops.anchor_generator(input, *attrs)
        return anchor, var

    helper = LayerHelper("anchor_generator", **locals())
    dtype = helper.input_dtype()
    attrs = {
        'anchor_sizes': anchor_sizes,
        'aspect_ratios': aspect_ratios,
        'variances': variance,
        'stride': stride,
        'offset': offset
    }

    anchor = helper.create_variable_for_type_inference(dtype)
    var = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="anchor_generator",
        inputs={"Input": input},
        outputs={"Anchors": anchor,
                 "Variances": var},
        attrs=attrs, )
    anchor.stop_gradient = True
    var.stop_gradient = True
    return anchor, var

    **Anchor generator operator**
    Generate anchors for Faster RCNN algorithm.
    Each position of the input produce N anchors, N =
    size(anchor_sizes) * size(aspect_ratios). The order of generated anchors
    is firstly aspect_ratios loop then anchor_sizes loop.
    Args:
       input(Variable): 4-D Tensor with shape [N,C,H,W]. The input feature map.
       anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated
          anchors, given in absolute pixels e.g. [64., 128., 256., 512.].
          For instance, the anchor size of 64 means the area of this anchor 
          equals to 64**2. None by default.
       aspect_ratios(float32|list|tuple, optional): The height / width ratios 
           of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.
       variance(list|tuple, optional): The variances to be used in box 
           regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by 
           default.
       stride(list|tuple, optional): The anchors stride across width and height.
           The data type is float32. e.g. [16.0, 16.0]. None by default.
       offset(float32, optional): Prior boxes center offset. 0.5 by default.
    Returns:
        Tuple:
        Anchors(Variable): The output anchors with a layout of [H, W, num_anchors, 4].
        H is the height of input, W is the width of input,
        num_anchors is the box count of each position. 
        Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
 
        Variances(Variable): The expanded variances of anchors
        with a layout of [H, W, num_priors, 4].
        H is the height of input, W is the width of input
        num_anchors is the box count of each position.
        Each variance is in (xcenter, ycenter, w, h) format.
    Examples:
        .. code-block:: python
            import paddle.fluid as fluid
            conv1 = fluid.data(name='conv1', shape=[None, 48, 16, 16], dtype='float32')
            anchor, var = fluid.layers.anchor_generator(
                input=conv1,
                anchor_sizes=[64, 128, 256, 512],
                aspect_ratios=[0.5, 1.0, 2.0],
                variance=[0.1, 0.1, 0.2, 0.2],
                stride=[16.0, 16.0],
                offset=0.5)
    
ppdet/modeling/ops.py