detection.py

#  Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
All layers just related to the detection neural network.
"""

import paddle

from .layer_function_generator import templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable, _non_static_mode, static_only, in_dygraph_mode
from .. import core
from paddle.fluid.layers import softmax_with_cross_entropy
from . import tensor
from . import nn
from ..data_feeder import check_variable_and_dtype, check_type, check_dtype
import math
import numpy as np
from functools import reduce
from ..data_feeder import (
    convert_dtype,
    check_variable_and_dtype,
    check_type,
    check_dtype,
)
from paddle.utils import deprecated
from paddle import _C_ops, _legacy_C_ops
from ..framework import in_dygraph_mode

__all__ = [
    'prior_box',
    'density_prior_box',
    'multi_box_head',
    'anchor_generator',
    'roi_perspective_transform',
    'generate_proposal_labels',
    'generate_proposals',
    'generate_mask_labels',
    'box_coder',
    'polygon_box_transform',
    'box_clip',
    'multiclass_nms',
    'locality_aware_nms',
    'matrix_nms',
    'retinanet_detection_output',
    'distribute_fpn_proposals',
    'box_decoder_and_assign',
    'collect_fpn_proposals',
]


@templatedoc()
def box_coder(
    prior_box,
    prior_box_var,
    target_box,
    code_type="encode_center_size",
    box_normalized=True,
    name=None,
    axis=0,
):
    r"""

    **Box Coder Layer**

    Encode/Decode the target bounding box with the priorbox information.

    The Encoding schema described below:

    .. math::

        ox = (tx - px) / pw / pxv

        oy = (ty - py) / ph / pyv

        ow = \log(\abs(tw / pw)) / pwv

        oh = \log(\abs(th / ph)) / phv

    The Decoding schema described below:

    .. math::

        ox = (pw * pxv * tx * + px) - tw / 2

        oy = (ph * pyv * ty * + py) - th / 2

        ow = \exp(pwv * tw) * pw + tw / 2

        oh = \exp(phv * th) * ph + th / 2

    where `tx`, `ty`, `tw`, `th` denote the target box's center coordinates,
    width and height respectively. Similarly, `px`, `py`, `pw`, `ph` denote
    the priorbox's (anchor) center coordinates, width and height. `pxv`,
    `pyv`, `pwv`, `phv` denote the variance of the priorbox and `ox`, `oy`,
    `ow`, `oh` denote the encoded/decoded coordinates, width and height.

    During Box Decoding, two modes for broadcast are supported. Say target
    box has shape [N, M, 4], and the shape of prior box can be [N, 4] or
    [M, 4]. Then prior box will broadcast to target box along the
    assigned axis.

    Args:
        prior_box(Variable): Box list prior_box is a 2-D Tensor with shape
            [M, 4] holds M boxes and data type is float32 or float64. Each box
            is represented as [xmin, ymin, xmax, ymax], [xmin, ymin] is the
            left top coordinate of the anchor box, if the input is image feature
            map, they are close to the origin of the coordinate system.
            [xmax, ymax] is the right bottom coordinate of the anchor box.
        prior_box_var(List|Variable|None): prior_box_var supports three types
            of input. One is variable with shape [M, 4] which holds M group and
            data type is float32 or float64. The second is list consist of
            4 elements shared by all boxes and data type is float32 or float64.
            Other is None and not involved in calculation.
        target_box(Variable): This input can be a 2-D LoDTensor with shape
            [N, 4] when code_type is 'encode_center_size'. This input also can
            be a 3-D Tensor with shape [N, M, 4] when code_type is
            'decode_center_size'. Each box is represented as
            [xmin, ymin, xmax, ymax]. The data type is float32 or float64.
            This tensor can contain LoD information to represent a batch of inputs.
        code_type(str): The code type used with the target box. It can be
            `encode_center_size` or `decode_center_size`. `encode_center_size`
            by default.
        box_normalized(bool): Whether treat the priorbox as a normalized box.
            Set true 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.
        axis(int): Which axis in PriorBox to broadcast for box decode,
            for example, if axis is 0 and TargetBox has shape [N, M, 4] and
            PriorBox has shape [M, 4], then PriorBox will broadcast to [N, M, 4]
            for decoding. It is only valid when code type is
            `decode_center_size`. Set 0 by default.

    Returns:
        Variable:

        output_box(Variable): When code_type is 'encode_center_size', the
        output tensor of box_coder_op with shape [N, M, 4] representing the
        result of N target boxes encoded with M Prior boxes and variances.
        When code_type is 'decode_center_size', N represents the batch size
        and M represents the number of decoded boxes.

    Examples:

        .. code-block:: python

            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            # For encode
            prior_box_encode = fluid.data(name='prior_box_encode',
                                  shape=[512, 4],
                                  dtype='float32')
            target_box_encode = fluid.data(name='target_box_encode',
                                   shape=[81, 4],
                                   dtype='float32')
            output_encode = fluid.layers.box_coder(prior_box=prior_box_encode,
                                    prior_box_var=[0.1,0.1,0.2,0.2],
                                    target_box=target_box_encode,
                                    code_type="encode_center_size")
            # For decode
            prior_box_decode = fluid.data(name='prior_box_decode',
                                  shape=[512, 4],
                                  dtype='float32')
            target_box_decode = fluid.data(name='target_box_decode',
                                   shape=[512, 81, 4],
                                   dtype='float32')
            output_decode = fluid.layers.box_coder(prior_box=prior_box_decode,
                                    prior_box_var=[0.1,0.1,0.2,0.2],
                                    target_box=target_box_decode,
                                    code_type="decode_center_size",
                                    box_normalized=False,
                                    axis=1)
    """
    return paddle.vision.ops.box_coder(
        prior_box=prior_box,
        prior_box_var=prior_box_var,
        target_box=target_box,
        code_type=code_type,
        box_normalized=box_normalized,
        axis=axis,
        name=name,
    )


@templatedoc()
def polygon_box_transform(input, name=None):
    """
    ${comment}

    Args:
        input(Variable): The input with shape [batch_size, geometry_channels, height, width].
                         A Tensor with type float32, float64.
        name(str, Optional): For details, please refer to :ref:`api_guide_Name`.
                        Generally, no setting is required. Default: None.

    Returns:
        Variable: The output with the same shape as input. A Tensor with type float32, float64.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            input = fluid.data(name='input', shape=[4, 10, 5, 5], dtype='float32')
            out = fluid.layers.polygon_box_transform(input)
    """
    check_variable_and_dtype(
        input, "input", ['float32', 'float64'], 'polygon_box_transform'
    )
    helper = LayerHelper("polygon_box_transform", **locals())
    output = helper.create_variable_for_type_inference(dtype=input.dtype)

    helper.append_op(
        type="polygon_box_transform",
        inputs={"Input": input},
        attrs={},
        outputs={"Output": output},
    )
    return output


def prior_box(
    input,
    image,
    min_sizes,
    max_sizes=None,
    aspect_ratios=[1.0],
    variance=[0.1, 0.1, 0.2, 0.2],
    flip=False,
    clip=False,
    steps=[0.0, 0.0],
    offset=0.5,
    name=None,
    min_max_aspect_ratios_order=False,
):
    """

    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(Variable): 4-D tensor(NCHW), the data type should be float32 or float64.
       image(Variable): 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(Variable): 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(Variable): 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

            #declarative mode
            import paddle.fluid as fluid
            import numpy as np
            import paddle
            paddle.enable_static()
            input = fluid.data(name="input", shape=[None,3,6,9])
            image = fluid.data(name="image", shape=[None,3,9,12])
            box, var = fluid.layers.prior_box(
                 input=input,
                 image=image,
                 min_sizes=[100.],
                 clip=True,
                 flip=True)

            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())

            # prepare a batch of data
            input_data = np.random.rand(1,3,6,9).astype("float32")
            image_data = np.random.rand(1,3,9,12).astype("float32")

            box_out, var_out = exe.run(fluid.default_main_program(),
                feed={"input":input_data,"image":image_data},
                fetch_list=[box,var],
                return_numpy=True)

            # print(box_out.shape)
            # (6, 9, 1, 4)
            # print(var_out.shape)
            # (6, 9, 1, 4)

            # imperative mode
            import paddle.fluid.dygraph as dg

            with dg.guard(place) as g:
                input = dg.to_variable(input_data)
                image = dg.to_variable(image_data)
                box, var = fluid.layers.prior_box(
                    input=input,
                    image=image,
                    min_sizes=[100.],
                    clip=True,
                    flip=True)
                # print(box.shape)
                # [6L, 9L, 1L, 4L]
                # print(var.shape)
                # [6L, 9L, 1L, 4L]

    """
    return paddle.vision.ops.prior_box(
        input=input,
        image=image,
        min_sizes=min_sizes,
        max_sizes=max_sizes,
        aspect_ratios=aspect_ratios,
        variance=variance,
        flip=flip,
        clip=clip,
        steps=steps,
        offset=offset,
        min_max_aspect_ratios_order=min_max_aspect_ratios_order,
        name=name,
    )


def density_prior_box(
    input,
    image,
    densities=None,
    fixed_sizes=None,
    fixed_ratios=None,
    variance=[0.1, 0.1, 0.2, 0.2],
    clip=False,
    steps=[0.0, 0.0],
    offset=0.5,
    flatten_to_2d=False,
    name=None,
):
    r"""

    This op generates density 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 densities, fixed_sizes and fixed_ratios.
    Boxes center at grid points around each input position is generated by
    this operator, and the grid points is determined by densities and
    the count of density prior box is determined by fixed_sizes and fixed_ratios.
    Obviously, the number of fixed_sizes is equal to the number of densities.

    For densities_i in densities:

    .. math::

        N\_density_prior\_box = SUM(N\_fixed\_ratios * densities\_i^2)

    N_density_prior_box is the number of density_prior_box and N_fixed_ratios is the number of fixed_ratios.

    Parameters:
       input(Variable): 4-D tensor(NCHW), the data type should be float32 of float64.
       image(Variable): 4-D tensor(NCHW), the input image data of PriorBoxOp, the data type should be float32 or float64.
            the layout is NCHW.
       densities(list|tuple|None): The densities of generated density prior
            boxes, this attribute should be a list or tuple of integers.
            Default: None.
       fixed_sizes(list|tuple|None): The fixed sizes of generated density
            prior boxes, this attribute should a list or tuple of same
            length with :attr:`densities`. Default: None.
       fixed_ratios(list|tuple|None): The fixed ratios of generated density
            prior boxes, if this attribute is not set and :attr:`densities`
            and :attr:`fix_sizes` is set, :attr:`aspect_ratios` will be used
            to generate density prior boxes.
       variance(list|tuple): The variances to be encoded in density prior boxes.
            Default:[0.1, 0.1, 0.2, 0.2].
       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 0.0 or step[1] equals 0.0, the density 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
       flatten_to_2d(bool): Whether to flatten output prior boxes and variance
           to 2D shape, the second dim is 4. 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: the output density prior boxes of PriorBox.
        4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False.
        2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True.
        H is the height of input, W is the width of input, and num_priors is the total box count of each position of input.

        variances: the expanded variances of PriorBox.
        4-D tensor, the layout is [H, W, num_priors, 4] when flatten_to_2d is False.
        2-D tensor, the layout is [H * W * num_priors, 4] when flatten_to_2d is True.
        H is the height of input, W is the width of input, and num_priors is the total box count of each position of input.


    Examples:

        .. code-block:: python

            #declarative mode

            import paddle.fluid as fluid
            import numpy as np
            import paddle
            paddle.enable_static()

            input = fluid.data(name="input", shape=[None,3,6,9])
            image = fluid.data(name="image", shape=[None,3,9,12])
            box, var = fluid.layers.density_prior_box(
                 input=input,
                 image=image,
                 densities=[4, 2, 1],
                 fixed_sizes=[32.0, 64.0, 128.0],
                 fixed_ratios=[1.],
                 clip=True,
                 flatten_to_2d=True)

            place = fluid.CPUPlace()
            exe = fluid.Executor(place)
            exe.run(fluid.default_startup_program())

            # prepare a batch of data
            input_data = np.random.rand(1,3,6,9).astype("float32")
            image_data = np.random.rand(1,3,9,12).astype("float32")

            box_out, var_out = exe.run(
                fluid.default_main_program(),
                feed={"input":input_data,
                      "image":image_data},
                fetch_list=[box,var],
                return_numpy=True)

            # print(box_out.shape)
            # (1134, 4)
            # print(var_out.shape)
            # (1134, 4)


            #imperative mode
            import paddle.fluid.dygraph as dg

            with dg.guard(place) as g:
                input = dg.to_variable(input_data)
                image = dg.to_variable(image_data)
                box, var = fluid.layers.density_prior_box(
                    input=input,
                    image=image,
                    densities=[4, 2, 1],
                    fixed_sizes=[32.0, 64.0, 128.0],
                    fixed_ratios=[1.],
                    clip=True)

                # print(box.shape)
                # [6L, 9L, 21L, 4L]
                # print(var.shape)
                # [6L, 9L, 21L, 4L]

    """
    helper = LayerHelper("density_prior_box", **locals())
    dtype = helper.input_dtype()
    check_variable_and_dtype(
        input, 'input', ['float32', 'float64'], 'density_prior_box'
    )

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

    check_type(densities, 'densities', (list, tuple), 'density_prior_box')
    check_type(fixed_sizes, 'fixed_sizes', (list, tuple), 'density_prior_box')
    check_type(fixed_ratios, 'fixed_ratios', (list, tuple), 'density_prior_box')
    if len(densities) != len(fixed_sizes):
        raise ValueError('densities and fixed_sizes length should be euqal.')

    if not (_is_list_or_tuple_(steps) and len(steps) == 2):
        raise ValueError(
            'steps should be a list or tuple ',
            'with length 2, (step_width, step_height).',
        )

    densities = list(map(int, densities))
    fixed_sizes = list(map(float, fixed_sizes))
    fixed_ratios = list(map(float, fixed_ratios))
    steps = list(map(float, steps))

    attrs = {
        'variances': variance,
        'clip': clip,
        'step_w': steps[0],
        'step_h': steps[1],
        'offset': offset,
        'densities': densities,
        'fixed_sizes': fixed_sizes,
        'fixed_ratios': fixed_ratios,
        'flatten_to_2d': flatten_to_2d,
    }
    box = helper.create_variable_for_type_inference(dtype)
    var = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="density_prior_box",
        inputs={"Input": input, "Image": image},
        outputs={"Boxes": box, "Variances": var},
        attrs=attrs,
    )
    box.stop_gradient = True
    var.stop_gradient = True
    return box, var


@static_only
def multi_box_head(
    inputs,
    image,
    base_size,
    num_classes,
    aspect_ratios,
    min_ratio=None,
    max_ratio=None,
    min_sizes=None,
    max_sizes=None,
    steps=None,
    step_w=None,
    step_h=None,
    offset=0.5,
    variance=[0.1, 0.1, 0.2, 0.2],
    flip=True,
    clip=False,
    kernel_size=1,
    pad=0,
    stride=1,
    name=None,
    min_max_aspect_ratios_order=False,
):
    """
        :api_attr: Static Graph

    Base on SSD ((Single Shot MultiBox Detector) algorithm, generate prior boxes,
    regression location and classification confidence on multiple input feature
    maps, then output the concatenate results. The details of this algorithm,
    please refer the section 2.2 of SSD paper `SSD: Single Shot MultiBox Detector
    <https://arxiv.org/abs/1512.02325>`_ .

    Args:
       inputs (list(Variable)|tuple(Variable)): The list of input variables,
           the format of all Variables are 4-D Tensor, layout is NCHW.
           Data type should be float32 or float64.
       image (Variable): The input image, layout is NCHW. Data type should be
           the same as inputs.
       base_size(int): the base_size is input image size. When len(inputs) > 2
           and `min_size` and `max_size` are None, the `min_size` and `max_size`
           are calculated by `baze_size`, 'min_ratio' and `max_ratio`. The
           formula is as follows:

              ..  code-block:: text

                  min_sizes = []
                  max_sizes = []
                  step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
                  for ratio in range(min_ratio, max_ratio + 1, step):
                      min_sizes.append(base_size * ratio / 100.)
                      max_sizes.append(base_size * (ratio + step) / 100.)
                      min_sizes = [base_size * .10] + min_sizes
                      max_sizes = [base_size * .20] + max_sizes

       num_classes(int): The number of classes.
       aspect_ratios(list(float) | tuple(float)): the aspect ratios of generated
           prior boxes. The length of input and aspect_ratios must be equal.
       min_ratio(int): the min ratio of generated prior boxes.
       max_ratio(int): the max ratio of generated prior boxes.
       min_sizes(list|tuple|None): If `len(inputs) <=2`,
            min_sizes must be set up, and the length of min_sizes
            should equal to the length of inputs. Default: None.
       max_sizes(list|tuple|None): If `len(inputs) <=2`,
            max_sizes must be set up, and the length of min_sizes
            should equal to the length of inputs. Default: None.
       steps(list|tuple): If step_w and step_h are the same,
            step_w and step_h can be replaced by steps.
       step_w(list|tuple): Prior boxes step
            across width. If step_w[i] == 0.0, the prior boxes step
            across width of the inputs[i] will be automatically
            calculated. Default: None.
       step_h(list|tuple): Prior boxes step across height, If
            step_h[i] == 0.0, the prior boxes step across height of
            the inputs[i] will be automatically calculated. Default: None.
       offset(float): Prior boxes center offset. Default: 0.5
       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.
       kernel_size(int): The kernel size of conv2d. Default: 1.
       pad(int|list|tuple): The padding of conv2d. Default:0.
       stride(int|list|tuple): The stride of conv2d. Default:1,
       name(str): 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`.
       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.

    Returns:
        tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances)

        mbox_loc (Variable): The predicted boxes' location of the inputs. The
        layout is [N, num_priors, 4], where N is batch size, ``num_priors``
        is the number of prior boxes. Data type is the same as input.

        mbox_conf (Variable): The predicted boxes' confidence of the inputs.
        The layout is [N, num_priors, C], where ``N`` and ``num_priors``
        has the same meaning as above. C is the number of Classes.
        Data type is the same as input.

        boxes (Variable): the output prior boxes. The layout is [num_priors, 4].
        The meaning of num_priors is the same as above.
        Data type is the same as input.

        variances (Variable): the expanded variances for prior boxes.
        The layout is [num_priors, 4]. Data type is the same as input.

    Examples 1: set min_ratio and max_ratio:
        .. code-block:: python

          import paddle
          paddle.enable_static()

          images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
          conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
          conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
          conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
          conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
          conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
          conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')

          mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head(
            inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
            image=images,
            num_classes=21,
            min_ratio=20,
            max_ratio=90,
            aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
            base_size=300,
            offset=0.5,
            flip=True,
            clip=True)

    Examples 2: set min_sizes and max_sizes:
        .. code-block:: python

          import paddle
          paddle.enable_static()

          images = paddle.static.data(name='data', shape=[None, 3, 300, 300], dtype='float32')
          conv1 = paddle.static.data(name='conv1', shape=[None, 512, 19, 19], dtype='float32')
          conv2 = paddle.static.data(name='conv2', shape=[None, 1024, 10, 10], dtype='float32')
          conv3 = paddle.static.data(name='conv3', shape=[None, 512, 5, 5], dtype='float32')
          conv4 = paddle.static.data(name='conv4', shape=[None, 256, 3, 3], dtype='float32')
          conv5 = paddle.static.data(name='conv5', shape=[None, 256, 2, 2], dtype='float32')
          conv6 = paddle.static.data(name='conv6', shape=[None, 128, 1, 1], dtype='float32')

          mbox_locs, mbox_confs, box, var = paddle.static.nn.multi_box_head(
            inputs=[conv1, conv2, conv3, conv4, conv5, conv6],
            image=images,
            num_classes=21,
            min_sizes=[60.0, 105.0, 150.0, 195.0, 240.0, 285.0],
            max_sizes=[[], 150.0, 195.0, 240.0, 285.0, 300.0],
            aspect_ratios=[[2.], [2., 3.], [2., 3.], [2., 3.], [2.], [2.]],
            base_size=300,
            offset=0.5,
            flip=True,
            clip=True)

    """

    def _reshape_with_axis_(input, axis=1):
        # Note : axis!=0 in current references to this func
        # if axis == 0:
        #     x = paddle.flatten(input, 0, -1)
        #     x = paddle.unsqueeze(x, 0)
        #     return x
        # else:
        x = paddle.flatten(input, axis, -1)
        x = paddle.flatten(x, 0, axis - 1)
        return x

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

    def _is_list_or_tuple_and_equal(data, length, err_info):
        if not (_is_list_or_tuple_(data) and len(data) == length):
            raise ValueError(err_info)

    if not _is_list_or_tuple_(inputs):
        raise ValueError('inputs should be a list or tuple.')

    num_layer = len(inputs)

    if num_layer <= 2:
        assert min_sizes is not None and max_sizes is not None
        assert len(min_sizes) == num_layer and len(max_sizes) == num_layer
    elif min_sizes is None and max_sizes is None:
        min_sizes = []
        max_sizes = []
        step = int(math.floor(((max_ratio - min_ratio)) / (num_layer - 2)))
        for ratio in range(min_ratio, max_ratio + 1, step):
            min_sizes.append(base_size * ratio / 100.0)
            max_sizes.append(base_size * (ratio + step) / 100.0)
        min_sizes = [base_size * 0.10] + min_sizes
        max_sizes = [base_size * 0.20] + max_sizes

    if aspect_ratios:
        _is_list_or_tuple_and_equal(
            aspect_ratios,
            num_layer,
            'aspect_ratios should be list or tuple, and the length of inputs '
            'and aspect_ratios should be the same.',
        )
    if step_h is not None:
        _is_list_or_tuple_and_equal(
            step_h,
            num_layer,
            'step_h should be list or tuple, and the length of inputs and '
            'step_h should be the same.',
        )
    if step_w is not None:
        _is_list_or_tuple_and_equal(
            step_w,
            num_layer,
            'step_w should be list or tuple, and the length of inputs and '
            'step_w should be the same.',
        )
    if steps is not None:
        _is_list_or_tuple_and_equal(
            steps,
            num_layer,
            'steps should be list or tuple, and the length of inputs and '
            'step_w should be the same.',
        )
        step_w = steps
        step_h = steps

    mbox_locs = []
    mbox_confs = []
    box_results = []
    var_results = []
    for i, input in enumerate(inputs):
        min_size = min_sizes[i]
        max_size = max_sizes[i]

        if not _is_list_or_tuple_(min_size):
            min_size = [min_size]
        if not _is_list_or_tuple_(max_size):
            max_size = [max_size]

        aspect_ratio = []
        if aspect_ratios is not None:
            aspect_ratio = aspect_ratios[i]
            if not _is_list_or_tuple_(aspect_ratio):
                aspect_ratio = [aspect_ratio]
        step = [step_w[i] if step_w else 0.0, step_h[i] if step_w else 0.0]

        box, var = prior_box(
            input,
            image,
            min_size,
            max_size,
            aspect_ratio,
            variance,
            flip,
            clip,
            step,
            offset,
            None,
            min_max_aspect_ratios_order,
        )

        box_results.append(box)
        var_results.append(var)

        num_boxes = box.shape[2]

        # get loc
        num_loc_output = num_boxes * 4
        mbox_loc = nn.conv2d(
            input=input,
            num_filters=num_loc_output,
            filter_size=kernel_size,
            padding=pad,
            stride=stride,
        )

        mbox_loc = paddle.transpose(mbox_loc, perm=[0, 2, 3, 1])
        mbox_loc_flatten = paddle.flatten(mbox_loc, 1, -1)
        mbox_locs.append(mbox_loc_flatten)

        # get conf
        num_conf_output = num_boxes * num_classes
        conf_loc = nn.conv2d(
            input=input,
            num_filters=num_conf_output,
            filter_size=kernel_size,
            padding=pad,
            stride=stride,
        )

        conf_loc = paddle.transpose(conf_loc, perm=[0, 2, 3, 1])
        conf_loc_flatten = paddle.flatten(conf_loc, 1, -1)
        mbox_confs.append(conf_loc_flatten)

    if len(box_results) == 1:
        box = box_results[0]
        var = var_results[0]
        mbox_locs_concat = mbox_locs[0]
        mbox_confs_concat = mbox_confs[0]
    else:
        reshaped_boxes = []
        reshaped_vars = []
        for i in range(len(box_results)):
            reshaped_boxes.append(_reshape_with_axis_(box_results[i], axis=3))
            reshaped_vars.append(_reshape_with_axis_(var_results[i], axis=3))

        box = tensor.concat(reshaped_boxes)
        var = tensor.concat(reshaped_vars)
        mbox_locs_concat = tensor.concat(mbox_locs, axis=1)
        mbox_locs_concat = paddle.reshape(mbox_locs_concat, shape=[0, -1, 4])
        mbox_confs_concat = tensor.concat(mbox_confs, axis=1)
        mbox_confs_concat = paddle.reshape(
            mbox_confs_concat, shape=[0, -1, num_classes]
        )

    box.stop_gradient = True
    var.stop_gradient = True
    return mbox_locs_concat, mbox_confs_concat, box, var


def anchor_generator(
    input,
    anchor_sizes=None,
    aspect_ratios=None,
    variance=[0.1, 0.1, 0.2, 0.2],
    stride=None,
    offset=0.5,
    name=None,
):
    """

    **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.
       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:

        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
            import paddle

            paddle.enable_static()
            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)
    """
    helper = LayerHelper("anchor_generator", **locals())
    dtype = helper.input_dtype()

    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))

    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


def roi_perspective_transform(
    input,
    rois,
    transformed_height,
    transformed_width,
    spatial_scale=1.0,
    name=None,
):
    """
    **The** `rois` **of this op should be a LoDTensor.**

    ROI perspective transform op applies perspective transform to map each roi into an
    rectangular region. Perspective transform is a type of transformation in linear algebra.

    Parameters:
        input (Variable): 4-D Tensor, input of ROIPerspectiveTransformOp. The format of
                          input tensor is NCHW. Where N is batch size, C is the
                          number of input channels, H is the height of the feature,
                          and W is the width of the feature. The data type is float32.
        rois (Variable):  2-D LoDTensor, ROIs (Regions of Interest) to be transformed.
                          It should be a 2-D LoDTensor of shape (num_rois, 8). Given as
                          [[x1, y1, x2, y2, x3, y3, x4, y4], ...], (x1, y1) is the
                          top left coordinates, and (x2, y2) is the top right
                          coordinates, and (x3, y3) is the bottom right coordinates,
                          and (x4, y4) is the bottom left coordinates. The data type is the
                          same as `input`
        transformed_height (int): The height of transformed output.
        transformed_width (int): The width of transformed output.
        spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0
        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:
            A tuple with three Variables. (out, mask, transform_matrix)

            out: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape
            (num_rois, channels, transformed_h, transformed_w). The data type is the same as `input`

            mask: The mask of ROIPerspectiveTransformOp which is a 4-D tensor with shape
            (num_rois, 1, transformed_h, transformed_w). The data type is int32

            transform_matrix: The transform matrix of ROIPerspectiveTransformOp which is
            a 2-D tensor with shape (num_rois, 9). The data type is the same as `input`

    Return Type:
        tuple

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid

            x = fluid.data(name='x', shape=[100, 256, 28, 28], dtype='float32')
            rois = fluid.data(name='rois', shape=[None, 8], lod_level=1, dtype='float32')
            out, mask, transform_matrix = fluid.layers.roi_perspective_transform(x, rois, 7, 7, 1.0)
    """
    check_variable_and_dtype(
        input, 'input', ['float32'], 'roi_perspective_transform'
    )
    check_variable_and_dtype(
        rois, 'rois', ['float32'], 'roi_perspective_transform'
    )
    check_type(
        transformed_height,
        'transformed_height',
        int,
        'roi_perspective_transform',
    )
    check_type(
        transformed_width, 'transformed_width', int, 'roi_perspective_transform'
    )
    check_type(
        spatial_scale, 'spatial_scale', float, 'roi_perspective_transform'
    )

    helper = LayerHelper('roi_perspective_transform', **locals())
    dtype = helper.input_dtype()
    out = helper.create_variable_for_type_inference(dtype)
    mask = helper.create_variable_for_type_inference(dtype="int32")
    transform_matrix = helper.create_variable_for_type_inference(dtype)
    out2in_idx = helper.create_variable_for_type_inference(dtype="int32")
    out2in_w = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="roi_perspective_transform",
        inputs={"X": input, "ROIs": rois},
        outputs={
            "Out": out,
            "Out2InIdx": out2in_idx,
            "Out2InWeights": out2in_w,
            "Mask": mask,
            "TransformMatrix": transform_matrix,
        },
        attrs={
            "transformed_height": transformed_height,
            "transformed_width": transformed_width,
            "spatial_scale": spatial_scale,
        },
    )
    return out, mask, transform_matrix


def generate_proposal_labels(
    rpn_rois,
    gt_classes,
    is_crowd,
    gt_boxes,
    im_info,
    batch_size_per_im=256,
    fg_fraction=0.25,
    fg_thresh=0.25,
    bg_thresh_hi=0.5,
    bg_thresh_lo=0.0,
    bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
    class_nums=None,
    use_random=True,
    is_cls_agnostic=False,
    is_cascade_rcnn=False,
    max_overlap=None,
    return_max_overlap=False,
):
    """

    **Generate Proposal Labels of Faster-RCNN**

    This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,
    to sample foreground boxes and background boxes, and compute loss target.

    RpnRois is the output boxes of RPN and was processed by generate_proposal_op, these boxes
    were combined with groundtruth boxes and sampled according to batch_size_per_im and fg_fraction,
    If an instance with a groundtruth overlap greater than fg_thresh, then it was considered as a foreground sample.
    If an instance with a groundtruth overlap greater than bg_thresh_lo and lower than bg_thresh_hi,
    then it was considered as a background sample.
    After all foreground and background boxes are chosen (so called Rois),
    then we apply random sampling to make sure
    the number of foreground boxes is no more than batch_size_per_im * fg_fraction.

    For each box in Rois, we assign the classification (class label) and regression targets (box label) to it.
    Finally BboxInsideWeights and BboxOutsideWeights are used to specify whether it would contribute to training loss.

    Args:
        rpn_rois(Variable): A 2-D LoDTensor with shape [N, 4]. N is the number of the GenerateProposalOp's output, each element is a bounding box with [xmin, ymin, xmax, ymax] format. The data type can be float32 or float64.
        gt_classes(Variable): A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a class label of groundtruth. The data type must be int32.
        is_crowd(Variable): A 2-D LoDTensor with shape [M, 1]. M is the number of groundtruth, each element is a flag indicates whether a groundtruth is crowd. The data type must be int32.
        gt_boxes(Variable): A 2-D LoDTensor with shape [M, 4]. M is the number of groundtruth, each element is a bounding box with [xmin, ymin, xmax, ymax] format.
        im_info(Variable): A 2-D LoDTensor with shape [B, 3]. B is the number of input images, each element consists of im_height, im_width, im_scale.

        batch_size_per_im(int): Batch size of rois per images. The data type must be int32.
        fg_fraction(float): Foreground fraction in total batch_size_per_im. The data type must be float32.
        fg_thresh(float): Overlap threshold which is used to chose foreground sample. The data type must be float32.
        bg_thresh_hi(float): Overlap threshold upper bound which is used to chose background sample. The data type must be float32.
        bg_thresh_lo(float): Overlap threshold lower bound which is used to chose background sample. The data type must be float32.
        bbox_reg_weights(list|tuple): Box regression weights. The data type must be float32.
        class_nums(int): Class number. The data type must be int32.
        use_random(bool): Use random sampling to choose foreground and background boxes.
        is_cls_agnostic(bool): bbox regression use class agnostic simply which only represent fg and bg boxes.
        is_cascade_rcnn(bool): it will filter some bbox crossing the image's boundary when setting True.
        max_overlap(Variable): Maximum overlap between each proposal box and ground-truth.
        return_max_overlap(bool): Whether return the maximum overlap between each sampled RoI and ground-truth.

    Returns:
        tuple:
        A tuple with format``(rois, labels_int32, bbox_targets, bbox_inside_weights, bbox_outside_weights, max_overlap)``.

        - **rois**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4]``. The data type is the same as ``rpn_rois``.
        - **labels_int32**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 1]``. The data type must be int32.
        - **bbox_targets**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The regression targets of all RoIs. The data type is the same as ``rpn_rois``.
        - **bbox_inside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of foreground boxes' regression loss. The data type is the same as ``rpn_rois``.
        - **bbox_outside_weights**: 2-D LoDTensor with shape ``[batch_size_per_im * batch_size, 4 * class_num]``. The weights of regression loss. The data type is the same as ``rpn_rois``.
        - **max_overlap**: 1-D LoDTensor with shape ``[P]``. P is the number of output ``rois``. The maximum overlap between each sampled RoI and ground-truth.

    Examples:
        .. code-block:: python

            import paddle
            import paddle.fluid as fluid
            paddle.enable_static()
            rpn_rois = fluid.data(name='rpn_rois', shape=[None, 4], dtype='float32')
            gt_classes = fluid.data(name='gt_classes', shape=[None, 1], dtype='int32')
            is_crowd = fluid.data(name='is_crowd', shape=[None, 1], dtype='int32')
            gt_boxes = fluid.data(name='gt_boxes', shape=[None, 4], dtype='float32')
            im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32')
            rois, labels, bbox, inside_weights, outside_weights = fluid.layers.generate_proposal_labels(
                           rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
                           class_nums=10)

    """

    helper = LayerHelper('generate_proposal_labels', **locals())

    check_variable_and_dtype(
        rpn_rois, 'rpn_rois', ['float32', 'float64'], 'generate_proposal_labels'
    )
    check_variable_and_dtype(
        gt_classes, 'gt_classes', ['int32'], 'generate_proposal_labels'
    )
    check_variable_and_dtype(
        is_crowd, 'is_crowd', ['int32'], 'generate_proposal_labels'
    )
    if is_cascade_rcnn:
        assert (
            max_overlap is not None
        ), "Input max_overlap of generate_proposal_labels should not be None if is_cascade_rcnn is True"

    rois = helper.create_variable_for_type_inference(dtype=rpn_rois.dtype)
    labels_int32 = helper.create_variable_for_type_inference(
        dtype=gt_classes.dtype
    )
    bbox_targets = helper.create_variable_for_type_inference(
        dtype=rpn_rois.dtype
    )
    bbox_inside_weights = helper.create_variable_for_type_inference(
        dtype=rpn_rois.dtype
    )
    bbox_outside_weights = helper.create_variable_for_type_inference(
        dtype=rpn_rois.dtype
    )
    max_overlap_with_gt = helper.create_variable_for_type_inference(
        dtype=rpn_rois.dtype
    )

    inputs = {
        'RpnRois': rpn_rois,
        'GtClasses': gt_classes,
        'IsCrowd': is_crowd,
        'GtBoxes': gt_boxes,
        'ImInfo': im_info,
    }
    if max_overlap is not None:
        inputs['MaxOverlap'] = max_overlap
    helper.append_op(
        type="generate_proposal_labels",
        inputs=inputs,
        outputs={
            'Rois': rois,
            'LabelsInt32': labels_int32,
            'BboxTargets': bbox_targets,
            'BboxInsideWeights': bbox_inside_weights,
            'BboxOutsideWeights': bbox_outside_weights,
            'MaxOverlapWithGT': max_overlap_with_gt,
        },
        attrs={
            'batch_size_per_im': batch_size_per_im,
            'fg_fraction': fg_fraction,
            'fg_thresh': fg_thresh,
            'bg_thresh_hi': bg_thresh_hi,
            'bg_thresh_lo': bg_thresh_lo,
            'bbox_reg_weights': bbox_reg_weights,
            'class_nums': class_nums,
            'use_random': use_random,
            'is_cls_agnostic': is_cls_agnostic,
            'is_cascade_rcnn': is_cascade_rcnn,
        },
    )

    rois.stop_gradient = True
    labels_int32.stop_gradient = True
    bbox_targets.stop_gradient = True
    bbox_inside_weights.stop_gradient = True
    bbox_outside_weights.stop_gradient = True
    max_overlap_with_gt.stop_gradient = True

    if return_max_overlap:
        return (
            rois,
            labels_int32,
            bbox_targets,
            bbox_inside_weights,
            bbox_outside_weights,
            max_overlap_with_gt,
        )
    return (
        rois,
        labels_int32,
        bbox_targets,
        bbox_inside_weights,
        bbox_outside_weights,
    )


def generate_mask_labels(
    im_info,
    gt_classes,
    is_crowd,
    gt_segms,
    rois,
    labels_int32,
    num_classes,
    resolution,
):
    r"""

    **Generate Mask Labels for Mask-RCNN**

    This operator can be, for given the RoIs and corresponding labels,
    to sample foreground RoIs. This mask branch also has
    a :math: `K \\times M^{2}` dimensional output targets for each foreground
    RoI, which encodes K binary masks of resolution M x M, one for each of the
    K classes. This mask targets are used to compute loss of mask branch.

    Please note, the data format of groud-truth segmentation, assumed the
    segmentations are as follows. The first instance has two gt objects.
    The second instance has one gt object, this object has two gt segmentations.

        .. code-block:: python

            #[
            #  [[[229.14, 370.9, 229.14, 370.9, ...]],
            #   [[343.7, 139.85, 349.01, 138.46, ...]]], # 0-th instance
            #  [[[500.0, 390.62, ...],[115.48, 187.86, ...]]] # 1-th instance
            #]

            batch_masks = []
            for semgs in batch_semgs:
                gt_masks = []
                for semg in semgs:
                    gt_segm = []
                    for polys in semg:
                        gt_segm.append(np.array(polys).reshape(-1, 2))
                    gt_masks.append(gt_segm)
                batch_masks.append(gt_masks)


            place = fluid.CPUPlace()
            feeder = fluid.DataFeeder(place=place, feed_list=feeds)
            feeder.feed(batch_masks)

    Args:
        im_info (Variable): A 2-D Tensor with shape [N, 3] and float32
            data type. N is the batch size, each element is
            [height, width, scale] of image. Image scale is
            target_size / original_size, target_size is the size after resize,
            original_size is the original image size.
        gt_classes (Variable): A 2-D LoDTensor with shape [M, 1]. Data type
            should be int. M is the total number of ground-truth, each
            element is a class label.
        is_crowd (Variable): A 2-D LoDTensor with same shape and same data type
            as gt_classes, each element is a flag indicating whether a
            groundtruth is crowd.
        gt_segms (Variable): This input is a 2D LoDTensor with shape [S, 2] and
            float32 data type, it's LoD level is 3.
            Usually users do not needs to understand LoD,
            The users should return correct data format in reader.
            The LoD[0] represents the ground-truth objects number of
            each instance. LoD[1] represents the segmentation counts of each
            objects. LoD[2] represents the polygons number of each segmentation.
            S the total number of polygons coordinate points. Each element is
            (x, y) coordinate points.
        rois (Variable): A 2-D LoDTensor with shape [R, 4] and float32 data type
            float32. R is the total number of RoIs, each element is a bounding
            box with (xmin, ymin, xmax, ymax) format in the range of original image.
        labels_int32 (Variable): A 2-D LoDTensor in shape of [R, 1] with type
            of int32. R is the same as it in `rois`. Each element represents
            a class label of a RoI.
        num_classes (int): Class number.
        resolution (int): Resolution of mask predictions.

    Returns:
        mask_rois (Variable):  A 2D LoDTensor with shape [P, 4] and same data
        type as `rois`. P is the total number of sampled RoIs. Each element
        is a bounding box with [xmin, ymin, xmax, ymax] format in range of
        original image size.

        mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1]
        and int data type, each element represents the output mask RoI
        index with regard to input RoIs.

        mask_int32 (Variable): A 2D LoDTensor with shape [P, K * M * M] and int
        data type, K is the classes number and M is the resolution of mask
        predictions. Each element represents the binary mask targets.

    Examples:
        .. code-block:: python

          import paddle.fluid as fluid

          im_info = fluid.data(name="im_info", shape=[None, 3],
              dtype="float32")
          gt_classes = fluid.data(name="gt_classes", shape=[None, 1],
              dtype="float32", lod_level=1)
          is_crowd = fluid.data(name="is_crowd", shape=[None, 1],
              dtype="float32", lod_level=1)
          gt_masks = fluid.data(name="gt_masks", shape=[None, 2],
              dtype="float32", lod_level=3)
          # rois, roi_labels can be the output of
          # fluid.layers.generate_proposal_labels.
          rois = fluid.data(name="rois", shape=[None, 4],
              dtype="float32", lod_level=1)
          roi_labels = fluid.data(name="roi_labels", shape=[None, 1],
              dtype="int32", lod_level=1)
          mask_rois, mask_index, mask_int32 = fluid.layers.generate_mask_labels(
              im_info=im_info,
              gt_classes=gt_classes,
              is_crowd=is_crowd,
              gt_segms=gt_masks,
              rois=rois,
              labels_int32=roi_labels,
              num_classes=81,
              resolution=14)
    """

    helper = LayerHelper('generate_mask_labels', **locals())

    mask_rois = helper.create_variable_for_type_inference(dtype=rois.dtype)
    roi_has_mask_int32 = helper.create_variable_for_type_inference(
        dtype=gt_classes.dtype
    )
    mask_int32 = helper.create_variable_for_type_inference(
        dtype=gt_classes.dtype
    )

    helper.append_op(
        type="generate_mask_labels",
        inputs={
            'ImInfo': im_info,
            'GtClasses': gt_classes,
            'IsCrowd': is_crowd,
            'GtSegms': gt_segms,
            'Rois': rois,
            'LabelsInt32': labels_int32,
        },
        outputs={
            'MaskRois': mask_rois,
            'RoiHasMaskInt32': roi_has_mask_int32,
            'MaskInt32': mask_int32,
        },
        attrs={'num_classes': num_classes, 'resolution': resolution},
    )

    mask_rois.stop_gradient = True
    roi_has_mask_int32.stop_gradient = True
    mask_int32.stop_gradient = True

    return mask_rois, roi_has_mask_int32, mask_int32


def generate_proposals(
    scores,
    bbox_deltas,
    im_info,
    anchors,
    variances,
    pre_nms_top_n=6000,
    post_nms_top_n=1000,
    nms_thresh=0.5,
    min_size=0.1,
    eta=1.0,
    return_rois_num=False,
    name=None,
):
    """

    **Generate proposal Faster-RCNN**

    This operation proposes RoIs according to each box with their
    probability to be a foreground object and
    the box can be calculated by anchors. Bbox_deltais and scores
    to be an object are the output of RPN. Final proposals
    could be used to train detection net.

    For generating proposals, this operation performs following steps:

    1. Transposes and resizes scores and bbox_deltas in size of
       (H*W*A, 1) and (H*W*A, 4)
    2. Calculate box locations as proposals candidates.
    3. Clip boxes to image
    4. Remove predicted boxes with small area.
    5. Apply NMS to get final proposals as output.

    Args:
        scores(Variable): A 4-D Tensor with shape [N, A, H, W] represents
            the probability for each box to be an object.
            N is batch size, A is number of anchors, H and W are height and
            width of the feature map. The data type must be float32.
        bbox_deltas(Variable): A 4-D Tensor with shape [N, 4*A, H, W]
            represents the difference between predicted box location and
            anchor location. The data type must be float32.
        im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin
            image information for N batch. Height and width are the input sizes
            and scale is the ratio of network input size and original size.
            The data type can be float32 or float64.
        anchors(Variable):   A 4-D Tensor represents the anchors with a layout
            of [H, W, A, 4]. H and W are height and width of the feature map,
            num_anchors is the box count of each position. Each anchor is
            in (xmin, ymin, xmax, ymax) format an unnormalized. The data type must be float32.
        variances(Variable): A 4-D Tensor. The expanded variances of anchors with a layout of
            [H, W, num_priors, 4]. Each variance is in
            (xcenter, ycenter, w, h) format. The data type must be float32.
        pre_nms_top_n(float): Number of total bboxes to be kept per
            image before NMS. The data type must be float32. `6000` by default.
        post_nms_top_n(float): Number of total bboxes to be kept per
            image after NMS. The data type must be float32. `1000` by default.
        nms_thresh(float): Threshold in NMS. The data type must be float32. `0.5` by default.
        min_size(float): Remove predicted boxes with either height or
            width < min_size. The data type must be float32. `0.1` by default.
        eta(float): Apply in adaptive NMS, if adaptive `threshold > 0.5`,
            `adaptive_threshold = adaptive_threshold * eta` in each iteration.
        return_rois_num(bool): When setting True, it will return a 1D Tensor with shape [N, ] that includes Rois's
            num of each image in one batch. The N is the image's num. For example, the tensor has values [4,5] that represents
            the first image has 4 Rois, the second image has 5 Rois. It only used in rcnn model.
            'False' 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:
        A tuple with format ``(rpn_rois, rpn_roi_probs)``.

        - **rpn_rois**: The generated RoIs. 2-D Tensor with shape ``[N, 4]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.
        - **rpn_roi_probs**: The scores of generated RoIs. 2-D Tensor with shape ``[N, 1]`` while ``N`` is the number of RoIs. The data type is the same as ``scores``.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            scores = fluid.data(name='scores', shape=[None, 4, 5, 5], dtype='float32')
            bbox_deltas = fluid.data(name='bbox_deltas', shape=[None, 16, 5, 5], dtype='float32')
            im_info = fluid.data(name='im_info', shape=[None, 3], dtype='float32')
            anchors = fluid.data(name='anchors', shape=[None, 5, 4, 4], dtype='float32')
            variances = fluid.data(name='variances', shape=[None, 5, 10, 4], dtype='float32')
            rois, roi_probs = fluid.layers.generate_proposals(scores, bbox_deltas,
                         im_info, anchors, variances)

    """
    return paddle.vision.ops.generate_proposals(
        scores=scores,
        bbox_deltas=bbox_deltas,
        img_size=im_info[:2],
        anchors=anchors,
        variances=variances,
        pre_nms_top_n=pre_nms_top_n,
        post_nms_top_n=post_nms_top_n,
        nms_thresh=nms_thresh,
        min_size=min_size,
        eta=eta,
        return_rois_num=return_rois_num,
        name=name,
    )


def box_clip(input, im_info, name=None):
    """

    Clip the box into the size given by im_info
    For each input box, The formula is given as follows:

    .. code-block:: text

        xmin = max(min(xmin, im_w - 1), 0)
        ymin = max(min(ymin, im_h - 1), 0)
        xmax = max(min(xmax, im_w - 1), 0)
        ymax = max(min(ymax, im_h - 1), 0)

    where im_w and im_h are computed from im_info:

    .. code-block:: text

        im_h = round(height / scale)
        im_w = round(weight / scale)

    Args:
        input(Variable): The input Tensor with shape :math:`[N_1, N_2, ..., N_k, 4]`,
            the last dimension is 4 and data type is float32 or float64.
        im_info(Variable): The 2-D Tensor with shape [N, 3] with layout
            (height, width, scale) representing the information of image.
            Height and width are the input sizes and scale is the ratio of network input
            size and original size. The data type is float32 or float64.
        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:
        Variable:

        output(Variable): The clipped tensor with data type float32 or float64.
        The shape is same as input.


    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            boxes = fluid.data(
                name='boxes', shape=[None, 8, 4], dtype='float32', lod_level=1)
            im_info = fluid.data(name='im_info', shape=[-1 ,3])
            out = fluid.layers.box_clip(
                input=boxes, im_info=im_info)
    """

    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'box_clip')
    check_variable_and_dtype(
        im_info, 'im_info', ['float32', 'float64'], 'box_clip'
    )

    helper = LayerHelper("box_clip", **locals())
    output = helper.create_variable_for_type_inference(dtype=input.dtype)
    inputs = {"Input": input, "ImInfo": im_info}
    helper.append_op(type="box_clip", inputs=inputs, outputs={"Output": output})

    return output


def retinanet_detection_output(
    bboxes,
    scores,
    anchors,
    im_info,
    score_threshold=0.05,
    nms_top_k=1000,
    keep_top_k=100,
    nms_threshold=0.3,
    nms_eta=1.0,
):
    """
    **Detection Output Layer for the detector RetinaNet.**

    In the detector `RetinaNet <https://arxiv.org/abs/1708.02002>`_ , many
    `FPN <https://arxiv.org/abs/1612.03144>`_ levels output the category
    and location predictions, this OP is to get the detection results by
    performing following steps:

    1. For each FPN level, decode box predictions according to the anchor
       boxes from at most :attr:`nms_top_k` top-scoring predictions after
       thresholding detector confidence at :attr:`score_threshold`.
    2. Merge top predictions from all levels and apply multi-class non
       maximum suppression (NMS) on them to get the final detections.

    Args:
        bboxes(List): A list of Tensors from multiple FPN levels represents
            the location prediction for all anchor boxes. Each element is
            a 3-D Tensor with shape :math:`[N, Mi, 4]`, :math:`N` is the
            batch size, :math:`Mi` is the number of bounding boxes from
            :math:`i`-th FPN level and each bounding box has four coordinate
            values and the layout is [xmin, ymin, xmax, ymax]. The data type
            of each element is float32 or float64.
        scores(List): A list of Tensors from multiple FPN levels represents
            the category prediction for all anchor boxes. Each element is a
            3-D Tensor with shape :math:`[N, Mi, C]`,  :math:`N` is the batch
            size, :math:`C` is the class number (**excluding background**),
            :math:`Mi` is the number of bounding boxes from :math:`i`-th FPN
            level. The data type of each element is float32 or float64.
        anchors(List): A list of Tensors from multiple FPN levels represents
            the locations of all anchor boxes. Each element is a 2-D Tensor
            with shape :math:`[Mi, 4]`, :math:`Mi` is the number of bounding
            boxes from :math:`i`-th FPN level, and each bounding box has four
            coordinate values and the layout is [xmin, ymin, xmax, ymax].
            The data type of each element is float32 or float64.
        im_info(Variable): A 2-D Tensor with shape :math:`[N, 3]` represents the size
            information of input images. :math:`N` is the batch size, the size
            information of each image is a 3-vector which are the height and width
            of the network input along with the factor scaling the origin image to
            the network input. The data type of :attr:`im_info` is float32.
        score_threshold(float): Threshold to filter out bounding boxes
            with a confidence score before NMS, default value is set to 0.05.
        nms_top_k(int): Maximum number of detections per FPN layer to be
            kept according to the confidences before NMS, default value is set to
            1000.
        keep_top_k(int): Number of total bounding boxes to be kept per image after
            NMS step. Default value is set to 100, -1 means keeping all bounding
            boxes after NMS step.
        nms_threshold(float): The Intersection-over-Union(IoU) threshold used to
            filter out boxes in NMS.
        nms_eta(float): The parameter for adjusting :attr:`nms_threshold` in NMS.
            Default value is set to 1., which represents the value of
            :attr:`nms_threshold` keep the same in NMS. If :attr:`nms_eta` is set
            to be lower than 1. and the value of :attr:`nms_threshold` is set to
            be higher than 0.5, everytime a bounding box is filtered out,
            the adjustment for :attr:`nms_threshold` like :attr:`nms_threshold`
            = :attr:`nms_threshold` * :attr:`nms_eta`  will not be stopped until
            the actual value of :attr:`nms_threshold` is lower than or equal to
            0.5.

    **Notice**: In some cases where the image sizes are very small, it's possible
    that there is no detection if :attr:`score_threshold` are used at all
    levels. Hence, this OP do not filter out anchors from the highest FPN level
    before NMS. And the last element in :attr:`bboxes`:, :attr:`scores` and
    :attr:`anchors` is required to be from the highest FPN level.

    Returns:
        Variable(The data type is float32 or float64):
            The detection output is a 1-level LoDTensor with shape :math:`[No, 6]`.
            Each row has six values: [label, confidence, xmin, ymin, xmax, ymax].
            :math:`No` is the total number of detections in this mini-batch.
            The :math:`i`-th image has `LoD[i + 1] - LoD[i]` detected
            results, if `LoD[i + 1] - LoD[i]` is 0, the :math:`i`-th image
            has no detected results. If all images have no detected results,
            LoD will be set to 0, and the output tensor is empty (None).

    Examples:
        .. code-block:: python

           import paddle.fluid as fluid

           bboxes_low = fluid.data(
               name='bboxes_low', shape=[1, 44, 4], dtype='float32')
           bboxes_high = fluid.data(
               name='bboxes_high', shape=[1, 11, 4], dtype='float32')
           scores_low = fluid.data(
               name='scores_low', shape=[1, 44, 10], dtype='float32')
           scores_high = fluid.data(
               name='scores_high', shape=[1, 11, 10], dtype='float32')
           anchors_low = fluid.data(
               name='anchors_low', shape=[44, 4], dtype='float32')
           anchors_high = fluid.data(
               name='anchors_high', shape=[11, 4], dtype='float32')
           im_info = fluid.data(
               name="im_info", shape=[1, 3], dtype='float32')
           nmsed_outs = fluid.layers.retinanet_detection_output(
               bboxes=[bboxes_low, bboxes_high],
               scores=[scores_low, scores_high],
               anchors=[anchors_low, anchors_high],
               im_info=im_info,
               score_threshold=0.05,
               nms_top_k=1000,
               keep_top_k=100,
               nms_threshold=0.45,
               nms_eta=1.0)
    """

    check_type(bboxes, 'bboxes', (list), 'retinanet_detection_output')
    for i, bbox in enumerate(bboxes):
        check_variable_and_dtype(
            bbox,
            'bbox{}'.format(i),
            ['float32', 'float64'],
            'retinanet_detection_output',
        )
    check_type(scores, 'scores', (list), 'retinanet_detection_output')
    for i, score in enumerate(scores):
        check_variable_and_dtype(
            score,
            'score{}'.format(i),
            ['float32', 'float64'],
            'retinanet_detection_output',
        )
    check_type(anchors, 'anchors', (list), 'retinanet_detection_output')
    for i, anchor in enumerate(anchors):
        check_variable_and_dtype(
            anchor,
            'anchor{}'.format(i),
            ['float32', 'float64'],
            'retinanet_detection_output',
        )
    check_variable_and_dtype(
        im_info, 'im_info', ['float32', 'float64'], 'retinanet_detection_output'
    )

    helper = LayerHelper('retinanet_detection_output', **locals())
    output = helper.create_variable_for_type_inference(
        dtype=helper.input_dtype('scores')
    )
    helper.append_op(
        type="retinanet_detection_output",
        inputs={
            'BBoxes': bboxes,
            'Scores': scores,
            'Anchors': anchors,
            'ImInfo': im_info,
        },
        attrs={
            'score_threshold': score_threshold,
            'nms_top_k': nms_top_k,
            'nms_threshold': nms_threshold,
            'keep_top_k': keep_top_k,
            'nms_eta': 1.0,
        },
        outputs={'Out': output},
    )
    output.stop_gradient = True
    return output


def multiclass_nms(
    bboxes,
    scores,
    score_threshold,
    nms_top_k,
    keep_top_k,
    nms_threshold=0.3,
    normalized=True,
    nms_eta=1.0,
    background_label=0,
    name=None,
):
    """

    **Multiclass NMS**

    This operator is to do multi-class non maximum suppression (NMS) on
    boxes and scores.

    In the NMS step, this operator greedily selects a subset of detection bounding
    boxes that have high scores larger than score_threshold, if providing this
    threshold, then selects the largest nms_top_k confidences scores if nms_top_k
    is larger than -1. Then this operator pruns away boxes that have high IOU
    (intersection over union) overlap with already selected boxes by adaptive
    threshold NMS based on parameters of nms_threshold and nms_eta.
    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.

    See below for an example:

    .. code-block:: text

        if:
            box1.data = (2.0, 3.0, 7.0, 5.0) format is (xmin, ymin, xmax, ymax)
            box1.scores = (0.7, 0.2, 0.4)  which is (label0.score=0.7, label1.score=0.2, label2.cores=0.4)

            box2.data = (3.0, 4.0, 8.0, 5.0)
            box2.score = (0.3, 0.3, 0.1)

            nms_threshold = 0.3
            background_label = 0
            score_threshold = 0


        Then:
            iou = 4/11 > 0.3
            out.data = [[1, 0.3, 3.0, 4.0, 8.0, 5.0],
                         [2, 0.4, 2.0, 3.0, 7.0, 5.0]]

            Out format is (label, confidence, xmin, ymin, xmax, ymax)
    Args:
        bboxes (Variable): Two types of bboxes are supported:
                           1. (Tensor) A 3-D Tensor with shape
                           [N, M, 4 or 8 16 24 32] represents the
                           predicted locations of M bounding bboxes,
                           N is the batch size. Each bounding box has four
                           coordinate values and the layout is
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           The data type is float32 or float64.
                           2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
                           M is the number of bounding boxes, C is the
                           class number. The data type is float32 or float64.
        scores (Variable): Two types of scores are supported:
                           1. (Tensor) A 3-D Tensor with shape [N, C, M]
                           represents the predicted confidence predictions.
                           N is the batch size, C is the class number, M is
                           number of bounding boxes. For each category there
                           are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension
                           of BBoxes.The data type is float32 or float64.
                           2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
                           M is the number of bbox, C is the class number.
                           In this case, input BBoxes should be the second
                           case with shape [M, C, 4].The data type is float32 or float64.
        background_label (int): The index of background label, the background
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: 0
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score. If not provided,
                                 consider all boxes.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences after the filtering detections based
                         on score_threshold.
        nms_threshold (float): The threshold to be used in NMS. Default: 0.3
        nms_eta (float): The threshold to be used in NMS. Default: 1.0
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        normalized (bool): Whether detections are normalized. Default: True
        name(str): Name of the multiclass nms op. Default: None.

    Returns:
        Variable: A 2-D LoDTensor with shape [No, 6] represents the detections.
             Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
             or A 2-D LoDTensor with shape [No, 10] represents the detections.
             Each row has 10 values:
             [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the
             total number of detections. If there is no detected boxes for all
             images, lod will be set to {1} and Out only contains one value
             which is -1.
             (After version 1.3, when no boxes detected, the lod is changed
             from {0} to {1})


    Examples:
        .. code-block:: python


            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            boxes = fluid.data(name='bboxes', shape=[None,81, 4],
                                      dtype='float32', lod_level=1)
            scores = fluid.data(name='scores', shape=[None,81],
                                      dtype='float32', lod_level=1)
            out = fluid.layers.multiclass_nms(bboxes=boxes,
                                              scores=scores,
                                              background_label=0,
                                              score_threshold=0.5,
                                              nms_top_k=400,
                                              nms_threshold=0.3,
                                              keep_top_k=200,
                                              normalized=False)
    """
    check_variable_and_dtype(
        bboxes, 'BBoxes', ['float32', 'float64'], 'multiclass_nms'
    )
    check_variable_and_dtype(
        scores, 'Scores', ['float32', 'float64'], 'multiclass_nms'
    )
    check_type(score_threshold, 'score_threshold', float, 'multicalss_nms')
    check_type(nms_top_k, 'nums_top_k', int, 'multiclass_nms')
    check_type(keep_top_k, 'keep_top_k', int, 'mutliclass_nms')
    check_type(nms_threshold, 'nms_threshold', float, 'multiclass_nms')
    check_type(normalized, 'normalized', bool, 'multiclass_nms')
    check_type(nms_eta, 'nms_eta', float, 'multiclass_nms')
    check_type(background_label, 'background_label', int, 'multiclass_nms')

    helper = LayerHelper('multiclass_nms', **locals())
    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
    helper.append_op(
        type="multiclass_nms",
        inputs={'BBoxes': bboxes, 'Scores': scores},
        attrs={
            'background_label': background_label,
            'score_threshold': score_threshold,
            'nms_top_k': nms_top_k,
            'nms_threshold': nms_threshold,
            'nms_eta': nms_eta,
            'keep_top_k': keep_top_k,
            'normalized': normalized,
        },
        outputs={'Out': output},
    )
    output.stop_gradient = True

    return output


def locality_aware_nms(
    bboxes,
    scores,
    score_threshold,
    nms_top_k,
    keep_top_k,
    nms_threshold=0.3,
    normalized=True,
    nms_eta=1.0,
    background_label=-1,
    name=None,
):
    """
    **Local Aware NMS**

    `Local Aware NMS <https://arxiv.org/abs/1704.03155>`_ is to do locality-aware non maximum
    suppression (LANMS) on boxes and scores.

    Firstly, this operator merge box and score according their IOU
    (intersection over union). In the NMS step, this operator greedily selects a
    subset of detection bounding boxes that have high scores larger than score_threshold,
    if providing this threshold, then selects the largest nms_top_k confidences scores
    if nms_top_k is larger than -1. Then this operator pruns away boxes that have high
    IOU overlap with already selected boxes by adaptive threshold NMS based on parameters
    of nms_threshold and nms_eta.

    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.

    Args:
        bboxes (Variable): A 3-D Tensor with shape [N, M, 4 or 8 16 24 32]
                           represents the predicted locations of M bounding
                           bboxes, N is the batch size. Each bounding box
                           has four coordinate values and the layout is
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           The data type is float32 or float64.
        scores (Variable): A 3-D Tensor with shape [N, C, M] represents the
                           predicted confidence predictions. N is the batch
                           size, C is the class number, M is number of bounding
                           boxes. Now only support 1 class. For each category
                           there are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension of
                           BBoxes. The data type is float32 or float64.
        background_label (int): The index of background label, the background
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: -1
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score. If not provided,
                                 consider all boxes.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences after the filtering detections based
                         on score_threshold.
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        nms_threshold (float): The threshold to be used in NMS. Default: 0.3
        nms_eta (float): The threshold to be used in NMS. Default: 1.0
        normalized (bool): Whether detections are normalized. Default: True
        name(str): Name of the locality aware nms op, please refer to :ref:`api_guide_Name` .
                          Default: None.

    Returns:
        Variable: A 2-D LoDTensor with shape [No, 6] represents the detections.
             Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
             or A 2-D LoDTensor with shape [No, 10] represents the detections.
             Each row has 10 values:
             [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the
             total number of detections. If there is no detected boxes for all
             images, lod will be set to {1} and Out only contains one value
             which is -1.
             (After version 1.3, when no boxes detected, the lod is changed
             from {0} to {1}). The data type is float32 or float64.


    Examples:
        .. code-block:: python


            import paddle.fluid as fluid
            boxes = fluid.data(name='bboxes', shape=[None, 81, 8],
                                      dtype='float32')
            scores = fluid.data(name='scores', shape=[None, 1, 81],
                                      dtype='float32')
            out = fluid.layers.locality_aware_nms(bboxes=boxes,
                                              scores=scores,
                                              score_threshold=0.5,
                                              nms_top_k=400,
                                              nms_threshold=0.3,
                                              keep_top_k=200,
                                              normalized=False)
    """
    check_variable_and_dtype(
        bboxes, 'bboxes', ['float32', 'float64'], 'locality_aware_nms'
    )
    check_variable_and_dtype(
        scores, 'scores', ['float32', 'float64'], 'locality_aware_nms'
    )
    check_type(background_label, 'background_label', int, 'locality_aware_nms')
    check_type(score_threshold, 'score_threshold', float, 'locality_aware_nms')
    check_type(nms_top_k, 'nms_top_k', int, 'locality_aware_nms')
    check_type(nms_eta, 'nms_eta', float, 'locality_aware_nms')
    check_type(nms_threshold, 'nms_threshold', float, 'locality_aware_nms')
    check_type(keep_top_k, 'keep_top_k', int, 'locality_aware_nms')
    check_type(normalized, 'normalized', bool, 'locality_aware_nms')

    shape = scores.shape
    assert len(shape) == 3, "dim size of scores must be 3"
    assert (
        shape[1] == 1
    ), "locality_aware_nms only support one class, Tensor score shape must be [N, 1, M]"

    helper = LayerHelper('locality_aware_nms', **locals())

    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
    out = {'Out': output}

    helper.append_op(
        type="locality_aware_nms",
        inputs={'BBoxes': bboxes, 'Scores': scores},
        attrs={
            'background_label': background_label,
            'score_threshold': score_threshold,
            'nms_top_k': nms_top_k,
            'nms_threshold': nms_threshold,
            'nms_eta': nms_eta,
            'keep_top_k': keep_top_k,
            'nms_eta': nms_eta,
            'normalized': normalized,
        },
        outputs={'Out': output},
    )
    output.stop_gradient = True

    return output


def matrix_nms(
    bboxes,
    scores,
    score_threshold,
    post_threshold,
    nms_top_k,
    keep_top_k,
    use_gaussian=False,
    gaussian_sigma=2.0,
    background_label=0,
    normalized=True,
    return_index=False,
    name=None,
):
    """
    **Matrix NMS**

    This operator does matrix non maximum suppression (NMS).

    First selects a subset of candidate bounding boxes that have higher scores
    than score_threshold (if provided), then the top k candidate is selected if
    nms_top_k is larger than -1. Score of the remaining candidate are then
    decayed according to the Matrix NMS scheme.
    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
    per image if keep_top_k is larger than -1.

    Args:
        bboxes (Variable): A 3-D Tensor with shape [N, M, 4] represents the
                           predicted locations of M bounding bboxes,
                           N is the batch size. Each bounding box has four
                           coordinate values and the layout is
                           [xmin, ymin, xmax, ymax], when box size equals to 4.
                           The data type is float32 or float64.
        scores (Variable): A 3-D Tensor with shape [N, C, M]
                           represents the predicted confidence predictions.
                           N is the batch size, C is the class number, M is
                           number of bounding boxes. For each category there
                           are total M scores which corresponding M bounding
                           boxes. Please note, M is equal to the 2nd dimension
                           of BBoxes. The data type is float32 or float64.
        score_threshold (float): Threshold to filter out bounding boxes with
                                 low confidence score.
        post_threshold (float): Threshold to filter out bounding boxes with
                                low confidence score AFTER decaying.
        nms_top_k (int): Maximum number of detections to be kept according to
                         the confidences after the filtering detections based
                         on score_threshold.
        keep_top_k (int): Number of total bboxes to be kept per image after NMS
                          step. -1 means keeping all bboxes after NMS step.
        use_gaussian (bool): Use Gaussian as the decay function. Default: False
        gaussian_sigma (float): Sigma for Gaussian decay function. Default: 2.0
        background_label (int): The index of background label, the background
                                label will be ignored. If set to -1, then all
                                categories will be considered. Default: 0
        normalized (bool): Whether detections are normalized. Default: True
        return_index(bool): Whether return selected index. Default: False
        name(str): Name of the matrix nms op. Default: None.

    Returns:
        A tuple with two Variables: (Out, Index) if return_index is True,
        otherwise, one Variable(Out) is returned.

        Out (Variable): A 2-D LoDTensor with shape [No, 6] containing the
             detection results.
             Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
             (After version 1.3, when no boxes detected, the lod is changed
             from {0} to {1})

        Index (Variable): A 2-D LoDTensor with shape [No, 1] containing the
            selected indices, which are absolute values cross batches.

    Examples:
        .. code-block:: python


            import paddle.fluid as fluid
            boxes = fluid.data(name='bboxes', shape=[None,81, 4],
                                      dtype='float32', lod_level=1)
            scores = fluid.data(name='scores', shape=[None,81],
                                      dtype='float32', lod_level=1)
            out = fluid.layers.matrix_nms(bboxes=boxes,
                                          scores=scores,
                                          background_label=0,
                                          score_threshold=0.5,
                                          post_threshold=0.1,
                                          nms_top_k=400,
                                          keep_top_k=200,
                                          normalized=False)
    """
    if in_dygraph_mode():
        attrs = (
            score_threshold,
            nms_top_k,
            keep_top_k,
            post_threshold,
            use_gaussian,
            gaussian_sigma,
            background_label,
            normalized,
        )

        out, index = _C_ops.matrix_nms(bboxes, scores, *attrs)
        if return_index:
            return out, index
        else:
            return out

    check_variable_and_dtype(
        bboxes, 'BBoxes', ['float32', 'float64'], 'matrix_nms'
    )
    check_variable_and_dtype(
        scores, 'Scores', ['float32', 'float64'], 'matrix_nms'
    )
    check_type(score_threshold, 'score_threshold', float, 'matrix_nms')
    check_type(post_threshold, 'post_threshold', float, 'matrix_nms')
    check_type(nms_top_k, 'nums_top_k', int, 'matrix_nms')
    check_type(keep_top_k, 'keep_top_k', int, 'matrix_nms')
    check_type(normalized, 'normalized', bool, 'matrix_nms')
    check_type(use_gaussian, 'use_gaussian', bool, 'matrix_nms')
    check_type(gaussian_sigma, 'gaussian_sigma', float, 'matrix_nms')
    check_type(background_label, 'background_label', int, 'matrix_nms')

    helper = LayerHelper('matrix_nms', **locals())
    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
    index = helper.create_variable_for_type_inference(dtype='int')
    helper.append_op(
        type="matrix_nms",
        inputs={'BBoxes': bboxes, 'Scores': scores},
        attrs={
            'score_threshold': score_threshold,
            'post_threshold': post_threshold,
            'nms_top_k': nms_top_k,
            'keep_top_k': keep_top_k,
            'use_gaussian': use_gaussian,
            'gaussian_sigma': gaussian_sigma,
            'background_label': background_label,
            'normalized': normalized,
        },
        outputs={'Out': output, 'Index': index},
    )
    output.stop_gradient = True

    if return_index:
        return output, index
    else:
        return output


def distribute_fpn_proposals(
    fpn_rois,
    min_level,
    max_level,
    refer_level,
    refer_scale,
    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.fluid as fluid
            import paddle
            paddle.enable_static()
            fpn_rois = fluid.data(
                name='data', shape=[None, 4], dtype='float32', lod_level=1)
            multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
                fpn_rois=fpn_rois,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224)
    """
    return paddle.vision.ops.distribute_fpn_proposals(
        fpn_rois=fpn_rois,
        min_level=min_level,
        max_level=max_level,
        refer_level=refer_level,
        refer_scale=refer_scale,
        rois_num=rois_num,
        name=name,
    )


@templatedoc()
def box_decoder_and_assign(
    prior_box, prior_box_var, target_box, box_score, box_clip, name=None
):
    """

    ${comment}
    Args:
        prior_box(${prior_box_type}): ${prior_box_comment}
        prior_box_var(${prior_box_var_type}): ${prior_box_var_comment}
        target_box(${target_box_type}): ${target_box_comment}
        box_score(${box_score_type}): ${box_score_comment}
        box_clip(${box_clip_type}): ${box_clip_comment}
        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:

        decode_box(${decode_box_type}): ${decode_box_comment}

        output_assign_box(${output_assign_box_type}): ${output_assign_box_comment}


    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            pb = fluid.data(
                name='prior_box', shape=[None, 4], dtype='float32')
            pbv = fluid.data(
                name='prior_box_var', shape=[4], dtype='float32')
            loc = fluid.data(
                name='target_box', shape=[None, 4*81], dtype='float32')
            scores = fluid.data(
                name='scores', shape=[None, 81], dtype='float32')
            decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign(
                pb, pbv, loc, scores, 4.135)

    """
    check_variable_and_dtype(
        prior_box, 'prior_box', ['float32', 'float64'], 'box_decoder_and_assign'
    )
    check_variable_and_dtype(
        target_box,
        'target_box',
        ['float32', 'float64'],
        'box_decoder_and_assign',
    )
    check_variable_and_dtype(
        box_score, 'box_score', ['float32', 'float64'], 'box_decoder_and_assign'
    )
    helper = LayerHelper("box_decoder_and_assign", **locals())

    decoded_box = helper.create_variable_for_type_inference(
        dtype=prior_box.dtype
    )
    output_assign_box = helper.create_variable_for_type_inference(
        dtype=prior_box.dtype
    )

    helper.append_op(
        type="box_decoder_and_assign",
        inputs={
            "PriorBox": prior_box,
            "PriorBoxVar": prior_box_var,
            "TargetBox": target_box,
            "BoxScore": box_score,
        },
        attrs={"box_clip": box_clip},
        outputs={
            "DecodeBox": decoded_box,
            "OutputAssignBox": output_assign_box,
        },
    )
    return decoded_box, output_assign_box


def collect_fpn_proposals(
    multi_rois,
    multi_scores,
    min_level,
    max_level,
    post_nms_top_n,
    rois_num_per_level=None,
    name=None,
):
    """

    **This OP only supports LoDTensor as input**. Concat multi-level RoIs
    (Region of Interest) and select N RoIs with respect to multi_scores.
    This operation performs the following steps:

    1. Choose num_level RoIs and scores as input: num_level = max_level - min_level
    2. Concat multi-level RoIs and scores
    3. Sort scores and select post_nms_top_n scores
    4. Gather RoIs by selected indices from scores
    5. Re-sort RoIs by corresponding batch_id

    Args:
        multi_rois(list): List of RoIs to collect. Element in list is 2-D
            LoDTensor with shape [N, 4] and data type is float32 or float64,
            N is the number of RoIs.
        multi_scores(list): List of scores of RoIs to collect. Element in list
            is 2-D LoDTensor with shape [N, 1] and data type is float32 or
            float64, N is the number of RoIs.
        min_level(int): The lowest level of FPN layer to collect
        max_level(int): The highest level of FPN layer to collect
        post_nms_top_n(int): The number of selected RoIs
        rois_num_per_level(list, optional): The List of RoIs' numbers.
            Each element is 1-D Tensor which contains the RoIs' number of each
            image on each level and the shape is [B] and data type is
            int32, B is the number of images. If it is not None then return
            a 1-D Tensor contains the output RoIs' number of each image and
            the shape is [B]. Default: None
        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:
        Variable:

        fpn_rois(Variable): 2-D LoDTensor with shape [N, 4] and data type is
        float32 or float64. Selected RoIs.

        rois_num(Tensor): 1-D Tensor contains the RoIs's number of each
        image. The shape is [B] and data type is int32. B is the number of
        images.

    Examples:
        .. code-block:: python

            import paddle.fluid as fluid
            import paddle
            paddle.enable_static()
            multi_rois = []
            multi_scores = []
            for i in range(4):
                multi_rois.append(fluid.data(
                    name='roi_'+str(i), shape=[None, 4], dtype='float32', lod_level=1))
            for i in range(4):
                multi_scores.append(fluid.data(
                    name='score_'+str(i), shape=[None, 1], dtype='float32', lod_level=1))

            fpn_rois = fluid.layers.collect_fpn_proposals(
                multi_rois=multi_rois,
                multi_scores=multi_scores,
                min_level=2,
                max_level=5,
                post_nms_top_n=2000)
    """
    num_lvl = max_level - min_level + 1
    input_rois = multi_rois[:num_lvl]
    input_scores = multi_scores[:num_lvl]

    if _non_static_mode():
        assert (
            rois_num_per_level is not None
        ), "rois_num_per_level should not be None in dygraph mode."
        attrs = ('post_nms_topN', post_nms_top_n)
        output_rois, rois_num = _legacy_C_ops.collect_fpn_proposals(
            input_rois, input_scores, rois_num_per_level, *attrs
        )

    check_type(multi_rois, 'multi_rois', list, 'collect_fpn_proposals')
    check_type(multi_scores, 'multi_scores', list, 'collect_fpn_proposals')
    helper = LayerHelper('collect_fpn_proposals', **locals())
    dtype = helper.input_dtype('multi_rois')
    check_dtype(
        dtype, 'multi_rois', ['float32', 'float64'], 'collect_fpn_proposals'
    )
    output_rois = helper.create_variable_for_type_inference(dtype)
    output_rois.stop_gradient = True

    inputs = {
        'MultiLevelRois': input_rois,
        'MultiLevelScores': input_scores,
    }
    outputs = {'FpnRois': output_rois}
    if rois_num_per_level is not None:
        inputs['MultiLevelRoIsNum'] = rois_num_per_level
        rois_num = helper.create_variable_for_type_inference(dtype='int32')
        rois_num.stop_gradient = True
        outputs['RoisNum'] = rois_num
    helper.append_op(
        type='collect_fpn_proposals',
        inputs=inputs,
        outputs=outputs,
        attrs={'post_nms_topN': post_nms_top_n},
    )
    if rois_num_per_level is not None:
        return output_rois, rois_num
    return output_rois