ops.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

import paddle

from paddle.fluid.framework import Variable, in_dygraph_mode
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph import layers
from paddle.fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
import math
import six
import numpy as np
from functools import reduce

__all__ = [
    'roi_pool',
    'roi_align',
    #'prior_box',
    #'anchor_generator',
    #'generate_proposals',
    'iou_similarity',
    #'box_coder',
    'yolo_box',
    #'multiclass_nms',
    'distribute_fpn_proposals',
    'collect_fpn_proposals',
    'matrix_nms',
]


def roi_pool(input,
             rois,
             output_size,
             spatial_scale=1.0,
             rois_num=None,
             name=None):
    """

    This operator implements the roi_pooling layer.
    Region of interest pooling (also known as RoI pooling) is to perform max pooling on inputs of nonuniform sizes to obtain fixed-size feature maps (e.g. 7*7).

    The operator has three steps:

        1. Dividing each region proposal into equal-sized sections with output_size(h, w);
        2. Finding the largest value in each section;
        3. Copying these max values to the output buffer.

    For more information, please refer to https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn

    Args:
        input (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W], 
            where N is the batch size, C is the input channel, H is Height, W is weight. 
            The data type is float32 or float64.
        rois (Tensor): ROIs (Regions of Interest) to pool over. 
            2D-Tensor or 2D-LoDTensor with the shape of [num_rois,4], the lod level is 1. 
            Given as [[x1, y1, x2, y2], ...], (x1, y1) is the top left coordinates, 
            and (x2, y2) is the bottom right coordinates.
        output_size (int or tuple[int, int]): The pooled output size(h, w), data type is int32. If int, h and w are both equal to output_size.
        spatial_scale (float, optional): Multiplicative spatial scale factor to translate ROI coords from their input scale to the scale used when pooling. Default: 1.0
        rois_num (Tensor): The number of RoIs in each image. 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:
        Tensor: The pooled feature, 4D-Tensor with the shape of [num_rois, C, output_size[0], output_size[1]].


    Examples:

    ..  code-block:: python

        import paddle
        paddle.enable_static()

        x = paddle.static.data(
                name='data', shape=[None, 256, 32, 32], dtype='float32')
        rois = paddle.static.data(
                name='rois', shape=[None, 4], dtype='float32')
        rois_num = paddle.static.data(name='rois_num', shape=[None], dtype='int32')

        pool_out = ops.roi_pool(
                input=x,
                rois=rois,
                output_size=(1, 1),
                spatial_scale=1.0,
                rois_num=rois_num)
    """
    check_type(output_size, 'output_size', (int, tuple), 'roi_pool')
    if isinstance(output_size, int):
        output_size = (output_size, output_size)

    pooled_height, pooled_width = output_size
    if in_dygraph_mode():
        assert rois_num is not None, "rois_num should not be None in dygraph mode."
        pool_out, argmaxes = core.ops.roi_pool(
            input, rois, rois_num, "pooled_height", pooled_height,
            "pooled_width", pooled_width, "spatial_scale", spatial_scale)
        return pool_out, argmaxes

    check_variable_and_dtype(input, 'input', ['float32'], 'roi_pool')
    check_variable_and_dtype(rois, 'rois', ['float32'], 'roi_pool')
    helper = LayerHelper('roi_pool', **locals())
    dtype = helper.input_dtype()
    pool_out = helper.create_variable_for_type_inference(dtype)
    argmaxes = helper.create_variable_for_type_inference(dtype='int32')

    inputs = {
        "X": input,
        "ROIs": rois,
    }
    if rois_num is not None:
        inputs['RoisNum'] = rois_num
    helper.append_op(
        type="roi_pool",
        inputs=inputs,
        outputs={"Out": pool_out,
                 "Argmax": argmaxes},
        attrs={
            "pooled_height": pooled_height,
            "pooled_width": pooled_width,
            "spatial_scale": spatial_scale
        })
    return pool_out, argmaxes


def roi_align(input,
              rois,
              output_size,
              spatial_scale=1.0,
              sampling_ratio=-1,
              rois_num=None,
              name=None):
    """

    Region of interest align (also known as RoI align) is to perform
    bilinear interpolation on inputs of nonuniform sizes to obtain 
    fixed-size feature maps (e.g. 7*7)

    Dividing each region proposal into equal-sized sections with
    the pooled_width and pooled_height. Location remains the origin
    result.

    In each ROI bin, the value of the four regularly sampled locations 
    are computed directly through bilinear interpolation. The output is
    the mean of four locations.
    Thus avoid the misaligned problem. 

    Args:
        input (Tensor): Input feature, 4D-Tensor with the shape of [N,C,H,W], 
            where N is the batch size, C is the input channel, H is Height, W is weight. 
            The data type is float32 or float64.
        rois (Tensor): ROIs (Regions of Interest) to pool over.It should be
            a 2-D Tensor or 2-D LoDTensor of shape (num_rois, 4), the lod level is 1. 
            The data type is float32 or float64. Given as [[x1, y1, x2, y2], ...],
            (x1, y1) is the top left coordinates, and (x2, y2) is the bottom right coordinates.
        output_size (int or tuple[int, int]): The pooled output size(h, w), data type is int32. If int, h and w are both equal to output_size.
        spatial_scale (float32, optional): Multiplicative spatial scale factor to translate ROI coords 
            from their input scale to the scale used when pooling. Default: 1.0
        sampling_ratio(int32, optional): number of sampling points in the interpolation grid. 
            If <=0, then grid points are adaptive to roi_width and pooled_w, likewise for height. Default: -1
        rois_num (Tensor): The number of RoIs in each image. 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:
        Tensor:

        Output: The output of ROIAlignOp is a 4-D tensor with shape (num_rois, channels, pooled_h, pooled_w). The data type is float32 or float64.


    Examples:
        .. code-block:: python

            import paddle
            paddle.enable_static()

            x = paddle.static.data(
                name='data', shape=[None, 256, 32, 32], dtype='float32')
            rois = paddle.static.data(
                name='rois', shape=[None, 4], dtype='float32')
            rois_num = paddle.static.data(name='rois_num', shape=[None], dtype='int32')
            align_out = ops.roi_align(input=x,
                                               rois=rois,
                                               ouput_size=(7, 7),
                                               spatial_scale=0.5,
                                               sampling_ratio=-1,
                                               rois_num=rois_num)
    """
    check_type(output_size, 'output_size', (int, tuple), 'roi_align')
    if isinstance(output_size, int):
        output_size = (output_size, output_size)

    pooled_height, pooled_width = output_size

    if in_dygraph_mode():
        assert rois_num is not None, "rois_num should not be None in dygraph mode."
        align_out = core.ops.roi_align(
            input, rois, rois_num, "pooled_height", pooled_height,
            "pooled_width", pooled_width, "spatial_scale", spatial_scale,
            "sampling_ratio", sampling_ratio)
        return align_out

    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
                             'roi_align')
    check_variable_and_dtype(rois, 'rois', ['float32', 'float64'], 'roi_align')
    helper = LayerHelper('roi_align', **locals())
    dtype = helper.input_dtype()
    align_out = helper.create_variable_for_type_inference(dtype)
    inputs = {
        "X": input,
        "ROIs": rois,
    }
    if rois_num is not None:
        inputs['RoisNum'] = rois_num
    helper.append_op(
        type="roi_align",
        inputs=inputs,
        outputs={"Out": align_out},
        attrs={
            "pooled_height": pooled_height,
            "pooled_width": pooled_width,
            "spatial_scale": spatial_scale,
            "sampling_ratio": sampling_ratio
        })
    return align_out


def iou_similarity(x, y, box_normalized=True, name=None):
    """
    Computes intersection-over-union (IOU) between two box lists.
    Box list 'X' should be a LoDTensor and 'Y' is a common Tensor,
    boxes in 'Y' are shared by all instance of the batched inputs of X.
    Given two boxes A and B, the calculation of IOU is as follows:

    $$
    IOU(A, B) = 
    \\frac{area(A\\cap B)}{area(A)+area(B)-area(A\\cap B)}
    $$

    Args:
        x (Tensor): Box list X is a 2-D Tensor with shape [N, 4] holds N 
             boxes, each box is represented as [xmin, ymin, xmax, ymax], 
             the shape of X is [N, 4]. [xmin, ymin] is the left top 
             coordinate of the 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 box.
             The data type is float32 or float64.
        y (Tensor): Box list Y holds M boxes, each box is represented as 
             [xmin, ymin, xmax, ymax], the shape of X is [N, 4]. 
             [xmin, ymin] is the left top coordinate of the box if the 
             input is image feature map, and [xmax, ymax] is the right 
             bottom coordinate of the box. The data type is float32 or float64.
        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. 

    Returns:
        Tensor: The output of iou_similarity op, a tensor with shape [N, M] 
              representing pairwise iou scores. The data type is same with x.

    Examples:
        .. code-block:: python

            import numpy as np
            import paddle
            paddle.enable_static()

            x = paddle.data(name='x', shape=[None, 4], dtype='float32')
            y = paddle.data(name='y', shape=[None, 4], dtype='float32')
            iou = ops.iou_similarity(x=x, y=y)
    """

    if in_dygraph_mode():
        out = core.ops.iou_similarity(x, y, 'box_normalized', box_normalized)
        return out

    helper = LayerHelper("iou_similarity", **locals())
    out = helper.create_variable_for_type_inference(dtype=x.dtype)

    helper.append_op(
        type="iou_similarity",
        inputs={"X": x,
                "Y": y},
        attrs={"box_normalized": box_normalized},
        outputs={"Out": out})
    return out


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(paddle.static.data(
                    name='roi_'+str(i), shape=[None, 4], dtype='float32', lod_level=1))
            for i in range(4):
                multi_scores.append(paddle.static.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)
    """
    check_type(multi_rois, 'multi_rois', list, 'collect_fpn_proposals')
    check_type(multi_scores, 'multi_scores', list, 'collect_fpn_proposals')
    num_lvl = max_level - min_level + 1
    input_rois = multi_rois[:num_lvl]
    input_scores = multi_scores[:num_lvl]

    if in_dygraph_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 = core.ops.collect_fpn_proposals(
            input_rois, input_scores, rois_num_per_level, *attrs)

    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


def distribute_fpn_proposals(fpn_rois,
                             min_level,
                             max_level,
                             refer_level,
                             refer_scale,
                             rois_num=None,
                             name=None):
    """
    
    **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 = paddle.static.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)
    """
    num_lvl = max_level - min_level + 1

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

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

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

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

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

    helper.append_op(
        type='distribute_fpn_proposals',
        inputs=inputs,
        outputs=outputs,
        attrs={
            'min_level': min_level,
            'max_level': max_level,
            'refer_level': refer_level,
            'refer_scale': refer_scale
        })
    if rois_num is not None:
        return multi_rois, restore_ind, rois_num_per_level
    return multi_rois, restore_ind


def yolo_box(
        x,
        origin_shape,
        anchors,
        class_num,
        conf_thresh,
        downsample_ratio,
        clip_bbox=True,
        scale_x_y=1.,
        name=None, ):
    """

    This operator generates YOLO detection boxes from output of YOLOv3 network.
     
     The output of previous network is in shape [N, C, H, W], while H and W
     should be the same, H and W specify the grid size, each grid point predict
     given number boxes, this given number, which following will be represented as S,
     is specified by the number of anchors. In the second dimension(the channel
     dimension), C should be equal to S * (5 + class_num), class_num is the object
     category number of source dataset(such as 80 in coco dataset), so the
     second(channel) dimension, apart from 4 box location coordinates x, y, w, h,
     also includes confidence score of the box and class one-hot key of each anchor
     box.
     Assume the 4 location coordinates are :math:`t_x, t_y, t_w, t_h`, the box
     predictions should be as follows:
     $$
     b_x = \\sigma(t_x) + c_x
     $$
     $$
     b_y = \\sigma(t_y) + c_y
     $$
     $$
     b_w = p_w e^{t_w}
     $$
     $$
     b_h = p_h e^{t_h}
     $$
     in the equation above, :math:`c_x, c_y` is the left top corner of current grid
     and :math:`p_w, p_h` is specified by anchors.
     The logistic regression value of the 5th channel of each anchor prediction boxes
     represents the confidence score of each prediction box, and the logistic
     regression value of the last :attr:`class_num` channels of each anchor prediction
     boxes represents the classifcation scores. Boxes with confidence scores less than
     :attr:`conf_thresh` should be ignored, and box final scores is the product of
     confidence scores and classification scores.
     $$
     score_{pred} = score_{conf} * score_{class}
     $$

    Args:
        x (Tensor): The input tensor of YoloBox operator is a 4-D tensor with shape of [N, C, H, W].
                    The second dimension(C) stores box locations, confidence score and
                    classification one-hot keys of each anchor box. Generally, X should be the output of YOLOv3 network.
                    The data type is float32 or float64.
        origin_shape (Tensor): The image size tensor of YoloBox operator, This is a 2-D tensor with shape of [N, 2].
                    This tensor holds height and width of each input image used for resizing output box in input image
                    scale. The data type is int32.
        anchors (list|tuple): The anchor width and height, it will be parsed pair by pair.
        class_num (int): The number of classes to predict.
        conf_thresh (float): The confidence scores threshold of detection boxes. Boxes with confidence scores
                    under threshold should be ignored.
        downsample_ratio (int): The downsample ratio from network input to YoloBox operator input,
                    so 32, 16, 8 should be set for the first, second, and thrid YoloBox operators.
        clip_bbox (bool): Whether clip output bonding box in Input(ImgSize) boundary. Default true.
        scale_x_y (float): Scale the center point of decoded bounding box. Default 1.0.
        name (string): 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:
        boxes Tensor: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,  N is the batch num,
                    M is output box number, and the 3rd dimension stores [xmin, ymin, xmax, ymax] coordinates of boxes.
        scores Tensor: A 3-D tensor with shape [N, M, :attr:`class_num`], the coordinates of boxes,  N is the batch num,
                    M is output box number.
                    
    Raises:
        TypeError: Attr anchors of yolo box must be list or tuple
        TypeError: Attr class_num of yolo box must be an integer
        TypeError: Attr conf_thresh of yolo box must be a float number

    Examples:

    .. code-block:: python

        import paddle
        
        paddle.enable_static()
        x = paddle.static.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
        img_size = paddle.static.data(name='img_size',shape=[None, 2],dtype='int64')
        anchors = [10, 13, 16, 30, 33, 23]
        boxes,scores = ops.yolo_box(x=x, img_size=img_size, class_num=80, anchors=anchors,
                                        conf_thresh=0.01, downsample_ratio=32)
    """
    helper = LayerHelper('yolo_box', **locals())

    if not isinstance(anchors, list) and not isinstance(anchors, tuple):
        raise TypeError("Attr anchors of yolo_box must be list or tuple")
    if not isinstance(class_num, int):
        raise TypeError("Attr class_num of yolo_box must be an integer")
    if not isinstance(conf_thresh, float):
        raise TypeError("Attr ignore_thresh of yolo_box must be a float number")

    if in_dygraph_mode():
        attrs = ('anchors', anchors, 'class_num', class_num, 'conf_thresh',
                 conf_thresh, 'downsample_ratio', downsample_ratio, 'clip_bbox',
                 clip_bbox, 'scale_x_y', scale_x_y)
        boxes, scores = core.ops.yolo_box(x, origin_shape, *attrs)
        return boxes, scores

    boxes = helper.create_variable_for_type_inference(dtype=x.dtype)
    scores = helper.create_variable_for_type_inference(dtype=x.dtype)

    attrs = {
        "anchors": anchors,
        "class_num": class_num,
        "conf_thresh": conf_thresh,
        "downsample_ratio": downsample_ratio,
        "clip_bbox": clip_bbox,
        "scale_x_y": scale_x_y,
    }

    helper.append_op(
        type='yolo_box',
        inputs={
            "X": x,
            "ImgSize": origin_shape,
        },
        outputs={
            'Boxes': boxes,
            'Scores': scores,
        },
        attrs=attrs)
    return boxes, scores


def matrix_nms(bboxes,
               scores,
               score_threshold,
               post_threshold,
               nms_top_k,
               keep_top_k,
               use_gaussian=False,
               gaussian_sigma=2.,
               background_label=0,
               normalized=True,
               return_index=False,
               return_rois_num=True,
               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 (Tensor): 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 (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.
        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
        return_rois_num(bool): whether return rois_num. Default: True
        name(str): Name of the matrix nms op. Default: None.

    Returns:
        A tuple with three Tensor: (Out, Index, RoisNum) if return_index is True,
        otherwise, a tuple with two Tensor (Out, RoisNum) is returned.

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

        rois_num (Tensor): A 1-D Tensor with shape [N] containing 
            the number of detected boxes in each image.

    Examples:
        .. code-block:: python


            import paddle
            from ppdet.modeling import ops

            boxes = paddle.static.data(name='bboxes', shape=[None,81, 4],
                                      dtype='float32', lod_level=1)
            scores = paddle.static.data(name='scores', shape=[None,81],
                                      dtype='float32', lod_level=1)
            out = ops.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)

    """
    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')

    if in_dygraph_mode():
        attrs = ('background_label', background_label, 'score_threshold',
                 score_threshold, 'post_threshold', post_threshold, 'nms_top_k',
                 nms_top_k, 'gaussian_sigma', gaussian_sigma, 'use_gaussian',
                 use_gaussian, 'keep_top_k', keep_top_k, 'normalized',
                 normalized)
        out, index, rois_num = core.ops.matrix_nms(bboxes, scores, *attrs)
        if return_index:
            if return_rois_num:
                return out, index, rois_num
            return out, index
        if return_rois_num:
            return out, rois_num
        return out

    helper = LayerHelper('matrix_nms', **locals())
    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
    index = helper.create_variable_for_type_inference(dtype='int')
    outputs = {'Out': output, 'Index': index}
    if return_rois_num:
        rois_num = helper.create_variable_for_type_inference(dtype='int')
        outputs['RoisNum'] = rois_num

    helper.append_op(
        type="matrix_nms",
        inputs={'BBoxes': bboxes,
                'Scores': scores},
        attrs={
            'background_label': background_label,
            'score_threshold': score_threshold,
            'post_threshold': post_threshold,
            'nms_top_k': nms_top_k,
            'gaussian_sigma': gaussian_sigma,
            'use_gaussian': use_gaussian,
            'keep_top_k': keep_top_k,
            'normalized': normalized
        },
        outputs=outputs)
    output.stop_gradient = True

    if return_index:
        if return_rois_num:
            return output, index, rois_num
        return output, index
    if return_rois_num:
        return output, rois_num
    return output