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.
"""

from __future__ import print_function

from .layer_function_generator import generate_layer_fn
from .layer_function_generator import autodoc, templatedoc
from ..layer_helper import LayerHelper
from ..framework import Variable
from . import tensor
from . import nn
from . import ops
from ... import compat as cpt
import math
import six
import numpy
from functools import reduce

__all__ = [
    'prior_box',
    'density_prior_box',
    'multi_box_head',
    'bipartite_match',
    'target_assign',
    'detection_output',
    'ssd_loss',
    'detection_map',
    'rpn_target_assign',
    'anchor_generator',
    'roi_perspective_transform',
    'generate_proposal_labels',
    'generate_proposals',
    'generate_mask_labels',
    'iou_similarity',
    'box_coder',
    'polygon_box_transform',
    'yolov3_loss',
    'yolo_box',
    'box_clip',
    'multiclass_nms',
    'distribute_fpn_proposals',
    'box_decoder_and_assign',
]


def rpn_target_assign(bbox_pred,
                      cls_logits,
                      anchor_box,
                      anchor_var,
                      gt_boxes,
                      is_crowd,
                      im_info,
                      rpn_batch_size_per_im=256,
                      rpn_straddle_thresh=0.0,
                      rpn_fg_fraction=0.5,
                      rpn_positive_overlap=0.7,
                      rpn_negative_overlap=0.3,
                      use_random=True):
    """
    **Target Assign Layer for region proposal network (RPN) in Faster-RCNN detection.**

    This layer can be, for given the  Intersection-over-Union (IoU) overlap
    between anchors and ground truth boxes, to assign classification and
    regression targets to each each anchor, these target labels are used for
    train RPN. The classification targets is a binary class label (of being
    an object or not). Following the paper of Faster-RCNN, the positive labels
    are two kinds of anchors: (i) the anchor/anchors with the highest IoU
    overlap with a ground-truth box, or (ii) an anchor that has an IoU overlap
    higher than rpn_positive_overlap(0.7) with any ground-truth box. Note
    that a single ground-truth box may assign positive labels to multiple
    anchors. A non-positive anchor is when its IoU ratio is lower than
    rpn_negative_overlap (0.3) for all ground-truth boxes. Anchors that are
    neither positive nor negative do not contribute to the training objective.
    The regression targets are the encoded ground-truth boxes associated with
    the positive anchors.

    Args:
        bbox_pred(Variable): A 3-D Tensor with shape [N, M, 4] represents the
            predicted locations of M bounding bboxes. N is the batch size,
            and each bounding box has four coordinate values and the layout
            is [xmin, ymin, xmax, ymax].
        cls_logits(Variable): A 3-D Tensor with shape [N, M, 1] represents the
            predicted confidence predictions. N is the batch size, 1 is the
            frontground and background sigmoid, M is number of bounding boxes.
        anchor_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
            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.
        anchor_var(Variable): A 2-D Tensor with shape [M,4] holds expanded 
            variances of anchors.
        gt_boxes (Variable): The ground-truth boudding boxes (bboxes) are a 2D
            LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
            bboxes of mini-batch input.
        is_crowd (Variable): A 1-D LoDTensor which indicates groud-truth is crowd.
        im_info (Variable): A 2-D LoDTensor with shape [N, 3]. N is the batch size,
        3 is the height, width and scale.
        rpn_batch_size_per_im(int): Total number of RPN examples per image.
        rpn_straddle_thresh(float): Remove RPN anchors that go outside the image
            by straddle_thresh pixels.
        rpn_fg_fraction(float): Target fraction of RoI minibatch that is labeled
            foreground (i.e. class > 0), 0-th class is background.
        rpn_positive_overlap(float): Minimum overlap required between an anchor
            and ground-truth box for the (anchor, gt box) pair to be a positive
            example.
        rpn_negative_overlap(float): Maximum overlap allowed between an anchor
            and ground-truth box for the (anchor, gt box) pair to be a negative
            examples.

    Returns:
        tuple:
               A tuple(predicted_scores, predicted_location, target_label,
               target_bbox, bbox_inside_weight) is returned. The predicted_scores 
               and predicted_location is the predicted result of the RPN.
               The target_label and target_bbox is the ground truth,
               respectively. The predicted_location is a 2D Tensor with shape
               [F, 4], and the shape of target_bbox is same as the shape of
               the predicted_location, F is the number of the foreground
               anchors. The predicted_scores is a 2D Tensor with shape
               [F + B, 1], and the shape of target_label is same as the shape
               of the predicted_scores, B is the number of the background
               anchors, the F and B is depends on the input of this operator.
               Bbox_inside_weight represents whether the predicted loc is fake_fg
               or not and the shape is [F, 4].

    Examples:
        .. code-block:: python

            bbox_pred = layers.data(name='bbox_pred', shape=[100, 4],
                              append_batch_size=False, dtype='float32')
            cls_logits = layers.data(name='cls_logits', shape=[100, 1],
                              append_batch_size=False, dtype='float32')
            anchor_box = layers.data(name='anchor_box', shape=[20, 4],
                              append_batch_size=False, dtype='float32')
            gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
                             append_batch_size=False, dtype='float32')
            loc_pred, score_pred, loc_target, score_target, bbox_inside_weight =
                fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
                                              cls_logits=cls_logits,
                                              anchor_box=anchor_box,
                                              gt_boxes=gt_boxes)

    """

    helper = LayerHelper('rpn_target_assign', **locals())
    # Assign target label to anchors
    loc_index = helper.create_variable_for_type_inference(dtype='int32')
    score_index = helper.create_variable_for_type_inference(dtype='int32')
    target_label = helper.create_variable_for_type_inference(dtype='int32')
    target_bbox = helper.create_variable_for_type_inference(
        dtype=anchor_box.dtype)
    bbox_inside_weight = helper.create_variable_for_type_inference(
        dtype=anchor_box.dtype)
    helper.append_op(
        type="rpn_target_assign",
        inputs={
            'Anchor': anchor_box,
            'GtBoxes': gt_boxes,
            'IsCrowd': is_crowd,
            'ImInfo': im_info
        },
        outputs={
            'LocationIndex': loc_index,
            'ScoreIndex': score_index,
            'TargetLabel': target_label,
            'TargetBBox': target_bbox,
            'BBoxInsideWeight': bbox_inside_weight
        },
        attrs={
            'rpn_batch_size_per_im': rpn_batch_size_per_im,
            'rpn_straddle_thresh': rpn_straddle_thresh,
            'rpn_positive_overlap': rpn_positive_overlap,
            'rpn_negative_overlap': rpn_negative_overlap,
            'rpn_fg_fraction': rpn_fg_fraction,
            'use_random': use_random
        })

    loc_index.stop_gradient = True
    score_index.stop_gradient = True
    target_label.stop_gradient = True
    target_bbox.stop_gradient = True
    bbox_inside_weight.stop_gradient = True

    cls_logits = nn.reshape(x=cls_logits, shape=(-1, 1))
    bbox_pred = nn.reshape(x=bbox_pred, shape=(-1, 4))
    predicted_cls_logits = nn.gather(cls_logits, score_index)
    predicted_bbox_pred = nn.gather(bbox_pred, loc_index)

    return predicted_cls_logits, predicted_bbox_pred, target_label, target_bbox, bbox_inside_weight


def detection_output(loc,
                     scores,
                     prior_box,
                     prior_box_var,
                     background_label=0,
                     nms_threshold=0.3,
                     nms_top_k=400,
                     keep_top_k=200,
                     score_threshold=0.01,
                     nms_eta=1.0):
    """
    **Detection Output Layer for Single Shot Multibox Detector (SSD).**

    This operation is to get the detection results by performing following
    two steps:

    1. Decode input bounding box predictions according to the prior boxes.
    2. Get the final detection results by applying multi-class non maximum
       suppression (NMS).

    Please note, this operation doesn't clip the final output bounding boxes
    to the image window.

    Args:
        loc(Variable): A 3-D Tensor with shape [N, M, 4] represents the
            predicted locations of M bounding bboxes. N is the batch size,
            and each bounding box has four coordinate values and the layout
            is [xmin, ymin, xmax, ymax].
        scores(Variable): A 3-D Tensor with shape [N, M, C] 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.
        prior_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
            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(Variable): A 2-D Tensor with shape [M, 4] holds M group
            of variance.
        background_label(float): The index of background label,
            the background label will be ignored. If set to -1, then all
            categories will be considered.
        nms_threshold(float): The threshold to be used in NMS.
        nms_top_k(int): Maximum number of detections to be kept according
            to the confidences aftern 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.
        score_threshold(float): Threshold to filter out bounding boxes with
            low confidence score. If not provided, consider all boxes.
        nms_eta(float): The parameter for adaptive NMS.

    Returns:
        Variable:

            The detection outputs is a LoDTensor with shape [No, 6].
            Each row has six values: [label, confidence, xmin, ymin, xmax, ymax].
            `No` is the total number of detections in this mini-batch. For each
            instance, the offsets in first dimension are called LoD, the offset
            number is N + 1, N is the batch size. The i-th image has
            `LoD[i + 1] - LoD[i]` detected results, if it is 0, the i-th image
            has no detected results. If all images have not detected results,
            LoD will be set to {1}, and output tensor 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

            pb = layers.data(name='prior_box', shape=[10, 4],
                         append_batch_size=False, dtype='float32')
            pbv = layers.data(name='prior_box_var', shape=[10, 4],
                          append_batch_size=False, dtype='float32')
            loc = layers.data(name='target_box', shape=[2, 21, 4],
                          append_batch_size=False, dtype='float32')
            scores = layers.data(name='scores', shape=[2, 21, 10],
                          append_batch_size=False, dtype='float32')
            nmsed_outs = fluid.layers.detection_output(scores=scores,
                                       loc=loc,
                                       prior_box=pb,
                                       prior_box_var=pbv)
    """
    helper = LayerHelper("detection_output", **locals())
    decoded_box = box_coder(
        prior_box=prior_box,
        prior_box_var=prior_box_var,
        target_box=loc,
        code_type='decode_center_size')
    scores = nn.softmax(input=scores)
    scores = nn.transpose(scores, perm=[0, 2, 1])
    scores.stop_gradient = True
    nmsed_outs = helper.create_variable_for_type_inference(
        dtype=decoded_box.dtype)
    helper.append_op(
        type="multiclass_nms",
        inputs={'Scores': scores,
                'BBoxes': decoded_box},
        outputs={'Out': nmsed_outs},
        attrs={
            'background_label': 0,
            'nms_threshold': nms_threshold,
            'nms_top_k': nms_top_k,
            'keep_top_k': keep_top_k,
            'score_threshold': score_threshold,
            'nms_eta': 1.0
        })
    nmsed_outs.stop_gradient = True
    return nmsed_outs


@templatedoc()
def iou_similarity(x, y, name=None):
    """
    ${comment}

    Args:
        x(${x_type}): ${x_comment}
        y(${y_type}): ${y_comment}

    Returns:
        out(${out_type}): ${out_comment}
    """
    helper = LayerHelper("iou_similarity", **locals())
    if name is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

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


@templatedoc()
def box_coder(prior_box,
              prior_box_var,
              target_box,
              code_type="encode_center_size",
              box_normalized=True,
              name=None,
              axis=0):
    """
    **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, 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(Variable|list|None): prior_box_var supports two types 
                              of input. One is variable with shape [M, 4] 
                              holds M group. The other one is list consist of 
                              4 elements shared by all boxes. 
        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]. This tensor can 
                              contain LoD information to represent a batch 
                              of inputs. 
        code_type(string): The code type used with the target box. It can be
                           encode_center_size or decode_center_size
        box_normalized(int): Whether treat the priorbox as a noramlized box.
                             Set true by default.
        name(string): The name of box coder.
        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:
        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 deocded boxes.

    Examples:
 
        .. code-block:: python
 
            prior_box = fluid.layers.data(name='prior_box', 
                                          shape=[512, 4], 
                                          dtype='float32',
                                          append_batch_size=False)
            target_box = fluid.layers.data(name='target_box',
                                           shape=[512,81,4],
                                           dtype='float32',
                                           append_batch_size=False)
            output = fluid.layers.box_coder(prior_box=prior_box,
                                            prior_box_var=[0.1,0.1,0.2,0.2],
                                            target_box=target_box,
                                            code_type="decode_center_size",
                                            box_normalized=False,
                                            axis=1)

    """
    helper = LayerHelper("box_coder", **locals())

    if name is None:
        output_box = helper.create_variable_for_type_inference(
            dtype=prior_box.dtype)
    else:
        output_box = helper.create_variable(
            name=name, dtype=prior_box.dtype, persistable=False)

    inputs = {"PriorBox": prior_box, "TargetBox": target_box}
    attrs = {
        "code_type": code_type,
        "box_normalized": box_normalized,
        "axis": axis
    }
    if isinstance(prior_box_var, Variable):
        inputs['PriorBoxVar'] = prior_box_var
    elif isinstance(prior_box_var, list):
        attrs['variance'] = prior_box_var
    else:
        raise TypeError("Input variance of box_coder must be Variable or lisz")
    helper.append_op(
        type="box_coder",
        inputs=inputs,
        attrs=attrs,
        outputs={"OutputBox": output_box})
    return output_box


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

    Args:
        input(${input_type}): ${input_comment}

    Returns:
        output(${output_type}): ${output_comment}
    """
    helper = LayerHelper("polygon_box_transform", **locals())
    if name is None:
        output = helper.create_variable_for_type_inference(dtype=input.dtype)
    else:
        output = helper.create_variable(
            name=name, dtype=prior_box.input, persistable=False)

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


@templatedoc(op_type="yolov3_loss")
def yolov3_loss(x,
                gtbox,
                gtlabel,
                anchors,
                anchor_mask,
                class_num,
                ignore_thresh,
                downsample_ratio,
                gtscore=None,
                use_label_smooth=True,
                name=None):
    """
    ${comment}

    Args:
        x (Variable): ${x_comment}
        gtbox (Variable): groud truth boxes, should be in shape of [N, B, 4],
                          in the third dimenstion, x, y, w, h should be stored 
                          and x, y, w, h should be relative value of input image.
                          N is the batch number and B is the max box number in 
                          an image.
        gtlabel (Variable): class id of ground truth boxes, shoud be in shape
                            of [N, B].
        anchors (list|tuple): ${anchors_comment}
        anchor_mask (list|tuple): ${anchor_mask_comment}
        class_num (int): ${class_num_comment}
        ignore_thresh (float): ${ignore_thresh_comment}
        downsample_ratio (int): ${downsample_ratio_comment}
        name (string): the name of yolov3 loss. Default None.
        gtscore (Variable): mixup score of ground truth boxes, shoud be in shape
                            of [N, B]. Default None.
        use_label_smooth (bool): ${use_label_smooth_comment}

    Returns:
        Variable: A 1-D tensor with shape [N], the value of yolov3 loss

    Raises:
        TypeError: Input x of yolov3_loss must be Variable
        TypeError: Input gtbox of yolov3_loss must be Variable
        TypeError: Input gtlabel of yolov3_loss must be Variable
        TypeError: Input gtscore of yolov3_loss must be None or Variable
        TypeError: Attr anchors of yolov3_loss must be list or tuple
        TypeError: Attr class_num of yolov3_loss must be an integer
        TypeError: Attr ignore_thresh of yolov3_loss must be a float number
        TypeError: Attr use_label_smooth of yolov3_loss must be a bool value

    Examples:
      .. code-block:: python

          x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
          gtbox = fluid.layers.data(name='gtbox', shape=[6, 4], dtype='float32')
          gtlabel = fluid.layers.data(name='gtlabel', shape=[6], dtype='int32')
          gtscore = fluid.layers.data(name='gtscore', shape=[6], dtype='float32')
          anchors = [10, 13, 16, 30, 33, 23, 30, 61, 62, 45, 59, 119, 116, 90, 156, 198, 373, 326]
          anchor_mask = [0, 1, 2]
          loss = fluid.layers.yolov3_loss(x=x, gtbox=gtbox, gtlabel=gtlabel,
                                          gtscore=gtscore, anchors=anchors, 
                                          anchor_mask=anchor_mask, class_num=80,
                                          ignore_thresh=0.7, downsample_ratio=32)
    """
    helper = LayerHelper('yolov3_loss', **locals())

    if not isinstance(x, Variable):
        raise TypeError("Input x of yolov3_loss must be Variable")
    if not isinstance(gtbox, Variable):
        raise TypeError("Input gtbox of yolov3_loss must be Variable")
    if not isinstance(gtlabel, Variable):
        raise TypeError("Input gtlabel of yolov3_loss must be Variable")
    if gtscore is not None and not isinstance(gtscore, Variable):
        raise TypeError("Input gtscore of yolov3_loss must be Variable")
    if not isinstance(anchors, list) and not isinstance(anchors, tuple):
        raise TypeError("Attr anchors of yolov3_loss must be list or tuple")
    if not isinstance(anchor_mask, list) and not isinstance(anchor_mask, tuple):
        raise TypeError("Attr anchor_mask of yolov3_loss must be list or tuple")
    if not isinstance(class_num, int):
        raise TypeError("Attr class_num of yolov3_loss must be an integer")
    if not isinstance(ignore_thresh, float):
        raise TypeError(
            "Attr ignore_thresh of yolov3_loss must be a float number")
    if not isinstance(use_label_smooth, bool):
        raise TypeError(
            "Attr use_label_smooth of yolov3_loss must be a bool value")

    if name is None:
        loss = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
        loss = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False)

    objectness_mask = helper.create_variable_for_type_inference(dtype='int32')
    gt_match_mask = helper.create_variable_for_type_inference(dtype='int32')

    inputs = {
        "X": x,
        "GTBox": gtbox,
        "GTLabel": gtlabel,
    }
    if gtscore:
        inputs["GTScore"] = gtscore

    attrs = {
        "anchors": anchors,
        "anchor_mask": anchor_mask,
        "class_num": class_num,
        "ignore_thresh": ignore_thresh,
        "downsample_ratio": downsample_ratio,
        "use_label_smooth": use_label_smooth,
    }

    helper.append_op(
        type='yolov3_loss',
        inputs=inputs,
        outputs={
            'Loss': loss,
            'ObjectnessMask': objectness_mask,
            'GTMatchMask': gt_match_mask
        },
        attrs=attrs)
    return loss


@templatedoc(op_type="yolo_box")
def yolo_box(x,
             img_size,
             anchors,
             class_num,
             conf_thresh,
             downsample_ratio,
             name=None):
    """
    ${comment}

    Args:
        x (Variable): ${x_comment}
        img_size (Variable): ${img_size_comment}
        anchors (list|tuple): ${anchors_comment}
        class_num (int): ${class_num_comment}
        conf_thresh (float): ${conf_thresh_comment}
        downsample_ratio (int): ${downsample_ratio_comment}
        name (string): the name of yolo box layer. Default None.

    Returns:
        Variable: A 3-D tensor with shape [N, M, 4], the coordinates of boxes,
        and a 3-D tensor with shape [N, M, :attr:`class_num`], the classification 
        scores of boxes.

    Raises:
        TypeError: Input x of yolov_box must be Variable
        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

        x = fluid.layers.data(name='x', shape=[255, 13, 13], dtype='float32')
        anchors = [10, 13, 16, 30, 33, 23]
        loss = fluid.layers.yolo_box(x=x, class_num=80, anchors=anchors, 
                                        conf_thresh=0.01, downsample_ratio=32)
    """
    helper = LayerHelper('yolo_box', **locals())

    if not isinstance(x, Variable):
        raise TypeError("Input x of yolo_box must be Variable")
    if not isinstance(img_size, Variable):
        raise TypeError("Input img_size of yolo_box must be Variable")
    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")

    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,
    }

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


@templatedoc()
def detection_map(detect_res,
                  label,
                  class_num,
                  background_label=0,
                  overlap_threshold=0.3,
                  evaluate_difficult=True,
                  has_state=None,
                  input_states=None,
                  out_states=None,
                  ap_version='integral'):
    """
    ${comment}

    Args:
        detect_res: ${detect_res_comment}
        label:  ${label_comment}
        class_num: ${class_num_comment}
        background_label: ${background_label_comment}
        overlap_threshold: ${overlap_threshold_comment}
        evaluate_difficult: ${evaluate_difficult_comment}
        has_state: ${has_state_comment}
        input_states: If not None, It contains 3 elements:
            1. pos_count ${pos_count_comment}.
            2. true_pos ${true_pos_comment}.
            3. false_pos ${false_pos_comment}.
        out_states: If not None, it contains 3 elements.
            1. accum_pos_count ${accum_pos_count_comment}.
            2. accum_true_pos ${accum_true_pos_comment}.
            3. accum_false_pos ${accum_false_pos_comment}.
        ap_version: ${ap_type_comment}

    Returns:
        ${map_comment}


    Examples:
          .. code-block:: python

            detect_res = fluid.layers.data(
                name='detect_res',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32')
            label = fluid.layers.data(
                name='label',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32')

            map_out = fluid.layers.detection_map(detect_res, label, 21)
    """
    helper = LayerHelper("detection_map", **locals())

    def __create_var(type):
        return helper.create_variable_for_type_inference(dtype=type)

    map_out = __create_var('float32')
    accum_pos_count_out = out_states[0] if out_states else __create_var('int32')
    accum_true_pos_out = out_states[1] if out_states else __create_var(
        'float32')
    accum_false_pos_out = out_states[2] if out_states else __create_var(
        'float32')

    pos_count = input_states[0] if input_states else None
    true_pos = input_states[1] if input_states else None
    false_pos = input_states[2] if input_states else None

    helper.append_op(
        type="detection_map",
        inputs={
            'Label': label,
            'DetectRes': detect_res,
            'HasState': has_state,
            'PosCount': pos_count,
            'TruePos': true_pos,
            'FalsePos': false_pos
        },
        outputs={
            'MAP': map_out,
            'AccumPosCount': accum_pos_count_out,
            'AccumTruePos': accum_true_pos_out,
            'AccumFalsePos': accum_false_pos_out
        },
        attrs={
            'overlap_threshold': overlap_threshold,
            'evaluate_difficult': evaluate_difficult,
            'ap_type': ap_version,
            'class_num': class_num,
        })
    return map_out


def bipartite_match(dist_matrix,
                    match_type=None,
                    dist_threshold=None,
                    name=None):
    """
    This operator implements a greedy bipartite matching algorithm, which is
    used to obtain the matching with the maximum distance based on the input
    distance matrix. For input 2D matrix, the bipartite matching algorithm can
    find the matched column for each row (matched means the largest distance),
    also can find the matched row for each column. And this operator only
    calculate matched indices from column to row. For each instance,
    the number of matched indices is the column number of the input distance
    matrix.

    There are two outputs, matched indices and distance.
    A simple description, this algorithm matched the best (maximum distance)
    row entity to the column entity and the matched indices are not duplicated
    in each row of ColToRowMatchIndices. If the column entity is not matched
    any row entity, set -1 in ColToRowMatchIndices.

    NOTE: the input DistMat can be LoDTensor (with LoD) or Tensor.
    If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
    If Tensor, the height of ColToRowMatchIndices is 1.

    NOTE: This API is a very low level API. It is used by :code:`ssd_loss`
    layer. Please consider to use :code:`ssd_loss` instead.

    Args:
        dist_matrix(Variable): This input is a 2-D LoDTensor with shape
            [K, M]. It is pair-wise distance matrix between the entities
            represented by each row and each column. For example, assumed one
            entity is A with shape [K], another entity is B with shape [M]. The
            dist_matrix[i][j] is the distance between A[i] and B[j]. The bigger
            the distance is, the better matching the pairs are.

            NOTE: This tensor can contain LoD information to represent a batch
            of inputs. One instance of this batch can contain different numbers
            of entities.
        match_type(string|None): The type of matching method, should be
           'bipartite' or 'per_prediction'. [default 'bipartite'].
        dist_threshold(float|None): If `match_type` is 'per_prediction',
            this threshold is to determine the extra matching bboxes based
            on the maximum distance, 0.5 by default.
    Returns:
        tuple: a tuple with two elements is returned. The first is
        matched_indices, the second is matched_distance.

        The matched_indices is a 2-D Tensor with shape [N, M] in int type.
        N is the batch size. If match_indices[i][j] is -1, it
        means B[j] does not match any entity in i-th instance.
        Otherwise, it means B[j] is matched to row
        match_indices[i][j] in i-th instance. The row number of
        i-th instance is saved in match_indices[i][j].

        The matched_distance is a 2-D Tensor with shape [N, M] in float type
        . N is batch size. If match_indices[i][j] is -1,
        match_distance[i][j] is also -1.0. Otherwise, assumed
        match_distance[i][j] = d, and the row offsets of each instance
        are called LoD. Then match_distance[i][j] =
        dist_matrix[d+LoD[i]][j].

    Examples:

        >>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
        >>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
        >>> iou = fluid.layers.iou_similarity(x=x, y=y)
        >>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
    """
    helper = LayerHelper('bipartite_match', **locals())
    match_indices = helper.create_variable_for_type_inference(dtype='int32')
    match_distance = helper.create_variable_for_type_inference(
        dtype=dist_matrix.dtype)
    helper.append_op(
        type='bipartite_match',
        inputs={'DistMat': dist_matrix},
        attrs={
            'match_type': match_type,
            'dist_threshold': dist_threshold,
        },
        outputs={
            'ColToRowMatchIndices': match_indices,
            'ColToRowMatchDist': match_distance
        })
    return match_indices, match_distance


def target_assign(input,
                  matched_indices,
                  negative_indices=None,
                  mismatch_value=None,
                  name=None):
    """
    This operator can be, for given the target bounding boxes or labels,
    to assign classification and regression targets to each prediction as well as
    weights to prediction. The weights is used to specify which prediction would
    not contribute to training loss.

    For each instance, the output `out` and`out_weight` are assigned based on
    `match_indices` and `negative_indices`.
    Assumed that the row offset for each instance in `input` is called lod,
    this operator assigns classification/regression targets by performing the
    following steps:

    1. Assigning all outpts based on `match_indices`:

    .. code-block:: text

        If id = match_indices[i][j] > 0,

            out[i][j][0 : K] = X[lod[i] + id][j % P][0 : K]
            out_weight[i][j] = 1.

        Otherwise,

            out[j][j][0 : K] = {mismatch_value, mismatch_value, ...}
            out_weight[i][j] = 0.

    2. Assigning out_weight based on `neg_indices` if `neg_indices` is provided:

    Assumed that the row offset for each instance in `neg_indices` is called neg_lod,
    for i-th instance and each `id` of neg_indices in this instance:

    .. code-block:: text

        out[i][id][0 : K] = {mismatch_value, mismatch_value, ...}
        out_weight[i][id] = 1.0

    Args:
       inputs (Variable): This input is a 3D LoDTensor with shape [M, P, K].
       matched_indices (Variable): Tensor<int>), The input matched indices
           is 2D Tenosr<int32> with shape [N, P], If MatchIndices[i][j] is -1,
           the j-th entity of column is not matched to any entity of row in
           i-th instance.
       negative_indices (Variable): The input negative example indices are
           an optional input with shape [Neg, 1] and int32 type, where Neg is
           the total number of negative example indices.
       mismatch_value (float32): Fill this value to the mismatched location.

    Returns:
        tuple:
               A tuple(out, out_weight) is returned. out is a 3D Tensor with
               shape [N, P, K], N and P is the same as they are in
               `neg_indices`, K is the same as it in input of X. If
               `match_indices[i][j]`. out_weight is the weight for output with
               the shape of [N, P, 1].

    Examples:

        .. code-block:: python

            matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
            gt = layers.data(
                        name='gt', shape=[1, 1], dtype='int32', lod_level=1)
            trg, trg_weight = layers.target_assign(
                            gt, matched_indices, mismatch_value=0)
    """
    helper = LayerHelper('target_assign', **locals())
    out = helper.create_variable_for_type_inference(dtype=input.dtype)
    out_weight = helper.create_variable_for_type_inference(dtype='float32')
    helper.append_op(
        type='target_assign',
        inputs={
            'X': input,
            'MatchIndices': matched_indices,
            'NegIndices': negative_indices
        },
        outputs={'Out': out,
                 'OutWeight': out_weight},
        attrs={'mismatch_value': mismatch_value})
    return out, out_weight


def ssd_loss(location,
             confidence,
             gt_box,
             gt_label,
             prior_box,
             prior_box_var=None,
             background_label=0,
             overlap_threshold=0.5,
             neg_pos_ratio=3.0,
             neg_overlap=0.5,
             loc_loss_weight=1.0,
             conf_loss_weight=1.0,
             match_type='per_prediction',
             mining_type='max_negative',
             normalize=True,
             sample_size=None):
    """
    **Multi-box loss layer for object detection algorithm of SSD**

    This layer is to compute dection loss for SSD given the location offset
    predictions, confidence predictions, prior boxes and ground-truth boudding
    boxes and labels, and the type of hard example mining. The returned loss
    is a weighted sum of the localization loss (or regression loss) and
    confidence loss (or classification loss) by performing the following steps:

    1. Find matched bounding box by bipartite matching algorithm.

      1.1 Compute IOU similarity between ground-truth boxes and prior boxes.

      1.2 Compute matched boundding box by bipartite matching algorithm.

    2. Compute confidence for mining hard examples

      2.1. Get the target label based on matched indices.

      2.2. Compute confidence loss.

    3. Apply hard example mining to get the negative example indices and update
       the matched indices.

    4. Assign classification and regression targets

      4.1. Encoded bbox according to the prior boxes.

      4.2. Assign regression targets.

      4.3. Assign classification targets.

    5. Compute the overall objective loss.

      5.1 Compute confidence loss.

      5.1 Compute localization loss.

      5.3 Compute the overall weighted loss.

    Args:
        location (Variable): The location predictions are a 3D Tensor with
            shape [N, Np, 4], N is the batch size, Np is total number of
            predictions for each instance. 4 is the number of coordinate values,
            the layout is [xmin, ymin, xmax, ymax].
        confidence (Variable): The confidence predictions are a 3D Tensor
            with shape [N, Np, C], N and Np are the same as they are in
            `location`, C is the class number.
        gt_box (Variable): The ground-truth boudding boxes (bboxes) are a 2D
            LoDTensor with shape [Ng, 4], Ng is the total number of ground-truth
            bboxes of mini-batch input.
        gt_label (Variable): The ground-truth labels are a 2D LoDTensor
            with shape [Ng, 1].
        prior_box (Variable): The prior boxes are a 2D Tensor with shape [Np, 4].
        prior_box_var (Variable): The variance of prior boxes are a 2D Tensor
            with shape [Np, 4].
        background_label (int): The index of background label, 0 by default.
        overlap_threshold (float): If match_type is 'per_prediction', use
            `overlap_threshold` to determine the extra matching bboxes when
             finding matched boxes. 0.5 by default.
        neg_pos_ratio (float): The ratio of the negative boxes to the positive
            boxes, used only when mining_type is 'max_negative', 3.0 by defalut.
        neg_overlap (float): The negative overlap upper bound for the unmatched
            predictions. Use only when mining_type is 'max_negative',
            0.5 by default.
        loc_loss_weight (float): Weight for localization loss, 1.0 by default.
        conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
        match_type (str): The type of matching method during training, should
            be 'bipartite' or 'per_prediction', 'per_prediction' by defalut.
        mining_type (str): The hard example mining type, should be 'hard_example'
            or 'max_negative', now only support `max_negative`.
        normalize (bool): Whether to normalize the SSD loss by the total number
            of output locations, True by default.
        sample_size (int): The max sample size of negative box, used only when
            mining_type is 'hard_example'.

    Returns:
        The weighted sum of the localization loss and confidence loss, with \
        shape [N * Np, 1], N and Np are the same as they are in `location`.

    Raises:
        ValueError: If mining_type is 'hard_example', now only support mining \
        type of `max_negative`.

    Examples:
        >>> pb = fluid.layers.data(
        >>>                   name='prior_box',
        >>>                   shape=[10, 4],
        >>>                   append_batch_size=False,
        >>>                   dtype='float32')
        >>> pbv = fluid.layers.data(
        >>>                   name='prior_box_var',
        >>>                   shape=[10, 4],
        >>>                   append_batch_size=False,
        >>>                   dtype='float32')
        >>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
        >>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
        >>> gt_box = fluid.layers.data(
        >>>         name='gt_box', shape=[4], lod_level=1, dtype='float32')
        >>> gt_label = fluid.layers.data(
        >>>         name='gt_label', shape=[1], lod_level=1, dtype='float32')
        >>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
    """

    helper = LayerHelper('ssd_loss', **locals())
    if mining_type != 'max_negative':
        raise ValueError("Only support mining_type == max_negative now.")

    num, num_prior, num_class = confidence.shape
    conf_shape = nn.shape(confidence)

    def __reshape_to_2d(var):
        return nn.flatten(x=var, axis=2)

    # 1. Find matched boundding box by prior box.
    #   1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
    iou = iou_similarity(x=gt_box, y=prior_box)
    #   1.2 Compute matched boundding box by bipartite matching algorithm.
    matched_indices, matched_dist = bipartite_match(iou, match_type,
                                                    overlap_threshold)

    # 2. Compute confidence for mining hard examples
    # 2.1. Get the target label based on matched indices
    gt_label = nn.reshape(
        x=gt_label, shape=(len(gt_label.shape) - 1) * (0, ) + (-1, 1))
    gt_label.stop_gradient = True
    target_label, _ = target_assign(
        gt_label, matched_indices, mismatch_value=background_label)
    # 2.2. Compute confidence loss.
    # Reshape confidence to 2D tensor.
    confidence = __reshape_to_2d(confidence)
    target_label = tensor.cast(x=target_label, dtype='int64')
    target_label = __reshape_to_2d(target_label)
    target_label.stop_gradient = True
    conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
    # 3. Mining hard examples
    actual_shape = nn.slice(conf_shape, axes=[0], starts=[0], ends=[2])
    actual_shape.stop_gradient = True
    conf_loss = nn.reshape(
        x=conf_loss, shape=(num, num_prior), actual_shape=actual_shape)
    conf_loss.stop_gradient = True
    neg_indices = helper.create_variable_for_type_inference(dtype='int32')
    dtype = matched_indices.dtype
    updated_matched_indices = helper.create_variable_for_type_inference(
        dtype=dtype)
    helper.append_op(
        type='mine_hard_examples',
        inputs={
            'ClsLoss': conf_loss,
            'LocLoss': None,
            'MatchIndices': matched_indices,
            'MatchDist': matched_dist,
        },
        outputs={
            'NegIndices': neg_indices,
            'UpdatedMatchIndices': updated_matched_indices
        },
        attrs={
            'neg_pos_ratio': neg_pos_ratio,
            'neg_dist_threshold': neg_overlap,
            'mining_type': mining_type,
            'sample_size': sample_size,
        })

    # 4. Assign classification and regression targets
    # 4.1. Encoded bbox according to the prior boxes.
    encoded_bbox = box_coder(
        prior_box=prior_box,
        prior_box_var=prior_box_var,
        target_box=gt_box,
        code_type='encode_center_size')
    # 4.2. Assign regression targets
    target_bbox, target_loc_weight = target_assign(
        encoded_bbox, updated_matched_indices, mismatch_value=background_label)
    # 4.3. Assign classification targets
    target_label, target_conf_weight = target_assign(
        gt_label,
        updated_matched_indices,
        negative_indices=neg_indices,
        mismatch_value=background_label)

    # 5. Compute loss.
    # 5.1 Compute confidence loss.
    target_label = __reshape_to_2d(target_label)
    target_label = tensor.cast(x=target_label, dtype='int64')

    conf_loss = nn.softmax_with_cross_entropy(confidence, target_label)
    target_conf_weight = __reshape_to_2d(target_conf_weight)
    conf_loss = conf_loss * target_conf_weight

    # the target_label and target_conf_weight do not have gradient.
    target_label.stop_gradient = True
    target_conf_weight.stop_gradient = True

    # 5.2 Compute regression loss.
    location = __reshape_to_2d(location)
    target_bbox = __reshape_to_2d(target_bbox)

    loc_loss = nn.smooth_l1(location, target_bbox)
    target_loc_weight = __reshape_to_2d(target_loc_weight)
    loc_loss = loc_loss * target_loc_weight

    # the target_bbox and target_loc_weight do not have gradient.
    target_bbox.stop_gradient = True
    target_loc_weight.stop_gradient = True

    # 5.3 Compute overall weighted loss.
    loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss
    # reshape to [N, Np], N is the batch size and Np is the prior box number.
    loss = nn.reshape(x=loss, shape=(num, num_prior), actual_shape=actual_shape)
    loss = nn.reduce_sum(loss, dim=1, keep_dim=True)
    if normalize:
        normalizer = nn.reduce_sum(target_loc_weight)
        loss = loss / normalizer

    return loss


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

    Generate 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.

    Args:
       input(Variable): The Input Variables, the format is NCHW.
       image(Variable): The input image data of PriorBoxOp,
            the layout is NCHW.
       min_sizes(list|tuple|float value): min sizes of generated prior boxes.
       max_sizes(list|tuple|None): max sizes of generated prior boxes.
            Default: None.
       aspect_ratios(list|tuple|float value): 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|turple): Prior boxes step across width and height, If
            step[0] == 0.0/step[1] == 0.0, the prior boxes step across
            height/weight of the input will be automatically calculated.
            Default: [0., 0.]
       offset(float): Prior boxes center offset. Default: 0.5
       name(str): Name of the prior box op. Default: None.
       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 two Variable (boxes, variances)

        boxes: the output prior boxes of PriorBox.
        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: the expanded variances of PriorBox.
        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


    Examples:
        .. code-block:: python

            box, var = fluid.layers.prior_box(
                input=conv1,
                image=images,
                min_sizes=[100.],
                flip=True,
                clip=True)
    """
    helper = LayerHelper("prior_box", **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_(min_sizes):
        min_sizes = [min_sizes]
    if not _is_list_or_tuple_(aspect_ratios):
        aspect_ratios = [aspect_ratios]
    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).')

    min_sizes = list(map(float, min_sizes))
    aspect_ratios = list(map(float, aspect_ratios))
    steps = list(map(float, steps))

    attrs = {
        'min_sizes': min_sizes,
        'aspect_ratios': aspect_ratios,
        'variances': variance,
        'flip': flip,
        'clip': clip,
        'step_w': steps[0],
        'step_h': steps[1],
        'offset': offset,
        'min_max_aspect_ratios_order': min_max_aspect_ratios_order
    }
    if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
        if not _is_list_or_tuple_(max_sizes):
            max_sizes = [max_sizes]
        attrs['max_sizes'] = max_sizes

    box = helper.create_variable_for_type_inference(dtype)
    var = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="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


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):
    """
    **Density Prior Box Operator**

    Generate 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:
    N_density_prior_box =sum(N_fixed_ratios * densities_i^2),

    Args:
       input(Variable): The Input Variables, the format is NCHW.
       image(Variable): The input image data of PriorBoxOp,
            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|turple): Prior boxes step across width and height, If
            step[0] == 0.0/step[1] == 0.0, the density prior boxes step across
            height/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): Name of the density prior box op. Default: None.

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

        boxes: the output density prior boxes of PriorBox.
            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
            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,
            num_priors is the total box count of each position of input.

        variances: the expanded variances of PriorBox.
            The layout is [H, W, num_priors, 4] when flatten_to_2d is False.
            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
            num_priors is the total box count of each position of input.


    Examples:
        .. code-block:: python

            box, var = fluid.layers.density_prior_box(
                input=conv1,
                image=images,
                densities=[4, 2, 1],
                fixed_sizes=[32.0, 64.0, 128.0],
                fixed_ratios=[1.],
                clip=True,
                flatten_to_2d=True)
    """
    helper = LayerHelper("density_prior_box", **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_(densities):
        raise TypeError('densities should be a list or a tuple or None.')
    if not _is_list_or_tuple_(fixed_sizes):
        raise TypeError('fixed_sizes should be a list or a tuple or None.')
    if not _is_list_or_tuple_(fixed_ratios):
        raise TypeError('fixed_ratios should be a list or a tuple or None.')
    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


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):
    """
    Generate prior boxes for SSD(Single Shot MultiBox Detector)
    algorithm. 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|tuple): The list of input Variables, the format
            of all Variables is NCHW.
       image(Variable): The input image data of PriorBoxOp,
            the layout is NCHW.
       base_size(int): the base_size is used to get min_size
            and max_size according to min_ratio and max_ratio.
       num_classes(int): The number of classes.
       aspect_ratios(list|tuple): 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): Name of the prior box layer. Default: None.
       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 fininal
            detection results. Default: False.

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

        mbox_loc: The predicted boxes' location of the inputs. The layout
        is [N, H*W*Priors, 4]. where Priors is the number of predicted
        boxes each position of each input.

        mbox_conf: The predicted boxes' confidence of the inputs. The layout
        is [N, H*W*Priors, C]. where Priors is the number of predicted boxes
        each position of each input and C is the number of Classes.

        boxes: the output prior boxes of PriorBox. The layout is [num_priors, 4].
        num_priors is the total box count of each position of inputs.

        variances: the expanded variances of PriorBox. The layout is
        [num_priors, 4]. num_priors is the total box count of each position of inputs


    Examples:
        .. code-block:: python

          mbox_locs, mbox_confs, box, var = fluid.layers.multi_box_head(
            inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
            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)
    """

    def _reshape_with_axis_(input, axis=1):
        out = nn.flatten(x=input, axis=axis)
        return out

    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 six.moves.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

    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_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_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_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 = nn.transpose(mbox_loc, perm=[0, 2, 3, 1])
        compile_shape = [
            mbox_loc.shape[0], cpt.floor_division(
                mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3], 4), 4
        ]
        run_shape = tensor.assign(numpy.array([0, -1, 4]).astype("int32"))
        mbox_loc_flatten = nn.reshape(
            mbox_loc, shape=compile_shape, actual_shape=run_shape)
        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 = nn.transpose(conf_loc, perm=[0, 2, 3, 1])
        new_shape = [0, -1, num_classes]
        compile_shape = [
            conf_loc.shape[0],
            cpt.floor_division(conf_loc.shape[1] * conf_loc.shape[2] *
                               conf_loc.shape[3], num_classes), num_classes
        ]
        run_shape = tensor.assign(
            numpy.array([0, -1, num_classes]).astype("int32"))
        conf_loc_flatten = nn.reshape(
            conf_loc, shape=compile_shape, actual_shape=run_shape)
        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_confs_concat = tensor.concat(mbox_confs, axis=1)

    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): The input feature map, the format is NCHW.
       anchor_sizes(list|tuple|float): 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.
       aspect_ratios(list|tuple|float): The height / width ratios of generated
                                        anchors, e.g. [0.5, 1.0, 2.0].
       variance(list|tuple): The variances to be used in box regression deltas.
                             Default:[0.1, 0.1, 0.2, 0.2].
       stride(list|turple): The anchors stride across width and height,e.g. [16.0, 16.0]
       offset(float): Prior boxes center offset. Default: 0.5
       name(str): Name of the prior box op. Default: None.

    Returns:
        Anchors(Variable),Variances(Variable):  
        
              two variables:
        
              - 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

            anchor, var = 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):
    """
    ROI perspective transform op.

    Args:
        input (Variable): The 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.
        rois (Variable):  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.
        transformed_height (integer): The height of transformed output.
        transformed_height (integer): The width of transformed output.
        spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0

    Returns:
        Variable: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape 
                  (num_rois, channels, transformed_h, transformed_w).

    Examples:
        .. code-block:: python

            out = fluid.layers.roi_perspective_transform(input, rois, 7, 7, 1.0)
    """
    helper = LayerHelper('roi_perspective_transform', **locals())
    dtype = helper.input_dtype()
    out = helper.create_variable_for_type_inference(dtype)
    helper.append_op(
        type="roi_perspective_transform",
        inputs={"X": input,
                "ROIs": rois},
        outputs={"Out": out},
        attrs={
            "transformed_height": transformed_height,
            "transformed_width": transformed_width,
            "spatial_scale": spatial_scale
        })
    return out


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):
    """
    ** 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.
        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.
        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.
        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.
        fg_fraction(float): Foreground fraction in total batch_size_per_im.
        fg_thresh(float): Overlap threshold which is used to chose foreground sample.
        bg_thresh_hi(float): Overlap threshold upper bound which is used to chose background sample.
        bg_thresh_lo(float): Overlap threshold lower bound which is used to chose background sample.
        bbox_reg_weights(list|tuple): Box regression weights.
        class_nums(int): Class number.
        use_random(bool): Use random sampling to choose foreground and background boxes.
    """

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

    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)

    helper.append_op(
        type="generate_proposal_labels",
        inputs={
            'RpnRois': rpn_rois,
            'GtClasses': gt_classes,
            'IsCrowd': is_crowd,
            'GtBoxes': gt_boxes,
            'ImInfo': im_info
        },
        outputs={
            'Rois': rois,
            'LabelsInt32': labels_int32,
            'BboxTargets': bbox_targets,
            'BboxInsideWeights': bbox_inside_weights,
            'BboxOutsideWeights': bbox_outside_weights
        },
        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
        })

    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

    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):
    """
    ** 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]. N is the batch size,
            each element is [height, width, scale] of image. Image scale is
            target_size) / original_size.
        gt_classes(Variable): A 2-D LoDTensor with shape [M, 1]. M is the total
            number of ground-truth, each element is a class label.
        is_crowd(Variable): A 2-D LoDTensor with shape 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],
            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 gt 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]. 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 repersents
            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]. P is the total
            number of sampled RoIs. Each element is a bounding box with
            [xmin, ymin, xmax, ymax] format in range of orignal image size.
        mask_rois_has_mask_int32 (Variable): A 2D LoDTensor with shape [P, 1],
            each element repersents the output mask RoI index with regard to
            to input RoIs.
        mask_int32 (Variable): A 2D LoDTensor with shape [P, K * M * M],
            K is the classes number and M is the resolution of mask predictions.
            Each element repersents the binary mask targets.

    Examples:
        .. code-block:: python

          im_info = fluid.layers.data(name="im_info", shape=[3],
              dtype="float32")
          gt_classes = fluid.layers.data(name="gt_classes", shape=[1],
              dtype="float32", lod_level=1)
          is_crowd = fluid.layers.data(name="is_crowd", shape=[1],
              dtype="float32", lod_level=1)
          gt_masks = fluid.layers.data(name="gt_masks", shape=[2],
              dtype="float32", lod_level=3)
          # rois, labels_int32 can be the output of
          # fluid.layers.generate_proposal_labels.
          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=labels_int32,
              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,
                       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.
        bbox_deltas(Variable): A 4-D Tensor with shape [N, 4*A, H, W]
            represents the differece between predicted box locatoin and
            anchor location.
        im_info(Variable): A 2-D Tensor with shape [N, 3] represents origin
            image information for N batch. Info contains height, width and scale
            between origin image size and the size of feature map.
        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.
        variances(Variable): The expanded variances of anchors with a layout of
            [H, W, num_priors, 4]. Each variance is in
            (xcenter, ycenter, w, h) format.
        pre_nms_top_n(float): Number of total bboxes to be kept per
            image before NMS. 6000 by default.
        post_nms_top_n(float): Number of total bboxes to be kept per
            image after NMS. 1000 by default.
        nms_thresh(float): Threshold in NMS, 0.5 by default.
        min_size(float): Remove predicted boxes with either height or
            width < min_size. 0.1 by default.
        eta(float): Apply in adaptive NMS, if adaptive threshold > 0.5,
            adaptive_threshold = adaptive_threshold * eta in each iteration.
    """
    helper = LayerHelper('generate_proposals', **locals())

    rpn_rois = helper.create_variable_for_type_inference(
        dtype=bbox_deltas.dtype)
    rpn_roi_probs = helper.create_variable_for_type_inference(
        dtype=scores.dtype)
    helper.append_op(
        type="generate_proposals",
        inputs={
            'Scores': scores,
            'BboxDeltas': bbox_deltas,
            'ImInfo': im_info,
            'Anchors': anchors,
            'Variances': variances
        },
        attrs={
            'pre_nms_topN': pre_nms_top_n,
            'post_nms_topN': post_nms_top_n,
            'nms_thresh': nms_thresh,
            'min_size': min_size,
            'eta': eta
        },
        outputs={'RpnRois': rpn_rois,
                 'RpnRoiProbs': rpn_roi_probs})
    rpn_rois.stop_gradient = True
    rpn_roi_probs.stop_gradient = True

    return rpn_rois, rpn_roi_probs


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 box, the last dimension is 4.
        im_info(variable): The information of image with shape [N, 3] with 
                            layout (height, width, scale). height and width
                            is the input size and scale is the ratio of input
                            size and original size.
        name (str): The name of this layer. It is optional.
    
    Returns:
        Variable: The cliped tensor variable.
        
    Examples:
        .. code-block:: python
        
            boxes = fluid.layers.data(
                name='data', shape=[8, 4], dtype='float32', lod_level=1)
            im_info = fluid.layers.data(name='im_info', shape=[3])
            out = fluid.layers.box_clip(
                input=boxes, im_info=im_info, inplace=True)
    """

    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 multiclass_nms(bboxes,
                   scores,
                   score_threshold,
                   nms_top_k,
                   keep_top_k,
                   nms_threshold=0.3,
                   normalized=True,
                   nms_eta=1.,
                   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.

    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.
                           2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
                           M is the number of bounding boxes, C is the 
                           class number   
        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.
                           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].
        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 aftern 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:
        Out: 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


            boxes = fluid.layers.data(name='bboxes', shape=[81, 4],
                                      dtype='float32', lod_level=1)
            scores = fluid.layers.data(name='scores', shape=[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)
    """
    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,
            'nms_eta': nms_eta,
            'normalized': normalized
        },
        outputs={'Out': output})
    output.stop_gradient = True

    return output


def distribute_fpn_proposals(fpn_rois,
                             min_level,
                             max_level,
                             refer_level,
                             refer_scale,
                             name=None):
    """
    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): The input fpn_rois, the second dimension is 4.
        min_level(int): The lowest level of FPN layer where the proposals come 
                        from.
        max_level(int): The highest level of FPN layer where the proposals
                        come from.
        refer_level(int): The referring level of FPN layer with specified scale.
        refer_scale(int): The referring scale of FPN layer with specified level.
        name(str|None): The name of this operator.        

    Returns:
        tuple: 
               A tuple(multi_rois, restore_ind) is returned. The multi_rois is 
               a list of segmented tensor variables. The restore_ind is a 2D 
               Tensor with shape [N, 1], N is the number of total rois. It is
               used to restore the order of fpn_rois.

    Examples:
        .. code-block:: python

            fpn_rois = fluid.layers.data(
                name='data', shape=[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)
    """

    helper = LayerHelper('distribute_fpn_proposals', **locals())
    dtype = helper.input_dtype()
    num_lvl = max_level - min_level + 1
    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')
    helper.append_op(
        type='distribute_fpn_proposals',
        inputs={'FpnRois': fpn_rois},
        outputs={'MultiFpnRois': multi_rois,
                 'RestoreIndex': restore_ind},
        attrs={
            'min_level': min_level,
            'max_level': max_level,
            'refer_level': refer_level,
            'refer_scale': refer_scale
        })
    return multi_rois, restore_ind


@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|None): The name of this operator
    Returns:
        decode_box(Variable), output_assign_box(Variable):

            two variables:

            - decode_box(${decode_box_type}): ${decode_box_comment}
            - output_assign_box(${output_assign_box_type}): ${output_assign_box_comment}

    Examples:
        .. code-block:: python

            pb = fluid.layers.data(
                name='prior_box', shape=[20, 4], dtype='float32')
            pbv = fluid.layers.data(
                name='prior_box_var', shape=[1, 4], dtype='float32')
            loc = fluid.layers.data(
                name='target_box', shape=[20, 4*81], dtype='float32')
            scores = fluid.layers.data(
                name='scores', shape=[20, 81], dtype='float32')
            decoded_box, output_assign_box = fluid.layers.box_decoder_and_assign(
                pb, pbv, loc, scores, 4.135)

    """
    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