person_monitor/dependence/PaddleDetection/static/ppdet/modeling/losses/diou_loss.py

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from paddle import fluid
from ppdet.core.workspace import register, serializable
from .giou_loss import GiouLoss

__all__ = ['DiouLoss']


@register
@serializable
class DiouLoss(GiouLoss):
    """
    Distance-IoU Loss, see https://arxiv.org/abs/1911.08287
    Args:
        loss_weight (float): diou loss weight, default as 10 in faster-rcnn
        is_cls_agnostic (bool): flag of class-agnostic
        num_classes (int): class num
        use_complete_iou_loss (bool): whether to use complete iou loss
    """

    def __init__(self,
                 loss_weight=10.,
                 is_cls_agnostic=False,
                 num_classes=81,
                 use_complete_iou_loss=True):
        super(DiouLoss, self).__init__(
            loss_weight=loss_weight,
            is_cls_agnostic=is_cls_agnostic,
            num_classes=num_classes)
        self.use_complete_iou_loss = use_complete_iou_loss

    def __call__(self,
                 x,
                 y,
                 inside_weight=None,
                 outside_weight=None,
                 bbox_reg_weight=[0.1, 0.1, 0.2, 0.2]):
        eps = 1.e-10
        x1, y1, x2, y2 = self.bbox_transform(x, bbox_reg_weight)
        x1g, y1g, x2g, y2g = self.bbox_transform(y, bbox_reg_weight)

        cx = (x1 + x2) / 2
        cy = (y1 + y2) / 2
        w = x2 - x1
        h = y2 - y1

        cxg = (x1g + x2g) / 2
        cyg = (y1g + y2g) / 2
        wg = x2g - x1g
        hg = y2g - y1g

        x2 = fluid.layers.elementwise_max(x1, x2)
        y2 = fluid.layers.elementwise_max(y1, y2)

        # A and B
        xkis1 = fluid.layers.elementwise_max(x1, x1g)
        ykis1 = fluid.layers.elementwise_max(y1, y1g)
        xkis2 = fluid.layers.elementwise_min(x2, x2g)
        ykis2 = fluid.layers.elementwise_min(y2, y2g)

        # A or B
        xc1 = fluid.layers.elementwise_min(x1, x1g)
        yc1 = fluid.layers.elementwise_min(y1, y1g)
        xc2 = fluid.layers.elementwise_max(x2, x2g)
        yc2 = fluid.layers.elementwise_max(y2, y2g)

        intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
        intsctk = intsctk * fluid.layers.greater_than(
            xkis2, xkis1) * fluid.layers.greater_than(ykis2, ykis1)
        unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
                                                        ) - intsctk + eps
        iouk = intsctk / unionk

        # DIOU term
        dist_intersection = (cx - cxg) * (cx - cxg) + (cy - cyg) * (cy - cyg)
        dist_union = (xc2 - xc1) * (xc2 - xc1) + (yc2 - yc1) * (yc2 - yc1)
        diou_term = (dist_intersection + eps) / (dist_union + eps)

        # CIOU term
        ciou_term = 0
        if self.use_complete_iou_loss:
            ar_gt = wg / hg
            ar_pred = w / h
            arctan = fluid.layers.atan(ar_gt) - fluid.layers.atan(ar_pred)
            ar_loss = 4. / np.pi / np.pi * arctan * arctan
            alpha = ar_loss / (1 - iouk + ar_loss + eps)
            alpha.stop_gradient = True
            ciou_term = alpha * ar_loss

        iou_weights = 1
        if inside_weight is not None and outside_weight is not None:
            inside_weight = fluid.layers.reshape(inside_weight, shape=(-1, 4))
            outside_weight = fluid.layers.reshape(outside_weight, shape=(-1, 4))

            inside_weight = fluid.layers.reduce_mean(inside_weight, dim=1)
            outside_weight = fluid.layers.reduce_mean(outside_weight, dim=1)

            iou_weights = inside_weight * outside_weight

        class_weight = 2 if self.is_cls_agnostic else self.num_classes
        diou = fluid.layers.reduce_mean(
            (1 - iouk + ciou_term + diou_term) * iou_weights) * class_weight

        return diou * self.loss_weight