forked from PulseFocusPlatform/PulseFocusPlatform
124 lines
4.4 KiB
Python
124 lines
4.4 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from paddle import fluid
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from ppdet.core.workspace import register, serializable
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from .giou_loss import GiouLoss
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__all__ = ['DiouLoss']
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@register
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@serializable
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class DiouLoss(GiouLoss):
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"""
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Distance-IoU Loss, see https://arxiv.org/abs/1911.08287
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Args:
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loss_weight (float): diou loss weight, default as 10 in faster-rcnn
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is_cls_agnostic (bool): flag of class-agnostic
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num_classes (int): class num
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use_complete_iou_loss (bool): whether to use complete iou loss
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"""
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def __init__(self,
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loss_weight=10.,
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is_cls_agnostic=False,
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num_classes=81,
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use_complete_iou_loss=True):
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super(DiouLoss, self).__init__(
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loss_weight=loss_weight,
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is_cls_agnostic=is_cls_agnostic,
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num_classes=num_classes)
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self.use_complete_iou_loss = use_complete_iou_loss
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def __call__(self,
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x,
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y,
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inside_weight=None,
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outside_weight=None,
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bbox_reg_weight=[0.1, 0.1, 0.2, 0.2]):
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eps = 1.e-10
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x1, y1, x2, y2 = self.bbox_transform(x, bbox_reg_weight)
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x1g, y1g, x2g, y2g = self.bbox_transform(y, bbox_reg_weight)
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cx = (x1 + x2) / 2
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cy = (y1 + y2) / 2
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w = x2 - x1
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h = y2 - y1
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cxg = (x1g + x2g) / 2
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cyg = (y1g + y2g) / 2
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wg = x2g - x1g
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hg = y2g - y1g
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x2 = fluid.layers.elementwise_max(x1, x2)
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y2 = fluid.layers.elementwise_max(y1, y2)
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# A and B
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xkis1 = fluid.layers.elementwise_max(x1, x1g)
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ykis1 = fluid.layers.elementwise_max(y1, y1g)
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xkis2 = fluid.layers.elementwise_min(x2, x2g)
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ykis2 = fluid.layers.elementwise_min(y2, y2g)
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# A or B
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xc1 = fluid.layers.elementwise_min(x1, x1g)
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yc1 = fluid.layers.elementwise_min(y1, y1g)
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xc2 = fluid.layers.elementwise_max(x2, x2g)
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yc2 = fluid.layers.elementwise_max(y2, y2g)
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intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
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intsctk = intsctk * fluid.layers.greater_than(
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xkis2, xkis1) * fluid.layers.greater_than(ykis2, ykis1)
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unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
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) - intsctk + eps
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iouk = intsctk / unionk
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# DIOU term
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dist_intersection = (cx - cxg) * (cx - cxg) + (cy - cyg) * (cy - cyg)
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dist_union = (xc2 - xc1) * (xc2 - xc1) + (yc2 - yc1) * (yc2 - yc1)
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diou_term = (dist_intersection + eps) / (dist_union + eps)
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# CIOU term
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ciou_term = 0
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if self.use_complete_iou_loss:
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ar_gt = wg / hg
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ar_pred = w / h
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arctan = fluid.layers.atan(ar_gt) - fluid.layers.atan(ar_pred)
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ar_loss = 4. / np.pi / np.pi * arctan * arctan
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alpha = ar_loss / (1 - iouk + ar_loss + eps)
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alpha.stop_gradient = True
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ciou_term = alpha * ar_loss
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iou_weights = 1
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if inside_weight is not None and outside_weight is not None:
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inside_weight = fluid.layers.reshape(inside_weight, shape=(-1, 4))
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outside_weight = fluid.layers.reshape(outside_weight, shape=(-1, 4))
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inside_weight = fluid.layers.reduce_mean(inside_weight, dim=1)
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outside_weight = fluid.layers.reduce_mean(outside_weight, dim=1)
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iou_weights = inside_weight * outside_weight
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class_weight = 2 if self.is_cls_agnostic else self.num_classes
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diou = fluid.layers.reduce_mean(
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(1 - iouk + ciou_term + diou_term) * iou_weights) * class_weight
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return diou * self.loss_weight
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