forked from PulseFocusPlatform/PulseFocusPlatform
111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
# Copyright (c) 2020 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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from paddle import fluid
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from ppdet.core.workspace import register, serializable
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from .iou_loss import IouLoss
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__all__ = ['DiouLossYolo']
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@register
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@serializable
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class DiouLossYolo(IouLoss):
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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 is 5
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max_height (int): max height of input to support random shape input
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max_width (int): max width of input to support random shape input
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"""
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def __init__(self, loss_weight=5, max_height=608, max_width=608):
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self._loss_weight = loss_weight
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self._MAX_HI = max_height
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self._MAX_WI = max_width
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def __call__(self,
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x,
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y,
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w,
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h,
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tx,
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ty,
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tw,
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th,
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anchors,
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downsample_ratio,
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batch_size,
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eps=1.e-10,
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**kwargs):
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'''
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Args:
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x | y | w | h ([Variables]): the output of yolov3 for encoded x|y|w|h
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tx |ty |tw |th ([Variables]): the target of yolov3 for encoded x|y|w|h
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anchors ([float]): list of anchors for current output layer
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downsample_ratio (float): the downsample ratio for current output layer
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batch_size (int): training batch size
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eps (float): the decimal to prevent the denominator eqaul zero
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'''
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x1, y1, x2, y2 = self._bbox_transform(
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x, y, w, h, anchors, downsample_ratio, batch_size, False, 1.0, eps)
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x1g, y1g, x2g, y2g = self._bbox_transform(tx, ty, tw, th, anchors,
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downsample_ratio, batch_size,
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True, 1.0, eps)
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#central coordinates
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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_loss
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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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loss_diou = 1. - iouk + diou_term
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loss_diou = loss_diou * self._loss_weight
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return loss_diou
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