105 lines
3.7 KiB
Python
105 lines
3.7 KiB
Python
# Copyright (c) 2022 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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import paddle
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import paddle.nn.functional as F
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from ppdet.core.workspace import register, serializable
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__all__ = ['ProbIoULoss']
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def gbb_form(boxes):
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xy, wh, angle = paddle.split(boxes, [2, 2, 1], axis=-1)
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return paddle.concat([xy, wh.pow(2) / 12., angle], axis=-1)
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def rotated_form(a_, b_, angles):
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cos_a = paddle.cos(angles)
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sin_a = paddle.sin(angles)
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a = a_ * paddle.pow(cos_a, 2) + b_ * paddle.pow(sin_a, 2)
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b = a_ * paddle.pow(sin_a, 2) + b_ * paddle.pow(cos_a, 2)
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c = (a_ - b_) * cos_a * sin_a
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return a, b, c
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def probiou_loss(pred, target, eps=1e-3, mode='l1'):
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"""
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pred -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours predicted box ;in case of HBB angle == 0
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target -> a matrix [N,5](x,y,w,h,angle - in radians) containing ours target box ;in case of HBB angle == 0
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eps -> threshold to avoid infinite values
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mode -> ('l1' in [0,1] or 'l2' in [0,inf]) metrics according our paper
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"""
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gbboxes1 = gbb_form(pred)
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gbboxes2 = gbb_form(target)
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x1, y1, a1_, b1_, c1_ = gbboxes1[:,
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0], gbboxes1[:,
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1], gbboxes1[:,
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2], gbboxes1[:,
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3], gbboxes1[:,
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4]
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x2, y2, a2_, b2_, c2_ = gbboxes2[:,
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0], gbboxes2[:,
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1], gbboxes2[:,
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2], gbboxes2[:,
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3], gbboxes2[:,
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4]
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a1, b1, c1 = rotated_form(a1_, b1_, c1_)
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a2, b2, c2 = rotated_form(a2_, b2_, c2_)
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t1 = 0.25 * ((a1 + a2) * (paddle.pow(y1 - y2, 2)) + (b1 + b2) * (paddle.pow(x1 - x2, 2))) + \
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0.5 * ((c1+c2)*(x2-x1)*(y1-y2))
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t2 = (a1 + a2) * (b1 + b2) - paddle.pow(c1 + c2, 2)
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t3_ = (a1 * b1 - c1 * c1) * (a2 * b2 - c2 * c2)
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t3 = 0.5 * paddle.log(t2 / (4 * paddle.sqrt(F.relu(t3_)) + eps))
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B_d = (t1 / t2) + t3
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# B_d = t1 + t2 + t3
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B_d = paddle.clip(B_d, min=eps, max=100.0)
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l1 = paddle.sqrt(1.0 - paddle.exp(-B_d) + eps)
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l_i = paddle.pow(l1, 2.0)
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l2 = -paddle.log(1.0 - l_i + eps)
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if mode == 'l1':
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probiou = l1
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if mode == 'l2':
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probiou = l2
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return probiou
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@serializable
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@register
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class ProbIoULoss(object):
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""" ProbIoU Loss, refer to https://arxiv.org/abs/2106.06072 for details """
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def __init__(self, mode='l1', eps=1e-3):
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super(ProbIoULoss, self).__init__()
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self.mode = mode
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self.eps = eps
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def __call__(self, pred_rboxes, assigned_rboxes):
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return probiou_loss(pred_rboxes, assigned_rboxes, self.eps, self.mode)
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