forked from PulseFocusPlatform/PulseFocusPlatform
78 lines
2.9 KiB
Python
78 lines
2.9 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__ = ['IouAwareLoss']
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@register
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@serializable
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class IouAwareLoss(IouLoss):
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"""
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iou aware loss, see https://arxiv.org/abs/1912.05992
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Args:
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loss_weight (float): iou aware loss weight, default is 1.0
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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=1.0, max_height=608, max_width=608):
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super(IouAwareLoss, self).__init__(
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loss_weight=loss_weight, max_height=max_height, max_width=max_width)
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def __call__(self,
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ioup,
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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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scale_x_y,
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eps=1.e-10):
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'''
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Args:
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ioup ([Variables]): the predicted iou
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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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pred = self._bbox_transform(x, y, w, h, anchors, downsample_ratio,
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batch_size, False, scale_x_y, eps)
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gt = self._bbox_transform(tx, ty, tw, th, anchors, downsample_ratio,
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batch_size, True, scale_x_y, eps)
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iouk = self._iou(pred, gt, ioup, eps)
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iouk.stop_gradient = True
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loss_iou_aware = fluid.layers.sigmoid_cross_entropy_with_logits(ioup,
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iouk)
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loss_iou_aware = loss_iou_aware * self._loss_weight
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return loss_iou_aware
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