forked from PulseFocusPlatform/PulseFocusPlatform
211 lines
7.1 KiB
Python
211 lines
7.1 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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import numpy as np
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import paddle
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from ppdet.core.workspace import register, serializable
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from ..bbox_utils import bbox_iou
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__all__ = ['IouLoss', 'GIoULoss', 'DIouLoss']
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@register
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@serializable
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class IouLoss(object):
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"""
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iou loss, see https://arxiv.org/abs/1908.03851
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loss = 1.0 - iou * iou
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Args:
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loss_weight (float): iou loss weight, default is 2.5
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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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ciou_term (bool): whether to add ciou_term
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loss_square (bool): whether to square the iou term
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"""
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def __init__(self,
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loss_weight=2.5,
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giou=False,
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diou=False,
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ciou=False,
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loss_square=True):
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self.loss_weight = loss_weight
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self.giou = giou
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self.diou = diou
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self.ciou = ciou
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self.loss_square = loss_square
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def __call__(self, pbox, gbox):
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iou = bbox_iou(
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pbox, gbox, giou=self.giou, diou=self.diou, ciou=self.ciou)
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if self.loss_square:
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loss_iou = 1 - iou * iou
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else:
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loss_iou = 1 - iou
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loss_iou = loss_iou * self.loss_weight
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return loss_iou
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@register
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@serializable
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class GIoULoss(object):
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"""
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Generalized Intersection over Union, see https://arxiv.org/abs/1902.09630
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Args:
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loss_weight (float): giou loss weight, default as 1
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eps (float): epsilon to avoid divide by zero, default as 1e-10
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reduction (string): Options are "none", "mean" and "sum". default as none
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"""
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def __init__(self, loss_weight=1., eps=1e-10, reduction='none'):
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self.loss_weight = loss_weight
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self.eps = eps
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assert reduction in ('none', 'mean', 'sum')
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self.reduction = reduction
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def bbox_overlap(self, box1, box2, eps=1e-10):
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"""calculate the iou of box1 and box2
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Args:
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box1 (Tensor): box1 with the shape (..., 4)
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box2 (Tensor): box1 with the shape (..., 4)
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eps (float): epsilon to avoid divide by zero
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Return:
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iou (Tensor): iou of box1 and box2
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overlap (Tensor): overlap of box1 and box2
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union (Tensor): union of box1 and box2
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"""
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x1, y1, x2, y2 = box1
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x1g, y1g, x2g, y2g = box2
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xkis1 = paddle.maximum(x1, x1g)
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ykis1 = paddle.maximum(y1, y1g)
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xkis2 = paddle.minimum(x2, x2g)
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ykis2 = paddle.minimum(y2, y2g)
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w_inter = (xkis2 - xkis1).clip(0)
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h_inter = (ykis2 - ykis1).clip(0)
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overlap = w_inter * h_inter
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area1 = (x2 - x1) * (y2 - y1)
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area2 = (x2g - x1g) * (y2g - y1g)
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union = area1 + area2 - overlap + eps
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iou = overlap / union
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return iou, overlap, union
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def __call__(self, pbox, gbox, iou_weight=1., loc_reweight=None):
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x1, y1, x2, y2 = paddle.split(pbox, num_or_sections=4, axis=-1)
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x1g, y1g, x2g, y2g = paddle.split(gbox, num_or_sections=4, axis=-1)
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box1 = [x1, y1, x2, y2]
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box2 = [x1g, y1g, x2g, y2g]
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iou, overlap, union = self.bbox_overlap(box1, box2, self.eps)
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xc1 = paddle.minimum(x1, x1g)
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yc1 = paddle.minimum(y1, y1g)
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xc2 = paddle.maximum(x2, x2g)
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yc2 = paddle.maximum(y2, y2g)
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area_c = (xc2 - xc1) * (yc2 - yc1) + self.eps
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miou = iou - ((area_c - union) / area_c)
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if loc_reweight is not None:
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loc_reweight = paddle.reshape(loc_reweight, shape=(-1, 1))
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loc_thresh = 0.9
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giou = 1 - (1 - loc_thresh
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) * miou - loc_thresh * miou * loc_reweight
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else:
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giou = 1 - miou
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if self.reduction == 'none':
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loss = giou
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elif self.reduction == 'sum':
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loss = paddle.sum(giou * iou_weight)
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else:
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loss = paddle.mean(giou * iou_weight)
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return loss * self.loss_weight
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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): giou loss weight, default as 1
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eps (float): epsilon to avoid divide by zero, default as 1e-10
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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, loss_weight=1., eps=1e-10, use_complete_iou_loss=True):
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super(DIouLoss, self).__init__(loss_weight=loss_weight, eps=eps)
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self.use_complete_iou_loss = use_complete_iou_loss
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def __call__(self, pbox, gbox, iou_weight=1.):
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x1, y1, x2, y2 = paddle.split(pbox, num_or_sections=4, axis=-1)
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x1g, y1g, x2g, y2g = paddle.split(gbox, num_or_sections=4, axis=-1)
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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 = paddle.maximum(x1, x2)
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y2 = paddle.maximum(y1, y2)
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# A and B
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xkis1 = paddle.maximum(x1, x1g)
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ykis1 = paddle.maximum(y1, y1g)
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xkis2 = paddle.minimum(x2, x2g)
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ykis2 = paddle.minimum(y2, y2g)
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# A or B
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xc1 = paddle.minimum(x1, x1g)
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yc1 = paddle.minimum(y1, y1g)
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xc2 = paddle.maximum(x2, x2g)
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yc2 = paddle.maximum(y2, y2g)
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intsctk = (xkis2 - xkis1) * (ykis2 - ykis1)
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intsctk = intsctk * paddle.greater_than(
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xkis2, xkis1) * paddle.greater_than(ykis2, ykis1)
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unionk = (x2 - x1) * (y2 - y1) + (x2g - x1g) * (y2g - y1g
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) - intsctk + self.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 + self.eps) / (dist_union + self.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 = paddle.atan(ar_gt) - paddle.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 + self.eps)
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alpha.stop_gradient = True
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ciou_term = alpha * ar_loss
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diou = paddle.mean((1 - iouk + ciou_term + diou_term) * iou_weight)
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return diou * self.loss_weight
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