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yolo_loss.py
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yolo_loss.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
from ..bbox_utils import decode_yolo, xywh2xyxy, batch_iou_similarity
__all__ = ['YOLOv3Loss']
def bbox_transform(pbox, anchor, downsample):
pbox = decode_yolo(pbox, anchor, downsample)
pbox = xywh2xyxy(pbox)
return pbox
@register
class YOLOv3Loss(nn.Layer):
__inject__ = ['iou_loss', 'iou_aware_loss']
__shared__ = ['num_classes']
def __init__(self,
num_classes=80,
ignore_thresh=0.7,
label_smooth=False,
downsample=[32, 16, 8],
scale_x_y=1.,
iou_loss=None,
iou_aware_loss=None):
"""
YOLOv3Loss layer
Args:
num_calsses (int): number of foreground classes
ignore_thresh (float): threshold to ignore confidence loss
label_smooth (bool): whether to use label smoothing
downsample (list): downsample ratio for each detection block
scale_x_y (float): scale_x_y factor
iou_loss (object): IoULoss instance
iou_aware_loss (object): IouAwareLoss instance
"""
super(YOLOv3Loss, self).__init__()
self.num_classes = num_classes
self.ignore_thresh = ignore_thresh
self.label_smooth = label_smooth
self.downsample = downsample
self.scale_x_y = scale_x_y
self.iou_loss = iou_loss
self.iou_aware_loss = iou_aware_loss
self.distill_pairs = []
def obj_loss(self, pbox, gbox, pobj, tobj, anchor, downsample):
# pbox
pbox = decode_yolo(pbox, anchor, downsample)
pbox = xywh2xyxy(pbox)
pbox = paddle.concat(pbox, axis=-1)
b = pbox.shape[0]
pbox = pbox.reshape((b, -1, 4))
# gbox
gxy = gbox[:, :, 0:2] - gbox[:, :, 2:4] * 0.5
gwh = gbox[:, :, 0:2] + gbox[:, :, 2:4] * 0.5
gbox = paddle.concat([gxy, gwh], axis=-1)
iou = batch_iou_similarity(pbox, gbox)
iou.stop_gradient = True
iou_max = iou.max(2) # [N, M1]
iou_mask = paddle.cast(iou_max <= self.ignore_thresh, dtype=pbox.dtype)
iou_mask.stop_gradient = True
pobj = pobj.reshape((b, -1))
tobj = tobj.reshape((b, -1))
obj_mask = paddle.cast(tobj > 0, dtype=pbox.dtype)
obj_mask.stop_gradient = True
loss_obj = F.binary_cross_entropy_with_logits(
pobj, obj_mask, reduction='none')
loss_obj_pos = (loss_obj * tobj)
loss_obj_neg = (loss_obj * (1 - obj_mask) * iou_mask)
return loss_obj_pos + loss_obj_neg
def cls_loss(self, pcls, tcls):
if self.label_smooth:
delta = min(1. / self.num_classes, 1. / 40)
pos, neg = 1 - delta, delta
# 1 for positive, 0 for negative
tcls = pos * paddle.cast(
tcls > 0., dtype=tcls.dtype) + neg * paddle.cast(
tcls <= 0., dtype=tcls.dtype)
loss_cls = F.binary_cross_entropy_with_logits(
pcls, tcls, reduction='none')
return loss_cls
def yolov3_loss(self, p, t, gt_box, anchor, downsample, scale=1.,
eps=1e-10):
na = len(anchor)
b, c, h, w = p.shape
if self.iou_aware_loss:
ioup, p = p[:, 0:na, :, :], p[:, na:, :, :]
ioup = ioup.unsqueeze(-1)
p = p.reshape((b, na, -1, h, w)).transpose((0, 1, 3, 4, 2))
x, y = p[:, :, :, :, 0:1], p[:, :, :, :, 1:2]
w, h = p[:, :, :, :, 2:3], p[:, :, :, :, 3:4]
obj, pcls = p[:, :, :, :, 4:5], p[:, :, :, :, 5:]
self.distill_pairs.append([x, y, w, h, obj, pcls])
t = t.transpose((0, 1, 3, 4, 2))
tx, ty = t[:, :, :, :, 0:1], t[:, :, :, :, 1:2]
tw, th = t[:, :, :, :, 2:3], t[:, :, :, :, 3:4]
tscale = t[:, :, :, :, 4:5]
tobj, tcls = t[:, :, :, :, 5:6], t[:, :, :, :, 6:]
tscale_obj = tscale * tobj
loss = dict()
x = scale * F.sigmoid(x) - 0.5 * (scale - 1.)
y = scale * F.sigmoid(y) - 0.5 * (scale - 1.)
if abs(scale - 1.) < eps:
loss_x = F.binary_cross_entropy(x, tx, reduction='none')
loss_y = F.binary_cross_entropy(y, ty, reduction='none')
loss_xy = tscale_obj * (loss_x + loss_y)
else:
loss_x = paddle.abs(x - tx)
loss_y = paddle.abs(y - ty)
loss_xy = tscale_obj * (loss_x + loss_y)
loss_xy = loss_xy.sum([1, 2, 3, 4]).mean()
loss_w = paddle.abs(w - tw)
loss_h = paddle.abs(h - th)
loss_wh = tscale_obj * (loss_w + loss_h)
loss_wh = loss_wh.sum([1, 2, 3, 4]).mean()
loss['loss_xy'] = loss_xy
loss['loss_wh'] = loss_wh
if self.iou_loss is not None:
# warn: do not modify x, y, w, h in place
box, tbox = [x, y, w, h], [tx, ty, tw, th]
pbox = bbox_transform(box, anchor, downsample)
gbox = bbox_transform(tbox, anchor, downsample)
loss_iou = self.iou_loss(pbox, gbox)
loss_iou = loss_iou * tscale_obj
loss_iou = loss_iou.sum([1, 2, 3, 4]).mean()
loss['loss_iou'] = loss_iou
if self.iou_aware_loss is not None:
box, tbox = [x, y, w, h], [tx, ty, tw, th]
pbox = bbox_transform(box, anchor, downsample)
gbox = bbox_transform(tbox, anchor, downsample)
loss_iou_aware = self.iou_aware_loss(ioup, pbox, gbox)
loss_iou_aware = loss_iou_aware * tobj
loss_iou_aware = loss_iou_aware.sum([1, 2, 3, 4]).mean()
loss['loss_iou_aware'] = loss_iou_aware
box = [x, y, w, h]
loss_obj = self.obj_loss(box, gt_box, obj, tobj, anchor, downsample)
loss_obj = loss_obj.sum(-1).mean()
loss['loss_obj'] = loss_obj
loss_cls = self.cls_loss(pcls, tcls) * tobj
loss_cls = loss_cls.sum([1, 2, 3, 4]).mean()
loss['loss_cls'] = loss_cls
return loss
def forward(self, inputs, targets, anchors):
np = len(inputs)
gt_targets = [targets['target{}'.format(i)] for i in range(np)]
gt_box = targets['gt_bbox']
yolo_losses = dict()
self.distill_pairs.clear()
for x, t, anchor, downsample in zip(inputs, gt_targets, anchors,
self.downsample):
yolo_loss = self.yolov3_loss(
x.astype('float32'), t, gt_box, anchor, downsample,
self.scale_x_y)
for k, v in yolo_loss.items():
if k in yolo_losses:
yolo_losses[k] += v
else:
yolo_losses[k] = v
loss = 0
for k, v in yolo_losses.items():
loss += v
yolo_losses['loss'] = loss
return yolo_losses