# 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, 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): 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 = 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, 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