更换文档检测模型
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paddle_detection/ppdet/modeling/architectures/ppyoloe.py
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paddle_detection/ppdet/modeling/architectures/ppyoloe.py
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# 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 copy
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import paddle
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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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__all__ = ['PPYOLOE', 'PPYOLOEWithAuxHead']
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# PP-YOLOE and PP-YOLOE+ are recommended to use this architecture, especially when use distillation or aux head
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# PP-YOLOE and PP-YOLOE+ can also use the same architecture of YOLOv3 in yolo.py when not use distillation or aux head
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@register
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class PPYOLOE(BaseArch):
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"""
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PPYOLOE network, see https://arxiv.org/abs/2203.16250
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Args:
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backbone (nn.Layer): backbone instance
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neck (nn.Layer): neck instance
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yolo_head (nn.Layer): anchor_head instance
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post_process (object): `BBoxPostProcess` instance
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ssod_loss (object): 'SSODPPYOLOELoss' instance, only used for semi-det(ssod)
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for_distill (bool): whether for distillation
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feat_distill_place (str): distill which feature for distillation
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for_mot (bool): whether return other features for multi-object tracking
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models, default False in pure object detection models.
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"""
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__category__ = 'architecture'
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__shared__ = ['for_distill']
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__inject__ = ['post_process', 'ssod_loss']
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def __init__(self,
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backbone='CSPResNet',
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neck='CustomCSPPAN',
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yolo_head='PPYOLOEHead',
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post_process='BBoxPostProcess',
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ssod_loss='SSODPPYOLOELoss',
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for_distill=False,
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feat_distill_place='neck_feats',
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for_mot=False):
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super(PPYOLOE, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.yolo_head = yolo_head
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self.post_process = post_process
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self.for_mot = for_mot
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# for ssod, semi-det
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self.is_teacher = False
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self.ssod_loss = ssod_loss
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# distill
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self.for_distill = for_distill
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self.feat_distill_place = feat_distill_place
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if for_distill:
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assert feat_distill_place in ['backbone_feats', 'neck_feats']
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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backbone = create(cfg['backbone'])
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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kwargs = {'input_shape': neck.out_shape}
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yolo_head = create(cfg['yolo_head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"yolo_head": yolo_head,
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}
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def _forward(self):
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body_feats = self.backbone(self.inputs)
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neck_feats = self.neck(body_feats, self.for_mot)
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self.is_teacher = self.inputs.get('is_teacher', False) # for semi-det
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if self.training or self.is_teacher:
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yolo_losses = self.yolo_head(neck_feats, self.inputs)
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if self.for_distill:
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if self.feat_distill_place == 'backbone_feats':
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self.yolo_head.distill_pairs['backbone_feats'] = body_feats
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elif self.feat_distill_place == 'neck_feats':
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self.yolo_head.distill_pairs['neck_feats'] = neck_feats
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else:
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raise ValueError
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return yolo_losses
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else:
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yolo_head_outs = self.yolo_head(neck_feats)
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if self.post_process is not None:
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bbox, bbox_num, nms_keep_idx = self.post_process(
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yolo_head_outs, self.yolo_head.mask_anchors,
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self.inputs['im_shape'], self.inputs['scale_factor'])
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else:
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bbox, bbox_num, nms_keep_idx = self.yolo_head.post_process(
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yolo_head_outs, self.inputs['scale_factor'])
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if self.use_extra_data:
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extra_data = {} # record the bbox output before nms, such like scores and nms_keep_idx
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"""extra_data:{
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'scores': predict scores,
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'nms_keep_idx': bbox index before nms,
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}
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"""
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extra_data['scores'] = yolo_head_outs[0] # predict scores (probability)
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extra_data['nms_keep_idx'] = nms_keep_idx
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output = {'bbox': bbox, 'bbox_num': bbox_num, 'extra_data': extra_data}
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else:
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output = {'bbox': bbox, 'bbox_num': bbox_num}
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return output
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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return self._forward()
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def get_loss_keys(self):
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return ['loss_cls', 'loss_iou', 'loss_dfl', 'loss_contrast']
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def get_ssod_loss(self, student_head_outs, teacher_head_outs, train_cfg):
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ssod_losses = self.ssod_loss(student_head_outs, teacher_head_outs,
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train_cfg)
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return ssod_losses
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@register
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class PPYOLOEWithAuxHead(BaseArch):
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__category__ = 'architecture'
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__inject__ = ['post_process']
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def __init__(self,
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backbone='CSPResNet',
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neck='CustomCSPPAN',
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yolo_head='PPYOLOEHead',
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aux_head='SimpleConvHead',
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post_process='BBoxPostProcess',
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for_mot=False,
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detach_epoch=5):
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"""
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PPYOLOE network, see https://arxiv.org/abs/2203.16250
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Args:
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backbone (nn.Layer): backbone instance
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neck (nn.Layer): neck instance
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yolo_head (nn.Layer): anchor_head instance
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post_process (object): `BBoxPostProcess` instance
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for_mot (bool): whether return other features for multi-object tracking
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models, default False in pure object detection models.
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"""
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super(PPYOLOEWithAuxHead, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.aux_neck = copy.deepcopy(self.neck)
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self.yolo_head = yolo_head
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self.aux_head = aux_head
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self.post_process = post_process
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self.for_mot = for_mot
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self.detach_epoch = detach_epoch
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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# backbone
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backbone = create(cfg['backbone'])
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# fpn
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kwargs = {'input_shape': backbone.out_shape}
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neck = create(cfg['neck'], **kwargs)
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aux_neck = copy.deepcopy(neck)
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# head
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kwargs = {'input_shape': neck.out_shape}
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yolo_head = create(cfg['yolo_head'], **kwargs)
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aux_head = create(cfg['aux_head'], **kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"yolo_head": yolo_head,
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'aux_head': aux_head,
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}
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def _forward(self):
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body_feats = self.backbone(self.inputs)
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neck_feats = self.neck(body_feats, self.for_mot)
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if self.training:
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if self.inputs['epoch_id'] >= self.detach_epoch:
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aux_neck_feats = self.aux_neck([f.detach() for f in body_feats])
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dual_neck_feats = (paddle.concat(
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[f.detach(), aux_f], axis=1) for f, aux_f in
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zip(neck_feats, aux_neck_feats))
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else:
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aux_neck_feats = self.aux_neck(body_feats)
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dual_neck_feats = (paddle.concat(
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[f, aux_f], axis=1) for f, aux_f in
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zip(neck_feats, aux_neck_feats))
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aux_cls_scores, aux_bbox_preds = self.aux_head(dual_neck_feats)
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loss = self.yolo_head(
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neck_feats,
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self.inputs,
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aux_pred=[aux_cls_scores, aux_bbox_preds])
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return loss
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else:
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yolo_head_outs = self.yolo_head(neck_feats)
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if self.post_process is not None:
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bbox, bbox_num, nms_keep_idx = self.post_process(
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yolo_head_outs, self.yolo_head.mask_anchors,
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self.inputs['im_shape'], self.inputs['scale_factor'])
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else:
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bbox, bbox_num, nms_keep_idx = self.yolo_head.post_process(
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yolo_head_outs, self.inputs['scale_factor'])
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if self.use_extra_data:
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extra_data = {} # record the bbox output before nms, such like scores and nms_keep_idx
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"""extra_data:{
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'scores': predict scores,
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'nms_keep_idx': bbox index before nms,
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}
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"""
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extra_data['scores'] = yolo_head_outs[0] # predict scores (probability)
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# Todo: get logits output
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extra_data['nms_keep_idx'] = nms_keep_idx
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output = {'bbox': bbox, 'bbox_num': bbox_num, 'extra_data': extra_data}
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else:
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output = {'bbox': bbox, 'bbox_num': bbox_num}
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return output
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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return self._forward()
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