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- # Copyright (c) 2021 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
- from ppdet.core.workspace import register, create
- from .meta_arch import BaseArch
- __all__ = ['S2ANet']
- @register
- class S2ANet(BaseArch):
- __category__ = 'architecture'
- __inject__ = ['head']
- def __init__(self, backbone, neck, head):
- """
- S2ANet, see https://arxiv.org/pdf/2008.09397.pdf
- Args:
- backbone (object): backbone instance
- neck (object): `FPN` instance
- head (object): `Head` instance
- """
- super(S2ANet, self).__init__()
- self.backbone = backbone
- self.neck = neck
- self.s2anet_head = head
- @classmethod
- def from_config(cls, cfg, *args, **kwargs):
- backbone = create(cfg['backbone'])
- kwargs = {'input_shape': backbone.out_shape}
- neck = cfg['neck'] and create(cfg['neck'], **kwargs)
- out_shape = neck and neck.out_shape or backbone.out_shape
- kwargs = {'input_shape': out_shape}
- head = create(cfg['head'], **kwargs)
- return {'backbone': backbone, 'neck': neck, "head": head}
- def _forward(self):
- body_feats = self.backbone(self.inputs)
- if self.neck is not None:
- body_feats = self.neck(body_feats)
- if self.training:
- loss = self.s2anet_head(body_feats, self.inputs)
- return loss
- else:
- head_outs = self.s2anet_head(body_feats)
- # post_process
- bboxes, bbox_num = self.s2anet_head.get_bboxes(head_outs)
- # rescale the prediction back to origin image
- im_shape = self.inputs['im_shape']
- scale_factor = self.inputs['scale_factor']
- bboxes = self.s2anet_head.get_pred(bboxes, bbox_num, im_shape,
- scale_factor)
- # output
- output = {'bbox': bboxes, 'bbox_num': bbox_num}
- return output
- def get_loss(self, ):
- loss = self._forward()
- return loss
- def get_pred(self):
- output = self._forward()
- return output
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