forked from PulseFocusPlatform/PulseFocusPlatform
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
# Copyright (c) 2021 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 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__ = ['S2ANet']
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@register
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class S2ANet(BaseArch):
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__category__ = 'architecture'
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__inject__ = [
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's2anet_head',
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's2anet_bbox_post_process',
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]
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def __init__(self, backbone, neck, s2anet_head, s2anet_bbox_post_process):
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"""
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S2ANet, see https://arxiv.org/pdf/2008.09397.pdf
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Args:
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backbone (object): backbone instance
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neck (object): `FPN` instance
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s2anet_head (object): `S2ANetHead` instance
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s2anet_bbox_post_process (object): `S2ANetBBoxPostProcess` instance
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"""
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super(S2ANet, self).__init__()
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self.backbone = backbone
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self.neck = neck
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self.s2anet_head = s2anet_head
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self.s2anet_bbox_post_process = s2anet_bbox_post_process
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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 = cfg['neck'] and create(cfg['neck'], **kwargs)
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out_shape = neck and neck.out_shape or backbone.out_shape
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kwargs = {'input_shape': out_shape}
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s2anet_head = create(cfg['s2anet_head'], **kwargs)
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s2anet_bbox_post_process = create(cfg['s2anet_bbox_post_process'],
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**kwargs)
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return {
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'backbone': backbone,
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'neck': neck,
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"s2anet_head": s2anet_head,
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"s2anet_bbox_post_process": s2anet_bbox_post_process,
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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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if self.neck is not None:
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body_feats = self.neck(body_feats)
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self.s2anet_head(body_feats)
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if self.training:
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loss = self.s2anet_head.get_loss(self.inputs)
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total_loss = paddle.add_n(list(loss.values()))
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loss.update({'loss': total_loss})
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return loss
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else:
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im_shape = self.inputs['im_shape']
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scale_factor = self.inputs['scale_factor']
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nms_pre = self.s2anet_bbox_post_process.nms_pre
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pred_scores, pred_bboxes = self.s2anet_head.get_prediction(nms_pre)
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# post_process
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pred_bboxes, bbox_num = self.s2anet_bbox_post_process(pred_scores,
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pred_bboxes)
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# rescale the prediction back to origin image
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pred_bboxes = self.s2anet_bbox_post_process.get_pred(
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pred_bboxes, bbox_num, im_shape, scale_factor)
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# output
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output = {'bbox': pred_bboxes, 'bbox_num': bbox_num}
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return output
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def get_loss(self, ):
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loss = self._forward()
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return loss
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def get_pred(self):
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output = self._forward()
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return output
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