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83 lines
2.7 KiB
83 lines
2.7 KiB
# 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 paddle |
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from paddlers_slim.models.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__ = ['head'] |
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def __init__(self, backbone, neck, head): |
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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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head (object): `Head` 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 = head |
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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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head = create(cfg['head'], **kwargs) |
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return {'backbone': backbone, 'neck': neck, "head": head} |
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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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if self.training: |
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loss = self.s2anet_head(body_feats, self.inputs) |
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return loss |
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else: |
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head_outs = self.s2anet_head(body_feats) |
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# post_process |
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bboxes, bbox_num = self.s2anet_head.get_bboxes(head_outs) |
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# rescale the prediction back to origin image |
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im_shape = self.inputs['im_shape'] |
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scale_factor = self.inputs['scale_factor'] |
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bboxes = self.s2anet_head.get_pred(bboxes, bbox_num, im_shape, |
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scale_factor) |
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# output |
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output = {'bbox': 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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