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108 lines
3.6 KiB
108 lines
3.6 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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from paddlers.models.ppdet.core.workspace import register, create |
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from .meta_arch import BaseArch |
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__all__ = ['CenterNet'] |
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@register |
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class CenterNet(BaseArch): |
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""" |
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CenterNet network, see http://arxiv.org/abs/1904.07850 |
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Args: |
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backbone (object): backbone instance |
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neck (object): FPN instance, default use 'CenterNetDLAFPN' |
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head (object): 'CenterNetHead' instance |
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post_process (object): 'CenterNetPostProcess' instance |
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for_mot (bool): whether return other features used in tracking model |
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""" |
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__category__ = 'architecture' |
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__inject__ = ['post_process'] |
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__shared__ = ['for_mot'] |
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def __init__(self, |
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backbone, |
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neck='CenterNetDLAFPN', |
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head='CenterNetHead', |
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post_process='CenterNetPostProcess', |
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for_mot=False): |
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super(CenterNet, self).__init__() |
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self.backbone = backbone |
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self.neck = neck |
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self.head = head |
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self.post_process = post_process |
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self.for_mot = for_mot |
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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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neck_feat = self.backbone(self.inputs) |
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if self.neck is not None: |
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neck_feat = self.neck(neck_feat) |
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head_out = self.head(neck_feat, self.inputs) |
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if self.for_mot: |
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head_out.update({'neck_feat': neck_feat}) |
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elif self.training: |
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head_out['loss'] = head_out.pop('det_loss') |
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return head_out |
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def get_pred(self): |
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head_out = self._forward() |
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if self.for_mot: |
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bbox, bbox_inds, topk_clses = self.post_process( |
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head_out['heatmap'], |
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head_out['size'], |
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head_out['offset'], |
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im_shape=self.inputs['im_shape'], |
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scale_factor=self.inputs['scale_factor']) |
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output = { |
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"bbox": bbox, |
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"bbox_inds": bbox_inds, |
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"topk_clses": topk_clses, |
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"neck_feat": head_out['neck_feat'] |
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} |
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else: |
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bbox, bbox_num, _ = self.post_process( |
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head_out['heatmap'], |
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head_out['size'], |
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head_out['offset'], |
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im_shape=self.inputs['im_shape'], |
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scale_factor=self.inputs['scale_factor']) |
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output = { |
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"bbox": bbox, |
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"bbox_num": bbox_num, |
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} |
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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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