101 lines
3.1 KiB
Python
101 lines
3.1 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__ = ['FairMOT']
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@register
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class FairMOT(BaseArch):
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"""
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FairMOT network, see http://arxiv.org/abs/2004.01888
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Args:
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detector (object): 'CenterNet' instance
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reid (object): 'FairMOTEmbeddingHead' instance
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tracker (object): 'JDETracker' instance
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loss (object): 'FairMOTLoss' instance
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"""
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__category__ = 'architecture'
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__inject__ = ['loss']
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def __init__(self,
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detector='CenterNet',
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reid='FairMOTEmbeddingHead',
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tracker='JDETracker',
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loss='FairMOTLoss'):
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super(FairMOT, self).__init__()
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self.detector = detector
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self.reid = reid
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self.tracker = tracker
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self.loss = loss
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@classmethod
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def from_config(cls, cfg, *args, **kwargs):
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detector = create(cfg['detector'])
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detector_out_shape = detector.neck and detector.neck.out_shape or detector.backbone.out_shape
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kwargs = {'input_shape': detector_out_shape}
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reid = create(cfg['reid'], **kwargs)
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loss = create(cfg['loss'])
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tracker = create(cfg['tracker'])
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return {
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'detector': detector,
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'reid': reid,
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'loss': loss,
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'tracker': tracker
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}
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def _forward(self):
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loss = dict()
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# det_outs keys:
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# train: neck_feat, det_loss, heatmap_loss, size_loss, offset_loss (optional: iou_loss)
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# eval/infer: neck_feat, bbox, bbox_inds
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det_outs = self.detector(self.inputs)
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neck_feat = det_outs['neck_feat']
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if self.training:
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reid_loss = self.reid(neck_feat, self.inputs)
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det_loss = det_outs['det_loss']
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loss = self.loss(det_loss, reid_loss)
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for k, v in det_outs.items():
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if 'loss' not in k:
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continue
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loss.update({k: v})
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loss.update({'reid_loss': reid_loss})
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return loss
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else:
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pred_dets, pred_embs = self.reid(
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neck_feat, self.inputs, det_outs['bbox'], det_outs['bbox_inds'],
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det_outs['topk_clses'])
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return pred_dets, pred_embs
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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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def get_loss(self):
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loss = self._forward()
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return loss
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