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100 lines
3.1 KiB
100 lines
3.1 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__ = ['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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