111 lines
3.5 KiB
Python
111 lines
3.5 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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from ppdet.core.workspace import register, create
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from .meta_arch import BaseArch
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__all__ = ['JDE']
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@register
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class JDE(BaseArch):
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__category__ = 'architecture'
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__shared__ = ['metric']
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"""
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JDE network, see https://arxiv.org/abs/1909.12605v1
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Args:
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detector (object): detector model instance
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reid (object): reid model instance
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tracker (object): tracker instance
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metric (str): 'MOTDet' for training and detection evaluation, 'ReID'
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for ReID embedding evaluation, or 'MOT' for multi object tracking
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evaluation.
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"""
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def __init__(self,
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detector='YOLOv3',
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reid='JDEEmbeddingHead',
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tracker='JDETracker',
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metric='MOT'):
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super(JDE, 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.metric = metric
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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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kwargs = {'input_shape': detector.neck.out_shape}
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reid = create(cfg['reid'], **kwargs)
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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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"tracker": tracker,
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}
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def _forward(self):
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det_outs = self.detector(self.inputs)
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if self.training:
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emb_feats = det_outs['emb_feats']
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loss_confs = det_outs['det_losses']['loss_confs']
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loss_boxes = det_outs['det_losses']['loss_boxes']
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jde_losses = self.reid(
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emb_feats,
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self.inputs,
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loss_confs=loss_confs,
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loss_boxes=loss_boxes)
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return jde_losses
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else:
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if self.metric == 'MOTDet':
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det_results = {
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'bbox': det_outs['bbox'],
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'bbox_num': det_outs['bbox_num'],
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}
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return det_results
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elif self.metric == 'MOT':
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emb_feats = det_outs['emb_feats']
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bboxes = det_outs['bbox']
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boxes_idx = det_outs['boxes_idx']
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nms_keep_idx = det_outs['nms_keep_idx']
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pred_dets, pred_embs = self.reid(
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emb_feats,
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self.inputs,
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bboxes=bboxes,
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boxes_idx=boxes_idx,
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nms_keep_idx=nms_keep_idx)
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return pred_dets, pred_embs
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else:
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raise ValueError("Unknown metric {} for multi object tracking.".
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format(self.metric))
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def get_loss(self):
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return self._forward()
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def get_pred(self):
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return self._forward()
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