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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from paddlers.models.ppdet.core.workspace import register, create
from .meta_arch import BaseArch
__all__ = ['JDE']
@register
class JDE(BaseArch):
__category__ = 'architecture'
__shared__ = ['metric']
"""
JDE network, see https://arxiv.org/abs/1909.12605v1
Args:
detector (object): detector model instance
reid (object): reid model instance
tracker (object): tracker instance
metric (str): 'MOTDet' for training and detection evaluation, 'ReID'
for ReID embedding evaluation, or 'MOT' for multi object tracking
evaluation.
"""
def __init__(self,
detector='YOLOv3',
reid='JDEEmbeddingHead',
tracker='JDETracker',
metric='MOT'):
super(JDE, self).__init__()
self.detector = detector
self.reid = reid
self.tracker = tracker
self.metric = metric
@classmethod
def from_config(cls, cfg, *args, **kwargs):
detector = create(cfg['detector'])
kwargs = {'input_shape': detector.neck.out_shape}
reid = create(cfg['reid'], **kwargs)
tracker = create(cfg['tracker'])
return {
"detector": detector,
"reid": reid,
"tracker": tracker,
}
def _forward(self):
det_outs = self.detector(self.inputs)
if self.training:
emb_feats = det_outs['emb_feats']
loss_confs = det_outs['det_losses']['loss_confs']
loss_boxes = det_outs['det_losses']['loss_boxes']
jde_losses = self.reid(
emb_feats,
self.inputs,
loss_confs=loss_confs,
loss_boxes=loss_boxes)
return jde_losses
else:
if self.metric == 'MOTDet':
det_results = {
'bbox': det_outs['bbox'],
'bbox_num': det_outs['bbox_num'],
}
return det_results
elif self.metric == 'MOT':
emb_feats = det_outs['emb_feats']
bboxes = det_outs['bbox']
boxes_idx = det_outs['boxes_idx']
nms_keep_idx = det_outs['nms_keep_idx']
pred_dets, pred_embs = self.reid(
emb_feats,
self.inputs,
bboxes=bboxes,
boxes_idx=boxes_idx,
nms_keep_idx=nms_keep_idx)
return pred_dets, pred_embs
else:
raise ValueError("Unknown metric {} for multi object tracking.".
format(self.metric))
def get_loss(self):
return self._forward()
def get_pred(self):
return self._forward()